ImmPort, toward repurposing of open access immunological assay data for translational and clinical research

Microarray Gene Expression Dataset Re-analysis Reveals Variability in Influenza Infection and Vaccination

Influenza, a communicable disease, affects thousands of people worldwide. Young children, elderly, immunocompromised individuals and pregnant women are at higher risk for being infected by the influenza virus. Our study aims to highlight differentially expressed genes in influenza disease compared to influenza vaccination, including variability due to age and sex. To accomplish our goals, we conducted a meta-analysis using publicly available microarray expression data. Our inclusion criteria included subjects with influenza, subjects who received the influenza vaccine and healthy controls. We curated 18 microarray datasets for a total of 3,481 samples (1,277 controls, 297 influenza infection, 1,907 influenza vaccination). We pre-processed the raw microarray expression data in R using packages available to pre-process Affymetrix and Illumina microarray platforms. We used a Box-Cox power transformation of the data prior to our down-stream analysis to identify differentially expressed genes. Statistical analyses were based on linear mixed effects model with all study factors and successive likelihood ratio tests (LRT) to identify differentially-expressed genes. We filtered LRT results by disease (Bonferroni adjusted p < 0.05) and used a two-tailed 10% quantile cutoff to identify biologically significant genes. Furthermore, we assessed age and sex effects on the disease genes by filtering for genes with a statistically significant (Bonferroni adjusted p < 0.05) interaction between disease and age, and disease and sex. We identified 4,889 statistically significant genes when we filtered the LRT results by disease factor, and gene enrichment analysis (gene ontology and pathways) included innate immune response, viral process, defense response to virus, Hematopoietic cell lineage and NF-kappa B signaling pathway. Our quantile filtered gene lists comprised of 978 genes each associated with influenza infection and vaccination. We also identified 907 and 48 genes with statistically significant (Bonferroni adjusted p < 0.05) disease-age and disease-sex interactions, respectively. Our meta-analysis approach highlights key gene signatures and their associated pathways for both influenza infection and vaccination. We also were able to identify genes with an age and sex effect. This gives potential for improving current vaccines and exploring genes that are expressed equally across ages when considering universal vaccinations for influenza.

A linked data graph approach to integration of immunological data

Systems biology involves the integration of multiple data types (across different data sources) to offer a more complete picture of the biological system being studied. While many existing biological databases are implemented using the traditional SQL (Structured Query Language) database technology, NoSQL database technologies have been explored as a more relationship-based, flexible and scalable method of data integration. In this paper, we describe how to use the Neo4J graph database to integrate a variety of types of data sets in the context of systems vaccinology. Specifically, we have converted into a common graph model diverse types of vaccine response measurement data from the NIH/NIAID ImmPort data repository, pathway data from Reactome, influenza virus strains from WHO, and taxonomic data from NCBI Taxon. While Neo4J provides a graph-based query language (Cypher) for data retrieval, we develop a web-based dashboard for users to easily browse and visualize data without the need to learn Cypher. In addition, we have prototyped a natural language query interface for users to interact with our system. In conclusion, we demonstrate the feasibility of using a graph-based database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to reveal novel relationships among heterogeneous biological data.

Transcriptome analysis of dog oral melanoma and its oncogenic analogy with human melanoma

Dogs have been considered as an excellent immunocompetent model for human melanoma due to the same tumor location and the common clinical and pathological features with human melanoma. However, the differences in the melanoma transcriptome between the two species have not been yet fully determined. Considering the role of oncogenes in melanoma development, in this study, we first characterized the transcriptome in canine oral melanoma and then compared the transcriptome with that of human melanoma. The global transcriptome from 8 canine oral melanoma samples and 3 healthy oral tissues were compared by RNA-Seq followed by RT-qPCR validation. The results revealed 2,555 annotated differentially expressed genes, as well as 364 novel differentially expressed genes. Dog chromosomes 1 and 9 were enriched with downregulated and upregulated genes, respectively. Along with 10 significant transcription site binding motifs; the NF-κB and ATF1 binding motifs were the most significant and 4 significant unknown motifs were indentified among the upregulated differentially expressed genes. Moreover, it was found that canine oral melanoma shared >80% significant oncogenes (upregulated genes) with human melanoma, and JAK-STAT was the most common significant pathway between the species. The results identified a 429 gene signature in melanoma, which was up-regulated in both species; these genes may be good candidates for therapeutic development. Furthermore, this study demonstrates that as regards oncogene expression, human melanoma contains an oncogene group that bears similarities with dog oral melanoma, which supports the use of dogs as a model for the development of novel therapeutics and experimental trials before human application.

