Peripheral blood RNA gene expression profiling in patients with bacterial meningitis

Quantitative and Longitudinal Assessment of Systemic Innate Immunity in Health and Disease Using a 2D Gene Model

Dysregulation of innate immunity is deeply involved in infectious and autoimmune diseases. For a better understanding of pathogenesis and improved management of these diseases, it is of vital importance to implement convenient monitoring of systemic innate immunity. Built upon our previous works on the host transcriptional response to infection in peripheral blood, we proposed a 2D gene model for the simultaneous assessment of two major components of systemic innate immunity, including VirSig as the signature of the host response to viral infection and BacSig as the signature of the host response to bacterial infection. The revelation of dysregulation in innate immunity by this 2D gene model was demonstrated with a wide variety of transcriptome datasets. In acute infection, distinctive patterns of VirSig and BacSig activation were observed in viral and bacterial infection. In comparison, both signatures were restricted to a defined range in the vast majority of healthy adults, regardless of age. In addition, BacSig showed significant elevation during pregnancy and an upward trend during development. In tuberculosis (TB), elevation of BacSig and VirSig was observed in a significant portion of active TB patients, and abnormal BacSig was also associated with a longer treatment course. In cystic fibrosis (CF), abnormal BacSig was observed in a subset of patients, and no overall change in BacSig abnormality was observed after the drug treatment. In systemic sclerosis-associated interstitial lung disease (SSc-ILD), significant elevation of VirSig and BacSig was observed in some patients, and treatment with a drug led to the further deviation of BacSig from the control level. In systemic lupus erythematosus (SLE), positivity for the anti-Ro autoantibody was associated with significant elevation of VirSig in SLE patients, and the additive effect of VirSig/BacSig activation was also observed in SLE patients during pregnancy. Overall, these data demonstrated that the 2D gene model can be used to assess systemic innate immunity in health and disease, with the potential clinical applications including patient stratification, prescription of antibiotics, understanding of pathogenesis, and longitudinal monitoring of treatment response.

Electrical Characterization and Analysis of Single Cells and Related Applications

Biological parameters extracted from electrical signals from various body parts have been used for many years to analyze the human body and its behavior. In addition, electrical signals from cancer cell lines, normal cells, and viruses, among others, have been widely used for the detection of various diseases. Single-cell parameters such as cell and cytoplasmic conductivity, relaxation frequency, and membrane capacitance are important. There are many techniques available to characterize biomaterials, such as nanotechnology, microstrip cavity resonance measurement, etc. This article reviews single-cell isolation and sorting techniques, such as the micropipette separation method, separation and sorting system (dual electrophoretic array system), DEPArray sorting system (dielectrophoretic array system), cell selector sorting system, and microfluidic and valve devices, and discusses their respective advantages and disadvantages. Furthermore, it summarizes common single-cell electrical manipulations, such as single-cell amperometry (SCA), electrical impedance sensing (EIS), impedance flow cytometry (IFC), cell-based electrical impedance (CEI), microelectromechanical systems (MEMS), and integrated microelectrode array (IMA). The article also enumerates the application and significance of single-cell electrochemical analysis from the perspectives of CTC liquid biopsy, recombinant adenovirus, tumor cells like lung cancer DTCs (LC-DTCs), and single-cell metabolomics analysis. The paper concludes with a discussion of the current limitations faced by single-cell analysis techniques along with future directions and potential application scenarios.

Changes in expression levels of erythrocyte and immune-related genes are associated with high altitude polycythemia

BACKGROUND

As a chronic mountain sickness(CMS) with the highest incidence and the greatest harm, the pathogenesis of high altitude polycythemia (HAPC) is still not fully understood.

METHODS

37 HAPC patients and 42 healthy subjects were selected from plateau, and peripheral venous blood samples were collected for transcriptome sequencing on Illumina NovaSeq platform. The sequenced data were analyzed by bioinformatics and phenotypic association analysis.

RESULTS

The results showed significant differences in multiple clinical indicators including RBC and HGB et al. existed between HAPC and control. Based on the RNA-seq data, 550 genes with significant differential expression were identified in HAPC patients. GO and KEGG pathway enrichment analysis showed that the up-regulated genes were mainly enriched in processes such as erythrocyte differentiation and development and homeostasis of number of cells, while the down-regulated genes were mainly enriched in categories such as immunoglobulin production, classical pathway of complement activation and other biological processes. The coupling analysis of differential expression genes(DEGs) and pathological phenotypes revealed that 91 DEGs were in close correlation with in the phenotype of red blood cell volume distribution (width-CV and width-SD), and they were all up-regulated in HAPC and involved in the process of erythrocyte metabolism. Combined with the functional annotation of DEGs and literature survey, we found that the expression of several potential genes might be responsible for pathogenesis of HAPC. Besides, cell type deconvolution analysis result suggested that the changes in the number of some immune cell types was significantly lower in HAPC patients than control, implying the autoimmune level of HAPC patients was affected to a certain extent.

CONCLUSION

This study provides an important data source for understanding the pathogenesis and screening pathogenic genes of HAPC. We found for the first time that there was a significant correlation between HAPC and the pathological phenotype of width-CV and width-SD, wherein the enriched genes were all up-regulated expressed and involved in the process of erythrocyte metabolism. Although the role of these genes needs to be further studied, the candidate genes can provide a starting point for functionally pinning down the underlying mechanism of HAPC.

Leveraging Genomic and Bioinformatic Analysis to Enhance Drug Repositioning for Dermatomyositis

Dermatomyositis (DM) is an autoimmune disease that is classified as a type of idiopathic inflammatory myopathy, which affects human skin and muscles. The most common clinical symptoms of DM are muscle weakness, rash, and scaly skin. There is currently no cure for DM. Genetic factors are known to play a pivotal role in DM progression, but few have utilized this information geared toward drug discovery for the disease. Here, we exploited genomic variation associated with DM and integrated this with genomic and bioinformatic analyses to discover new drug candidates. We first integrated genome-wide association study (GWAS) and phenome-wide association study (PheWAS) catalogs to identify disease-associated genomic variants. Biological risk genes for DM were prioritized using strict functional annotations, further identifying candidate drug targets based on druggable genes from databases. Overall, we analyzed 1239 variants associated with DM and obtained 43 drugs that overlapped with 13 target genes (JAK2, FCGR3B, CD4, CD3D, LCK, CD2, CD3E, FCGR3A, CD3G, IFNAR1, CD247, JAK1, IFNAR2). Six drugs clinically investigated for DM, as well as eight drugs under pre-clinical investigation, are candidate drugs that could be repositioned for DM. Further studies are necessary to validate potential biomarkers for novel DM therapeutics from our findings.

