Host immune transcriptional profiles reflect the variability in clinical disease manifestations in patients with Staphylococcus aureus infections

International multi-cohort analysis identifies novel framework for quantifying immune dysregulation in critical illness: results of the SUBSPACE consortium

Progress in the management of critical care syndromes such as sepsis, Acute Respiratory Distress Syndrome (ARDS), and trauma has slowed over the last two decades, limited by the inherent heterogeneity within syndromic illnesses. Numerous immune endotypes have been proposed in sepsis and critical care, however the overlap of the endotypes is unclear, limiting clinical translation. The SUBSPACE consortium is an international consortium that aims to advance precision medicine through the sharing of transcriptomic data. By evaluating the overlap of existing immune endotypes in sepsis across over 6,000 samples, we developed cell-type specific signatures to quantify dysregulation in these immune compartments. Myeloid and lymphoid dysregulation were associated with disease severity and mortality across all cohorts. This dysregulation was not only observed in sepsis but also in ARDS, trauma, and burn patients, indicating a conserved mechanism across various critical illness syndromes. Moreover, analysis of randomized controlled trial data revealed that myeloid and lymphoid dysregulation is linked to differential mortality in patients treated with anakinra or corticosteroids, underscoring its prognostic and therapeutic significance. In conclusion, this novel immunology-based framework for quantifying cellular compartment dysregulation offers a valuable tool for prognosis and therapeutic decision-making in critical illness.

Examination of the shared genetic architecture between multiple sclerosis and systemic lupus erythematosus facilitates discovery of novel lupus risk loci

Systemic Lupus Erythematosus (SLE) is an autoimmune disease with heterogeneous manifestations, including neurological and psychiatric symptoms. Genetic association studies in SLE have been hampered by insufficient sample size and limited power compared to many other diseases. Multiple Sclerosis (MS) is a chronic relapsing autoimmune disease of the central nervous system (CNS) that also manifests neurological and immunological features. Here, we identify a method of leveraging large-scale genome wide association studies (GWAS) in MS to identify novel genetic risk loci in SLE. Statistical genetic comparison methods including linkage disequilibrium score regression (LDSC) and cross-phenotype association analysis (CPASSOC) to identify genetic overlap in disease pathophysiology, traditional 2-sample and novel PPI-based mendelian randomization to identify causal associations and Bayesian colocalization were applied to association studies conducted in MS to facilitate discovery in the smaller, more limited datasets available for SLE. Pathway analysis using SNP-to-gene mapping identified biological networks composed of molecular pathways with causal implications for CNS disease in SLE specifically, as well as pathways likely causal of both pathologies, providing key insights for therapeutic selection.

An interpretable machine learning pipeline based on transcriptomics predicts phenotypes of lupus patients

Machine learning (ML) has the potential to identify subsets of patients with distinct phenotypes from gene expression data. However, phenotype prediction using ML has often relied on identifying important genes without a systems biology context. To address this, we created an interpretable ML approach based on blood transcriptomics to predict phenotype in systemic lupus erythematosus (SLE), a heterogeneous autoimmune disease. We employed a sequential grouped feature importance algorithm to assess the performance of gene sets, including immune and metabolic pathways and cell types, known to be abnormal in SLE in predicting disease activity and organ involvement. Gene sets related to interferon, tumor necrosis factor, the mitoribosome, and T cell activation were the best predictors of phenotype with excellent performance. These results suggest potential relationships between the molecular pathways identified in each model and manifestations of SLE. This ML approach to phenotype prediction can be applied to other diseases and tissues.

Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature

BACKGROUND

Appropriate treatment and management of children presenting with fever depend on accurate and timely diagnosis, but current diagnostic tests lack sensitivity and specificity and are frequently too slow to inform initial treatment. As an alternative to pathogen detection, host gene expression signatures in blood have shown promise in discriminating several infectious and inflammatory diseases in a dichotomous manner. However, differential diagnosis requires simultaneous consideration of multiple diseases. Here, we show that diverse infectious and inflammatory diseases can be discriminated by the expression levels of a single panel of genes in blood.

METHODS

A multi-class supervised machine-learning approach, incorporating clinical consequence of misdiagnosis as a "cost" weighting, was applied to a whole-blood transcriptomic microarray dataset, incorporating 12 publicly available datasets, including 1,212 children with 18 infectious or inflammatory diseases. The transcriptional panel identified was further validated in a new RNA sequencing dataset comprising 411 febrile children.

FINDINGS

We identified 161 transcripts that classified patients into 18 disease categories, reflecting individual causative pathogen and specific disease, as well as reliable prediction of broad classes comprising bacterial infection, viral infection, malaria, tuberculosis, or inflammatory disease. The transcriptional panel was validated in an independent cohort and benchmarked against existing dichotomous RNA signatures.

CONCLUSIONS

Our data suggest that classification of febrile illness can be achieved with a single blood sample and opens the way for a new approach for clinical diagnosis.

FUNDING

European Union's Seventh Framework no. 279185; Horizon2020 no. 668303 PERFORM; Wellcome Trust (206508/Z/17/Z); Medical Research Foundation (MRF-160-0008-ELP-KAFO-C0801); NIHR Imperial BRC.

Staphylococcus aureus isolates from children with clinically differentiated osteomyelitis exhibit distinct transcriptomic signatures

There is substantial genomic heterogeneity among Staphylococcus aureus isolates of children with acute hematogenous osteomyelitis (AHO) but transcriptional behavior of clinically differentiated strains has not been previously described. This study evaluates transcriptional activity of S. aureus isolates of children with AHO that may regulate metabolism, biosynthesis, or virulence during bacterial growth and pathogenesis. In vitro growth kinetics were compared between three S. aureus clinical isolates from children with AHO who had mild, moderate, and severe illness. Total RNA sequencing was performed for each isolate at six separate time points throughout the logarithmic phase of growth. The NASA RNA-Sequencing Consensus Pipeline was used to identify differentially expressed genes allowing for 54 comparisons between the three isolates during growth. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathways were used to evaluate transcriptional variation in metabolism, biosynthesis pathways and virulence potential of the isolates. The S. aureus isolates demonstrated differing growth kinetics under standardized conditions with the mild isolate having higher optical densities with earlier and higher peak rates of growth than that of the other isolates (p<0.001). Enrichment pathway analysis established distinct transcriptional signatures according to both sampling time and clinical severity. Moderate and severe isolates demonstrated pathways of bacterial invasion, S. aureus infection, quorum sensing and two component systems. In comparison, the mild strain favored biosynthesis and metabolism. These findings suggest that transcriptional regulation during the growth of S. aureus may impact the pathogenetic mechanisms involved in the progression of severity of illness in childhood osteomyelitis. The clinical isolates studied demonstrated a tradeoff between growth and virulence. Further investigation is needed to evaluate these transcriptional pathways in an animal model or during active clinical infections of children with AHO.

Comparison of gene set scoring methods for reproducible evaluation of multiple tuberculosis gene signatures

Rationale

Many blood-based transcriptional gene signatures for tuberculosis (TB) have been developed with potential use to diagnose disease, predict risk of progression from infection to disease, and monitor TB treatment outcomes. However, an unresolved issue is whether gene set enrichment analysis (GSEA) of the signature transcripts alone is sufficient for prediction and differentiation, or whether it is necessary to use the original statistical model created when the signature was derived. Intra-method comparison is complicated by the unavailability of original training data, missing details about the original trained model, and inadequate publicly-available software tools or source code implementing models. To facilitate these signatures' replicability and appropriate utilization in TB research, comprehensive comparisons between gene set scoring methods with cross-data validation of original model implementations are needed.

Objectives

We compared the performance of 19 TB gene signatures across 24 transcriptomic datasets using both re-rebuilt original models and gene set scoring methods to evaluate whether gene set scoring is a reasonable proxy to the performance of the original trained model. We have provided an open-access software implementation of the original models for all 19 signatures for future use.

Methods

We considered existing gene set scoring and machine learning methods, including ssGSEA, GSVA, PLAGE, Singscore, and Zscore, as alternative approaches to profile gene signature performance. The sample-size-weighted mean area under the curve (AUC) value was computed to measure each signature's performance across datasets. Correlation analysis and Wilcoxon paired tests were used to analyze the performance of enrichment methods with the original models.

Measurement and Main Results

For many signatures, the predictions from gene set scoring methods were highly correlated and statistically equivalent to the results given by the original diagnostic models. PLAGE outperformed all other gene scoring methods. In some cases, PLAGE outperformed the original models when considering signatures' weighted mean AUC values and the AUC results within individual studies.

Conclusion

Gene set enrichment scoring of existing blood-based biomarker gene sets can distinguish patients with active TB disease from latent TB infection and other clinical conditions with equivalent or improved accuracy compared to the original methods and models. These data justify using gene set scoring methods of published TB gene signatures for predicting TB risk and treatment outcomes, especially when original models are difficult to apply or implement.