The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system

Machine learning has the potential to identify novel biological factors underlying successful antibody responses to influenza vaccines. The first attempts have revealed a high level of complexity in establishing influenza immunity, and many different cellular and molecular components are involved. Of note is that the previously identified correlates of protection fail to account for the majority of individual responses across different age groups and influenza seasons. Challenges remain from the small sample sizes in most studies and from often limited data sets, such as transcriptomic data. Here we report the creation of a unified database, FluPRINT, to enable large-scale studies exploring the cellular and molecular underpinnings of successful antibody responses to influenza vaccines. Over 3,000 parameters were considered, including serological responses to influenza strains, serum cytokines, cell phenotypes, and cytokine stimulations. FluPRINT, facilitates the application of machine learning algorithms for data mining. The data are publicly available and represent a resource to uncover new markers and mechanisms that are important for influenza vaccine immunogenicity.

Measurement(s)	immune response trait
Technology Type(s)	digital curation
Factor Type(s)	gender • race • visit_age • bmi • flu_vaccination_history • statin_use • influenza_infection_history • influenza_hospitalization • cmv_status • ebv_status
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9902447

Deciphering host immunity to malaria using systems immunology

A century of conceptual and technological advances in infectious disease research has changed the face of medicine. However, there remains a lack of effective interventions and a poor understanding of host immunity to the most significant and complex pathogens, including malaria. The development of successful interventions against such intractable diseases requires a comprehensive understanding of host-pathogen immune responses. A major advance of the past decade has been a paradigm switch in thinking from the contemporary reductionist (gene-by-gene or protein-by-protein) view to a more holistic (whole organism) view. Also, a recognition that host-pathogen immunity is composed of complex, dynamic interactions of cellular and molecular components and networks that cannot be represented by any individual component in isolation. Systems immunology integrates the field of immunology with omics technologies and computational sciences to comprehensively interrogate the immune response at a systems level. Herein, we describe the system immunology toolkit and report recent studies deploying systems-level approaches in the context of natural exposure to malaria or controlled human malaria infection. We contribute our perspective on the potential of systems immunity for the rational design and development of effective interventions to improve global public health.

Computational approaches for characterizing the tumor immune microenvironment

Recent advances in high-throughput molecular profiling technologies and multiplexed imaging platforms have revolutionized our ability to characterize the tumor immune microenvironment. As a result, studies of tumor-associated immune cells increasingly involve complex data sets that require sophisticated methods of computational analysis. In this review, we present an overview of key assays and related bioinformatics tools for analyzing the tumor-associated immune system in bulk tissues and at the single-cell level. In parallel, we describe how data science strategies and novel technologies have advanced tumor immunology and opened the door for new opportunities to exploit host immunity to improve cancer clinical outcomes.

Lossless Compression of Cytometric Data

Nowadays, most cytometrists apply lossless compression by storing their FCS files in ZIP archives. Unfortunately, ZIP only achieves modest space savings in cytometric data, due to DEFLATE being used as the underlying lossless compression algorithm (LCA). Presumably, other modern LCA can outperform DEFLATE, especially in terms of space savings. Twenty-one codecs (programs implementing LCA) were evaluated in 167,131 publicly available FCS files. Within floating-point data, as produced by modern instruments, most favorable compression ratios (CRs) were achieved by ZPAQ (median 0.469), BCM (median 0.523), and LZMA (median 0.545). In comparison, the DEFLATE-based codecs only achieved median CR of 0.728 under the most optimal conditions. By default, ZIP offers nine compression level (CL) settings, where lower ZIP-CL optimizes for time efficiency, while higher ZIP-CL optimizes for space efficiency. Interestingly, the third ZIP-CL already resulted in near optimal CR in 90% of the files with floating-point data, as produced by digital cytometers. LZMA is well established, widely supported, and actively maintained (in sharp contrast to ZPAQ and BCM) and therefore arguably the most attractive alternative for ZIP. Within floating-point data, by shifting from ZIP (under optimal conditions) to LZMA (at default settings), the median CR can be improved by 25%. Based on our results, cytometrists can benefit from state-of-the-art compression by choosing the appropriate codec for their situation. Our results are likely to speed-up the adaptation of modern codecs, as CR around 0.5 were beyond all expectations, and such space savings will benefit the field of cytometry. © 2019 International Society for Advancement of Cytometry.