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.

Non-reference genome transposable elements (TEs) have a significant impact on the progression of the Parkinson's disease

The pathophysiology of Parkinson's disease (PD) is a complex process of the interaction between genetic and environmental factors. Studies on the genetic component of PD have predominantly focused on single nucleotide polymorphisms (SNPs) using a cross-sectional case-control design in large genome-wide association studies. This approach while giving insight into a significant portion of the genetics of PD does not fully account for all the genetic components resulting in missing heritability. In this study, we approached this problem by focusing on the non-reference genome transposable elements (TEs) and their impact on the progression of PD using a longitudinal study design within the Parkinson's progression markers initiative (PPMI) cohort. We analyzed 2886 Alu repeats, 360 LINE1 and 128 SINE-VNTR-Alus (SVAs) that were called from the whole-genome sequence data which are not within the reference genome. The presence or absence of these non-reference TE variants is known as a retrotransposon insertion polymorphism, and measuring this polymorphism describes the impact of TEs on the traits. The variations for the presence or absence of the non-reference TE elements were modeled to align with the changes in the 114 outcome measures during the five-year follow-up period of the PPMI cohort. Linear mixed-effects models were used, and many TEs were found to have a highly significant effect on the longitudinal changes in the clinically important PD outcomes such as UPDRS subscale II, UPDRS total scores, and modified Schwab and England ADL scale. In addition, the progression of several imaging and functional measures, including the Caudate/Putamen ratio and levodopa equivalent daily dose (LEDD) were also significantly affected by the TEs. In conclusion, this study identified the overwhelming effect of the non-reference TEs on the progression of PD and is a good example of the impact the variations in the "junk DNA" have on complex diseases.

Mortality Prediction in Sepsis With an Immune-Related Transcriptomics Signature: A Multi-Cohort Analysis

Background

Novel biomarkers are needed to progress toward individualized patient care in sepsis. The immune profiling panel (IPP) prototype has been designed as a fully-automated multiplex tool measuring expression levels of 26 genes in sepsis patients to explore immune functions, determine sepsis endotypes and guide personalized clinical management. The performance of the IPP gene set to predict 30-day mortality has not been extensively characterized in heterogeneous cohorts of sepsis patients.

Methods

Publicly available microarray data of sepsis patients with widely variable demographics, clinical characteristics and ethnical background were co-normalized, and the performance of the IPP gene set to predict 30-day mortality was assessed using a combination of machine learning algorithms.

Results

We collected data from 1,801 arrays sampled on sepsis patients and 598 sampled on controls in 17 studies. When gene expression was assayed at day 1 following admission (1,437 arrays sampled on sepsis patients, of whom 1,161 were alive and 276 (19.2%) were dead at day 30), the IPP gene set showed good performance to predict 30-day mortality, with an area under the receiving operating characteristics curve (AUROC) of 0.710 (CI 0.652-0.768). Importantly, there was no statistically significant improvement in predictive performance when training the same models with all genes common to the 17 microarray studies (n = 7,122 genes), with an AUROC = 0.755 (CI 0.697-0.813, p = 0.286). In patients with gene expression data sampled at day 3 following admission or later, the IPP gene set had higher performance, with an AUROC = 0.804 (CI 0.643-0.964), while the total gene pool had an AUROC = 0.787 (CI 0.610-0.965, p = 0.811).

Conclusion

Using pooled publicly-available gene expression data from multiple cohorts, we showed that the IPP gene set, an immune-related transcriptomics signature conveys relevant information to predict 30-day mortality when sampled at day 1 following admission. Our data also suggests that higher predictive performance could be obtained when assaying gene expression at later time points during the course of sepsis. Prospective studies are needed to confirm these findings using the IPP gene set on its dedicated measurement platform.

Carboxypeptidase A3 in the structure of the protease phenotype of mast cells: cytophysiological aspects

Carboxypeptidase A3 (CPA3) is a specific protease of mast cells (MC) with variable expression and appears to be one of the preformed components of the secretome. CPA3 is involved in regulation of the state of a specifi tissue microenvironment and components of the integrative-buffer metabolic environment in adaptive and pathological processes; it affects implementation of the innate immunity, mechanisms of angiogenesis, processes of the extracellular matrix remodeling, etc. CPA3 identification using protocols of multiplex immunohistochemistry allows specifying details of the organ-specific mast cell population features, including the protease phenotype, mechanisms of biogenesis with cytoand histotopographic criteria, and features of secretory pathways. Numerous biological effects of CPA3, including participation in the regulation of the pulmonary parenchyma and systemic blood flow, in biogenesis and remodeling of the fibrous component of the extracellular matrix, in epigenetic reprogramming, determine the importance of fundamental investigation of the physiological activity of protease and its involvement in the implementation of pathological processes. Further studies will contribute to the detection of the translational value of the mast cell CPA3 expression features as a prognostic factor and a promising molecular target for treatment of socially significant diseases.

Deciphering the TLR transcriptome and downstream signaling pathway in cerebrospinal fluid in pediatric meningitis

OBJECTIVE/DESIGN

Pediatric meningitis is characterized by a colossal inflammatory response to the pathogen in the central nervous system (CNS). This unabated inflammatory response persists even after the removal of the pathogen by antibiotics/steroids causing collateral damage to CNS tissue. Toll-like receptors (TLRs) are the key players in the recognition and elicitation of innate-immune response against bacterial/viral components in cerebrospinal fluid (CSF). Till date, the precise understanding of TLR-triggered inflammatory response in pediatric meningitis is lacking. The present study was designed to delineate the role of TLR transcriptome and downstream signaling pathways in CSF of pediatric meningitis.

METHODS

Children in the age group of > 3 months to 12 years with pediatric meningitis were included. A total of 249 cases of pediatric meningitis (bacterial = 89, viral = 160) were included. In addition, 71 children who tested negative to the pathogen in CSF tap and did not have signs of infection clinically constituted the controls. RNA was extracted from the CSF samples of both cases and controls. The relative gene expression profile of 42 TLR signaling pathway genes was performed. For the analysis of secretory cytokines and chemokines in CSF, Luminex assay was performed.