A robust host-response-based signature distinguishes bacterial and viral infections across diverse global populations

Limited sensitivity and specificity of current diagnostics lead to the erroneous prescription of antibiotics. Host-response-based diagnostics could address these challenges. However, using 4,200 samples across 69 blood transcriptome datasets from 20 countries from patients with bacterial or viral infections representing a broad spectrum of biological, clinical, and technical heterogeneity, we show current host-response-based gene signatures have lower accuracy to distinguish intracellular bacterial infections from viral infections than extracellular bacterial infections. Using these 69 datasets, we identify an 8-gene signature to distinguish intracellular or extracellular bacterial infections from viral infections with an area under the receiver operating characteristic curve (AUROC) > 0.91 (85.9% specificity and 90.2% sensitivity). In prospective cohorts from Nepal and Laos, the 8-gene classifier distinguished bacterial infections from viral infections with an AUROC of 0.94 (87.9% specificity and 91% sensitivity). The 8-gene signature meets the target product profile proposed by the World Health Organization and others for distinguishing bacterial and viral infections.

Tissue specificity drives protective immunity against Staphylococcus aureus infection

Infections caused by Staphylococcus aureus range from mild to severe and frequently recur. Emerging evidence suggests that the site and severity of infection drive the potency of elicited immune responses and susceptibility to recurrent infection. In this study, we used tractable mouse models of S. aureus skin infection (SSTI) and pneumonia to determine the relative magnitude of elicited protective immunity. Surprisingly, despite both SSTI and pneumonia eliciting antibody and local effector T cell responses, only SSTI elicited protective antibody and memory T cell responses and subsequent protection against secondary SSTI and pneumonia. The failure of pneumonia to elicit protective immunity was attributed to an inability of S. aureus pneumonia to elicit toxin-specific antibodies that confer protection during secondary infection and was associated with a failure to expand antigen-specific memory T cells. Taken together, these findings emphasize the importance of understanding protective immunity in the context of the tissue-specificity.

Staphylococcus aureus induces a muted host response in human blood that blunts the recruitment of neutrophils

Staphylococcus aureus is an opportunistic pathogen and chief among bloodstream-infecting bacteria. S. aureus produces an array of human-specific virulence factors that may contribute to immune suppression. Here, we defined the response of primary human phagocytes following infection with S. aureus using RNA-sequencing (RNA-Seq). We found that the overall transcriptional response to S. aureus was weak both in the number of genes and in the magnitude of response. Using an ex vivo bacteremia model with fresh human blood, we uncovered that infection with S. aureus resulted in the down-regulation of genes related to innate immune response and cytokine and chemokine signaling. This muted transcriptional response was conserved across diverse S. aureus clones but absent in blood exposed to heat-killed S. aureus or blood infected with the less virulent staphylococcal species Staphylococcus epidermidis. Notably, this signature was also present in patients with S. aureus bacteremia. We identified the master regulator S. aureus exoprotein expression (SaeRS) and the SaeRS-regulated pore-forming toxins as key mediators of the transcriptional suppression. The S. aureus-mediated suppression of chemokine and cytokine transcription was reflected by circulating protein levels in the plasma. Wild-type S. aureus elicited a soluble milieu that was restrictive in the recruitment of human neutrophils compared with strains lacking saeRS. Thus, S. aureus blunts the inflammatory response resulting in impaired neutrophil recruitment, which could promote the survival of the pathogen during invasive infection.

Increasing reproducibility, robustness, and generalizability of biomarker selection from meta-analysis using Bayesian methodology

A major limitation of gene expression biomarker studies is that they are not reproducible as they simply do not generalize to larger, real-world, heterogeneous populations. Frequentist multi-cohort gene expression meta-analysis has been frequently used as a solution to this problem to identify biomarkers that are truly differentially expressed. However, the frequentist meta-analysis framework has its limitations-it needs at least 4-5 datasets with hundreds of samples, is prone to confounding from outliers and relies on multiple-hypothesis corrected p-values. To address these shortcomings, we have created a Bayesian meta-analysis framework for the analysis of gene expression data. Using real-world data from three different diseases, we show that the Bayesian method is more robust to outliers, creates more informative estimates of between-study heterogeneity, reduces the number of false positive and false negative biomarkers and selects more generalizable biomarkers with less data. We have compared the Bayesian framework to a previously published frequentist framework and have developed a publicly available R package for use.

The Underlying Molecular Basis and Mechanisms of Venous Thrombosis in Patients with Osteomyelitis: A Data-Driven Analysis

Objective

Osteomyelitis (OM) is one of the most risky and challenging diseases. Emerging evidence indicates OM is a risk factor for increasing incidence of venous thromboembolism (VTE) development. However, the mechanisms have not been intensively investigated.

Methods

The OM-related dataset GSE30119 and VTE-related datasets GSE19151 and GSE48000 were downloaded from the Gene Expression Omnibus (GEO) database and analyzed to identify the differentially expressed genes (DEGs) (OMGs1 and VTEGs1, respectively). Functional enrichment analyses of Gene Ontology (GO) terms were performed. VTEGs2 and OMGs2 sharing the common GO biological process (GO-BP) ontology between OMGs1 and VTEGs1 were detected. The TRRUST database was used to identify the upstream transcription factors (TFs) that regulate VTEGs2 and OMGs2. The protein-protein interaction (PPI) network between VTEGs2 and OMGs2 was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and then visualized in Cytoscape. Topological properties of the PPI network were calculated by NetworkAnalyzer. The Molecular Complex Detection (MCODE) plugin was utilized to perform module analysis and choose the hub modules of the PPI network.

Results

A total of 587 OMGs1 and 382 VTEGs1 were identified from the related dataset, respectively. GO-BP terms of OMGs1 and shared DGEs1 were mainly enriched in the neutrophil-related immune response process, and the shared GO-BP terms of OMGs1 and VTEGs1 seemed to be focused on cell activation, immune, defense, and inflammatory response to stress or biotic stimulus. 230 VTEGs2, 333 OMGs2, and 13 shared DEGs2 were detected. 3 TF-target gene pairs (SP1-LSP1, SPI1-FCGR1A, and STAT1-FCGR1A) were identified. The PPI network contained 1611 interactions among 467 nodes. The top 10 hub proteins were TP53, IL4, MPO, ELANE, FOS, CD86, HP, SOCS3, ICAM1, and SNRPG. Several core nodes (such as MPO, ELANE, and CAMP) were essential components of the neutrophil extracellular traps (NETs) network.

Conclusion

This is the first data-mining study to explore shared signatures between OM and VTE by the integrated bioinformatic approach, which can help uncover potential biomarkers and therapeutic targets of OM-related VTE.

Transcriptome Architecture of Osteoblastic Cells Infected With Staphylococcus aureus Reveals Strong Inflammatory Responses and Signatures of Metabolic and Epigenetic Dysregulation

Staphylococcus aureus is an opportunistic pathogen that causes a range of devastating diseases including chronic osteomyelitis, which partially relies on the internalization and persistence of S. aureus in osteoblasts. The identification of the mechanisms of the osteoblast response to intracellular S. aureus is thus crucial to improve the knowledge of this infectious pathology. Since the signal from specifically infected bacteria-bearing cells is diluted and the results are confounded by bystander effects of uninfected cells, we developed a novel model of long-term infection. Using a flow cytometric approach we isolated only S. aureus-bearing cells from mixed populations that allows to identify signals specific to intracellular infection. Here we present an in-depth analysis of the effect of long-term S. aureus infection on the transcriptional program of human osteoblast-like cells. After RNA-seq and KEGG and Reactome pathway enrichment analysis, the remodeled transcriptomic profile of infected cells revealed exacerbated immune and inflammatory responses, as well as metabolic dysregulations that likely influence the intracellular life of bacteria. Numerous genes encoding epigenetic regulators were downregulated. The later included genes coding for components of chromatin-repressive complexes (e.g., NuRD, BAHD1 and PRC1) and epifactors involved in DNA methylation. Sets of genes encoding proteins of cell adhesion or neurotransmission were also deregulated. Our results suggest that intracellular S. aureus infection has a long-term impact on the genome and epigenome of host cells, which may exert patho-physiological dysfunctions additionally to the defense response during the infection process. Overall, these results not only improve our conceptual understanding of biological processes involved in the long-term S. aureus infections of osteoblast-like cells, but also provide an atlas of deregulated host genes and biological pathways and identify novel markers and potential candidates for prophylactic and therapeutic approaches.

Investigation of hub gene associated with the infection of Staphylococcus aureus via weighted gene co-expression network analysis

Introduction

Staphylococcus aureus is a gram-positive bacterium that causes serious infection. With the increasing resistance of bacteria to current antibiotics, it is necessary to learn more about the molecular mechanism and cellular pathways involved in the Staphylococcus aureus infection.

