Epithelial cell responses to rhinovirus identify an early-life-onset asthma phenotype in adults.

BACKGROUND: The study of pathogenic mechanisms in adult asthma is often marred by a lack of precise information about the natural history of the disease. Children who have persistent wheezing (PW) during the first 6 years of life and whose symptoms start before age 3 years (PW+) are much more likely to have wheezing illnesses due to rhinovirus (RV) in infancy and to have asthma into adult life than are those who do not have PW (PW-). OBJECTIVE: Our aim was to determine whether nasal epithelial cells from PW+ asthmatic adults as compared with cells from PW- asthmatic adults show distinct biomechanistic processes activated by RV exposure. METHODS: Air-liquid interface cultures derived from nasal epithelial cells of 36-year old participants with active asthma with and without a history of PW in childhood (10 PW+ participants and 20 PW- participants) from the Tucson Children's Respiratory Study were challenged with a human RV-A strain (RV-A16) or control, and their RNA was sequenced. RESULTS: A total of 35 differentially expressed genes involved in extracellular remodeling and angiogenesis distinguished the PW+ group from the PW- group at baseline and after RV-A stimulation. Notably, 22 transcriptomic pathways showed PW-by-RV interactions; the pathways were invariably overactivated in PW+ patients, and were involved in Toll-like receptor- and cytokine-mediated responses, remodeling, and angiogenic processes. CONCLUSIONS: Asthmatic adults with a history of persistent wheeze in the first 6 years of life have specific biomolecular alterations in response to RV-A that are not present in patients without such a history. Targeting these mechanisms may slow the progression of asthma in these patients.

'Single-subject studies'-derived analyses unveil altered biomechanisms between very small cohorts: implications for rare diseases.

MOTIVATION: Identifying altered transcripts between very small human cohorts is particularly challenging and is compounded by the low accrual rate of human subjects in rare diseases or sub-stratified common disorders. Yet, single-subject studies (S3) can compare paired transcriptome samples drawn from the same patient under two conditions (e.g. treated versus pre-treatment) and suggest patient-specific responsive biomechanisms based on the overrepresentation of functionally defined gene sets. These improve statistical power by: (i) reducing the total features tested and (ii) relaxing the requirement of within-cohort uniformity at the transcript level. We propose Inter-N-of-1, a novel method, to identify meaningful differences between very small cohorts by using the effect size of 'single-subject-study'-derived responsive biological mechanisms. RESULTS: In each subject, Inter-N-of-1 requires applying previously published S3-type N-of-1-pathways MixEnrich to two paired samples (e.g. diseased versus unaffected tissues) for determining patient-specific enriched genes sets: Odds Ratios (S3-OR) and S3-variance using Gene Ontology Biological Processes. To evaluate small cohorts, we calculated the precision and recall of Inter-N-of-1 and that of a control method (GLM+EGS) when comparing two cohorts of decreasing sizes (from 20 versus 20 to 2 versus 2) in a comprehensive six-parameter simulation and in a proof-of-concept clinical dataset. In simulations, the Inter-N-of-1 median precision and recall are > 90% and >75% in cohorts of 3 versus 3 distinct subjects (regardless of the parameter values), whereas conventional methods outperform Inter-N-of-1 at sample sizes 9 versus 9 and larger. Similar results were obtained in the clinical proof-of-concept dataset. AVAILABILITY AND IMPLEMENTATION: R software is available at Lussierlab.net/BSSD.

Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes.

The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile ('personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about 'average' disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subject's 'personalome' for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive 'personalomes' through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments.

Correction to: binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions.

An amendment to this paper has been published and can be accessed via the original article.

binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions.

BACKGROUND: In this era of data science-driven bioinformatics, machine learning research has focused on feature selection as users want more interpretation and post-hoc analyses for biomarker detection. However, when there are more features (i.e., transcripts) than samples (i.e., mice or human samples) in a study, it poses major statistical challenges in biomarker detection tasks as traditional statistical techniques are underpowered in high dimension. Second and third order interactions of these features pose a substantial combinatoric dimensional challenge. In computational biology, random forest (RF) classifiers are widely used due to their flexibility, powerful performance, their ability to rank features, and their robustness to the "P > > N" high-dimensional limitation that many matrix regression algorithms face. We propose binomialRF, a feature selection technique in RFs that provides an alternative interpretation for features using a correlated binomial distribution and scales efficiently to analyze multiway interactions. RESULTS: In both simulations and validation studies using datasets from the TCGA and UCI repositories, binomialRF showed computational gains (up to 5 to 300 times faster) while maintaining competitive variable precision and recall in identifying biomarkers' main effects and interactions. In two clinical studies, the binomialRF algorithm prioritizes previously-published relevant pathological molecular mechanisms (features) with high classification precision and recall using features alone, as well as with their statistical interactions alone. CONCLUSION: binomialRF extends upon previous methods for identifying interpretable features in RFs and brings them together under a correlated binomial distribution to create an efficient hypothesis testing algorithm that identifies biomarkers' main effects and interactions. Preliminary results in simulations demonstrate computational gains while retaining competitive model selection and classification accuracies. Future work will extend this framework to incorporate ontologies that provide pathway-level feature selection from gene expression input data.