RESULTS

We report global upregulation of TLR genes in patients with acute bacterial meningitis (ABM). The downstream signaling molecules were upregulated as well. The CSF of pediatric ABM patients revealed a predominant pro-inflammatory milieu marked by increased levels of pro-inflammatory cytokines. A significant correlation between poor clinical outcomes of patients and an increased expression of TLR/pro-inflammatory cytokine genes was observed.

CONCLUSION

Our findings provide support for future studies exploring TLR-based adjunct therapy to limit the neurological sequelae, owing to persistent inflammation in pediatric ABM patients.

Carboxypeptidase A3 in the structure of the protease phenotype of mast cells: cytophysiological aspects

Carboxypeptidase A3 (CPA3) is a specific protease of mast cells (MC) with variable expression and appears to be one of the preformed components of the secretome. CPA3 is involved in regulation of the state of a specifi tissue microenvironment and components of the integrative-buffer metabolic environment in adaptive and pathological processes; it affects implementation of the innate immunity, mechanisms of angiogenesis, processes of the extracellular matrix remodeling, etc. CPA3 identification using protocols of multiplex immunohistochemistry allows specifying details of the organ-specific mast cell population features, including the protease phenotype, mechanisms of biogenesis with cyto- and histotopographic criteria, and features of secretory pathways. Numerous biological effects of CPA3, including participation in the regulation of the pulmonary parenchyma and systemic blood flow, in biogenesis and remodeling of the fibrous component of the extracellular matrix, in epigenetic reprogramming, determine the importance of fundamental investigation of the biological activity and regulation of pathological processes of CPA3. Further studies will contribute to the detection of the true value of the mast cell CPA3 expression features as a prognostic factor and a promising molecular target for treatment of socially significant diseases.

Carboxypeptidase A3—A Key Component of the Protease Phenotype of Mast Cells

Carboxypeptidase A3 (CPA3) is a specific mast cell (MC) protease with variable expression. This protease is one of the preformed components of the secretome. During maturation of granules, CPA3 becomes an active enzyme with a characteristic localization determining the features of the cytological and ultrastructural phenotype of MC. CPA3 takes part in the regulation of a specific tissue microenvironment, affecting the implementation of innate immunity, the mechanisms of angiogenesis, the processes of remodeling of the extracellular matrix, etc. Characterization of CPA3 expression in MC can be used to refine the MC classification, help in a prognosis, and increase the effectiveness of targeted therapy.

Expression Quantitative Trait Loci (eQTLs) Associated with Retrotransposons Demonstrate their Modulatory Effect on the Transcriptome

Transposable elements (TEs) are repetitive elements that belong to a variety of functional classes and have an important role in shaping genome evolution. Around 50% of the human genome contains TEs, and they have been termed the "dark matter" of the genome because relatively little is known about their function. While TEs have been shown to participate in aberrant gene regulation and the pathogenesis of diseases, only a few studies have explored the systemic effect of TEs on gene expression. In the present study, we analysed whole genome sequences and blood whole transcriptome data from 570 individuals within the Parkinson's Progressive Markers Initiative (PPMI) cohort to identify expression quantitative trait loci (eQTL) regulating genome-wide gene expression associated with TEs. We identified 2132 reference TEs that were polymorphic for their presence or absence in our study cohort. The presence or absence of the TE element could change the expression of the gene or gene clusters from zero to tens of thousands of copies of RNA. The main finding is that many TEs possess very strong regulatory effects, and they have the potential to modulate large genetic networks with hundreds of target genes over the genome. We illustrate the plethora of regulatory mechanisms using examples of their action at the HLA gene cluster and data showing different TEs' convergence to modulate WFS1 gene expression. In conclusion, the presence or absence of polymorphisms of TEs has an eminent genome-wide regulatory function with large effect size at the level of the whole transcriptome. The role of TEs in explaining, in part, the missing heritability for complex traits is convincing and should be considered.

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission

A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subsequent death. Owing to the heterogeneity, complexity, and unpredictability of the disease progression, ICU patient care is challenging. Identifying the predictors of complicated courses and subsequent mortality at the early stages of the disease and recognizing the trajectory of the disease from the vast array of longitudinal quantitative clinical data is difficult. Therefore, we attempted to perform a meta-analysis of previously published gene expression datasets to identify novel early biomarkers and train the artificial intelligence systems to recognize the disease trajectories and subsequent clinical outcomes. Using the gene expression profile of peripheral blood cells obtained within 24 h of pediatric ICU (PICU) admission and numerous clinical data from 228 septic patients from pediatric ICU, we identified 20 differentially expressed genes predictive of complicated course outcomes and developed a new machine learning model. After 5-fold cross-validation with 10 iterations, the overall mean area under the curve reached 0.82. Using a subset of the same set of genes, we further achieved an overall area under the curve of 0.72, 0.96, 0.83, and 0.82, respectively, on four independent external validation sets. This model was highly effective in identifying the clinical trajectories of the patients and mortality. Artificial intelligence systems identified eight out of twenty novel genetic markers (SDC4, CLEC5A, TCN1, MS4A3, HCAR3, OLAH, PLCB1, and NLRP1) that help predict sepsis severity or mortality. While these genes have been previously associated with sepsis mortality, in this work, we show that these genes are also implicated in complex disease courses, even among survivors. The discovery of eight novel genetic biomarkers related to the overactive innate immune system, including neutrophil function, and a new predictive machine learning method provides options to effectively recognize sepsis trajectories, modify real-time treatment options, improve prognosis, and patient survival.