Methods

We downloaded the GSE33341 dataset from the GEO database and applied the weighted gene co-expression network analysis (WGCNA), from which we obtained some critical modules. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were applied to illustrate the biological functions of genes in these modules. We constructed the protein-protein interaction (PPI) network by Cytoscape and selected five candidate hub genes. Five potential hub genes were validated in GSE30119 by GraphPad Prism 8.0. The diagnostic values of these genes were calculated and present in the ROC curve based on the GSE13670 dataset. Their gene functions were analyzed by Gene Set Enrichment Analysis (GSEA).

Results

A co-expression network was built with 5000 genes divided into 11 modules. The genes in green and turquoise modules demonstrated a high correlation. According to the KEGG and GO analyses, genes in the green module were closely related to ubiquitination and autophagy. Subsequently, we picked out the top five hub genes in the green module. And UBB was determined as the hub gene in the GSE30119 dataset. The expression level of UBB, ASB, and MKRN1 could significantly differentiate between Staphylococcus aureus infection and healthy controls based on the ROC curve. The GSEA analysis indicated that lower expression levels of UBB were associated with the P53 signal pathway.

Conclusions

We identified some hub genes and significant signal enrichment pathways in Staphylococcus aureus infection via bioinformatics analysis, which may facilitate the development of potential clinical therapeutic strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02392-y.

Improved radiation expression profiling in blood by sequential application of sensitive and specific gene signatures

PURPOSE

Combinations of expressed genes can discriminate radiation-exposed from normal control blood samples by machine learning (ML) based signatures (with 8-20% misclassification rates). These signatures can quantify therapeutically relevant as well as accidental radiation exposures. The prodromal symptoms of acute radiation syndrome (ARS) overlap those present in influenza and dengue fever infections. Surprisingly, these human radiation signatures misclassified gene expression profiles of virally infected samples as false positive exposures. The present study investigates these and other confounders, and then mitigates their impact on signature accuracy.

METHODS

This study investigated recall by previous and novel radiation signatures independently derived from multiple Gene Expression Omnibus datasets on common and rare non-neoplastic blood disorders and blood-borne infections (thromboembolism, S. aureus bacteremia, malaria, sickle cell disease, polycythemia vera, and aplastic anemia). Normalized expression levels of signature genes are used as input to ML-based classifiers to predict radiation exposure in other hematological conditions.

RESULTS

Except for aplastic anemia, these blood-borne disorders modify the normal baseline expression values of genes present in radiation signatures, leading to false-positive misclassification of radiation exposures in 8-54% of individuals. Shared changes, predominantly in DNA damage response and apoptosis-related gene transcripts in radiation and confounding hematological conditions, compromise the utility of these signatures for radiation assessment. These confounding conditions (sickle cell disease, thrombosis, S. aureus bacteremia, malaria) induce neutrophil extracellular traps, initiated by chromatin decondensation, DNA damage response and fragmentation followed by programmed cell death or extrusion of DNA fragments. Riboviral infections (e.g. influenza or dengue fever) have been proposed to bind and deplete host RNA binding proteins, inducing R-loops in chromatin. R-loops that collide with incoming replication forks can result in incompletely repaired DNA damage, inducing apoptosis and releasing mature virus. To mitigate the effects of confounders, we evaluated predicted radiation-positive samples with novel gene expression signatures derived from radiation-responsive transcripts encoding secreted blood plasma proteins whose expression levels are unperturbed by these conditions.

CONCLUSIONS

This approach identifies and eliminates misclassified samples with underlying hematological or infectious conditions, leaving only samples with true radiation exposures. Diagnostic accuracy is significantly improved by selecting genes that maximize both sensitivity and specificity in the appropriate tissue using combinations of the best signatures for each of these classes of signatures.

Immune System Disequilibrium-Neutrophils, Their Extracellular Traps, and COVID-19-Induced Sepsis

Equilibrium within the immune system can often determine the fate of its host. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogen responsible for the coronavirus disease 2019 (COVID-19) pandemic. Immune dysregulation remains one of the main pathophysiological components of SARS-CoV-2-associated organ injury, with over-activation of the innate immune system, and induced apoptosis of adaptive immune cells. Here, we provide an overview of the innate immune system, both in general and relating to COVID-19. We specifically discuss "NETosis," the process of neutrophil release of their extracellular traps, which may be a more recently described form of cell death that is different from apoptosis, and how this may propagate organ dysfunction in COVID-19. We complete this review by discussing Stem Cell Therapies in COVID-19 and emerging COVID-19 phenotypes, which may allow for more targeted therapy in the future. Finally, we consider the array of potential therapeutic targets in COVID-19, and associated therapeutics.

Sex Differences in Blood Transcriptional Profiles and Clinical Phenotypes in Pediatric Patients with Eosinophilic Esophagitis

BACKGROUND

Eosinophilic esophagitis (EoE) is an increasingly recognized, chronic inflammatory disease. Recent reports suggest clinical differences between males and females.

OBJECTIVE

To define the relevant molecular pathways that could be related to clinical phenotypes in children with EoE.

METHODS

We performed blood RNA expression analysis in children with newly diagnosed EoE and matched, healthy controls, and applied bioinformatics tools to define EoE host immune biosignatures. Questionnaires and medical records were used to characterize symptoms, esophagogastroduodenoscopy results, and treatment response.

RESULTS

Forty-one subjects (aged 2-17 years) were enrolled; the cohort consisted of 27 males and 14 females. Patients were randomly divided into a discovery cohort (21 EoE patients and 12 controls) that identified 544 significant differentially expressed transcripts (P ≤ .01; 1.25-fold change). Those 544 transcripts correctly classified most EoE patients in the validation cohort (n = 20) from healthy controls. Global transcriptional perturbation relative to healthy controls, Molecular Distance to Health scores were greater in EoE patients than controls (P = .003). When we analyzed subjects based on age and sex, males 13 years of age and older were more likely to have food impactions (P = .033) and to have higher endoscopic severity scores (P = .036). Separate group comparisons according to sex identified 294 differentially expressed transcripts in males and 643 transcripts in female EoE patients. Of those, 37 genes were shared and similarly expressed irrespective of sex.

CONCLUSIONS

Whole blood transcriptional analysis represents a promising noninvasive tool to assess activity of the immune/inflammatory response in children with EoE. Male and female EoE patients showed robust differences in gene expression suggesting distinct pathogenic endotypes.

BloodGen3Module: Blood transcriptional module repertoire analysis and visualization using R

Motivation

We previously described the construction and characterization of fixed reusable blood transcriptional module repertoires. More recently we released a third iteration (‘BloodGen3’ module repertoire) that comprises 382 functionally annotated modules and encompasses 14 168 transcripts. Custom bioinformatic tools are needed to support downstream analysis, visualization and interpretation relying on such fixed module repertoires.

Results

We have developed and describe here an R package, BloodGen3Module. The functions of our package permit group comparison analyses to be performed at the module-level, and to display the results as annotated fingerprint grid plots. A parallel workflow for computing module repertoire changes for individual samples rather than groups of samples is also available; these results are displayed as fingerprint heatmaps. An illustrative case is used to demonstrate the steps involved in generating blood transcriptome repertoire fingerprints of septic patients. Taken together, this resource could facilitate the analysis and interpretation of changes in blood transcript abundance observed across a wide range of pathological and physiological states.

Availability and implementation

The BloodGen3Module package and documentation are freely available from Github: https://github.com/Drinchai/BloodGen3Module.

Supplementary information

Supplementary data are available at Bioinformatics online.

SOS GEFs in health and disease

SOS1 and SOS2 are the most universal and widely expressed family of guanine exchange factors (GEFs) capable or activating RAS or RAC1 proteins in metazoan cells. SOS proteins contain a sequence of modular domains that are responsible for different intramolecular and intermolecular interactions modulating mechanisms of self-inhibition, allosteric activation and intracellular homeostasis. Despite their homology, analyses of SOS1/2-KO mice demonstrate functional prevalence of SOS1 over SOS2 in cellular processes including proliferation, migration, inflammation or maintenance of intracellular redox homeostasis, although some functional redundancy cannot be excluded, particularly at the organismal level. Specific SOS1 gain-of-function mutations have been identified in inherited RASopathies and various sporadic human cancers. SOS1 depletion reduces tumorigenesis mediated by RAS or RAC1 in mouse models and is associated with increased intracellular oxidative stress and mitochondrial dysfunction. Since WT RAS is essential for development of RAS-mutant tumors, the SOS GEFs may be considered as relevant biomarkers or therapy targets in RAS-dependent cancers. Inhibitors blocking SOS expression, intrinsic GEF activity, or productive SOS protein-protein interactions with cellular regulators and/or RAS/RAC targets have been recently developed and shown preclinical and clinical effectiveness blocking aberrant RAS signaling in RAS-driven and RTK-driven tumors.

