Airway gene expression correlates of RSV disease severity and microbiome composition in infants

Abstract Rationale Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants. The causes and correlates of severe illness in the majority of infants are poorly defined. Objectives We identified molecular correlates of illness severity from the airways of infants infected with RSV. Methods We recruited a cohort of RSV-infected infants and simultaneously assayed the molecular status of their airways and the presence of airway microbiota. Rigorous statistical approaches identified gene expression patterns associated with disease severity and microbiota composition, separately and in combination. Measurements and Main Results We measured comprehensive airway gene expression patterns in 106 infants with primary RSV infection. We identified an airway gene expression signature of severe illness dominated by excessive chemokine expression. We also found an association between H. influenzae, disease severity and airway lymphocyte accumulation. Exploring the time of onset of clinical symptoms revealed acute activation of interferon (IFN) signaling following RSV infection in infants with mild or moderate illness, which was absent in subjects with severe illness. Conclusion Our data reveal that airway gene expression patterns distinguish mild/moderate from severe illness severity. Furthermore, our data identify biomarkers that may be therapeutic targets or useful for measuring efficacy of intervention responses.

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Insufficiency in airway interferon activation defines clinical severity to infant RSV infection

10.1101/641795 ◽

2019 ◽

Cited By ~ 1

Author(s):

Chin-Yi Chu ◽

Xing Qiu ◽

Matthew N. McCall ◽

Lu Wang ◽

Anthony Corbett ◽

...

Keyword(s):

Gene Expression ◽

Clinical Symptoms ◽

Gene Expression Signature ◽

Airway Epithelial Cells ◽

Clinical Severity ◽

Severe Illness ◽

Expression Signature ◽

Rsv Infection ◽

Syncytial Virus ◽

The Relationship

AbstractRespiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants. Other than age at the time of infection, the causes and correlates of severe illness in infants lacking known risk factors are poorly defined. We recruited a cohort of confirmed RSV-infected infants and simultaneously assayed the presence of resident airway microbiota and the molecular status of their airways using a novel method. Rigorous statistical analyses identified a molecular airway gene expression signature of severe illness dominated by excessive chemokine expression. Global 16S rRNA sequencing confirmed an association between H. influenzae and clinical severity. Interestingly, adjusting for H. influenzae in our gene expression analysis revealed an association between severity and airway lymphocyte accumulation. Exploring the relationship between airway gene expression and the time of onset of clinical symptoms revealed a robust, acute activation of interferon (IFN) signaling, which was absent in subjects with severe illness. Finally, we explored the relationship between IFN activity, airway gene expression and productive RSV infection using a novel in vitro model of bona fide pediatric human airway epithelial cells. Interestingly, blocking IFN signaling, but not IFN ligand production, in these cells leads to increased viral infection. Our data reveal that acute airway interferon responses are physiologically relevant in the context of infant RSV infection and may be a target for therapeutic intervention. Additionally, the airway gene expression signature we define may be useful as a biomarker for efficacy of intervention responses.

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Airway Gene-Expression Classifiers for Respiratory Syncytial Virus (RSV) Disease Severity in Infants

10.1101/628701 ◽

2019 ◽

Author(s):

Lu Wang ◽

Chin-Yi Chu ◽

Matthew N. McCall ◽

Christopher Slaunwhite ◽

Jeanne Holden-Wiltse ◽

...

Keyword(s):

Gene Expression ◽

Disease Severity ◽

Expression Profiles ◽

Expression Patterns ◽

Acute Infection ◽

Gene Expression Profiles ◽

Severe Infection ◽

Invasive Procedure ◽

Severe Illness ◽

Syncytial Virus

AbstractBackgroundA substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness.MethodWe defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1-10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2).ResultsNGSS1 is strongly correlated with the disease severity, demonstrating a naïve correlation (ρ) of ρ=0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%.ConclusionAirway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection.

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Airway Gene-Expression Classifiers for Respiratory Syncytial Virus (RSV) Disease Severity in Infants

10.21203/rs.3.rs-63436/v1 ◽

2020 ◽

Author(s):

Lu Wang ◽

Chin-Yi Chu ◽

Matthew N McCall ◽

Christopher Slaunwhite ◽

Jeanne Holden-Wiltse ◽

...

Keyword(s):

Gene Expression ◽

Disease Severity ◽

Expression Profiles ◽

Expression Patterns ◽

Acute Infection ◽

Gene Expression Profiles ◽

Severe Infection ◽

Invasive Procedure ◽

Severe Illness ◽

Syncytial Virus

Abstract BackgroundA substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness. MethodWe defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1-10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2). ResultsNGSS1 is strongly correlated with the disease severity, demonstrating a naïve correlation (ρ) of ρ=0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%. ConclusionAirway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection.

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Airway gene-expression classifiers for respiratory syncytial virus (RSV) disease severity in infants

BMC Medical Genomics ◽

10.1186/s12920-021-00913-2 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Lu Wang ◽

Chin-Yi Chu ◽

Matthew N. McCall ◽

Christopher Slaunwhite ◽

Jeanne Holden-Wiltse ◽

...

Keyword(s):

Gene Expression ◽

Disease Severity ◽

Expression Profiles ◽

Expression Patterns ◽

Acute Infection ◽

Gene Expression Profiles ◽

Severe Infection ◽

Invasive Procedure ◽

Severe Illness ◽

Syncytial Virus

Abstract Background A substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness. Method We defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1–10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2). Results NGSS1 is strongly correlated with the disease severity, demonstrating a naïve correlation (ρ) of ρ = 0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%. Conclusion Airway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection.

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415. Airway Gene-Expression Classifiers for Respiratory Syncytial Virus (RSV) Disease Severity in Infants

Open Forum Infectious Diseases ◽

10.1093/ofid/ofz360.488 ◽

2019 ◽

Vol 6 (Supplement_2) ◽

pp. S210-S210

Author(s):

Mary T Caserta ◽

Lu Wang ◽

Chin-Yi Chu ◽

Christopher Slaunwhite ◽

Jeanne Holden-Wiltse ◽

...

Keyword(s):

Gene Expression ◽

Model Selection ◽

Disease Severity ◽

Severity Score ◽

Cross Validation ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Multivariate Linear Regression Analysis ◽

Linear Predictor

Abstract Background RSV infection is common in infants with a majority of those affected displaying mild clinical symptoms. However, a substantial number develop severe symptoms requiring hospitalization. We currently lack sensitive and specific predictors to identify a majority of those who develop severe disease. Methods High throughput RNA sequencing (RNAseq) of nasal epithelial cells defined airway gene expression patterns in RSV-infected subjects. Using multivariate linear regression analysis with AIC-based model selection, we built a sparse linear predictor of RSV disease severity, the Nasal Gene Severity Score-NGSS1. Using a similar statistical approach, we built an alternate predictor based upon genes displaying stable expression over time (NGSS2). We evaluated predictive performance of both models using leave-one-out cross-validation analyses. Results We defined comprehensive airway gene expression profiles from 106 full-tem previously healthy RSV-infected subjects with a range of RSV disease severity prospectively enrolled in the AsPIRES study. Nasal samples were obtained during acute infection (day 1–10 of illness; 106 samples), and convalescence (day 14–28 of illness; 69 samples). All subjects had a primary infection and were assigned a cumulative clinical illness severity score (GRSS) (Table 1). From the RNA seq data 41 genes were identified as the NGSS1 which is strongly correlated with disease severity (GRSS) in both the naive (ρ=0.935) and cross-validated analysis (ρ of 0.813). As a binary classifier (mild vs. severe), NGSS1 correctly classifies 89.6% of the subjects following cross-validation (Figure 1). Next, we evaluated genes that were stably expressed in both acute illness and convalescence samples in 54 subjects with data from both time points. Repeating the regression based step wise model selection identified 13 genes as NGSS2, which was significantly correlated with GRSS (ρ = 0.741). This model has slightly less, but comparable, prediction accuracy with a cross-validated correlation of 0.741 and cross-validated classification accuracy of 84.0% (Figure 2). Conclusion Airway gene expression patterns, obtained following a minimally-invasive nasal procedure, have potential utility as prognostic biomarkers for severe infant RSV infections. Disclosures All authors: No reported disclosures.

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Gene Expression Risk Scores for COVID-19 Illness Severity

10.1101/2021.08.24.457521 ◽

2021 ◽

Author(s):

Derick R Peterson ◽

Andrea M Baran ◽

Soumyaroop Bhattacharya ◽

Angela Ramona Branche ◽

Daniel P Croft ◽

...

Keyword(s):

Gene Expression ◽

Intensive Care ◽

Severe Disease ◽

Expression Patterns ◽

Illness Severity ◽

Risk Scores ◽

Severe Illness ◽

T Cell Signaling ◽

Training Cohort ◽

Peripheral Blood Gene Expression

Background: The correlates of COVID-19 illness severity following infection with SARS-Coronavirus 2 (SARS-CoV-2) are incompletely understood. Methods: We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2-infection clinically adjudicated as having mild, moderate or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and non-severe COVID. Results: Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus non-severe illness, we identified >4000 genes differentially expressed (FDR<0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated ROC-AUC=0.98), and need for intensive care in an independent cohort (ROC-AUC=0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort. Conclusion: These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.

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Gliosarcoma Is Driven by Alterations in PI3K/Akt, RAS/MAPK Pathways and Characterized by Collagen Gene Expression Signature

Cancers ◽

10.3390/cancers11030284 ◽

2019 ◽

Vol 11 (3) ◽

pp. 284 ◽

Cited By ~ 4

Author(s):

Bartosz Wojtas ◽

Bartlomiej Gielniewski ◽

Kamil Wojnicki ◽

Marta Maleszewska ◽

Shamba Mondal ◽

...

Keyword(s):

Gene Expression ◽

Copy Number ◽

Somatic Mutations ◽

Complex Analysis ◽

Expression Patterns ◽

Gene Expression Signature ◽

Genetic Alterations ◽

Copy Number Variations ◽

Gene Expression Patterns ◽

Sequencing Analysis

Gliosarcoma is a very rare brain tumor reported to be a variant of glioblastoma (GBM), IDH-wildtype. While differences in molecular and histological features between gliosarcoma and GBM were reported, detailed information on the genetic background of this tumor is lacking. We intend to fill in this knowledge gap by the complex analysis of somatic mutations, indels, copy number variations, translocations and gene expression patterns in gliosarcomas. Using next generation sequencing, we determined somatic mutations, copy number variations (CNVs) and translocations in 10 gliosarcomas. Six tumors have been further subjected to RNA sequencing analysis and gene expression patterns have been compared to those of GBMs. We demonstrate that gliosarcoma bears somatic alterations in gene coding for PI3K/Akt (PTEN, PI3K) and RAS/MAPK (NF1, BRAF) signaling pathways that are crucial for tumor growth. Interestingly, the frequency of PTEN alterations in gliosarcomas was much higher than in GBMs. Aberrations of PTEN were the most frequent and occurred in 70% of samples. We identified genes differentially expressed in gliosarcoma compared to GBM (including collagen signature) and confirmed a difference in the protein level by immunohistochemistry. We found several novel translocations (including translocations in the RABGEF1 gene) creating potentially unfavorable combinations. Collected results on genetic alterations and transcriptomic profiles offer new insights into gliosarcoma pathobiology, highlight differences in gliosarcoma and GBM genetic backgrounds and point out to distinct molecular cues for targeted treatment.

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