Papillary Thyroid Carcinoma Variants are Characterized by Co-dysregulation of Immune and Cancer Associated Genes

Papillary thyroid carcinoma (PTC) variants exhibit different prognosis, but critical characteristics of PTC variants that contribute to differences in pathogenesis are not well-known. This study aims to characterize dysregulated immune-associated and cancer-associated genes in three PTC subtypes to explore how the interplay between cancer and immune processes causes differential prognosis. RNA-sequencing data from The Cancer Genome Atlas (TCGA) were used to identify dysregulated genes in each variant. The dysregulation profiles of the subtypes were compared using functional pathways clustering and correlations to relevant clinical variables, genomic alterations, and microRNA regulation. We discovered that the dysregulation profiles of classical PTC (CPTC) and the tall cell variant (TCPTC) are similar and are distinct from that of the follicular variant (FVPTC). However, unique cancer or immune-associated genes are associated with clinical variables for each subtype. Cancer-related genes MUC1, FN1, and S100-family members were the most clinically relevant in CPTC, while APLN and IL16, both immune-related, were clinically relevant in FVPTC. RAET-family members, also immune-related, were clinically relevant in TCPTC. Collectively, our data suggest that dysregulation of both cancer and immune associated genes defines the gene expression landscapes of PTC variants, but different cancer or immune related genes may drive the phenotype of each variant.

SIMON, an Automated Machine Learning System, Reveals Immune Signatures of Influenza Vaccine Responses

Machine learning holds considerable promise for understanding complex biological processes such as vaccine responses. Capturing interindividual variability is essential to increase the statistical power necessary for building more accurate predictive models. However, available approaches have difficulty coping with incomplete datasets which is often the case when combining studies. Additionally, there are hundreds of algorithms available and no simple way to find the optimal one. In this study, we developed Sequential Iterative Modeling "OverNight" (SIMON), an automated machine learning system that compares results from 128 different algorithms and is particularly suitable for datasets containing many missing values. We applied SIMON to data from five clinical studies of seasonal influenza vaccination. The results reveal previously unrecognized CD4+ and CD8+ T cell subsets strongly associated with a robust Ab response to influenza Ags. These results demonstrate that SIMON can greatly speed up the choice of analysis modalities. Hence, it is a highly useful approach for data-driven hypothesis generation from disparate clinical datasets. Our strategy could be used to gain biological insight from ever-expanding heterogeneous datasets that are publicly available.

The Pregnancy Pickle: Evolved Immune Compensation Due to Pregnancy Underlies Sex Differences in Human Diseases

We hypothesize that, ancestrally, sex-specific immune modulation evolved to facilitate survival of the pregnant person in the presence of an invasive placenta and an immunologically challenging pregnancy - an idea we term the 'pregnancy compensation hypothesis' (PCH). Further, we propose that sex differences in immune function are mediated, at least in part, by the evolution of gene content and dosage on the sex chromosomes, and are regulated by reproductive hormones. Finally, we propose that changes in reproductive ecology in industrialized environments exacerbate these evolved sex differences, resulting in the increasing risk of autoimmune disease observed in females, and a counteracting reduction in diseases such as cancer that can be combated by heightened immune surveillance. The PCH generates a series of expectations that can be tested empirically and that may help to identify the mechanisms underlying sex differences in modern human diseases.