Mapping systemic lupus erythematosus heterogeneity at the single-cell level

Patients with systemic lupus erythematosus (SLE) display a complex blood transcriptome whose cellular origin is poorly resolved. Using single-cell RNA sequencing, we profiled ~276,000 peripheral blood mononuclear cells from 33 children with SLE with different degrees of disease activity and 11 matched controls. Increased expression of interferon-stimulated genes (ISGs) distinguished cells from children with SLE from healthy control cells. The high ISG expression signature (ISGhi) derived from a small number of transcriptionally defined subpopulations within major cell types, including monocytes, CD4+ and CD8+ T cells, natural killer cells, conventional and plasmacytoid dendritic cells, B cells and especially plasma cells. Expansion of unique subpopulations enriched in ISGs and/or in monogenic lupus-associated genes classified patients with the highest disease activity. Profiling of ~82,000 single peripheral blood mononuclear cells from adults with SLE confirmed the expansion of similar subpopulations in patients with the highest disease activity. This study lays the groundwork for resolving the origin of the SLE transcriptional signatures and the disease heterogeneity towards precision medicine applications.

Annexin A3 in sepsis: novel perspectives from an exploration of public transcriptome data

According to publicly available transcriptome datasets, the abundance of Annexin A3 (ANXA3) is robustly increased during the course of sepsis; however, no studies have examined the biological significance or clinical relevance of ANXA3 in this pathology. Here we explored this interpretation gap and identified possible directions for future research. Based on reference transcriptome datasets, we found that ANXA3 expression is restricted to neutrophils, is upregulated in vitro after exposure to plasma obtained from septic patients, and is associated with adverse clinical outcomes. Secondly, an increase in ANXA3 transcript abundance was also observed in vivo, in the blood of septic patients in multiple independent studies. ANXA3 is known to mediate calcium-dependent granules-phagosome fusion in support of microbicidal activity in neutrophils. More recent work has also shown that ANXA3 enhances proliferation and survival of tumour cells via a Caspase-3-dependent mechanism. And this same molecule is also known to play a critical role in regulation of apoptotic events in neutrophils. Thus, we posit that during sepsis ANXA3 might either play a beneficial role, by facilitating microbial clearance and resolution of the infection; or a detrimental role, by prolonging neutrophil survival, which is known to contribute to sepsis-mediated organ damage.

Characterization of peripheral blood mononuclear cells gene expression profiles of pediatric Staphylococcus aureus persistent and non-carriers using a targeted assay

Defects in innate immunity affect many different physiologic systems and several studies of patients with primary immunodeficiency disorders demonstrated the importance of innate immune system components in disease prevention or colonization of bacterial pathogens. To assess the role of the innate immune system on nasal colonization with Staphylococcus aureus, innate immune responses in pediatric S. aureus nasal persistent carriers (n = 14) and non-carriers (n = 15) were profiled by analyzing co-clustered gene sets (modules). We stimulated previously frozen peripheral blood mononuclear cells (PBMCs) from these subjects with i) a panel of TLR ligands, ii) live S. aureus (either a mixture of strains or stimulation with respective carriage isolates), or iii) heat-killed S. aureus. We found no difference in responses between carriers and non-carriers when PBMCs were stimulated with a panel of TLR ligands. However, PBMC gene expression profiles differed between persistent and non-S. aureus carriers following stimulation with either live or dead S. aureus. These observations suggest that individuals susceptible to persistent carriage with S. aureus may possess differences in their live/dead bacteria recognition pathway and that innate pathway signaling is different between persistent and non-carriers of S. aureus.

Baseline and Disease-Induced Transcriptional Profiles in Children with Sickle Cell Disease

Acute chest syndrome (ACS) is a significant cause of morbidity and mortality in sickle cell disease (SCD), but preventive, diagnostic, and therapeutic options are limited. Further, ACS and acute vasoccclusive pain crises (VOC) have overlapping features, which causes diagnostic dilemmas. We explored changes in gene expression profiles among patients with SCD hospitalized for VOC and ACS episodes to better understand ACS disease pathogenesis. Whole blood RNA-Seq was performed for 20 samples from children with SCD at baseline and during a hospitalization for either an ACS (n = 10) or a VOC episode (n = 10). Respiratory viruses were identified from nasopharyngeal swabs. Functional gene analyses were performed using modular repertoires, IPA, Gene Ontology, and NetworkAnalyst 3.0. The VOC group had a numerically higher percentage of female, older, and hemoglobin SS participants compared to the ACS group. Viruses were detected in 50% of ACS cases and 20% of VOC cases. We identified 3004 transcripts that were differentially expressed during ACS episodes, and 1802 transcripts during VOC episodes. Top canonical pathways during ACS episodes were related to interferon signaling, neuro-inflammation, pattern recognition receptors, and macrophages. Top canonical pathways in patients with VOC included IL-10 signaling, iNOS signaling, IL-6 signaling, and B cell signaling. Several genes related to antimicrobial function were down-regulated during ACS compared to VOC. Gene enrichment nodal interactions demonstrated significantly altered pathways during ACS and VOC. A complex network of changes in innate and adaptive immune gene expression were identified during both ACS and VOC episodes. These results provide unique insights into changes during acute events in children with SCD.

Global Transcriptome Analysis Identifies a Diagnostic Signature for Early Disseminated Lyme Disease and Its Resolution

A bioinformatics approach was employed to identify transcriptome alterations in the peripheral blood mononuclear cells of well-characterized human subjects who were diagnosed with early disseminated Lyme disease (LD) based on stringent microbiological and clinical criteria. Transcriptomes were assessed at the time of presentation and also at approximately 1 month (early convalescence) and 6 months (late convalescence) after initiation of an appropriate antibiotic regimen. Comparative transcriptomics identified 335 transcripts, representing 233 unique genes, with significant alterations of at least 2-fold expression in acute- or convalescent-phase blood samples from LD subjects relative to healthy donors. Acute-phase blood samples from LD subjects had the largest number of differentially expressed transcripts (187 induced, 54 repressed). This transcriptional profile, which was dominated by interferon-regulated genes, was sustained during early convalescence. 6 months after antibiotic treatment the transcriptome of LD subjects was indistinguishable from that of healthy controls based on two separate methods of analysis. Return of the LD expression profile to levels found in control subjects was concordant with disease outcome; 82% of subjects with LD experienced at least one symptom at the baseline visit compared to 43% at the early convalescence time point and only a single patient (9%) at the 6-month convalescence time point. Using the random forest machine learning algorithm, we developed an efficient computational framework to identify sets of 20 classifier genes that discriminated LD from other bacterial and viral infections. These novel LD biomarkers not only differentiated subjects with acute disseminated LD from healthy controls with 96% accuracy but also distinguished between subjects with acute and resolved (late convalescent) disease with 97% accuracy.IMPORTANCE Lyme disease (LD), caused by Borrelia burgdorferi, is the most common tick-borne infectious disease in the United States. We examined gene expression patterns in the blood of individuals with early disseminated LD at the time of diagnosis (acute) and also at approximately 1 month and 6 months following antibiotic treatment. A distinct acute LD profile was observed that was sustained during early convalescence (1 month) but returned to control levels 6 months after treatment. Using a computer learning algorithm, we identified sets of 20 classifier genes that discriminate LD from other bacterial and viral infections. In addition, these novel LD biomarkers are highly accurate in distinguishing patients with acute LD from healthy subjects and in discriminating between individuals with active and resolved infection. This computational approach offers the potential for more accurate diagnosis of early disseminated Lyme disease. It may also allow improved monitoring of treatment efficacy and disease resolution.

Molecular Diagnostics in Pulmonary Infections

Infection of the lung parenchyma, or pneumonia, accounts for over four million deaths per year worldwide (Ferkol and Schraufnagel, Ann Am Thorac Soc 11:404–406, 2014). The condition is common, but also over-diagnosed, in part due to relatively poor laboratory and radiographic diagnostics. Indeed, we continue to rely on antiquated tools such as sputum culture and chest X-ray – the former of which lacks speed and sensitivity, and the latter specificity (Albaum et al. Chest 110:343–50, 1996). The resulting presumptive diagnoses of pneumonia lead to excessive use of empiric broad spectrum antibiotics; indeed, by some estimates, 30–70% of antibiotic prescriptions for lower respiratory tract infection are inappropriate (Kraus, PLoS One 12(3): e0174584, 2017). This approach begets microbial resistance, exposes patients to medication side effects, and puts patients at risk of potentially life-threatening complications including Clostridium difficile colitis.

To improve diagnostic certainty in patients with suspected pneumonia, we must begin to consider and implement emerging technologies for efficient and accurate characterization of host responses to infection and identification of pathogens. In this chapter, we will discuss precision diagnostics already in common practice and those poised to be, and how these tools may ultimately enable personalization in the diagnosis of pneumonia.