Lung Mast Cells Have a High Constitutive Expression of Carboxypeptidase A3 mRNA That Is Independent from Granule-Stored CPA3

The mast cell granule metalloprotease CPA3 is proposed to have important tissue homeostatic functions. However, the basal CPA3 mRNA and protein expression among mast cell populations has remained poorly investigated. Using a novel histology-based methodology that yields quantitative data on mRNA and protein expression at a single-cell level, the present study maps CPA3 mRNA and protein throughout the MCT and MCTC populations in healthy skin, gut and lung tissues. MCTC cells had both a higher frequency of CPA3 protein-containing cells and a higher protein-staining intensity than the MCT population. Among the tissues, skin MCs had highest CPA3 protein intensity. The expression pattern at the mRNA level was reversed. Lung mast cells had the highest mean CPA3 mRNA staining. Intriguingly, the large alveolar MCT population, that lack CPA3 protein, had uniquely high CPA3 mRNA intensity. A broader multi-tissue RNA analysis confirmed the uniquely high CPA3 mRNA quantities in the lung and corroborated the dissociation between chymase and CPA3 at the mRNA level. Taken together, our novel data suggest a hitherto underestimated contribution of mucosal-like MCT to baseline CPA3 mRNA production. The functional consequence of this high constitutive expression now reveals an important area for further research.

Integrating biological knowledge and gene expression data using pathway-guided random forests: a benchmarking study

MOTIVATION

High-throughput technologies allow comprehensive characterization of individuals on many molecular levels. However, training computational models to predict disease status based on omics data is challenging. A promising solution is the integration of external knowledge about structural and functional relationships into the modeling process. We compared four published random forest-based approaches using two simulation studies and nine experimental datasets.

RESULTS

The self-sufficient prediction error approach should be applied when large numbers of relevant pathways are expected. The competing methods hunting and learner of functional enrichment should be used when low numbers of relevant pathways are expected or the most strongly associated pathways are of interest. The hybrid approach synthetic features is not recommended because of its high false discovery rate.

AVAILABILITY AND IMPLEMENTATION

An R package providing functions for data analysis and simulation is available at GitHub (https://github.com/szymczak-lab/PathwayGuidedRF). An accompanying R data package (https://github.com/szymczak-lab/DataPathwayGuidedRF) stores the processed and quality controlled experimental datasets downloaded from Gene Expression Omnibus (GEO).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of Methylated Gene Biomarkers in Patients with Alzheimer's Disease Based on Machine Learning

Background

Alzheimer's disease (AD) is a neurodegenerative disorder and characterized by the cognitive impairments. It is essential to identify potential gene biomarkers for AD pathology.

Methods

DNA methylation expression data of patients with AD were downloaded from the Gene Expression Omnibus (GEO) database. Differentially methylated sites were identified. The functional annotation analysis of corresponding genes in the differentially methylated sites was performed. The optimal diagnostic gene biomarkers for AD were identified by using random forest feature selection procedure. In addition, receiver operating characteristic (ROC) diagnostic analysis of differentially methylated genes was performed.

Results

A total of 10 differentially methylated sites including 5 hypermethylated sites and 5 hypomethylated sites were identified in AD. There were a total of 8 genes including thioredoxin interacting protein (TXNIP), noggin (NOG), regulator of microtubule dynamics 2 (FAM82A1), myoneurin (MYNN), ankyrin repeat domain 34B (ANKRD34B), STAM-binding protein like 1, ALMalpha (STAMBPL1), cyclin-dependent kinase inhibitor 1C (CDKN1C), and coronin 2B (CORO2B) that correspond to 10 differentially methylated sites. The cell cycle (FDR = 0.0284087) and TGF-beta signaling pathway (FDR = 0.0380372) were the only two significantly enriched pathways of these genes. MYNN was selected as optimal diagnostic biomarker with great diagnostic value. The random forests model could effectively predict AD.

Conclusion

Our study suggested that MYNN could be served as optimal diagnostic biomarker of AD. Cell cycle and TGF-beta signaling pathway may be associated with AD.

Identification of non-invasive biomarkers for predicting the radiosensitivity of nasopharyngeal carcinoma from serum microRNAs

Serum microRNAs (miRNAs) have been reported as novel biomarkers for various diseases. But circulating biomarkers for predicting the radiosensitivity of nasopharyngeal carcinoma (NPC) have not been used in clinical practice. To screen out of differently expressed serum miRNAs from NPC patients with different radiosensitivity may be helpful for its individual therapy. NPC patients with different radiosensitivity were enrolled according to the inclusion and exclusion criteria. RNA was isolated from serum of these NPC patients before treatment. We investigated the differential miRNA expression profiles using microarray test (GSE139164), and the candidate miRNAs were validated by reverse transcription-quantitative real time polymerase chain reaction (RT-qPCR) experiments. Receiver operating characteristic (ROC) analysis has been applied to estimate the diagnostic value. In this study, 37 serum-specific miRNAs were screened out from 12 NPC patients with different radiosensitivity by microarray test. Furthermore, RT-qPCR analysis confirmed that hsa-miR-1281 and hsa-miR-6732-3p were significantly downregulated in the serum of radioresistant NPC patients (P < 0.05), which was consistent with the results of microarray test. ROC curves demonstrated that the AUC for hsa-miR-1281 was 0.750 (95% CI: 0.574-0.926, SE 87.5%, SP 57.1%). While the AUC for hsa-miR-6732-3p was 0.696 (95% CI: 0.507-0.886, SE 56.3%, SP 78.6%). These results suggested that hsa-miR-1281 and hsa-miR-6732-3p in serum might serve as potential biomarkers for predicting the radiosensitivity of NPC.

Cytokine characteristic of cerebrospinal fluid from children with enteroviral meningitis compared to bacterial meningitis

Background

Enteroviruses are the most common etiological agent for viral encephalitis, but it is uncertain whether the cytokines have the ability to differentiate enteroviral meningitis (EVM) from bacterial meningitis (BM).

Methods

A retrospective study was performed at the Children's Hospital, Zhejiang University School of Medicine from August 2016 and August 2019. CSF and/or blood specimens were collected for microbiological culture, viruses, and cytokine detection.

Results

Forty‐three patients were confirmed with meningitis, 27 patients with EVM, and 16 with BM. Children with EVM were older compared with BM and Control group (P < .001). The most common presenting symptom in children with EVM was fever (96.3%) followed by headache (88.9%) and vomiting (66.7%). The occurrence of seizure was lower in both EVM and BM groups (P < .001). Serum IL‐6 and serum IL‐10 were lower in EVM group than BM (P = .02) and control group (IL‐6, P = .01; IL‐10, P < .001). IL‐6, IL‐10, and IFN‐γ levels showed obviously increase in CSF (P < .001, respectively) in EVM group, while only IL‐6 increased in CSF (P < .001) in BM group. CSF concentrations of cytokines IL‐6, IL‐10, TNF, and IFN‐γ in children with EVM and BM were both higher than Control group (P < .001). But compared EVM group to BM group, CSF IL‐2 (P = .13), IL‐6 (P = .37), IL‐10 (P = .98), TNF (P = .54), and IFN‐γ (P = .53) showed no difference between two groups.