Differential transcriptomics in sarcoidosis lung and lymph node granulomas with comparisons to pathogen-specific granulomas.

RATIONALE: Despite the availability of multi-"omics" strategies, insights into the etiology and pathogenesis of sarcoidosis have been elusive. This is partly due to the lack of reliable preclinical models and a paucity of validated biomarkers. As granulomas are a key feature of sarcoidosis, we speculate that direct genomic interrogation of sarcoid tissues, may lead to identification of dysregulated gene pathways or biomarker signatures. OBJECTIVE: To facilitate the development sarcoidosis genomic biomarkers by gene expression profiling of sarcoidosis granulomas in lung and lymph node tissues (most commonly affected organs) and comparison to infectious granulomas (coccidiodomycosis and tuberculosis). METHODS: Transcriptomic profiles of immune-related gene from micro-dissected sarcoidosis granulomas within lung and mediastinal lymph node tissues and compared to infectious granulomas from paraffin-embedded blocks. Differentially-expressed genes (DEGs) were profiled, compared among the three granulomatous diseases and analyzed for functional enrichment pathways. RESULTS: Despite histologic similarities, DEGs and pathway enrichment markedly differed in sarcoidosis granulomas from lymph nodes and lung. Lymph nodes showed a clear immunological response, whereas a structural regenerative response was observed in lung. Sarcoidosis granuloma gene expression data corroborated previously reported genomic biomarkers (STAB1, HBEGF, and NOTCH4), excluded others and identified new genomic markers present in lung and lymph nodes, ADAMTS1, NPR1 and CXCL2. Comparisons between sarcoidosis and pathogen granulomas identified pathway divergences and commonalities at gene expression level. CONCLUSION: These findings suggest the importance of tissue and disease-specificity evaluation when exploring sarcoidosis genomic markers. This relevant translational information in sarcoidosis and other two histopathological similar infections provides meaningful specific genomic-derived biomarkers for sarcoidosis diagnosis and prognosis.

Association of circulating transcriptomic profiles with mortality in sickle cell disease.

Sickle cell disease (SCD) complications are associated with increased morbidity and risk of mortality. We sought to identify a circulating transcriptomic profile predictive of these poor outcomes in SCD. Training and testing cohorts consisting of adult patients with SCD were recruited and prospectively followed. A pathway-based signature derived from grouping peripheral blood mononuclear cell transcriptomes distinguished 2 patient clusters with differences in survival in the training cohort. These findings were validated in a testing cohort in which the association between cluster 1 molecular profiling and mortality remained significant in a fully adjusted model. In a third cohort of West African children with SCD, cluster 1 differentiated SCD severity using a published scoring index. Finally, a risk score composed of assigning weights to cluster 1 profiling, along with established clinical risk factors using tricuspid regurgitation velocity, white blood cell count, history of acute chest syndrome, and hemoglobin levels, demonstrated a higher hazard ratio for mortality in both the training and testing cohorts compared with clinical risk factors or cluster 1 data alone. Circulating transcriptomic profiles are a powerful method to risk-stratify severity of disease and poor outcomes in both children and adults, respectively, with SCD and highlight potential associated molecular pathways.

Development of a biomarker mortality risk model in acute respiratory distress syndrome.

BACKGROUND: There is a compelling unmet medical need for biomarker-based models to risk-stratify patients with acute respiratory distress syndrome. Effective stratification would optimize participant selection for clinical trial enrollment by focusing on those most likely to benefit from new interventions. Our objective was to develop a prognostic, biomarker-based model for predicting mortality in adult patients with acute respiratory distress syndrome. METHODS: This is a secondary analysis using a cohort of 252 mechanically ventilated subjects with the diagnosis of acute respiratory distress syndrome. Survival to day 7 with both day 0 (first day of presentation) and day 7 sample availability was required. Blood was collected for biomarker measurements at first presentation to the intensive care unit and on the seventh day. Biomarkers included cytokine-chemokines, dual-functioning cytozymes, and vascular injury markers. Logistic regression, latent class analysis, and classification and regression tree analysis were used to identify the plasma biomarkers most predictive of 28-day ARDS mortality. RESULTS: From eight biologically relevant biomarker candidates, six demonstrated an enhanced capacity to predict mortality at day 0. Latent-class analysis identified two biomarker-based phenotypes. Phenotype A exhibited significantly higher plasma levels of angiopoietin-2, macrophage migration inhibitory factor, interleukin-8, interleukin-1 receptor antagonist, interleukin-6, and extracellular nicotinamide phosphoribosyltransferase (eNAMPT) compared to phenotype B. Mortality at 28 days was significantly higher for phenotype A compared to phenotype B (32% vs 19%, p = 0.04). CONCLUSIONS: An adult biomarker-based risk model reliably identifies ARDS subjects at risk of death within 28 days of hospitalization.

Genome-Wide Association Study in African Americans with Acute Respiratory Distress Syndrome Identifies the Selectin P Ligand Gene as a Risk Factor.

RATIONALE: Genetic factors are involved in acute respiratory distress syndrome (ARDS) susceptibility. Identification of novel candidate genes associated with increased risk and severity will improve our understanding of ARDS pathophysiology and enhance efforts to develop novel preventive and therapeutic approaches. OBJECTIVES: To identify genetic susceptibility targets for ARDS. METHODS: A genome-wide association study was performed on 232 African American patients with ARDS and 162 at-risk control subjects. The Identify Candidate Causal SNPs and Pathways platform was used to infer the association of known gene sets with the top prioritized intragenic SNPs. Preclinical validation of SELPLG (selectin P ligand gene) was performed using mouse models of LPS- and ventilator-induced lung injury. Exonic variation within SELPLG distinguishing patients with ARDS from sepsis control subjects was confirmed in an independent cohort. MEASUREMENTS AND MAIN RESULTS: Pathway prioritization analysis identified a nonsynonymous coding SNP (rs2228315) within SELPLG, encoding P-selectin glycoprotein ligand 1, to be associated with increased susceptibility. In an independent cohort, two exonic SELPLG SNPs were significantly associated with ARDS susceptibility. Additional support for SELPLG as an ARDS candidate gene was derived from preclinical ARDS models where SELPLG gene expression in lung tissues was significantly increased in both ventilator-induced (twofold increase) and LPS-induced (5.7-fold increase) murine lung injury models compared with controls. Furthermore, Selplg-/- mice exhibited significantly reduced LPS-induced inflammatory lung injury compared with wild-type C57/B6 mice. Finally, an antibody that neutralizes P-selectin glycoprotein ligand 1 significantly attenuated LPS-induced lung inflammation. CONCLUSIONS: These findings identify SELPLG as a novel ARDS susceptibility gene among individuals of European and African descent.

Microbes Are Associated with Host Innate Immune Response in Idiopathic Pulmonary Fibrosis.

RATIONALE: Differences in the lung microbial community influence idiopathic pulmonary fibrosis (IPF) progression. Whether the lung microbiome influences IPF host defense remains unknown. OBJECTIVES: To explore the host immune response and microbial interaction in IPF as they relate to progression-free survival (PFS), fibroblast function, and leukocyte phenotypes. METHODS: Paired microarray gene expression data derived from peripheral blood mononuclear cells as well as 16S ribosomal RNA sequencing data from bronchoalveolar lavage obtained as part of the COMET-IPF (Correlating Outcomes with Biochemical Markers to Estimate Time-Progression in Idiopathic Pulmonary Fibrosis) study were used to conduct association pathway analyses. The responsiveness of paired lung fibroblasts to Toll-like receptor 9 (TLR9) stimulation by CpG-oligodeoxynucleotide (CpG-ODN) was integrated into microbiome-gene expression association analyses for a subset of individuals. The relationship between associated pathways and circulating leukocyte phenotypes was explored by flow cytometry. MEASUREMENTS AND MAIN RESULTS: Down-regulation of immune response pathways, including nucleotide-binding oligomerization domain (NOD)-, Toll-, and RIG1-like receptor pathways, was associated with worse PFS. Ten of the 11 PFS-associated pathways correlated with microbial diversity and individual genus, with species accumulation curve richness as a hub. Higher species accumulation curve richness was significantly associated with inhibition of NODs and TLRs, whereas increased abundance of Streptococcus correlated with increased NOD-like receptor signaling. In a network analysis, expression of up-regulated signaling pathways was strongly associated with decreased abundance of operational taxonomic unit 1341 (OTU1341; Prevotella) among individuals with fibroblasts responsive to CpG-ODN stimulation. The expression of TLR signaling pathways was also linked to CpG-ODN responsive fibroblasts, OTU1341 (Prevotella), and Shannon index of microbial diversity in a network analysis. Lymphocytes expressing C-X-C chemokine receptor 3 CD8 significantly correlated with OTU1348 (Staphylococcus). CONCLUSIONS: These findings suggest that host-microbiome interactions influence PFS and fibroblast responsiveness.