Musculoskeletal Infection in Pediatrics: Assessment of the 2018 International Consensus Meeting on Musculoskeletal Infection

The Second International Consensus Meeting (ICM) on Musculoskeletal Infection was held in July 2018 in Philadelphia, Pennsylvania. This meeting involved contributions from an international multidisciplinary consortium of experts from orthopaedic surgery, infectious disease, pharmacology, rheumatology, microbiology, and others. Through strict delegate engagement in a comprehensive 13-step consensus process based on the Delphi technique, evidence-based consensus guidelines on musculoskeletal infection were developed. The 2018 ICM produced updates to recommendations from the inaugural ICM that was held in 2013, which primarily focused on periprosthetic infection of the hip and the knee, and added new guidelines with the expansion to encompass all subspecialties of orthopaedic surgery. The following proceedings from the pediatrics section are an overview of the ICM consensus recommendations on the prevention, diagnosis, and treatment of pediatric musculoskeletal infection.

Changes in the Composition of the Gut Microbiota and the Blood Transcriptome in Preterm Infants at Less than 29 Weeks Gestation Diagnosed with Bronchopulmonary Dysplasia

Bronchopulmonary dysplasia (BPD) is a common chronic lung condition in preterm infants that results in abnormal lung development and leads to considerable morbidity and mortality, making BPD one of the most common complications of preterm birth. We employed RNA sequencing and 16S rRNA gene sequencing to profile gene expression in blood and the composition of the fecal microbiota in infants born at <29 weeks gestational age and diagnosed with BPD in comparison to those of preterm infants that were not diagnosed with BPD. 16S rRNA gene sequencing, performed longitudinally on 255 fecal samples collected from 50 infants in the first months of life, identified significant differences in the relative levels of abundance of Klebsiella, Salmonella, Escherichia/Shigella, and Bifidobacterium in the BPD infants in a manner that was birth mode dependent. Transcriptome sequencing (RNA-Seq) analysis revealed that more than 400 genes were upregulated in infants with BPD. Genes upregulated in BPD infants were significantly enriched for functions related to red blood cell development and oxygen transport, while several immune-related pathways were downregulated. We also identified a gene expression signature consistent with an enrichment of immunosuppressive CD71+ early erythroid cells in infants with BPD. Intriguingly, genes that were correlated in their expression with the relative abundances of specific taxa in the microbiota were significantly enriched for roles in the immune system, suggesting that changes in the microbiota might influence immune gene expression systemically.IMPORTANCE Bronchopulmonary dysplasia (BPD) is a serious inflammatory condition of the lung and is the most common complication associated with preterm birth. A large body of evidence now suggests that the gut microbiota can influence immunity and inflammation systemically; however, the role of the gut microbiota in BPD has not been evaluated to date. Here, we report that there are significant differences in the gut microbiota of infants born at <29 weeks gestation and subsequently diagnosed with BPD, which are particularly pronounced when infants are stratified by birth mode. We also show that erythroid and immune gene expression levels are significantly altered in BPD infants. Interestingly, we identified an association between the composition of the microbiota and immune gene expression in blood in early life. Together, these findings suggest that the composition of the microbiota may influence the risk of developing BPD and, more generally, may shape systemic immune gene expression.

Long-Chain Acyl-CoA Synthetase 1 Role in Sepsis and Immunity: Perspectives From a Parallel Review of Public Transcriptome Datasets and of the Literature

A potential role for the long-chain acyl-CoA synthetase family member 1 (ACSL1) in the immunobiology of sepsis was explored during a hands-on training workshop. Participants first assessed the robustness of the potential gap in biomedical knowledge identified via an initial screen of public transcriptome data and of the literature associated with ACSL1. Increase in ACSL1 transcript abundance during sepsis was confirmed in several independent datasets. Querying the ACSL1 literature also confirmed the absence of reports associating ACSL1 with sepsis. Inferences drawn from both the literature (via indirect associations) and public transcriptome data (via correlation) point to the likely participation of ACSL1 and ACSL4, another family member, in inflammasome activation in neutrophils during sepsis. Furthermore, available clinical data indicate that levels of ACSL1 and ACSL4 induction was significantly higher in fatal cases of sepsis. This denotes potential translational relevance and is consistent with involvement in pathways driving potentially deleterious systemic inflammation. Finally, while ACSL1 expression was induced in blood in vitro by a wide range of pathogen-derived factors as well as TNF, induction of ACSL4 appeared restricted to flagellated bacteria and pathogen-derived TLR5 agonists and IFNG. Taken together, this joint review of public literature and omics data records points to two members of the acyl-CoA synthetase family potentially playing a role in inflammasome activation in neutrophils. Translational relevance of these observations in the context of sepsis and other inflammatory conditions remain to be investigated.

Whole-blood transcriptomic responses to lumacaftor/ivacaftor therapy in cystic fibrosis

BACKGROUND

Cystic fibrosis (CF) remains without a definitive cure. Novel therapeutics targeting the causative defect in the cystic fibrosis transmembrane conductance regulator (CFTR) gene are in clinical use. Lumacaftor/ivacaftor is a CFTR modulator approved for patients homozygous for the CFTR variant p.Phe508del, but there are wide variations in treatment responses preventing prediction of patient responses. We aimed to determine changes in gene expression related to treatment initiation and response.

METHODS

Whole-blood transcriptomics was performed using RNA-Seq in 20 patients with CF pre- and 6 months post-lumacaftor/ivacaftor (drug) initiation and 20 non-CF healthy controls. Correlation of gene expression with clinical variables was performed by stratification via clinical responses.

RESULTS

We identified 491 genes that were differentially expressed in CF patients (pre-drug) compared with non-CF controls and 36 genes when comparing pre-drug to post-drug profiles. Both pre- and post-drug CF profiles were associated with marked overexpression of inflammation-related genes and apoptosis genes, and significant under-expression of T cell and NK cell-related genes compared to non-CF. CF patients post-drug demonstrated normalized protein synthesis expression, and decreased expression of cell-death genes compared to pre-drug profiles, irrespective of clinical response. However, CF clinical responders demonstrated changes in eIF2 signaling, oxidative phosphorylation, IL-17 signaling, and mitochondrial function compared to non-responders. Top overexpressed genes (MMP9 and SOCS3) that decreased post-drug were validated by qRT-PCR. Functional assays demonstrated that CF monocytes normalized calcium (increases MMP9 expression) concentrations post-drug.

CONCLUSIONS

Transcriptomics revealed differentially regulated pathways in CF patients at baseline compared to non-CF, and in clinical responders to lumacaftor/ivacaftor.

Transketolase and vitamin B1 influence on ROS-dependent neutrophil extracellular traps (NETs) formation

Neutrophil extracellular traps (NETs) are a recently identified, web-like, extracellular structure composed of decondensed nuclear DNA and associated antimicrobial granules. NETs are extruded into the extracellular environment via the reactive oxygen species (ROS)-dependent cell death pathway participating in inflammation and autoimmune diseases. Transketolase (TKT) is a thiamine pyrophosphate (vitamin B1)-dependent enzyme that links the pentose phosphate pathway with the glycolytic pathway by feeding excess sugar phosphates into the main carbohydrate metabolic pathways to generate biosynthetic reducing capacity in the form of NADPH as a substrate for ROS generation. In this work, TKT was selected as a lead candidate from 24 NET-associated proteins obtained by literature screening and knowledge gap assessment. Consequently, we determined whether TKT influenced NET formation in vitro. We firstly established that the release of ROS-dependent NETs was significantly decreased after purified human PMNs were pretreated with oxythiamine, a TKT inhibitor, and in a concentration dependent manner. As a cofactor for TKT reaction, we evaluated the release of NET formation either in vitamin B1 treatment or in combined use of oxythiamine and vitamin B1, and found that those treatments also exerted a significant suppressive effect on the amount of NET-DNA and ROS production. The regulation of TKT by oxythiamine and/or vitamin B1 may therefore be associated with response to the modulation of NET formation by preventing generation of excessive NETs in inflammatory diseases.