Conclusions

CSF cytokines elevated in both virus and bacterial meningitis, while serum elevation only occurred in bacterial infection. Still, we could not distinguish enteroviral meningitis from bacterial meningitis with the parameters of CSF cytokines IL‐2, IL‐6, IL‐10, TNF, and IFN‐γ.

Factors and measures predicting external CSF drain-associated ventriculitis

Objective

To determine the diagnostic value of clinical factors and biochemical or microbiological measures for diagnosing a drain-associated ventriculitis, we summarized the available evidence.

Methods

We performed a systematic review and meta-analysis of studies of patients with external ventricular CSF drains who developed drain-associated ventriculitis by searching MEDLINE, EMBASE, and CENTRAL electronic database. We reported the occurrence of abnormal test results in patients with and without drain-associated ventriculitis. For continuous variables, we recalculated mean values presented in multiple studies.

Results

We identified 42 articles published between 1984 and 2018 including 3,035 patients with external CSF drains of whom 697 (23%) developed drain-associated bacterial ventriculitis. Indications for drain placement were subarachnoid, intraventricular or cerebral hemorrhage or hemorrhage not further specified (69%), traumatic brain injury (13%), and obstructive hydrocephalus secondary to a brain tumor (10%). Fever was present in 116 of 162 patients with ventriculitis (72%) compared with 80 of 275 (29%) patients without ventriculitis. The CSF cell count was increased for 74 of 80 patients (93%) with bacterial ventriculitis and 30 of 95 patients (32%) without ventriculitis. CSF culture was positive in 125 of 156 episodes classified as ventriculitis (80%), and CSF Gram stain was positive in 44 of 81 patients (54%). In patients with ventriculitis, PCR on ribosomal RNA was positive on 54 of 78 CSF samples (69%).

Conclusion

Clinical factors and biochemical and microbiological measures have limited diagnostic value in differentiating between ventriculitis and sterile inflammation in patients with external CSF drains. Prospective well-designed diagnostic accuracy studies in drain-associated ventriculitis are needed.

Low Interleukin-7 Receptor Messenger RNA Expression Is Independently Associated With Day 28 Mortality in Septic Shock Patients

OBJECTIVES

Septic shock is the primary cause of death in ICUs. A better comprehension of its pathophysiology, in particular, the immune alteration mechanisms, opened new therapeutic perspectives such as the recombinant interleukin-7. The use of biomarkers could improve the identification of eligible patients for this therapy. The soluble form of the interleukin-7 appears as a promising candidate in this regard since an association between its high plasmatic level and mortality in critically ill patients has been demonstrated. Because there are no data available on the transcriptional regulation of the interleukin-7 receptor in such patients, this study aimed to explore the expression level of different interleukin-7 receptor transcripts after septic shock and evaluate their association with mortality.

DESIGN

Retrospective discovery cohort (30 patients) and validation cohort (177 patients).

SETTING

Two French ICUs (discovery study) and six French ICUs (validation study).

PATIENTS

Adult septic shock patients.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The quantification of several interleukin-7 receptor transcripts using specific reverse transcription quantitative polymerase chain reaction designs allowed for global evaluation of interleukin-7 receptor gene expression in whole blood. In the discovery cohort, all interleukin-7 receptor transcripts studied were expressed at lower levels in septic shock patients than in healthy volunteers. Interleukin-7 receptor gene expression at day 3 after septic shock diagnosis was associated with day 28 mortality. Patients at a lower risk of death showed higher expression levels. These results were confirmed in the independent validation cohort. Interestingly, using a threshold obtained on the discovery cohort, we observed in the validation cohort a high negative predictive value for day 28 mortality for the transcript encoding the membrane form of interleukin-7 receptor (0.86; 95% CI, 0.79-0.93).

CONCLUSIONS

Interleukin-7 receptor transcripts appear as biomarkers of impaired adaptive immune response in septic shock patients and as a promising tool for patient stratification in clinical trials evaluating immunoadjuvant therapies.

Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters

OBJECTIVES

To find and validate generalizable sepsis subtypes using data-driven clustering.

DESIGN

We used advanced informatics techniques to pool data from 14 bacterial sepsis transcriptomic datasets from eight different countries (n = 700).

SETTING

Retrospective analysis.

SUBJECTS

Persons admitted to the hospital with bacterial sepsis.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A unified clustering analysis across 14 discovery datasets revealed three subtypes, which, based on functional analysis, we termed "Inflammopathic, Adaptive, and Coagulopathic." We then validated these subtypes in nine independent datasets from five different countries (n = 600). In both discovery and validation data, the Adaptive subtype is associated with a lower clinical severity and lower mortality rate, and the Coagulopathic subtype is associated with higher mortality and clinical coagulopathy. Further, these clusters are statistically associated with clusters derived by others in independent single sepsis cohorts.

CONCLUSIONS

The three sepsis subtypes may represent a unifying framework for understanding the molecular heterogeneity of the sepsis syndrome. Further study could potentially enable a precision medicine approach of matching novel immunomodulatory therapies with septic patients most likely to benefit.

Diagnosing enterovirus meningitis via blood transcriptomics: an alternative for lumbar puncture?

BACKGROUND

Meningitis can be caused by several viruses and bacteria. Identifying the causative pathogen as quickly as possible is crucial to initiate the most optimal therapy, as acute bacterial meningitis is associated with a significant morbidity and mortality. Bacterial meningitis requires antibiotics, as opposed to enteroviral meningitis, which only requires supportive therapy. Clinical presentation is usually not sufficient to differentiate between viral and bacterial meningitis, thereby necessitating cerebrospinal fluid (CSF) analysis by PCR and/or time-consuming bacterial cultures. However, collecting CSF in children is not always feasible and a rather invasive procedure.

METHODS

In 12 Belgian hospitals, we obtained acute blood samples from children with signs of meningitis (49 viral and 7 bacterial cases) (aged between 3 months and 16 years). After pathogen confirmation on CSF, the patient was asked to give a convalescent sample after recovery. 3' mRNA sequencing was performed to determine differentially expressed genes (DEGs) to create a host transcriptomic profile.

