Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases

Intestinal B cells license metabolic T-cell activation in NASH microbiota/antigen-independently and contribute to fibrosis by IgA-FcR signalling

BACKGROUND & AIMS

The progression of non-alcoholic steatohepatitis (NASH) to fibrosis and hepatocellular carcinoma (HCC) is aggravated by auto-aggressive T cells. The gut-liver axis contributes to NASH, but the mechanisms involved and the consequences for NASH-induced fibrosis and liver cancer remain unknown. We investigated the role of gastrointestinal B cells in the development of NASH, fibrosis and NASH-induced HCC.

METHODS

C57BL/6J wild-type (WT), B cell-deficient and different immunoglobulin-deficient or transgenic mice were fed distinct NASH-inducing diets or standard chow for 6 or 12 months, whereafter NASH, fibrosis, and NASH-induced HCC were assessed and analysed. Specific pathogen-free/germ-free WT and μMT mice (containing B cells only in the gastrointestinal tract) were fed a choline-deficient high-fat diet, and treated with an anti-CD20 antibody, whereafter NASH and fibrosis were assessed. Tissue biopsy samples from patients with simple steatosis, NASH and cirrhosis were analysed to correlate the secretion of immunoglobulins to clinicopathological features. Flow cytometry, immunohistochemistry and single-cell RNA-sequencing analysis were performed in liver and gastrointestinal tissue to characterise immune cells in mice and humans.

RESULTS

Activated intestinal B cells were increased in mouse and human NASH samples and licensed metabolic T-cell activation to induce NASH independently of antigen specificity and gut microbiota. Genetic or therapeutic depletion of systemic or gastrointestinal B cells prevented or reverted NASH and liver fibrosis. IgA secretion was necessary for fibrosis induction by activating CD11b+CCR2+F4/80+CD11c-FCGR1+ hepatic myeloid cells through an IgA-FcR signalling axis. Similarly, patients with NASH had increased numbers of activated intestinal B cells; additionally, we observed a positive correlation between IgA levels and activated FcRg+ hepatic myeloid cells, as well the extent of liver fibrosis.

CONCLUSIONS

Intestinal B cells and the IgA-FcR signalling axis represent potential therapeutic targets for the treatment of NASH.

IMPACT AND IMPLICATIONS

There is currently no effective treatment for non-alcoholic steatohepatitis (NASH), which is associated with a substantial healthcare burden and is a growing risk factor for hepatocellular carcinoma (HCC). We have previously shown that NASH is an auto-aggressive condition aggravated, amongst others, by T cells. Therefore, we hypothesized that B cells might have a role in disease induction and progression. Our present work highlights that B cells have a dual role in NASH pathogenesis, being implicated in the activation of auto-aggressive T cells and the development of fibrosis via activation of monocyte-derived macrophages by secreted immunoglobulins (e.g., IgA). Furthermore, we show that the absence of B cells prevented HCC development. B cell-intrinsic signalling pathways, secreted immunoglobulins, and interactions of B cells with other immune cells are potential targets for combinatorial NASH therapies against inflammation and fibrosis.

TGF-β1 Drives Inflammatory Th Cell But Not Treg Cell Compartment Upon Allergen Exposure

TGF-β1 is known to have a pro-inflammatory impact by inducing Th9 and Th17 cells, while it also induces anti-inflammatory Treg cells (Tregs). In the context of allergic airway inflammation (AAI) its dual role can be of critical importance in influencing the outcome of the disease. Here we demonstrate that TGF-β is a major player in AAI by driving effector T cells, while Tregs differentiate independently. Induction of experimental AAI and airway hyperreactivity in a mouse model with inducible genetic ablation of the gene encoding for TGFβ-receptor 2 (Tgfbr2) on CD4⁺T cells significantly reduced the disease phenotype. Further, it blocked the induction of pro-inflammatory T cell frequencies (Th2, Th9, Th17), but increased Treg cells. To translate these findings into a human clinically relevant context, Th2, Th9 and Treg cells were quantified both locally in induced sputum and systemically in blood of allergic rhinitis and asthma patients with or without allergen-specific immunotherapy (AIT). Natural allergen exposure induced local and systemic Th2, Th9, and reduced Tregs cells, while therapeutic allergen exposure by AIT suppressed Th2 and Th9 cell frequencies along with TGF-β and IL-9 secretion. Altogether, these findings support that neutralization of TGF-β represents a viable therapeutic option in allergy and asthma, not posing the risk of immune dysregulation by impacting Tregs cells.

Cancer cell immune mimicry delineates onco-immunologic modulation

Immune transcripts are essential for depicting onco-immunologic interactions. However, whether cancer cells mimic immune transcripts to reprogram onco-immunologic interaction remains unclear. Here, single-cell transcriptomic analyses of 7,737 normal and 37,476 cancer cells reveal increased immune transcripts in cancer cells. Cells gradually acquire immune transcripts in malignant transformation. Notably, cancer cell-derived immune transcripts contribute to distinct prognoses of immune gene signatures. Optimized immune response signature (oIRS), obtained by excluding cancer-related immune genes from immune gene signatures, and offers a more reliable prognostic value. oIRS reveals that antigen presentation, NK cell killing and T cell signaling are associated with favorable prognosis. Patients with higher oIRS expression are associated with favorable responses to immunotherapy. Indeed, CD83⁺ cell infiltration, which indicates antigen presentation activity, predicts favorable prognosis in breast cancer. These findings unveil that immune mimicry is a distinct cancer hallmark, providing an example of cancer cell plasticity and a refined view of tumor microenvironment.

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Single-cell transcriptome reveals immune mimicry as a distinct cancer hallmark

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Mimicry transcripts contribute to distinct prognoses of immune gene signatures

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oIRS refines prognostic evaluation and points to core favorable immune processes

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oIRS signifies the role of antigen presentation and indicates immunotherapy response

Comparing Host Module Activation Patterns and Temporal Dynamics in Infection by Influenza H1N1 Viruses

Influenza is a serious global health threat that shows varying pathogenicity among different virus strains. Understanding similarities and differences among activated functional pathways in the host responses can help elucidate therapeutic targets responsible for pathogenesis. To compare the types and timing of functional modules activated in host cells by four influenza viruses of varying pathogenicity, we developed a new DYNAmic MOdule (DYNAMO) method that addresses the need to compare functional module utilization over time. This integrative approach overlays whole genome time series expression data onto an immune-specific functional network, and extracts conserved modules exhibiting either different temporal patterns or overall transcriptional activity. We identified a common core response to influenza virus infection that is temporally shifted for different viruses. We also identified differentially regulated functional modules that reveal unique elements of responses to different virus strains. Our work highlights the usefulness of combining time series gene expression data with a functional interaction map to capture temporal dynamics of the same cellular pathways under different conditions. Our results help elucidate conservation of the immune response both globally and at a granular level, and provide mechanistic insight into the differences in the host response to infection by influenza strains of varying pathogenicity.

System-Level Analysis of Alzheimer's Disease Prioritizes Candidate Genes for Neurodegeneration

Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder. Since the advent of the genome-wide association study (GWAS) we have come to understand much about the genes involved in AD heritability and pathophysiology. Large case-control meta-GWAS studies have increased our ability to prioritize weaker effect alleles, while the recent development of network-based functional prediction has provided a mechanism by which we can use machine learning to reprioritize GWAS hits in the functional context of relevant brain tissues like the hippocampus and amygdala. In parallel with these developments, groups like the Alzheimer’s Disease Neuroimaging Initiative (ADNI) have compiled rich compendia of AD patient data including genotype and biomarker information, including derived volume measures for relevant structures like the hippocampus and the amygdala. In this study we wanted to identify genes involved in AD-related atrophy of these two structures, which are often critically impaired over the course of the disease. To do this we developed a combined score prioritization method which uses the cumulative distribution function of a gene’s functional and positional score, to prioritize top genes that not only segregate with disease status, but also with hippocampal and amygdalar atrophy. Our method identified a mix of genes that had previously been identified in AD GWAS including APOE, TOMM40, and NECTIN2(PVRL2) and several others that have not been identified in AD genetic studies, but play integral roles in AD-effected functional pathways including IQSEC1, PFN1, and PAK2. Our findings support the viability of our novel combined score as a method for prioritizing region- and even cell-specific AD risk genes.

Predicting Immunogenicity Risk in Biopharmaceuticals

The assessment of immunogenicity of biopharmaceuticals is a crucial step in the process of their development. Immunogenicity is related to the activation of adaptive immunity. The complexity of the immune system manifests through numerous different mechanisms, which allows the use of different approaches for predicting the immunogenicity of biopharmaceuticals. The direct experimental approaches are sometimes expensive and time consuming, or their results need to be confirmed. In this case, computational methods for immunogenicity prediction appear as an appropriate complement in the process of drug design. In this review, we analyze the use of various In silico methods and approaches for immunogenicity prediction of biomolecules: sequence alignment algorithms, predicting subcellular localization, searching for major histocompatibility complex (MHC) binding motifs, predicting T and B cell epitopes based on machine learning algorithms, molecular docking, and molecular dynamics simulations. Computational tools for antigenicity and allergenicity prediction also are considered.

Big Data Applications in Medical Field: A Literature Review

Digital imaging and medical reporting have acquired an essential role in healthcare, but the main challenge is the storage of a high volume of patient data. Although newer technologies are already introduced in the medical sciences to save records size, Big Data provides advancements by storing a large amount of data to improve the efficiency and quality of patient treatment with better care. It provides intelligent automation capabilities to reduce errors than manual inputs. Large numbers of research papers on big data in the medical field are studied and analyzed for their impacts, benefits, and applications. Big data has great potential to support the digitalization of all medical and clinical records and then save the entire data regarding the medical history of an individual or a group. This paper discusses big data usage for various industries and sectors. Finally, 12 significant applications for the medical field by the implementation of big data are identified and studied with a brief description. This technology can be gainfully used to extract useful information from the available data by analyzing and managing them through a combination of hardware and software. With technological advancement, big data provides health-related information for millions of patient-related to life issues such as lab tests reporting, clinical narratives, demographics, prescription, medical diagnosis, and related documentation. Thus, Big Data is essential in developing a better yet efficient analysis and storage healthcare services. The demand for big data applications is increasing due to its capability of handling and analyzing massive data. Not only in the future but even now, Big Data is proving itself as an axiom of storing, developing, analyzing, and providing overall health information to the physicians.

ImmunoGlobe: enabling systems immunology with a manually curated intercellular immune interaction network

BACKGROUND

While technological advances have made it possible to profile the immune system at high resolution, translating high-throughput data into knowledge of immune mechanisms has been challenged by the complexity of the interactions underlying immune processes. Tools to explore the immune network are critical for better understanding the multi-layered processes that underlie immune function and dysfunction, but require a standardized network map of immune interactions. To facilitate this we have developed ImmunoGlobe, a manually curated intercellular immune interaction network extracted from Janeway's Immunobiology textbook.

RESULTS

ImmunoGlobe is the first graphical representation of the immune interactome, and is comprised of 253 immune system components and 1112 unique immune interactions with detailed functional and characteristic annotations. Analysis of this network shows that it recapitulates known features of the human immune system and can be used uncover novel multi-step immune pathways, examine species-specific differences in immune processes, and predict the response of immune cells to stimuli. ImmunoGlobe is publicly available through a user-friendly interface at www.immunoglobe.org and can be downloaded as a computable graph and network table.

CONCLUSION

While the fields of proteomics and genomics have long benefited from network analysis tools, no such tool yet exists for immunology. ImmunoGlobe provides a ground truth immune interaction network upon which such tools can be built. These tools will allow us to predict the outcome of complex immune interactions, providing mechanistic insight that allows us to precisely modulate immune responses in health and disease.

Proteomic Applications in Antimicrobial Resistance and Clinical Microbiology Studies

Sequences of the genomes of all-important bacterial pathogens of man, plants, and animals have been completed. Still, it is not enough to achieve complete information of all the mechanisms controlling the biological processes of an organism. Along with all advances in different proteomics technologies, proteomics has completed our knowledge of biological processes all around the world. Proteomics is a valuable technique to explain the complement of proteins in any organism. One of the fields that has been notably benefited from other systems approaches is bacterial pathogenesis. An emerging field is to use proteomics to examine the infectious agents in terms of, among many, the response the host and pathogen to the infection process, which leads to a deeper knowledge of the mechanisms of bacterial virulence. This trend also enables us to identify quantitative measurements for proteins extracted from microorganisms. The present review study is an attempt to summarize a variety of different proteomic techniques and advances. The significant applications in bacterial pathogenesis studies are also covered. Moreover, the areas where proteomics may lead the future studies are introduced.

Downregulated transferrin receptor in the blood predicts recurrent MDD in the elderly cohort: A fuzzy forests approach

BACKGROUND

At present, no predictive markers for Major Depressive Disorder (MDD) exist. The search for such markers has been challenging due to clinical and molecular heterogeneity of MDD, the lack of statistical power in studies and suboptimal statistical tools applied to multidimensional data. Machine learning is a powerful approach to mitigate some of these limitations.

METHODS

We aimed to identify the predictive markers of recurrent MDD in the elderly using peripheral whole blood from the Sydney Memory and Aging Study (SMAS) (N = 521, aged over 65) and adopting machine learning methodology on transcriptome data. Fuzzy Forests is a Random Forests-based classification algorithm that takes advantage of the co-expression network structure between genes; it allows to alleviate the problem of p >> n via reducing the dimensionality of transcriptomic feature space.

RESULTS

By adopting Fuzzy Forests on transcriptome data, we found that the downregulated TFRC (transferrin receptor) can predict recurrent MDD with an accuracy of 63%.

LIMITATIONS

Although we corrected our data for several important confounders, we were not able to account for the comorbidities and medication taken, which may be numerous in the elderly and might have affected the levels of gene transcription.

CONCLUSIONS

We found that downregulated TFRC is predictive of recurrent MDD, which is consistent with the previous literature, indicating the role of the innate immune system in depression. This study is the first to successfully apply Fuzzy Forests methodology on psychiatric condition, opening, therefore, a methodological avenue that can lead to clinically useful predictive markers of complex traits.

Silencing the lncRNA Maclpil in pro-inflammatory macrophages attenuates acute experimental ischemic stroke via LCP1 in mice

Long noncoding RNAs (lncRNA) expression profiles change in the ischemic brain after stroke, but their roles in specific cell types after stroke have not been studied. We tested the hypothesis that lncRNA modulates brain injury by altering macrophage functions. Using RNA deep sequencing, we identified 73 lncRNAs that were differentially expressed in monocyte-derived macrophages (MoDMs) and microglia-derived macrophages (MiDMs) isolated in the ischemic brain three days after stroke. Among these, the lncRNA, GM15628, is highly expressed in pro-inflammatory MoDMs but not in MiDMs, and are functionally related to its neighbor gene, lymphocyte cytosolic protein 1 (LCP1), which plays a role in maintaining cell shape and cell migration. We termed this lncRNA as Macrophage contained LCP1 related pro-inflammatory lncRNA, Maclpil. Using cultured macrophages polarized by LPS, M(LPS), we found that downregulation of Maclpil in M(LPS) decreased pro-inflammatory gene expression while promoting anti-inflammatory gene expression. Maclpil inhibition also reduced the migration and phagocytosis ability of MoDMs by inhibiting LCP1. Furthermore, adoptive transfer of Maclpil silenced M(LPS), reduced ischemic brain infarction, improved behavioral performance and attenuated penetration of MoDMs in the ischemic hemisphere. We conclude that by blocking macrophage, Maclpil protects against acute ischemic stroke by inhibiting neuroinflammation.

Genetic influences on susceptibility to rheumatoid arthritis in African-Americans

Large meta-analyses of rheumatoid arthritis (RA) susceptibility in European (EUR) and East Asian (EAS) populations have identified >100 RA risk loci, but genome-wide studies of RA in African-Americans (AAs) are absent. To address this disparity, we performed an analysis of 916 AA RA patients and 1392 controls and aggregated our data with genotyping data from >100 000 EUR and Asian RA patients and controls. We identified two novel risk loci that appear to be specific to AAs: GPC5 and RBFOX1 (PAA < 5 × 10-9). Most RA risk loci are shared across different ethnicities, but among discordant loci, we observed strong enrichment of variants having large effect sizes. We found strong evidence of effect concordance for only 3 of the 21 largest effect index variants in EURs. We used the trans-ethnic fine-mapping algorithm PAINTOR3 to prioritize risk variants in >90 RA risk loci. Addition of AA data to those of EUR and EAS descent enabled identification of seven novel high-confidence candidate pathogenic variants (defined by posterior probability > 0.8). In summary, our trans-ethnic analyses are the first to include AAs, identified several new RA risk loci and point to candidate pathogenic variants that may underlie this common autoimmune disease. These findings may lead to better ways to diagnose or stratify treatment approaches in RA.

Seasonal Variability and Shared Molecular Signatures of Inactivated Influenza Vaccination in Young and Older Adults

The seasonal influenza vaccine is an important public health tool but is only effective in a subset of individuals. The identification of molecular signatures provides a mechanism to understand the drivers of vaccine-induced immunity. Most previously reported molecular signatures of human influenza vaccination were derived from a single age group or season, ignoring the effects of immunosenescence or vaccine composition. Thus, it remains unclear how immune signatures of vaccine response change with age across multiple seasons. In this study we profile the transcriptional landscape of young and older adults over five consecutive vaccination seasons to identify shared signatures of vaccine response as well as marked seasonal differences. Along with substantial variability in vaccine-induced signatures across seasons, we uncovered a common transcriptional signature 28 days postvaccination in both young and older adults. However, gene expression patterns associated with vaccine-induced Ab responses were distinct in young and older adults; for example, increased expression of killer cell lectin-like receptor B1 (KLRB1; CD161) 28 days postvaccination positively and negatively predicted vaccine-induced Ab responses in young and older adults, respectively. These findings contribute new insights for developing more effective influenza vaccines, particularly in older adults.

A multiscale signalling network map of innate immune response in cancer reveals cell heterogeneity signatures

The lack of integrated resources depicting the complexity of the innate immune response in cancer represents a bottleneck for high-throughput data interpretation. To address this challenge, we perform a systematic manual literature mining of molecular mechanisms governing the innate immune response in cancer and represent it as a signalling network map. The cell-type specific signalling maps of macrophages, dendritic cells, myeloid-derived suppressor cells and natural killers are constructed and integrated into a comprehensive meta map of the innate immune response in cancer. The meta-map contains 1466 chemical species as nodes connected by 1084 biochemical reactions, and it is supported by information from 820 articles. The resource helps to interpret single cell RNA-Seq data from macrophages and natural killer cells in metastatic melanoma that reveal different anti- or pro-tumor sub-populations within each cell type. Here, we report a new open source analytic platform that supports data visualisation and interpretation of tumour microenvironment activity in cancer.

The complexity of the innate immune response to cancer makes interpretation of large data sets challenging. Here, the authors provide an integrated multi-scale map of signalling networks representing the different immune cells and their interactions and show its utility for data interpretation.

Comparative analyses of sperm DNA methylomes among human, mouse and cattle provide insights into epigenomic evolution and complex traits

Sperm DNA methylation is crucial for fertility and viability of offspring but epigenome evolution in mammals is largely understudied. By comparing sperm DNA methylomes and large-scale genome-wide association study (GWAS) signals between human and cattle, we aimed to examine the DNA methylome evolution and its associations with complex phenotypes in mammals. Our analysis revealed that genes with conserved non-methylated promoters (e.g., ANKS1A and WNT7A) among human and cattle were involved in common system and embryo development, and enriched for GWAS signals of body conformation traits in both species, while genes with conserved hypermethylated promoters (e.g., TCAP and CD80) were engaged in immune responses and highlighted by immune-related traits. On the other hand, genes with human-specific hypomethylated promoters (e.g., FOXP2 and HYDIN) were engaged in neuron system development and enriched for GWAS signals of brain-related traits, while genes with cattle-specific hypomethylated promoters (e.g., LDHB and DGAT2) mainly participated in lipid storage and metabolism. We validated our findings using sperm-retained nucleosome, preimplantation transcriptome, and adult tissue transcriptome data, as well as sequence evolutionary features, including motif binding sites, mutation rates, recombination rates and evolution signatures. In conclusion, our results demonstrate important roles of epigenome evolution in shaping the genetic architecture underlying complex phenotypes, hence enhance signal prioritization in GWAS and provide valuable information for human neurological disorders and livestock genetic improvement.

The prognostic landscape of interactive biological processes presents treatment responses in cancer

Background

Differential gene expression patterns are commonly used as biomarkers to predict treatment responses among heterogeneous tumors. However, the link between response biomarkers and treatment-targeting biological processes remain poorly understood. Here, we develop a prognosis-guided approach to establish the determinants of treatment response.

Methods

The prognoses of biological processes were evaluated by integrating the transcriptomes and clinical outcomes of ~26,000 cases across 39 malignancies. Gene-prognosis scores of 39 malignancies (GEO datasets) were used for examining the prognoses, and TCGA datasets were selected for validation. The Oncomine and GEO datasets were used to establish and validate transcriptional signatures for treatment responses.

Findings

The prognostic landscape of biological processes was established across 39 malignancies. Notably, the prognoses of biological processes varied among cancer types, and transcriptional features underlying these prognostic patterns distinguished response to treatment targeting specific biological process. Applying this metric, we found that low tumor proliferation rates predicted favorable prognosis, whereas elevated cellular stress response signatures signified resistance to anti-proliferation treatment. Moreover, while high immune activities were associated with favorable prognosis, enhanced lipid metabolism signatures distinguished immunotherapy resistant patients.

Interpretation

These findings between prognosis and treatment response provide further insights into patient stratification for precision treatments, providing opportunities for further experimental and clinical validations.

Fund

National Natural Science Foundation, Innovative Research Team in University of Ministry of Education of China, National Key Research and Development Program, Natural Science Foundation of Guangdong, Science and Technology Planning Project of Guangzhou, MRC, CRUK, Breast Cancer Now, Imperial ECMC, NIHR Imperial BRC and NIH.

Germline genetic variants in somatically significantly mutated genes in tumors are associated with renal cell carcinoma risk and outcome

Genome-wide association studies (GWAS) have identified 13 susceptibility loci for renal cell carcinoma (RCC). Additional genetic loci of risk remain to be explored. Moreover, the role of germline genetic variants in predicting RCC recurrence and overall survival (OS) is less understood. In this study, we focused on 127 significantly mutated genes from The Cancer Genome Atlas (TCGA) Pan-Cancer Analysis across 12 major cancer sites to identify potential genetic variants predictive of RCC risk and clinical outcomes. In a three-phase design with a total of 2657 RCC cases and 5315 healthy controls, two single nucleotide polymorphisms (SNPs) that map to PIK3CG (rs6466135:A, ORmeta = 0.85, 95% CI = 0.77-0.94, Pmeta = 1.4 × 10-3) and ATM (rs611646:T, ORmeta = 1.17, 95% CI = 1.05-1.31, Pmeta = 3.5 × 10-3) were significantly associated with RCC risk. With respect to RCC recurrence and OS, two separate datasets with a total of 661 stages I-III RCC patients (discovery: 367; validation: 294) were analyzed. The most significant association was observed for rs10932384:C (ERBB4) with both outcomes (recurrence: HRmeta = 0.52, 95% CI = 0.39-0.68, Pmeta = 3.81 × 10-6; OS: HRmeta = 0.50, 95% CI = 0.37-0.67, Pmeta = 6.00 × 10-6). In addition, six SNPs were significantly associated with either RCC recurrence or OS but not both (Pmeta < 0.01). Rs10932384:C was significantly correlated with mutation frequency of ERBB4 in clear cell RCC (ccRCC) patients (P = 0.003, Fisher's exact test). Cis-eQTL was observed for several SNPs in blood/transformed fibroblasts but not in RCC tumor tissues. In summary, we identified promising genetic predictors of recurrence and OS among RCC patients with localized disease.

Early-onset inflammatory bowel disease as a model disease to identify key regulators of immune homeostasis mechanisms

Rare, monogenetic diseases present unique models to dissect gene functions and biological pathways, concomitantly enhancing our understanding of the etiology of complex (and often more common) traits. Although inflammatory bowel disease (IBD) is a generally prototypic complex disease, it can also manifest in an early-onset, monogenic fashion, often following Mendelian modes of inheritance. Recent advances in genomic technologies have spurred the identification of genetic defects underlying rare, very early-onset IBD (VEO-IBD) as a disease subgroup driven by strong genetic influence, pinpointing key players in the delicate homeostasis of the immune system in the gut and illustrating the intimate relationships between bowel inflammation, systemic immune dysregulation, and primary immunodeficiency with increased susceptibility to infections. As for other human diseases, it is likely that adult-onset diseases may represent complex diseases integrating the effects of host genetic susceptibility and environmental triggers. Comparison of adult-onset IBD and VEO-IBD thus provides beautiful models to investigate the relationship between monogenic and multifactorial/polygenic diseases. This review discusses the present and novel findings regarding monogenic IBD as well as key questions and future directions of IBD research.

Interpretation of an individual functional genomics experiment guided by massive public data

A key unmet challenge in interpreting omics experiments is inferring biological meaning in the context of public functional genomics data. We developed a computational framework, Your Evidence Tailored Integration (YETI; http://yeti.princeton.edu/ ), which creates specialized functional interaction maps from large public datasets relevant to an individual omics experiment. Using this tailored integration, we predicted and experimentally confirmed an unexpected divergence in viral replication after seasonal or pandemic human influenza virus infection.

Multiple network-constrained regressions expand insights into influenza vaccination responses

Motivation

Systems immunology leverages recent technological advancements that enable broad profiling of the immune system to better understand the response to infection and vaccination, as well as the dysregulation that occurs in disease. An increasingly common approach to gain insights from these large-scale profiling experiments involves the application of statistical learning methods to predict disease states or the immune response to perturbations. However, the goal of many systems studies is not to maximize accuracy, but rather to gain biological insights. The predictors identified using current approaches can be biologically uninterpretable or present only one of many equally predictive models, leading to a narrow understanding of the underlying biology.

Results

Here we show that incorporating prior biological knowledge within a logistic modeling framework by using network-level constraints on transcriptional profiling data significantly improves interpretability. Moreover, incorporating different types of biological knowledge produces models that highlight distinct aspects of the underlying biology, while maintaining predictive accuracy. We propose a new framework, Logistic Multiple Network-constrained Regression (LogMiNeR), and apply it to understand the mechanisms underlying differential responses to influenza vaccination. Although standard logistic regression approaches were predictive, they were minimally interpretable. Incorporating prior knowledge using LogMiNeR led to models that were equally predictive yet highly interpretable. In this context, B cell-specific genes and mTOR signaling were associated with an effective vaccination response in young adults. Overall, our results demonstrate a new paradigm for analyzing high-dimensional immune profiling data in which multiple networks encoding prior knowledge are incorporated to improve model interpretability.

Availability and implementation

The R source code described in this article is publicly available at https://bitbucket.org/kleinstein/logminer .

Contact

steven.kleinstein@yale.edu or stefan.avey@yale.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Big Data's Role in Precision Public Health

Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.

Discovery and validation of a glioblastoma co-expressed gene module

Tumors exhibit complex patterns of aberrant gene expression. Using a knowledge-independent, noise-reducing gene co-expression network construction software called KINC, we created multiple RNAseq-based gene co-expression networks relevant to brain and glioblastoma biology. In this report, we describe the discovery and validation of a glioblastoma-specific gene module that contains 22 co-expressed genes. The genes are upregulated in glioblastoma relative to normal brain and lower grade glioma samples; they are also hypo-methylated in glioblastoma relative to lower grade glioma tumors. Among the proneural, neural, mesenchymal, and classical glioblastoma subtypes, these genes are most-highly expressed in the mesenchymal subtype. Furthermore, high expression of these genes is associated with decreased survival across each glioblastoma subtype. These genes are of interest to glioblastoma biology and our gene interaction discovery and validation workflow can be used to discover and validate co-expressed gene modules derived from any co-expression network.

System analysis of the regulation of the immune response by CD147 and FOXC1 in cancer cell lines

CD147, encoded by BSG, is a highly glycosylated transmembrane protein that belongs to the immunological superfamily and expressed on the surface of many types of cancer cells. While CD147 is best known as a potent inducer of extracellular matrix metalloproteinases, it can also function as a key mediator of inflammatory and immune responses. To systematically elucidate the function of CD147 in cancer cells, we performed an analysis of genome-wide profiling across the Cancer Cell Line Encyclopedia (CCLE). We showed that CD147 mRNA expression was much higher than that of most other genes in cancer cell lines. CD147 varied widely across these cell lines, with the highest levels in the ovary (COLO704) and stomach (SNU668), intermediate levels in the lung (RERFLCKJ, NCIH596 and NCIH1651) and lowest levels in hematopoietic and lymphoid tissue (UT7, HEL9217, HEL and MHHCALL3) and the kidney (A704 and SLR20). Genome-wide analyses showed that CD147 expression was significantly negatively correlated with immune-related genes. Our findings implicated CD147 as a novel regulator of immune-related genes and suggest its important role as a master regulator of immune-related responses in cancer cell lines. We also found a high correlation between the expression of CD147 and FOXC1, and proved that CD147 was a direct transcriptional target of FOXC1. Our findings demonstrate that FOXC1 is a novel regulator of CD147 and confirms its role as a master regulator of the immune response.

Interferon-γ Represses M2 Gene Expression in Human Macrophages by Disassembling Enhancers Bound by the Transcription Factor MAF

Mechanisms by which interferon (IFN)-γ activates genes to promote macrophage activation are well studied, but little is known about mechanisms and functions of IFN-γ-mediated gene repression. We used an integrated transcriptomic and epigenomic approach to analyze chromatin accessibility, histone modifications, transcription-factor binding, and gene expression in IFN-γ-primed human macrophages. IFN-γ suppressed basal expression of genes corresponding to an "M2"-like homeostatic and reparative phenotype. IFN-γ repressed genes by suppressing the function of enhancers enriched for binding by transcription factor MAF. Mechanistically, IFN-γ disassembled a subset of enhancers by inducing coordinate suppression of binding by MAF, lineage-determining transcription factors, and chromatin accessibility. Genes associated with MAF-binding enhancers were suppressed in macrophages isolated from rheumatoid-arthritis patients, revealing a disease-associated signature of IFN-γ-mediated repression. These results identify enhancer inactivation and disassembly as a mechanism of IFN-γ-mediated gene repression and reveal that MAF regulates the macrophage enhancer landscape and is suppressed by IFN-γ to augment macrophage activation.

Developmental Control of NRAMP1 (SLC11A1) Expression in Professional Phagocytes

NRAMP1 (SLC11A1) is a professional phagocyte membrane importer of divalent metals that contributes to iron recycling at homeostasis and to nutritional immunity against infection. Analyses of data generated by several consortia and additional studies were integrated to hypothesize mechanisms restricting NRAMP1 expression to mature phagocytes. Results from various epigenetic and transcriptomic approaches were collected for mesodermal and hematopoietic cell types and compiled for combined analysis with results of genetic studies associating single nucleotide polymorphisms (SNPs) with variations in NRAMP1 expression (eQTLs). Analyses establish that NRAMP1 is part of an autonomous topologically associated domain delimited by ubiquitous CCCTC-binding factor (CTCF) sites. NRAMP1 locus contains five regulatory regions: a predicted super-enhancer (S-E) key to phagocyte-specific expression; the proximal promoter; two intronic areas, including 3' inhibitory elements that restrict expression during development; and a block of upstream sites possibly extending the S-E domain. Also the downstream region adjacent to the 3' CTCF locus boundary may regulate expression during hematopoiesis. Mobilization of the locus 14 predicted transcriptional regulatory elements occurs in three steps, beginning with hematopoiesis; at the onset of myelopoiesis and through myelo-monocytic differentiation. Basal expression level in mature phagocytes is further influenced by genetic variation, tissue environment, and in response to infections that induce various epigenetic memories depending on microorganism nature. Constitutively associated transcription factors (TFs) include CCAAT enhancer binding protein beta (C/EBPb), purine rich DNA binding protein (PU.1), early growth response 2 (EGR2) and signal transducer and activator of transcription 1 (STAT1) while hypoxia-inducible factors (HIFs) and interferon regulatory factor 1 (IRF1) may stimulate iron acquisition in pro-inflammatory conditions. Mouse orthologous locus is generally conserved; chromatin patterns typify a de novo myelo-monocytic gene whose expression is tightly controlled by TFs Pu.1, C/ebps and Irf8; Irf3 and nuclear factor NF-kappa-B p 65 subunit (RelA) regulate expression in inflammatory conditions. Functional differences in the determinants identified at these orthologous loci imply that species-specific mechanisms control gene expression.

Proteomics and integrative omic approaches for understanding host-pathogen interactions and infectious diseases

Organisms are constantly exposed to microbial pathogens in their environments. When a pathogen meets its host, a series of intricate intracellular interactions shape the outcome of the infection. The understanding of these host–pathogen interactions is crucial for the development of treatments and preventive measures against infectious diseases. Over the past decade, proteomic approaches have become prime contributors to the discovery and understanding of host–pathogen interactions that represent anti‐ and pro‐pathogenic cellular responses. Here, we review these proteomic methods and their application to studying viral and bacterial intracellular pathogens. We examine approaches for defining spatial and temporal host–pathogen protein interactions upon infection of a host cell. Further expanding the understanding of proteome organization during an infection, we discuss methods that characterize the regulation of host and pathogen proteomes through alterations in protein abundance, localization, and post‐translational modifications. Finally, we highlight bioinformatic tools available for analyzing such proteomic datasets, as well as novel strategies for integrating proteomics with other omic tools, such as genomics, transcriptomics, and metabolomics, to obtain a systems‐level understanding of infectious diseases.

IL-10 Receptor Signaling Is Essential for TR1 Cell Function In Vivo

IL-10 is essential to maintain intestinal homeostasis. CD4⁺ T regulatory type 1 (T_R1) cells produce large amounts of this cytokine and are therefore currently being examined in clinical trials as T cell therapy in patients with inflammatory bowel disease. However, factors and molecular signals sustaining T_R1 cell regulatory activity still need to be identified to optimize the efficiency and ensure the safety of these trials. We investigated the role of IL-10 signaling in mature T_R1 cells in vivo. Double IL-10^eGFP Foxp3^mRFP reporter mice and transgenic mice with impairment in IL-10 receptor signaling were used to test the activity of T_R1 cells in a murine inflammatory bowel disease model, a model that resembles the trials performed in humans. The molecular signaling was elucidated in vitro. Finally, we used human T_R1 cells, currently employed for cell therapy, to confirm our results. We found that murine T_R1 cells expressed functional IL-10Rα. T_R1 cells with impaired IL-10 receptor signaling lost their regulatory activity in vivo. T_R1 cells required IL-10 receptor signaling to activate p38 MAPK, thereby sustaining IL-10 production, which ultimately mediated their suppressive activity. Finally, we confirmed these data using human T_R1 cells. In conclusion, T_R1 cell regulatory activity is dependent on IL-10 receptor signaling. These data suggest that to optimize T_R1 cell-based therapy, IL-10 receptor expression has to be taken into consideration.

A Functional Genomic Meta-Analysis of Clinical Trials in Systemic Sclerosis: Toward Precision Medicine and Combination Therapy

Systemic sclerosis is an orphan, systemic autoimmune disease with no FDA-approved treatments. Its heterogeneity and rarity often result in underpowered clinical trials making the analysis and interpretation of associated molecular data challenging. We performed a meta-analysis of gene expression data from skin biopsies of patients with systemic sclerosis treated with five therapies: mycophenolate mofetil, rituximab, abatacept, nilotinib, and fresolimumab. A common clinical improvement criterion of -20% or -5 modified Rodnan skin score was applied to each study. We applied a machine learning approach that captured features beyond differential expression and was better at identifying targets of therapies than the differential expression alone. Regardless of treatment mechanism, abrogation of inflammatory pathways accompanied clinical improvement in multiple studies suggesting that high expression of immune-related genes indicates active and targetable disease. Our framework allowed us to compare different trials and ask if patients who failed one therapy would likely improve on a different therapy, based on changes in gene expression. Genes with high expression at baseline in fresolimumab nonimprovers were downregulated in mycophenolate mofetil improvers, suggesting that immunomodulatory or combination therapy may have benefitted these patients. This approach can be broadly applied to increase tissue specificity and sensitivity of differential expression results.

Solving Immunology?

Emergent responses of the immune system result from the integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for the systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the National Institute of Allergy and Infectious Disease (NIAID) workshop 'Complex Systems Science, Modeling and Immunity' and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling, and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies.

Embracing Complexity beyond Systems Medicine: A New Approach to Chronic Immune Disorders

In order to combat chronic immune disorders (CIDs), it is an absolute necessity to understand the bigger picture, one that goes beyond insights at a one-disease, molecular, cellular, and static level. To unravel this bigger picture we advocate an integral, cross-disciplinary approach capable of embracing the complexity of the field. This paper discusses the current knowledge on common pathways in CIDs including general psychosocial and lifestyle factors associated with immune functioning. We demonstrate the lack of more in-depth psychosocial and lifestyle factors in current research cohorts and most importantly the need for an all-encompassing analysis of these factors. The second part of the paper discusses the challenges of understanding immune system dynamics and effectively integrating all key perspectives on immune functioning, including the patient’s perspective itself. This paper suggests the use of techniques from complex systems science in describing and simulating healthy or deviating behavior of the immune system in its biopsychosocial surroundings. The patient’s perspective data are suggested to be generated by using specific narrative techniques. We conclude that to gain more insight into the behavior of the whole system and to acquire new ways of combatting CIDs, we need to construct and apply new techniques in the field of computational and complexity science, to an even wider variety of dynamic data than used in today’s systems medicine.

PathCellNet: Cell-type specific pathogen-response network explorer

Pathogen specific immune response is a complex interplay between several innate and adaptive immune cell-types. Innate immune cells play a critical role in pathogen recognition and initiating the antigen specific adaptive immune response. Despite specific functional roles of the innate immune cells, they share several anti-viral pathways. The question then becomes, what is the overlap in the transcriptional changes induced upon viral infections across different cell-types? Here we investigate the extent to which gene signatures are conserved across innate immune cell-types by performing a comparative analysis of transcriptomic data. Particularly, we integrate transcriptomic datasets measuring response of two innate immune cells (epithelial and dendritic cells) to influenza virus. The study reveals cell-type specific regulatory genes and a conserved network between the two cell-types. Additionally, novel functionally associated gene clusters are identified which are robustly defined across multiple independent studies. These gene clusters can be used in future investigation, and to facilitate their use we release PathCellNet (version 0), a cloud based tool to explore cell-type specific connectivity of user-defined genes. In the future, expansion of PathCellNet will allow exploration of cell-type specific responses across a variety of pathogens and cell-types.

Standardized Whole-Blood Transcriptional Profiling Enables the Deconvolution of Complex Induced Immune Responses

Systems approaches for the study of immune signaling pathways have been traditionally based on purified cells or cultured lines. However, in vivo responses involve the coordinated action of multiple cell types, which interact to establish an inflammatory microenvironment. We employed standardized whole-blood stimulation systems to test the hypothesis that responses to Toll-like receptor ligands or whole microbes can be defined by the transcriptional signatures of key cytokines. We found 44 genes, identified using Support Vector Machine learning, that captured the diversity of complex innate immune responses with improved segregation between distinct stimuli. Furthermore, we used donor variability to identify shared inter-cellular pathways and trace cytokine loops involved in gene expression. This provides strategies for dimension reduction of large datasets and deconvolution of innate immune responses applicable for characterizing immunomodulatory molecules. Moreover, we provide an interactive R-Shiny application with healthy donor reference values for induced inflammatory genes.

Immuno-Navigator, a batch-corrected coexpression database, reveals cell type-specific gene networks in the immune system

High-throughput gene expression data are one of the primary resources for exploring complex intracellular dynamics in modern biology. The integration of large amounts of public data may allow us to examine general dynamical relationships between regulators and target genes. However, obstacles for such analyses are study-specific biases or batch effects in the original data. Here we present Immuno-Navigator, a batch-corrected gene expression and coexpression database for 24 cell types of the mouse immune system. We systematically removed batch effects from the underlying gene expression data and showed that this removal considerably improved the consistency between inferred correlations and prior knowledge. The data revealed widespread cell type-specific correlation of expression. Integrated analysis tools allow users to use this correlation of expression for the generation of hypotheses about biological networks and candidate regulators in specific cell types. We show several applications of Immuno-Navigator as examples. In one application we successfully predicted known regulators of importance in naturally occurring Treg cells from their expression correlation with a set of Treg-specific genes. For one high-scoring gene, integrin β8 (Itgb8), we confirmed an association between Itgb8 expression in forkhead box P3 (Foxp3)-positive T cells and Treg-specific epigenetic remodeling. Our results also suggest that the regulation of Treg-specific genes within Treg cells is relatively independent of Foxp3 expression, supporting recent results pointing to a Foxp3-independent component in the development of Treg cells.

Modeling-Enabled Systems Nutritional Immunology

This review highlights the fundamental role of nutrition in the maintenance of health, the immune response, and disease prevention. Emerging global mechanistic insights in the field of nutritional immunology cannot be gained through reductionist methods alone or by analyzing a single nutrient at a time. We propose to investigate nutritional immunology as a massively interacting system of interconnected multistage and multiscale networks that encompass hidden mechanisms by which nutrition, microbiome, metabolism, genetic predisposition, and the immune system interact to delineate health and disease. The review sets an unconventional path to apply complex science methodologies to nutritional immunology research, discovery, and development through “use cases” centered around the impact of nutrition on the gut microbiome and immune responses. Our systems nutritional immunology analyses, which include modeling and informatics methodologies in combination with pre-clinical and clinical studies, have the potential to discover emerging systems-wide properties at the interface of the immune system, nutrition, microbiome, and metabolism.

Antiviral innate immunity through the lens of systems biology

Cellular innate immunity poses the first hurdle against invading viruses in their attempt to establish infection. This antiviral response is manifested with the detection of viral components by the host cell, followed by transduction of antiviral signals, transcription and translation of antiviral effectors and leads to the establishment of an antiviral state. These events occur in a rather branched and interconnected sequence than a linear path. Traditionally, these processes were studied in the context of a single virus and a host component. However, with the advent of rapid and affordable OMICS technologies it has become feasible to address such questions on a global scale. In the discipline of Systems Biology', extensive omics datasets are assimilated using computational tools and mathematical models to acquire deeper understanding of complex biological processes. In this review we have catalogued and discussed the application of Systems Biology approaches in dissecting the antiviral innate immune responses.

Human Dendritic Cell Response Signatures Distinguish 1918, Pandemic, and Seasonal H1N1 Influenza Viruses

Influenza viruses continue to present global threats to human health. Antigenic drift and shift, genetic reassortment, and cross-species transmission generate new strains with differences in epidemiology and clinical severity. We compared the temporal transcriptional responses of human dendritic cells (DC) to infection with two pandemic (A/Brevig Mission/1/1918, A/California/4/2009) and two seasonal (A/New Caledonia/20/1999, A/Texas/36/1991) H1N1 influenza viruses. Strain-specific response differences included stronger activation of NF-κB following infection with A/New Caledonia/20/1999 and a unique cluster of genes expressed following infection with A/Brevig Mission/1/1918. A common antiviral program showing strain-specific timing was identified in the early DC response and found to correspond with reported transcript changes in blood during symptomatic human influenza virus infection. Comparison of the global responses to the seasonal and pandemic strains showed that a dramatic divergence occurred after 4 h, with only the seasonal strains inducing widespread mRNA loss.

IMPORTANCE

Continuously evolving influenza viruses present a global threat to human health; however, these host responses display strain-dependent differences that are incompletely understood. Thus, we conducted a detailed comparative study assessing the immune responses of human DC to infection with two pandemic and two seasonal H1N1 influenza strains. We identified in the immune response to viral infection both common and strain-specific features. Among the stain-specific elements were a time shift of the interferon-stimulated gene response, selective induction of NF-κB signaling by one of the seasonal strains, and massive RNA degradation as early as 4 h postinfection by the seasonal, but not the pandemic, viruses. These findings illuminate new aspects of the distinct differences in the immune responses to pandemic and seasonal influenza viruses.