G-CSF partially mediates bone loss induced by Staphylococcus aureus infection in mice

Bone loss in Staphylococcus aureus (S. aureus) osteomyelitis poses a serious challenge to orthopedic treatment. The present study aimed to elucidate how S. aureus infection in bone might induce bone loss. The C57BL/6 mice were injected with S. aureus (10⁶ CFU/ml, 100 μl) or with the same amount of vehicle (control) via the tail vein. Microcomputed tomography (microCT) analysis showed bone loss progressing from week 1 to week 5 after infection, accompanied by a decreased number of osteocalcin-positive stained osteoblasts and the suppressed mRNA expression of Runx2 and osteocalcin. Transcriptome profiles of GSE30119 were downloaded and analyzed to determine the differences in expression of inflammatory factors between patients with S. aureus infected osteomyelitis and healthy controls, the data showed significantly higher mRNA expression of granulocyte colony-stimulating factor (G-CSF) in the whole blood from patients with S. aureus infection. Enzyme-linked immunosorbent assay (ELISA) analysis confirmed an increased level of G-CSF in the bone marrow and serum from S. aureus infected mice, which might have been due to the increased amount of F4/80⁺ macrophages. Interestingly, G-CSF neutralizing antibody treatment significantly rescued the bone loss after S. aureus infection, as evidenced by its roles in improving BV/TV and preserving osteocalcin- and osterix-positive stained cells. Importantly, we found that G-CSF level was significantly up-regulated in the serum from osteomyelitis patients infected by S. aureus Together, S. aureus infection might suppress the function of osteoblastic cells and induce progressive bone loss by up-regulating the level G-CSF, suggesting a therapeutic potential for G-CSF neutralization in combating bone loss in S. aureus osteomyelitis.

An imbalance of T cell subgroups exists in children with sepsis

The purpose of this study is to explore the role of different T cell subgroups in the pathogenesis of sepsis in children. Flow cytometry was used to detect the changes in the activation status and the number of T cell subgroups in the peripheral blood of children with sepsis; healthy children were selected as the control group. Compared with healthy children, the number of CD4+ T cells in the peripheral blood of children with sepsis did not change significantly (Z = 1.945, P = 0.052); though the ratio decreased and the median level dropped from 34.6% to 30.7% (Z = 2.257, P = 0.024). However, the number of CD8+ T cells in the blood of children with sepsis increased, and the median level also increased from 0.2 × 10⁹/L to 0.4 × 10⁹/L (Z = -2.404, P = 0.016). In addition, CD3+CD8+HLA-DR + cell level significantly increased, and the median level increased from 4.2% to 24.3% (Z = -5.370, P = 0.000). There was a large heterogeneity in the hospitalization time of sepsis in clinical patients. Compared to patients with a mean hospital stay of 6 days, patients with a median hospital stay of 13 days had a lower CD3+CD4+CD25 + cells percentage, while the percentage of CD3+CD8+HLA-DR+ was higher, resulting in a more apparent increase of CD3+ CD8+HLA-DR+/CD3+CD4+CD25+. Therefore, the failure of CD4+ T cell activation and proliferation, and the excessive activation and proliferation of CD8+ T cells play an important role in the pathogenesis of sepsis. The increase of CD3+CD8+HLA-DR+/CD3+CD4+CD25 + ratio was associated with the extended course of sepsis.

Differentiation of Deep Venous Thrombosis Among Children With or Without Osteomyelitis

BACKGROUND

Children with osteomyelitis are at risk for deep venous thrombosis (DVT). This study evaluates the characteristics of DVT among children to differentiate between those with and without osteomyelitis.

METHODS

Children with DVT of any cause were studied between 2008 and 2016. Children with DVT and osteomyelitis were compared with those with DVT without osteomyelitis. Another comparison cohort included children with osteomyelitis but without DVT. Comorbidities, severity of illness (SOI), and clinical course were compared between cohorts.

RESULTS

DVT was identified in 224 children, a prevalence of 2.5 per 10,000 children. Among those with DVT, 28 (12.1%) had osteomyelitis. The DVT rate among 466 children with osteomyelitis was 6.0%. Children with osteomyelitis and DVT had greater SOI (9.1 vs. 2.7), bacteremia rate (82.1% vs. 38.4%), methicillin-resistant Staphylococcus aureus rate (89.3% vs. 21.2%), surgeries per child (2.1 vs. 0.7), and intensive care unit admission rate (67.9% vs. 5.9%) than that of children without DVT (P<0.00001). Of 196 children who had DVT without osteomyelitis, 166 (84.7%) had comorbidities including defined hypercoagulability (27 or 13.8%). Children with DVT due to osteomyelitis were without comorbidities or hypercoagulability (P<0.00001). The rate of pulmonary embolism was similar for children with DVT with or without osteomyelitis (3/28, or 10.7% vs. 18/196, or 9.2%).

CONCLUSIONS

Children with DVT and osteomyelitis differ substantially from other children with DVT by the absence of comorbidities or post-thrombotic syndrome. They also differ from children with osteomyelitis without DVT by higher SOI, methicillin-resistant S. aureus rate, and occurrence of intensive care. Awareness of for the characteristics of DVT among children with osteomyelitis will reduce delay to diagnostic ultrasound and improve anticoagulation management which must be carefully coordinated given the high rate of surgery of these children.

LEVEL OF EVIDENCE

Level II-prognostic, retrospective cohort comparison.

Approach to Neonates and Young Infants with Fever without a Source Who Are at Risk for Severe Bacterial Infection

INTRODUCTION

Among neonates and infants <3 months of age with fever without a source (FWS), 5% to 15% of cases are patients with fever caused by a serious bacterial infection (SBI). To favour the differentiation between low- and high-risk infants, several algorithms based on analytical and clinical parameters have been developed. The aim of this review is to describe the management of young infants with FWS and to discuss the impact of recent knowledge regarding FWS management on clinical practice.

MATERIALS AND METHODS

PubMed was used to search for all of the studies published over the last 35 years using the keywords: "fever without source" or "fever of unknown origin" or "meningitis" or "sepsis" or "urinary tract infection" and "neonate" or "newborn" or "infant <90 days of life" or "infant <3 months".

RESULTS AND DISCUSSION

The selection of neonates and young infants who are <3 months old with FWS who are at risk for SBI remains a problem without a definitive solution. The old Rochester criteria remain effective for identifying young infants between 29 and 60 days old who do not have severe bacterial infections (SBIs). However, the addition of laboratory tests such as C-reactive protein (CRP) and procalcitonin (PCT) can significantly improve the identification of children with SBI. The approach in evaluating neonates is significantly more complicated, as their risk of SBIs, including bacteremia and meningitis, remains relevant and none of the suggested approaches can reduce the risk of dramatic mistakes. In both groups, the best antibiotic must be carefully selected considering the clinical findings, the laboratory data, the changing epidemiology, and increasing antibiotic resistance of the most common infectious bacteria.

SyNDI: synchronous network data integration framework

BACKGROUND

Systems biology takes a holistic approach by handling biomolecules and their interactions as big systems. Network based approach has emerged as a natural way to model these systems with the idea of representing biomolecules as nodes and their interactions as edges. Very often the input data come from various sorts of omics analyses. Those resulting networks sometimes describe a wide range of aspects, for example different experiment conditions, species, tissue types, stimulating factors, mutants, or simply distinct interaction features of the same network produced by different algorithms. For these scenarios, synchronous visualization of more than one distinct network is an excellent mean to explore all the relevant networks efficiently. In addition, complementary analysis methods are needed and they should work in a workflow manner in order to gain maximal biological insights.

RESULTS

In order to address the aforementioned needs, we have developed a Synchronous Network Data Integration (SyNDI) framework. This framework contains SyncVis, a Cytoscape application for user-friendly synchronous and simultaneous visualization of multiple biological networks, and it is seamlessly integrated with other bioinformatics tools via the Galaxy platform. We demonstrated the functionality and usability of the framework with three biological examples - we analyzed the distinct connectivity of plasma metabolites in networks associated with high or low latent cardiovascular disease risk; deeper insights were obtained from a few similar inflammatory response pathways in Staphylococcus aureus infection common to human and mouse; and regulatory motifs which have not been reported associated with transcriptional adaptations of Mycobacterium tuberculosis were identified.

CONCLUSIONS

Our SyNDI framework couples synchronous network visualization seamlessly with additional bioinformatics tools. The user can easily tailor the framework for his/her needs by adding new tools and datasets to the Galaxy platform.

Molecular Distance to Health Transcriptional Score and Disease Severity in Children Hospitalized With Community-Acquired Pneumonia

Background: Community-acquired pneumonia (CAP) is a leading cause of hospitalization and mortality in children. Diagnosis remains challenging and there are no reliable tools to objectively risk stratify patients or predict clinical outcomes. Molecular distance to health (MDTH) is a genomic score that measures the global perturbation of the transcriptional profile and may help classify patients by disease severity. We evaluated the value of MDTH to assess disease severity in children hospitalized with CAP.

Methods: Children hospitalized with CAP and matched healthy controls were enrolled in a prospective observational study. Blood samples were obtained for transcriptome analyses within 24 h of hospitalization. MDTH scores were calculated to assess disease severity and correlated with laboratory markers, such as white blood cell count, c-reactive protein (CRP), and procalcitonin (PCT), and clinical outcomes, including duration of fever and duration of hospitalization (LOS). Univariate and multivariable logistic regression were applied to assess factors associated with LOS and duration of fever after hospitalization.