RESULTS

Enteroviral meningitis cases displayed the largest upregulated fold change enrichment in type I interferon production, response and signaling pathways. Patients with bacterial meningitis showed a significant upregulation of genes related to macrophage and neutrophil activation. We found several significantly DEGs between enteroviral and bacterial meningitis. Random forest classification showed that we were able to differentiate enteroviral from bacterial meningitis with an AUC of 0.982 on held-out samples.

CONCLUSIONS

Enteroviral meningitis has an innate immunity signature with type 1 interferons as key players. Our classifier, based on blood host transcriptomic profiles of different meningitis cases, is a possible strong alternative for diagnosing enteroviral meningitis.

Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells

Complex interactions between different host immune cell types can determine the outcome of pathogen infections. Advances in single cell RNA-sequencing (scRNA-seq) allow probing of these immune interactions, such as cell-type compositions, which are then interpreted by deconvolution algorithms using bulk RNA-seq measurements. However, not all aspects of immune surveillance are represented by current algorithms. Here, using scRNA-seq of human peripheral blood cells infected with Salmonella, we develop a deconvolution algorithm for inferring cell-type specific infection responses from bulk measurements. We apply our dynamic deconvolution algorithm to a cohort of healthy individuals challenged ex vivo with Salmonella, and to three cohorts of tuberculosis patients during different stages of disease. We reveal cell-type specific immune responses associated not only with ex vivo infection phenotype but also with clinical disease stage. We propose that our approach provides a predictive power to identify risk for disease, and human infection outcomes.

Complex interactions between different host immune cell types can determine the outcome of pathogen infections. Here, Avraham and colleagues present a deconvolution algorithm that uses single-cell RNA and bulk RNA sequencing measurements of pathogen-infected cells to predict disease risk outcomes.

Identification of a transcriptome profile associated with improvement of organ function in septic shock patients after early supportive therapy

Background

Septic shock is the most severe complication of sepsis and this syndrome is associated with high mortality. Treatment of septic shock remains largely supportive of hemodynamics and tissue perfusion. Early changes in organ function assessed by the Sequential Organ Function Assessment (SOFA) score are highly predictive of the outcome. However, the individual patient’s response to supportive therapy is very heterogeneous, and the mechanisms underlying this variable response remain elusive. The aim of the study was to investigate the transcriptome of whole blood in septic shock patients with different responses to early supportive hemodynamic therapy assessed by changes in SOFA scores within the first 48 h from intensive care unit (ICU) admission.

Methods

We performed whole blood RNA sequencing in 31 patients: 17 classified as responders (R) and 14 as non-responders (NR). Gene expression was investigated at ICU admission (time point 1, or T1), comparing R with NR [padj < 0.01; Benjamini–Hochberg (BH)] and over time from T1 to T2 (48 h later) in R and NR independently (paired analysis, padj < 0.01; BH). Then the differences in gene expression trends over time were evaluated (Mann–Whitney, P <0.01). To identify enriched biological processes, we performed an over-representation analysis based on a right-sided hypergeometric test with Bonferroni step-down as multiple testing correction (padj < 0.05).

Results

At ICU admission, we did not identify differentially expressed genes (DEGs) between the two groups. In the transition from T1 to T2, the activation of genes involved in T cell–mediated immunity, granulocyte and natural killer (NK) cell functions, and pathogen lipid clearance was noted in the R group. Genes involved in acute inflammation were downregulated in both groups.

Conclusions

Within the limits of a small sample size, our results could suggest that early activation of genes of the adaptive immune response is associated with an improvement in organ function.

Electronic supplementary material

The online version of this article (10.1186/s13054-018-2242-3) contains supplementary material, which is available to authorized users.

The relative resistance of children to sepsis mortality: from pathways to drug candidates

Attempts to develop drugs that address sepsis based on leads developed in animal models have failed. We sought to identify leads based on human data by exploiting a natural experiment: the relative resistance of children to mortality from severe infections and sepsis. Using public datasets, we identified key differences in pathway activity (Pathprint) in blood transcriptome profiles of septic adults and children. To find drugs that could promote beneficial (child) pathways or inhibit harmful (adult) ones, we built an in silico pathway drug network (PDN) using expression correlation between drug, disease, and pathway gene signatures across 58,475 microarrays. Specific pathway clusters from children or adults were assessed for correlation with drug‐based signatures. Validation by literature curation and by direct testing in an endotoxemia model of murine sepsis of the most correlated drug candidates demonstrated that the Pathprint‐PDN methodology is more effective at generating positive drug leads than gene‐level methods (e.g., CMap). Pathway‐centric Pathprint‐PDN is a powerful new way to identify drug candidates for intervention against sepsis and provides direct insight into pathways that may determine survival.

A community approach to mortality prediction in sepsis via gene expression analysis

Improved risk stratification and prognosis prediction in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here, we present prognostic models for 30-day mortality generated independently by three scientific groups by using 12 discovery cohorts containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance is validated in five cohorts of community-onset sepsis patients in which the models show summary AUROCs ranging from 0.765-0.89. Similar performance is observed in four cohorts of hospital-acquired sepsis. Combining the new gene-expression-based prognostic models with prior clinical severity scores leads to significant improvement in prediction of 30-day mortality as measured via AUROC and net reclassification improvement index These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis.

Robust classification of bacterial and viral infections via integrated host gene expression diagnostics

Improved diagnostics for acute infections could decrease morbidity and mortality by increasing early antibiotics for patients with bacterial infections and reducing unnecessary antibiotics for patients without bacterial infections. Several groups have used gene expression microarrays to build classifiers for acute infections, but these have been hampered by the size of the gene sets, use of overfit models, or lack of independent validation. We used multicohort analysis to derive a set of seven genes for robust discrimination of bacterial and viral infections, which we then validated in 30 independent cohorts. We next used our previously published 11-gene Sepsis MetaScore together with the new bacterial/viral classifier to build an integrated antibiotics decision model. In a pooled analysis of 1057 samples from 20 cohorts (excluding infants), the integrated antibiotics decision model had a sensitivity and specificity for bacterial infections of 94.0 and 59.8%, respectively (negative likelihood ratio, 0.10). Prospective clinical validation will be needed before these findings are implemented for patient care.

Usefulness of Transcriptional Blood Biomarkers as a Non-invasive Surrogate Marker of Mucosal Healing and Endoscopic Response in Ulcerative Colitis

BACKGROUND AND AIMS

Ulcerative colitis [UC] is a chronic inflammatory disease of the colon. Colonoscopy remains the gold standard for evaluating disease activity, as clinical symptoms are not sufficiently accurate. The aim of this study is to identify new accurate non-invasive biomarkers based on whole-blood transcriptomics that can predict mucosal lesions and response to treatment in UC patients.

METHODS

Whole-blood samples were collected for a total of 152 UC patients at endoscopy. Blood RNA from 25 UC individuals and 20 controls was analysed using microarrays. Genes that correlated with endoscopic activity were validated using real-time polymerase chain reaction in an independent group of 111 UC patients, and a prediction model for mucosal lesions was evaluated. Responsiveness to treatment was assessed in a longitudinal cohort of 16 UC patients who started anti-tumour necrosis factor [TNF] therapy and were followed up for 14 weeks.

RESULTS

Microarray analysis identified 122 genes significantly altered in the blood of endoscopically active UC patients. A significant correlation with the degree of endoscopic activity was observed in several genes, including HP, CD177, GPR84, and S100A12. Using HP as a predictor of endoscopic disease activity, an accuracy of 67.3% was observed, compared with 52.4%, 45.2%, and 30.3% for C-reactive protein, erythrocyte sedimentation rate, and platelet count, respectively. Finally, at 14 weeks of treatment, response to anti-TNF therapy induced alterations in blood HP, CD177, GPR84, and S100A12 transcripts that correlated with changes in endoscopic activity.

CONCLUSIONS

Transcriptional changes in UC patients are sensitive to endoscopic improvement and appear to be an effective tool to monitor patients over time.

Current meningitis outbreak in Ghana: Historical perspectives and the importance of diagnostics

Bacterial meningitis continues to be one of the most dreaded infections in sub-Saharan Africa and other countries that fall in the "meningitis belt" due to recurrent nature of the infection and the sequel of deliberating effects among survivors even after treatment. Ghana has had recurrent epidemics in the past but has been free from high mortality levels. Whereas reasons for the low reported number of deaths in the past are unclear, we hypothesize that it may be due to increased vaccination from expanded program on immunization (EPI) and consequent herd immunity of the general population. As at the end of February, 2016, 100 individuals were reported to have died out of 500 recorded cases. The infection may cause severe brain damage and kills at least 1 out of 10 individuals if quick interventions are not provided. The Ghana Health Service (GHS) and the Ministry of Health (MoH), together with other local and international stakeholders are working intensely to control the spread of the infection in affected communities with treatment and other health management programmes. This review presents a quick overview of meningitis in Ghana with emphasis on S. pneumoniae (responsible for about 70% of cases in the recent epidemic) together with some recommendations aimed at ensuring a "meningitis-free Ghana".

Neonatal Meningitis: Overcoming Challenges in Diagnosis, Prognosis, and Treatment with Omics

Neonatal meningitis is a devastating condition. Prognosis has not improved in decades, despite the advent of improved antimicrobial therapy and heightened index of suspicion among clinicians caring for affected infants. One in ten infants die from meningitis, and up to half of survivors develop significant lifelong complications, including seizures, impaired hearing and vision, and delayed or arrested development of such basic skills as talking and walking. At present, it is not possible to predict which infants will suffer poor outcomes. Early treatment is critical to promote more favorable outcomes, though diagnosis of meningitis in infants is technically challenging, time-intensive, and invasive. Profound neuronal injury has long been described in the setting of neonatal meningitis, as has elevated levels of many pro- and anti-inflammatory cytokines. Mechanisms of the host immune response that drive clearance of the offending organism and underlie brain injury due to meningitis are not well understood, however. In this review, we will discuss challenges in diagnosis, prognosis, and treatment of neonatal meningitis. We will highlight transcriptomic, proteomic, and metabolomic data that contribute to suggested mechanisms of inflammation and brain injury in this setting with a view toward fruitful areas for future investigation.

Genomic landscape of the individual host response and outcomes in sepsis: a prospective cohort study

BACKGROUND

Effective targeted therapy for sepsis requires an understanding of the heterogeneity in the individual host response to infection. We investigated this heterogeneity by defining interindividual variation in the transcriptome of patients with sepsis and related this to outcome and genetic diversity.

METHODS

We assayed peripheral blood leucocyte global gene expression for a prospective discovery cohort of 265 adult patients admitted to UK intensive care units with sepsis due to community-acquired pneumonia and evidence of organ dysfunction. We then validated our findings in a replication cohort consisting of a further 106 patients. We mapped genomic determinants of variation in gene transcription between patients as expression quantitative trait loci (eQTL).

FINDINGS

We discovered that following admission to intensive care, transcriptomic analysis of peripheral blood leucocytes defines two distinct sepsis response signatures (SRS1 and SRS2). The presence of SRS1 (detected in 108 [41%] patients in discovery cohort) identifies individuals with an immunosuppressed phenotype that included features of endotoxin tolerance, T-cell exhaustion, and downregulation of human leucocyte antigen (HLA) class II. SRS1 was associated with higher 14 day mortality than was SRS2 (discovery cohort hazard ratio (HR) 2·4, 95% CI 1·3-4·5, p=0·005; validation cohort HR 2·8, 95% CI 1·5-5·1, p=0·0007). We found that a predictive set of seven genes enabled the classification of patients as SRS1 or SRS2. We identified cis-acting and trans-acting eQTL for key immune and metabolic response genes and sepsis response networks. Sepsis eQTL were enriched in endotoxin-induced epigenetic marks and modulated the individual host response to sepsis, including effects specific to SRS group. We identified regulatory genetic variants involving key mediators of gene networks implicated in the hypoxic response and the switch to glycolysis that occurs in sepsis, including HIF1α and mTOR, and mediators of endotoxin tolerance, T-cell activation, and viral defence.

INTERPRETATION

Our integrated genomics approach advances understanding of heterogeneity in sepsis by defining subgroups of patients with different immune response states and prognoses, as well as revealing the role of underlying genetic variation. Our findings provide new insights into the pathogenesis of sepsis and create opportunities for a precision medicine approach to enable targeted therapeutic intervention to improve sepsis outcomes.

FUNDING

European Commission, Medical Research Council (UK), and the Wellcome Trust.

Repositioning of Memantine as a Potential Novel Therapeutic Agent against Meningitic E. coli-Induced Pathogenicities through Disease-Associated Alpha7 Cholinergic Pathway and RNA Sequencing-Based Transcriptome Analysis of Host Inflammatory Responses

Neonatal sepsis and meningitis (NSM) remains a leading cause worldwide of mortality and morbidity in newborn infants despite the availability of antibiotics over the last several decades. E. coli is the most common gram-negative pathogen causing NSM. Our previous studies show that α7 nicotinic receptor (α7 nAChR), an essential regulator of inflammation, plays a detrimental role in the host defense against NSM. Despite notable successes, there still exists an unmet need for new effective therapeutic approaches to treat this disease. Using the in vitro/in vivo models of the blood-brain barrier (BBB) and RNA-seq, we undertook a drug repositioning study to identify unknown antimicrobial activities for known drugs. We have demonstrated for the first time that memantine (MEM), a FDA-approved drug for treatment of Alzheimer’s disease, could very efficiently block E. coli-caused bacteremia and meningitis in a mouse model of NSM in a manner dependent on α7 nAChR. MEM was able to synergistically enhance the antibacterial activity of ampicillin in HBMEC infected with E. coli K1 (E44) and in neonatal mice with E44-caused bacteremia and meningitis. Differential gene expression analysis of RNA-Seq data from mouse BMEC infected with E. coli K1 showed that several E44-increased inflammatory factors, including IL33, IL18rap, MMP10 and Irs1, were significantly reduced by MEM compared to the infected cells without drug treatment. MEM could also significantly up-regulate anti-inflammatory factors, including Tnfaip3, CISH, Ptgds and Zfp36. Most interestingly, these factors may positively and negatively contribute to regulation of NF-κB, which is a hallmark feature of bacterial meningitis. Furthermore, we have demonstrated that circulating BMEC (cBMEC) are the potential novel biomarkers for NSM. MEM could significantly reduce E44-increased blood level of cBMEC in mice. Taken together, our data suggest that memantine can efficiently block host inflammatory responses to bacterial infection through modulation of both inflammatory and anti-inflammatory pathways.

The T-Cell Inhibitory Molecule Butyrophilin-Like 2 Is Up-regulated in Mild Plasmodium falciparum Infection and Is Protective During Experimental Cerebral Malaria

Plasmodium falciparum infection can result in severe disease that is associated with elevated inflammation and vital organ dysfunction; however, malaria-endemic residents gain protection from lethal outcomes and manifest only mild symptoms during infection. To characterize host responses associated with this more effective antimalarial response, we characterized whole-blood transcriptional profiles in Rwandan adults during a mild malaria episode and compared them with findings from a convalescence sample. We observed transcriptional up-regulation in many pathways, including type I interferon, interferon γ, complement activation, and nitric oxide during malaria infection, which provide benchmarks of mild disease physiology. Transcripts encoding negative regulators of T-cell activation, such as programmed death ligand 1 (PD-L1), programmed death 1 ligand 2 (PD-L2), and the butyrophilin family member butyrophilin-like 2 (BTNL2) were also increased. To support an important functional role for BTNL2 during malaria infection, we studied chimeric mice reconstituted with BTNL2(-/-) or wild-type hematopoietic cells that were inoculated with Plasmodium berghei ANKA, a murine model of cerebral malaria. We found that BTNL2(-/-) chimeric mice had a significant decrease in survival compared with wild-type counterparts. Collectively these data characterize the immune responses associated with mild malaria and uncover a novel role for BTNL2 in the host response to malaria.

The application of transcriptional blood signatures to enhance our understanding of the host response to infection: the example of tuberculosis

Despite advances in antimicrobials, vaccination and public health measures, infectious diseases remain a leading cause of morbidity and mortality worldwide. With the increase in antimicrobial resistance and the emergence of new pathogens, there remains a need for new and more accurate diagnostics, the ability to monitor adequate treatment response as well as the ability to predict prognosis for an individual. Transcriptional approaches using blood signatures have enabled a better understanding of the host response to diseases, leading not only to new avenues of basic research, but also to the identification of potential biomarkers for use in diagnosis, prognosis and treatment monitoring.

A transcriptomic reporter assay employing neutrophils to measure immunogenic activity of septic patients' plasma

Background

There are diverse molecules present in blood plasma that regulate immune functions and also present a potential source of disease biomarkers and therapeutic targets. Genome-wide profiling has become a powerful method for assessing immune responses on a systems scale, but technologies that can measure the plasma proteome still face considerable challenges. An alternative approach to direct proteome assessment is to measure transcriptome responses in reporter cells exposed in vitro to plasma. In this report we describe such a “transcriptomic reporter assay” to assess plasma from patients with sepsis, which is a common and severe systemic infectious process for which physicians lack efficient diagnostic or prognostic markers.

Methods

Plasma samples collected from patients with culture-confirmed bacterial sepsis and uninfected healthy controls were used to stimulate three separate cell types – neutrophils, peripheral blood mononuclear cells, and monocyte-derived dendritic cells. Whole genome microarrays were generated from stimulated cells to assess transcriptional responses. Unsupervised analysis and enriched functional networks were evaluated for each cell type. Principal component analyses were used to assess variability in responses. A random K-nearest neighbor – feature selection algorithm was used to identify markers predictive of sepsis severity, which were then validated in an independent data set.

Results

Neutrophils demonstrated the most distinct response to plasma from septic patients with 709 genes showing altered expression profiles, many of which are involved in established immunologic pathways. The amplitude of the neutrophil transcriptomic response was shown to be correlated with sepsis severity in two independent sets of patients comprised of 64 total septic patients. A subset of 30 transcripts selected using one set of patients was demonstrated to have a high degree of accuracy (82-90%) in predicting sepsis severity and outcomes in the other independent set. This subset included several genes previously established in sepsis pathogenesis as well as novel genes.

Conclusions

These results demonstrate both the suitability and potential clinical relevance of a neutrophil reporter assay for studying plasma, in this case from septic patients. The distinctive transcriptional signature we found could potentially help predict severity of disease and guide treatment. Our findings also shed new light on mechanisms of immune dysregulation in sepsis.