Immunomics in Pediatric Rheumatic Diseases

The inherent complexity in the immune landscape of pediatric rheumatic disease necessitates a holistic system approach. Uncertainty in the mechanistic workings and etiological driving forces presents difficulty in personalized treatments. The development and progression of immunomics are well suited to deal with this complexity. Immunomics encompasses a spectrum of biological processes that entail genomics, transcriptomics, epigenomics, proteomics, and cytomics. In this review, we will discuss how various high dimensional technologies in immunomics have helped to grow a wealth of data that provide salient clues and biological insights into the pathogenesis of autoimmunity. Interfaced with critical unresolved clinical questions and unmet medical needs, these platforms have helped to identify candidate immune targets, refine patient stratification, and understand treatment response or resistance. Yet the unprecedented growth in data has presented both opportunities and challenges. Researchers are now facing huge heterogeneous data sets from different origins that need to be integrated and exploited for further data mining. We believe that the utilization and integration of these platforms will help unravel the complexities and expedite both discovery and validation of clinical targets.

Predicting Host Immune Cell Dynamics and Key Disease-Associated Genes Using Tissue Transcriptional Profiles

Motivation: Immune cell dynamics is a critical factor of disease-associated pathology (immunopathology) that also impacts the levels of mRNAs in diseased tissue. Deconvolution algorithms attempt to infer cell quantities in a tissue/organ sample based on gene expression profiles and are often evaluated using artificial, non-complex samples. Their accuracy on estimating cell counts given temporal tissue gene expression data remains not well characterized and has never been characterized when using diseased lung. Further, how to remove the effects of cell migration on transcript counts to improve discovery of disease factors is an open question. Results: Four cell count inference (i.e., deconvolution) tools are evaluated using microarray data from influenza-infected lung sampled at several time points post-infection. The analysis finds that inferred cell quantities are accurate only for select cell types and there is a tendency for algorithms to have a good relative fit (R 2 ) but a poor absolute fit (normalized mean squared error; NMSE), which suggests systemic biases exist. Nonetheless, using cell fraction estimates to adjust gene expression data, we show that genes associated with influenza virus replication and increased infection pathology are more likely to be identified as significant than when applying traditional statistical tests.

Comprehensive Genomic Profiling Identifies Novel Genetic Predictors of Response to Anti-PD-(L)1 Therapies in Non-Small Cell Lung Cancer

PURPOSE

Immune checkpoint inhibitors (ICI) have revolutionized cancer management. However, molecular determinants of response to ICIs remain incompletely understood.

EXPERIMENTAL DESIGN

We performed genomic profiling of 78 patients with non-small cell lung cancer (NSCLC) who underwent anti-PD-(L)1 therapies by both whole-exome and targeted next-generation sequencing (a 422-cancer-gene panel) to explore the predictive biomarkers of ICI response. Tumor mutation burden (TMB), and specific somatic mutations and copy-number alterations (CNA) were evaluated for their associations with immunotherapy response.

RESULTS

We confirmed that high TMB was associated with improved clinical outcomes, and TMB quantified by gene panel strongly correlated with WES results (Spearman's ρ = 0.81). Compared with wild-type, patients with FAT1 mutations had higher durable clinical benefit (DCB, 71.4% vs. 22.7%, P = 0.01) and objective response rates (ORR, 57.1% vs. 15.2%, P = 0.02). On the other hand, patients with activating mutations in EGFR/ERBB2 had reduced median progression-free survival (mPFS) compared with others [51.0 vs. 70.5 days, P = 0.0037, HR, 2.47; 95% confidence interval (CI), 1.32-4.62]. In addition, copy-number loss in specific chromosome 3p segments containing the tumor-suppressor ITGA9 and several chemokine receptor pathway genes, were highly predictive of poor clinical outcome (survival rates at 6 months, 0% vs. 31%, P = 0.012, HR, 2.08; 95% CI, 1.09-4.00). Our findings were further validated in two independently published datasets comprising multiple cancer types.

CONCLUSIONS

We identified novel genomic biomarkers that were predictive of response to anti-PD-(L)1 therapies. Our findings suggest that comprehensive profiling of TMB and the aforementioned molecular markers could result in greater predictive power of response to ICI therapies in NSCLC.

Reporting and connecting cell type names and gating definitions through ontologies

Background

Human immunology studies often rely on the isolation and quantification of cell populations from an input sample based on flow cytometry and related techniques. Such techniques classify cells into populations based on the detection of a pattern of markers. The description of the cell populations targeted in such experiments typically have two complementary components: the description of the cell type targeted (e.g. ‘T cells’), and the description of the marker pattern utilized (e.g. CD14−, CD3+).

Results

We here describe our attempts to use ontologies to cross-compare cell types and marker patterns (also referred to as gating definitions). We used a large set of such gating definitions and corresponding cell types submitted by different investigators into ImmPort, a central database for immunology studies, to examine the ability to parse gating definitions using terms from the Protein Ontology (PRO) and cell type descriptions, using the Cell Ontology (CL). We then used logical axioms from CL to detect discrepancies between the two.

Conclusions

We suggest adoption of our proposed format for describing gating and cell type definitions to make comparisons easier. We also suggest a number of new terms to describe gating definitions in flow cytometry that are not based on molecular markers captured in PRO, but on forward- and side-scatter of light during data acquisition, which is more appropriate to capture in the Ontology for Biomedical Investigations (OBI). Finally, our approach results in suggestions on what logical axioms and new cell types could be considered for addition to the Cell Ontology.

Cells in ExperimentaL Life Sciences (CELLS-2018): capturing the knowledge of normal and diseased cells with ontologies

Cell cultures and cell lines are widely used in life science experiments. In conjunction with the 2018 International Conference on Biomedical Ontology (ICBO-2018), the 2nd International Workshop on Cells in ExperimentaL Life Science (CELLS-2018) focused on two themes of knowledge representation, for newly-discovered cell types and for cells in disease states. This workshop included five oral presentations and a general discussion session. Two new ontologies, including the Cancer Cell Ontology (CCL) and the Ontology for Stem Cell Investigations (OSCI), were reported in the workshop. In another representation, the Cell Line Ontology (CLO) framework was applied and extended to represent cell line cells used in China and their Chinese representation. Other presentations included a report on the application of ontologies to cross-compare cell types and marker patterns used in flow cytometry studies, and a presentation on new experimental findings about novel cell types based on single cell RNA sequencing assay and their corresponding ontological representation. The general discussion session focused on the ontology design patterns in representing newly-discovered cell types and cells in disease states.

Assessment of Postdonation Outcomes in US Living Kidney Donors Using Publicly Available Data Sets

Importance

There are limited resources providing postdonation conditions that can occur in living donors (LDs) of solid-organ transplant. Consequently, it is difficult to visualize and understand possible postdonation outcomes in LDs.

Objective

To assemble an open access resource that is representative of the demographic characteristics in the US national registry, maintained by the Organ Procurement and Transplantation Network and administered by the United Network for Organ Sharing, but contains more follow-up information to help to examine postdonation outcomes in LDs.

Design, Setting, and Participants

Cohort study in which the data for the resource and analyses stemmed from the transplant data set derived from 27 clinical studies from the ImmPort database, which is an open access repository for clinical studies. The studies included data collected from 1963 to 2016. Data from the United Network for Organ Sharing Organ Procurement and Transplantation Network national registry collected from October 1987 to March 2016 were used to determine representativeness. Data analysis took place from June 2016 to May 2018. Data from 20 ImmPort clinical studies (including clinical trials and observational studies) were curated, and a cohort of 11 263 LDs was studied, excluding deceased donors, LDs with 95% or more missing data, and studies without a complete data dictionary. The harmonization process involved the extraction of common features from each clinical study based on categories that included demographic characteristics as well as predonation and postdonation data.

Main Outcomes and Measures

Thirty-six postdonation events were identified, represented, and analyzed via a trajectory network analysis.

Results

The curated data contained 10 869 living kidney donors (median [interquartile range] age, 39 [31-48] years; 6175 [56.8%] women; and 9133 [86.6%] of European descent). A total of 9558 living kidney donors with postdonation data were analyzed. Overall, 1406 LDs (14.7%) had postdonation events. The 4 most common events were hypertension (806 [8.4%]), diabetes (190 [2.0%]), proteinuria (171 [1.8%]), and postoperative ileus (147 [1.5%]). Relatively few events (n = 269) occurred before the 2-year postdonation mark. Of the 1746 events that took place 2 years or more after donation, 1575 (90.2%) were nonsurgical; nonsurgical conditions tended to occur in the wide range of 2 to 40 years after donation (odds ratio, 38.3; 95% CI, 4.12-1956.9).

Conclusions and Relevance

Most events that occurred more than 2 years after donation were nonsurgical and could occur up to 40 years after donation. Findings support the construction of a national registry for long-term monitoring of LDs and confirm the value of secondary reanalysis of clinical studies.

MicroRNA-155 coordinates the immunological landscape within murine melanoma and correlates with immunity in human cancers

miR-155 has recently emerged as an important promoter of antitumor immunity through its functions in T lymphocytes. However, the impact of T cell-expressed miR-155 on immune cell dynamics in solid tumors remains unclear. In the present study, we used single-cell RNA sequencing to define the CD45+ immune cell populations at different time points within B16F10 murine melanoma tumors growing in either wild-type or miR-155 T cell conditional knockout (TCKO) mice. miR-155 was required for optimal T cell activation and reinforced the T cell response at the expense of infiltrating myeloid cells. Further, myeloid cells from tumors growing in TCKO mice were defined by an increase in wound healing genes and a decreased IFN-γ-response gene signature. Finally, we found that miR-155 expression predicted a favorable outcome in human melanoma patients and was associated with a strong immune signature. Moreover, gene expression analysis of The Cancer Genome Atlas (TCGA) data revealed that miR-155 expression also correlates with an immune-enriched subtype in 29 other human solid tumors. Together, our study provides an unprecedented analysis of the cell types and gene expression signatures of immune cells within experimental melanoma tumors and elucidates the role of miR-155 in coordinating antitumor immune responses in mammalian tumors.

A pilot study showing a stronger H1N1 influenza vaccination response during pregnancy in women who subsequently deliver preterm

PROBLEM

Preterm birth (PTB), or the delivery of an infant prior to 37 weeks of gestation, is a major health concern. Although a variety of social, environmental, and maternal factors have been implicated in PTB, causes of preterm labor have remained largely unknown. There is evidence of effectiveness and safety of influenza vaccination during pregnancy, however fewer studies have looked at vaccination response as an indicator of an innate host response that may be associated with adverse pregnancy outcomes. We carried out a pilot study to analyze the flu vaccine response during pregnancy of women who later deliver preterm or term.

METHOD OF STUDY

We performed a secondary analysis of the individual-level data from an influenza vaccination response study (openly available from ImmPort) measured by hemagglutination inhibition assay of 91 pregnant women with term deliveries and 11 women who went on to deliver preterm. Flu vaccination responses for H1N1 and H3N2 influenza strains were compared between term and preterm deliveries.

RESULTS

Women who went on to deliver preterm showed a significantly (P <  0.001) greater flu vaccine response for the H1N1 strain than women who delivered at term. The vaccine response for H3N2 was not significantly different between these two groups (P =  0.97).

CONCLUSIONS

Although the sample size is limited and additional validation is required, our findings suggest an increased activation of the maternal immune system as shown by the stronger vaccination response to H1N1 in women who subsequently delivered preterm, in comparison to women who delivered at term.

Prototype of running clinical trials in an untrustworthy environment using blockchain

Monitoring and ensuring the integrity of data within the clinical trial process is currently not always feasible with the current research system. We propose a blockchain-based system to make data collected in the clinical trial process immutable, traceable, and potentially more trustworthy. We use raw data from a real completed clinical trial, simulate the trial onto a proof of concept web portal service, and test its resilience to data tampering. We also assess its prospects to provide a traceable and useful audit trail of trial data for regulators, and a flexible service for all members within the clinical trials network. We also improve the way adverse events are currently reported. In conclusion, we advocate that this service could offer an improvement in clinical trial data management, and could bolster trust in the clinical research process and the ease at which regulators can oversee trials.

Ensuring the integrity of clinical trial data is crucial to securing trust in the process. Here, the authors present a prototype of a blockchain-based clinical trial management system that ensures immutability and traceability of trial data, and demonstrate a proof of concept web portal service.

Open data informatics and data repurposing for IBD

Biomedical ‘big data’ has opened opportunities for data repurposing to reveal new insights into complex diseases. Public data on IBD have been repurposed for novel diagnostics and therapeutics, and these datasets continue to grow. Here, we discuss the practicalities and implications of open data informatics for IBD.

Elucidating the Interaction of CF Airway Epithelial Cells and Rhinovirus: Using the Host-Pathogen Relationship to Identify Future Therapeutic Strategies

Chronic lung disease remains the primary cause of mortality in cystic fibrosis (CF). Growing evidence suggests respiratory viral infections are often more severe in CF compared to healthy peers and contributes to pulmonary exacerbations (PEx) and deterioration of lung function. Rhinovirus is the most prevalent respiratory virus detected, particularly during exacerbations in children with CF <5 years old. However, even though rhinoviral infections are likely to be one of the factors initiating the onset of CF lung disease, there is no effective targeted treatment. A better understanding of the innate immune responses by CF airway epithelial cells, the primary site of infection for viruses, is needed to identify why viral infections are more severe in CF. The aim of this review is to present the clinical impact of virus infection in both young children and adults with CF, focusing on rhinovirus infection. Previous in vitro and in vivo investigations looking at the mechanisms behind virus infection will also be summarized. The review will finish on the potential of transcriptomics to elucidate the host-pathogen responses by CF airway cells to viral infection and identify novel therapeutic targets.

Enabling precision medicine in neonatology, an integrated repository for preterm birth research

Preterm birth, or the delivery of an infant prior to 37 weeks of gestation, is a significant cause of infant morbidity and mortality. In the last decade, the advent and continued development of molecular profiling technologies has enabled researchers to generate vast amount of 'omics' data, which together with integrative computational approaches, can help refine the current knowledge about disease mechanisms, diagnostics, and therapeutics. Here we describe the March of Dimes' Database for Preterm Birth Research (http://www.immport.org/resources/mod), a unique resource that contains a variety of 'omics' datasets related to preterm birth. The database is open publicly, and as of January 2018, links 13 molecular studies with data across tens of thousands of patients from 6 measurement modalities. The data in the repository are highly diverse and include genomic, transcriptomic, immunological, and microbiome data. Relevant datasets are augmented with additional molecular characterizations of almost 25,000 biological samples from public databases. We believe our data-sharing efforts will lead to enhanced research collaborations and coordination accelerating the overall pace of discovery in preterm birth research.

Observed Antibody Space: A Resource for Data Mining Next-Generation Sequencing of Antibody Repertoires

Abs are immune system proteins that recognize noxious molecules for elimination. Their sequence diversity and binding versatility have made Abs the primary class of biopharmaceuticals. Recently, it has become possible to query their immense natural diversity using next-generation sequencing of Ig gene repertoires (Ig-seq). However, Ig-seq outputs are currently fragmented across repositories and tend to be presented as raw nucleotide reads, which means nontrivial effort is required to reuse the data for analysis. To address this issue, we have collected Ig-seq outputs from 55 studies, covering more than half a billion Ab sequences across diverse immune states, organisms (primarily human and mouse), and individuals. We have sorted, cleaned, annotated, translated, and numbered these sequences and make the data available via our Observed Antibody Space (OAS) resource at http://antibodymap.org The data within OAS will be regularly updated with newly released Ig-seq datasets. We believe OAS will facilitate data mining of immune repertoires for improved understanding of the immune system and development of better biotherapeutics.

High-quality science requires high-quality open data infrastructure

Resources for data management, discovery and (re)use are numerous and diverse, and more specifically we need data resources that enable the FAIR principles1 of Findability, Accessibility, Interoperability and Reusability of data.