Results: Among children hospitalized with CAP (n = 152), pyogenic bacteria (PB) were detected in 16 (11%), Mycoplasma pneumoniae was detected in 41 (28%), respiratory viruses (RV) alone were detected in 78 (51%), and no pathogen was detected in 17 (11%) children. Statistical group comparisons identified 6,726 genes differentially expressed in patients with CAP vs. healthy controls (n = 39). Children with confirmed PB had higher MDTH scores than those with RV (p < 0.05) or M. pneumoniae (p < 0.01) detected alone. CRP (r = 0.39, p < 0.0001), PCT (r = 0.39, p < 0.0001), and MDTHs (r = 0.24, p < 0.01) correlated with duration of fever, while only MDTHs correlated with LOS (r = 0.33, p < 0.0001). Unadjusted analyses showed that both higher CRP and MDTHs were associated with longer LOS (OR 1.04 [1–1.07] and 1.12 [1.04–1.20], respectively), however, only MDTH remained significant when adjusting for other covariates (aOR 1.11 [1.01–1.22]).

Conclusions: In children hospitalized with CAP MDTH score measured within 24 h of admission was independently associated with longer duration of hospitalization, regardless of the pathogen detected. This suggests that transcriptional biomarkers may represent a promising approach to assess disease severity in children with CAP.

Altered expression of TIAM1 in endotoxin-challenged airway epithelial cells and rodent septic models

BACKGROUND

In sepsis, reorganization of the actin cytoskeleton in the epithelium during inflammation will lead to a breakdown of epithelial barrier integrity, and contribute to the pathogenesis of sepsis, but the exact changes of various components regulating the actin cytoskeleton pathway remain unclear.

METHODS

We used lipopolysaccharide (LPS) challenged primary epithelial cells cultured at the air-liquid interface (ALI) to mimic epithelial barrier dysfunction during sepsis. Then we detected differential expression of T-lymphoma invasion and metastasis 1 (TIAM1) gene in lung epithelial cells and septic models.

RESULTS

LPS induced barrier dysfunction in human tracheobronchial epithelial cells (HTBEs) as measured by statistically significant changes in ionic and macromolecular permeability. We observed differential expression of TIAM1 gene. The protein expression of TIAM1 was decreased after LPS challenge, in human bronchial epithelial cells. Furthermore, the expression levels of both TIAM1 mRNA and protein were decreased in lungs of septic rodent models.

CONCLUSIONS

Given that expression levels of TIAM1 have been associated with mortality among sepsis patients, our findings have the potential for the development of diagnostic and treatment strategies relevant for patient management.

Whole blood transcriptional profiles as a prognostic tool in complete and incomplete Kawasaki Disease

Background

Early identification of children with Kawasaki Disease (KD) is key for timely initiation of intravenous immunoglobulin (IVIG) therapy. However, the diagnosis of the disease remains challenging, especially in children with an incomplete presentation (inKD). Moreover, we currently lack objective tools for identification of non-response (NR) to IVIG.

Methods

Children with KD were enrolled and samples obtained before IVIG treatment and sequentially at 24 h and 4–6 weeks post-IVIG in a subset of patients. We also enrolled children with other febrile illnesses [adenovirus (AdV); group A streptococcus (GAS)] and healthy controls (HC) for comparative analyses. Blood transcriptional profiles were analyzed to define: a) the cKD and inKD biosignature, b) compare the KD signature with other febrile illnesses and, c) identify biomarkers predictive of clinical outcomes.

Results

We identified a cKD biosignature (n = 39; HC, n = 16) that was validated in two additional cohorts of children with cKD (n = 37; HC, n = 20) and inKD (n = 13; HC, n = 8) and was characterized by overexpression of inflammation, platelets, apoptosis and neutrophil genes, and underexpression of T and NK cell genes. Classifier genes discriminated KD from adenovirus with higher sensitivity and specificity (92% and 100%, respectively) than for GAS (75% and 87%, respectively). We identified a genomic score (MDTH) that was higher at baseline in IVIG-NR [median 12,290 vs. 5,572 in responders, p = 0.009] and independently predicted IVIG-NR.

Conclusion

A reproducible biosignature from KD patients was identified, and was similar in children with cKD and inKD. A genomic score allowed early identification of children at higher risk for non-response to IVIG.

Differentially Expressed Genes in Osteomyelitis Induced by Staphylococcus aureus Infection

Osteomyelitis (OM) is a complicated and serious disease and its underlying molecular signatures of disease initiation and progression remain unclear. Staphylococcus aureus (S. aureus) is the most common causative agent of OM. Previous study of Banchereau et al. has established a link between whole blood transcription profiles and clinical manifestations in patients infected with S. aureus. However, the differentially expressed genes (DEGs) in OM induced by S. aureus infection have not been intensively investigated. In this study, we downloaded the gene expression profile dataset GSE30119 from Gene Expression Omnibus, and performed bioinformatic analysis to identify DEGs in S. aureus infection induced OM from the transcriptional level. The study consisted of 143 whole blood samples, including 44 healthy controls, 42 OM-free, and 57 OM infection patients. A total of 209 S. aureus infection-related genes (SARGs) and 377 OM-related genes (OMRGs) were identified. The SARGs were primarily involved in the immune response by GO functional and pathway enrichment analysis. Several proteins adhere to neutrophil extracellular traps may be critical for the immune response to the process of S. aureus infection. By contrast, the OMRGs differ from the SARGs. The OMRGs were enriched in transmembrane signaling receptor and calcium channel activity, cilium morphogenesis, chromatin silencing, even multicellular organism development. Several key proteins, including PHLPP2 and EGF, were hub nodes in protein–protein interaction network of the OMRGs. In addition, alcoholism, systemic lupus erythematosus and proteoglycans in cancer were the top pathways influenced by the OMRGs associated with OM. Thus, this study has further explored the DEGs and their biological functions associated with S. aureus infection and OM, comparing with the previous study, and may light the further insight into the underlying molecular mechanisms and the potential critical biomarkers in OM development.

Persons with Chronic Spinal Cord Injury Have Decreased Natural Killer Cell and Increased Toll-Like Receptor/Inflammatory Gene Expression

Infections are the leading cause of death for individuals with traumatic spinal cord injury (SCI). Along with increased infection rates, inflammation is often also observed in persons with chronic SCI. Together, immunological changes post-SCI are also poised to impede neurological recovery and mediate common medical consequences of SCI, including atherogenesis and neuropathic pain. The molecular mechanisms contributing to increased infection susceptibility and inflammation in persons living with SCI are poorly understood. Here, we used tools of functional genomics to perform a pilot study to compare whole-blood gene expression in individuals with chronic SCI (≥1 year from initial injury; N = 31) and uninjured individuals (N = 26). We identified 1815 differentially expressed genes in all SCI participants and 2226 differentially expressed genes in persons with SCI rostral to thoracic level 5, compared to uninjured participants. This included marked downregulation of natural killer cell genes and upregulation of the proinflammatory Toll-like receptor signaling pathway. These data provide novel mechanistic insights into the causes underlying the symptoms of immune dysfunction in individuals living with SCI.

Respiratory Syncytial Virus Genotypes, Host Immune Profiles, and Disease Severity in Young Children Hospitalized With Bronchiolitis

Background

Data on how respiratory syncytial virus (RSV) genotypes influence disease severity and host immune responses is limited. Here, we characterized the genetic variability of RSV during 5 seasons, and evaluated the role of RSV subtypes, genotypes, and viral loads in disease severity and host transcriptional profiles.

Methods

A prospective, observational study was carried out, including a convenience sample of healthy infants hospitalized with RSV bronchiolitis. Nasopharyngeal samples for viral load quantitation, typing, and genotyping, and blood samples for transcriptome analyses were obtained within 24 hours of hospitalization. Multivariate models were constructed to identify virologic and clinical variables predictive of clinical outcomes.

Results

We enrolled 253 infants (median age 2.1 [25%-75% interquartile range] months). RSV A infections predominated over RSV B and showed greater genotype variability. RSV A/GA2, A/GA5, and RSV B/BA were the most common genotypes identified. Compared to GA2 or BA, infants with GA5 infections had higher viral loads. GA5 infections were associated with longer hospital stay, and with less activation of interferon and increased overexpression of neutrophil genes.

Conclusions

RSV A infections were more frequent than RSV B, and displayed greater variability. GA5 infections were associated with enhanced disease severity and distinct host immune responses.

Defects in innate and adaptive immunity in patients with sepsis and health care associated infection

Recent advances in sepsis therapy exclusively involve improvements in supportive care, while sepsis mortality rates remain disturbingly high at 30%. These persistently high sepsis mortality rates arise from the absence of sepsis specific therapies. However with improvements in supportive care, patients with septic shock commonly partially recover from the infection that precipitated their initial illness, yet they frequently succumb to subsequent health care associated infections. Remarkably today the pathophysiology of sepsis in humans, a common disease in western society, remains largely a conundrum. Conventionally sepsis was regarded as primarily a disorder of inflammation. More recently the importance of immune compromise in the pathophysiology of sepsis and health care associated infection has now become more widely accepted. Accordingly a review of the human evidence for this novel sepsis paradigm is timely. Septic patients appear to exhibit a complex and long-lasting immune deficiency state, involving lymphocytes of both the innate and adaptive immune responses that have been linked with mortality and the occurrence of health care associated infection. Such is the pervasive nature of immune compromise in sepsis that ultimately immune modulation will play a crucial role in sepsis therapies of the future.

Molecular and genetic inflammation networks in major human diseases

It has been well-recognized that inflammation alongside tissue repair and damage maintaining tissue homeostasis determines the initiation and progression of complex diseases. Albeit with the accomplishment of having captured the most critical inflammation-involved molecules, genetic susceptibilities, epigenetic factors, and environmental factors, our schemata on the role of inflammation in complex diseases remain largely patchy, in part due to the success of reductionism in terms of research methodology per se. Omics data alongside the advances in data integration technologies have enabled reconstruction of molecular and genetic inflammation networks which shed light on the underlying pathophysiology of complex diseases or clinical conditions. Given the proven beneficial role of anti-inflammation in coronary heart disease as well as other complex diseases and immunotherapy as a revolutionary transition in oncology, it becomes timely to review our current understanding of the molecular and genetic inflammation networks underlying major human diseases. In this review, we first briefly discuss the complexity of infectious diseases and then highlight recently uncovered molecular and genetic inflammation networks in other major human diseases including obesity, type II diabetes, coronary heart disease, late onset Alzheimer's disease, Parkinson's disease, and sporadic cancer. The commonality and specificity of these molecular networks are addressed in the context of genetics based on genome-wide association study (GWAS). The double-sword role of inflammation, such as how the aberrant type 1 and/or type 2 immunity leads to chronic and severe clinical conditions, remains open in terms of the inflammasome and the core inflammatome network features. Increasingly available large Omics and clinical data in tandem with systems biology approaches have offered an exciting yet challenging opportunity toward reconstruction of more comprehensive and dynamic molecular and genetic inflammation networks, which hold great promise in transiting network snapshots to video-style multi-scale interplays of disease mechanisms, in turn leading to effective clinical intervention.

Heterogeneity in tuberculosis

Infection with Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), results in a range of clinical presentations in humans. Most infections manifest as a clinically asymptomatic, contained state that is termed latent TB infection (LTBI); a smaller subset of infected individuals present with symptomatic, active TB. Within these two seemingly binary states, there is a spectrum of host outcomes that have varying symptoms, microbiologies, immune responses and pathologies. Recently, it has become apparent that there is diversity of infection even within a single individual. A good understanding of the heterogeneity that is intrinsic to TB - at both the population level and the individual level - is crucial to inform the development of intervention strategies that account for and target the unique, complex and independent nature of the local host-pathogen interactions that occur in this infection. In this Review, we draw on model systems and human data to discuss multiple facets of TB biology and their relationship to the overall heterogeneity observed in the human disease.

A Four-Biomarker Blood Signature Discriminates Systemic Inflammation Due to Viral Infection Versus Other Etiologies

The innate immune system of humans and other mammals responds to pathogen-associated molecular patterns (PAMPs) that are conserved across broad classes of infectious agents such as bacteria and viruses. We hypothesized that a blood-based transcriptional signature could be discovered indicating a host systemic response to viral infection. Previous work identified host transcriptional signatures to individual viruses including influenza, respiratory syncytial virus and dengue, but the generality of these signatures across all viral infection types has not been established. Based on 44 publicly available datasets and two clinical studies of our own design, we discovered and validated a four-gene expression signature in whole blood, indicative of a general host systemic response to many types of viral infection. The signature’s genes are: Interferon Stimulated Gene 15 (ISG15), Interleukin 16 (IL16), 2′,5′-Oligoadenylate Synthetase Like (OASL), and Adhesion G Protein Coupled Receptor E5 (ADGRE5). In each of 13 validation datasets encompassing human, macaque, chimpanzee, pig, mouse, rat and all seven Baltimore virus classification groups, the signature provides statistically significant (p < 0.05) discrimination between viral and non-viral conditions. The signature may have clinical utility for differentiating host systemic inflammation (SI) due to viral versus bacterial or non-infectious causes.

Early Whole Blood Transcriptional Signatures Are Associated with Severity of Lung Inflammation in Cynomolgus Macaques with Mycobacterium tuberculosis Infection

Whole blood transcriptional profiling offers great diagnostic and prognostic potential. Although studies identified signatures for pulmonary tuberculosis (TB) and transcripts that predict the risk for developing active TB in humans, the early transcriptional changes immediately following Mycobacterium tuberculosis infection have not been evaluated. We evaluated the gene expression changes in the cynomolgus macaque model of TB, which recapitulates all clinical aspects of human M. tuberculosis infection, using a human microarray and analytics platform. We performed genome-wide blood transcriptional analysis on 38 macaques at 11 postinfection time points during the first 6 mo of M. tuberculosis infection. Of 6371 differentially expressed transcripts between preinfection and postinfection, the greatest change in transcriptional activity occurred 20-56 d postinfection, during which fluctuation of innate and adaptive immune response-related transcripts was observed. Modest transcriptional differences between active TB and latent infection were observed over the time course with substantial overlap. The pattern of module activity previously published for human active TB was similar in macaques with active disease. Blood transcript activity was highly correlated with lung inflammation (lung [¹⁸F]fluorodeoxyglucose [FDG] avidity) measured by positron emission tomography and computed tomography at early time points postinfection. The differential signatures between animals with high and low lung FDG were stronger than between clinical outcomes. Analysis of preinfection signatures of macaques revealed that IFN signatures could influence eventual clinical outcomes and lung FDG avidity, even before infection. Our data support that transcriptional changes in the macaque model are translatable to human M. tuberculosis infection and offer important insights into early events of M. tuberculosis infection.

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

IMPORTANCE

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

OBJECTIVE

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

DESIGN, SETTING, AND PARTICIPANTS

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

EXPOSURE

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

MAIN OUTCOMES AND MEASURES

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

RESULTS

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

CONCLUSIONS AND RELEVANCE

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

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

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

Vaccinology in the era of high-throughput biology

Vaccination has been tremendously successful saving lives and preventing infections. However, the development of vaccines against global pandemics such as HIV, malaria and tuberculosis has been obstructed by several challenges. A major challenge is the lack of knowledge about the correlates and mechanisms of protective immunity. Recent advances in the application of systems biological approaches to analyse immune responses to vaccination in humans are beginning to yield new insights about mechanisms of vaccine immunity, and to define molecular signatures, induced rapidly after vaccination, that correlate with and predict vaccine induced immunity. Here, we review these advances and discuss the potential of this systems vaccinology approach in defining novel correlates of protection in clinical trials, and in infection-induced 'experimental challenge models' in humans.

A review of clinical models for the evaluation of human TB vaccines

While much progress has been made in the fight against the scourge of tuberculosis (TB), we are still some way from reaching the ambitious targets of eliminating it as a global public health problem by the mid twenty-first century. A new and effective vaccine that protects against pulmonary TB disease will be an essential element of any control strategy. Over a dozen vaccines are currently in development, but recent efficacy trial data from one of the most advanced candidates have been disappointing. Limitations of current preclinical animal models exist, together with a lack of a complete understanding of host immunity to TB or robust correlates of disease risk and protection. Therefore, in the context of such obstacles, we discuss the lessons identified from recent efficacy trials, current concepts of biomarkers and correlates of protection, the potential of innovative clinical models such as human challenge and conducting trials in high-incidence settings to evaluate TB vaccines in humans, and the use of systems vaccinology and novel technologies including transcriptomics and metabolomics, that may facilitate their utility.

Host gene expression classifiers diagnose acute respiratory illness etiology

Acute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical care. Despite improvements in pathogen-based diagnostics, most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. This observational cohort study determined whether host gene expression patterns discriminate noninfectious from infectious illness and bacterial from viral causes of acute respiratory infection in the acute care setting. Peripheral whole blood gene expression from 273 subjects with community-onset acute respiratory infection (ARI) or noninfectious illness, as well as 44 healthy controls, was measured using microarrays. Sparse logistic regression was used to develop classifiers for bacterial ARI (71 probes), viral ARI (33 probes), or a noninfectious cause of illness (26 probes). Overall accuracy was 87% (238 of 273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, P < 0.03) and three published classifiers of bacterial versus viral infection (78 to 83%). The classifiers developed here externally validated in five publicly available data sets (AUC, 0.90 to 0.99). A sixth publicly available data set included 25 patients with co-identification of bacterial and viral pathogens. Applying the ARI classifiers defined four distinct groups: a host response to bacterial ARI, viral ARI, coinfection, and neither a bacterial nor a viral response. These findings create an opportunity to develop and use host gene expression classifiers as diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance.