A Generalized Higher-order Correlation Analysis Framework for Multi-Omics Network Inference

Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest.

Commercially Available Blocking Oligonucleotides Effectively Suppress Unwanted Hemolysis-Related miRNAs in a Large Whole-Blood RNA Cohort

When sequencing small RNA libraries derived from whole blood, the most abundant microRNAs (miRs) detected are often miR-486-5p, miR-451a, and miR-92a-3p. These highly expressed erythropoietic miRs are released into the sample from red blood cell hemolysis. Next-generation sequencing of these unwanted miRs leads to a waste in sequencing cost and diminished detection of lowly expressed miRNAs, including many potential miRNA biomarkers. Previous work has developed a method to reduce targeted miRNAs using oligonucleotides that bind their target miRNA and prevent its ligation during library construction, although the extent to which oligonucleotides can be multiplexed and their effect on larger cohorts has not been thoroughly explored. We present a method for suppressing detection of three highly abundant heme miRs in a single multiplexed blocking oligonucleotide reaction. In a small paired-sample pilot (n = 8) and a large cohort of samples (n = 901), multiplexed oligos reduced detection of their target miRNAs by approximately 70%, allowing for an approximately 10-fold increase in reads mapping to nonheme miRs and increased detection of very lowly expressed miRs, with minimal off-target effects. By removing all three highly expressed erythropoietic miRNAs from next-generational sequencing libraries, this commercially available multiplexed blocking oligonucleotide method allows for greater detection of lowly expressed biomarkers, improving the efficacy, cost-efficiency, and sensitivity of biomarker studies and diagnostic tests.

Clarifying the Risk of Lung Disease in SZ Alpha-1 Antitrypsin Deficiency

Rationale: The ZZ genotype of alpha-1 antitrypsin deficiency (AATD) is associated with chronic obstructive pulmonary disease (COPD), even among never-smokers. The SZ genotype is also considered severe; yet, its effect on lung health remains unclear.Objectives: To determine the effect of SZ-AATD on spirometry compared with a normal-risk population and to determine the effect of smoking cessation in this genotype.Methods: We prospectively enrolled 166 related individuals, removing lung index cases to reduce bias, and compared spirometry between 70 SZ and 46 MM/MS individuals (control subjects). The effect of AAT concentrations on outcomes was assessed in 82 SZ individuals (including lung index cases). Subsequently, we analyzed retrospective SZ registry data to determine the effect of smoking cessation on spirometry decline (n = 60) and plasma anti-neutrophil elastase capacity (n = 20).Measurements and Main Results: No difference between SZ and control never-smokers was seen. Ever smoking was associated with a lower FEV1% predicted (-14.3%; P = 0.0092) and a lower FEV1/FVC ratio (-0.075; P = 0.0041) in SZ-AATD. No association was found between AAT concentration and outcomes for SZ-AATD. Longitudinal analysis of 60 SZ individuals demonstrated that COPD at baseline, but not former smoking or AAT concentrations, predicted greater spirometry decline. Finally, anti-neutrophil elastase capacity did not differ between former smokers and never-smokers (P = 0.67).Conclusions: SZ never-smokers demonstrated no increased risk of COPD, regardless of AAT concentration. Smoking interacts with SZ-AATD to significantly increase airflow obstruction. Former smoking alone is not associated with greater spirometry decline in SZ-AATD, suggesting that cessation attenuates the obstructive process. We found no evidence that the putative protective threshold or AAT concentrations predict risk within the SZ genotype, raising further doubts over the need for intravenous AAT augmentation in this cohort.

Integrated transcriptomic correlation network analysis identifies COPD molecular determinants

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous syndrome. Network-based analysis implemented by SWIM software can be exploited to identify key molecular switches - called "switch genes" - for the disease. Genes contributing to common biological processes or defining given cell types are usually co-regulated and co-expressed, forming expression network modules. Consistently, we found that the COPD correlation network built by SWIM consists of three well-characterized modules: one populated by switch genes, all up-regulated in COPD cases and related to the regulation of immune response, inflammatory response, and hypoxia (like TIMP1, HIF1A, SYK, LY96, BLNK and PRDX4); one populated by well-recognized immune signature genes, all up-regulated in COPD cases; one where the GWAS genes AGER and CAVIN1 are the most representative module genes, both down-regulated in COPD cases. Interestingly, 70% of AGER negative interactors are switch genes including PRDX4, whose activation strongly correlates with the activation of known COPD GWAS interactors SERPINE2, CD79A, and POUF2AF1. These results suggest that SWIM analysis can identify key network modules related to complex diseases like COPD.

The value of blood cytokines and chemokines in assessing COPD

BACKGROUND

Blood biomarkers are increasingly used to stratify high risk chronic obstructive pulmonary disease (COPD) patients; however, there are fewer studies that have investigated multiple biomarkers and replicated in multiple large well-characterized cohorts of susceptible current and former smokers.

METHODS

We used two MSD multiplex panels to measure 9 cytokines and chemokines in 2123 subjects from COPDGene and 1117 subjects from SPIROMICS. These biomarkers included: interleukin (IL)-2, IL-6, IL-8, IL-10, tumor necrosis factor (TNF)-α, interferon (IFN)-γ, eotaxin/CCL-11, eotaxin-3/CCL-26, and thymus and activation-regulated chemokine (TARC)/CCL-17. Regression models adjusted for clinical covariates were used to determine which biomarkers were associated with the following COPD phenotypes: airflow obstruction (forced expiratory flow at 1 s (FEV1%) and FEV1/forced vital capacity (FEV1/FVC), chronic bronchitis, COPD exacerbations, and emphysema. Biomarker-genotype associations were assessed by genome-wide association of single nucleotide polymorphisms (SNPs).

RESULTS

Eotaxin and IL-6 were strongly associated with airflow obstruction and accounted for 3-5% of the measurement variance on top of clinical variables. IL-6 was associated with progressive airflow obstruction over 5 years and both IL-6 and IL-8 were associated with progressive emphysema over 5 years. None of the biomarkers were consistently associated with chronic bronchitis or COPD exacerbations. We identified one novel SNP (rs9302690 SNP) that was associated with CCL17 plasma measurements.

CONCLUSION

When assessing smoking related pulmonary disease, biomarkers of inflammation such as IL-2, IL-6, IL-8, and eotaxin may add additional modest predictive value on top of clinical variables alone.

TRIAL REGISTRATION

COPDGene (ClinicalTrials.gov Identifier: NCT02445183 ). Subpopulations and Intermediate Outcomes Measures in COPD Study (SPIROMICS) ( ClinicalTrials.gov Identifier: NCT 01969344 ).

Association of COPD candidate genes with computed tomography emphysema and airway phenotypes in severe COPD

The principal determining factors influencing the development of the airway disease and emphysema components of chronic obstructive pulmonary disease (COPD) have not been clearly defined. Genetic variability in COPD patients might influence the varying degrees of involvement of airway disease and emphysema. Therefore, we investigated the genetic association of single nucleotide polymorphisms (SNPs) in COPD candidate genes for association with emphysema severity and airway wall thickness phenotypes. Polymorphisms in six candidate genes were analysed in 379 subjects of the National Emphysema Treatment Trial (NETT) Genetics Ancillary Study with quantitative chest computed tomography (CT) data. Genetic association with per cent of lung area below -950 HU (LAA950), airway wall thickness, and derived square root wall area (SRWA) of 10-mm internal perimeter airways were investigated. Three SNPs in EPHX1, five SNPs in SERPINE2 and one SNP in GSTP1 were significantly associated with LAA950. Five SNPs in TGFB1, two SNPs in EPHX1, one SNP in SERPINE2 and two SNPs in ADRB2 were associated with airway wall phenotypes in NETT. In conclusion, several COPD candidate genes showed evidence for association with airway wall thickness and emphysema severity using CT in a severe COPD population. Further investigation will be required to replicate these genetic associations for emphysema and airway wall phenotypes.

Bronchogenic carcinoma complicating lung transplantation

BACKGROUND

Malignancy is a well-recognized complication of solid-organ transplantation. Although a variety of malignancies have been reported in lung transplant recipients, a paucity of information exists regarding the incidence and clinical course of bronchogenic carcinoma in this patient population.

METHODS

We conducted a retrospective cohort study of our lung transplant experience at the University of Pennsylvania.

RESULTS

We identified 6 patients with bronchogenic carcinoma detected at the time of, or developing after, transplantation. The incidence of bronchogenic carcinoma was 2.4%. All patients with lung cancer had a history of smoking, with an average of 79 +/- 39 pack-years. A total of 5 patients had chronic obstructive pulmonary disease, and 1 had idiopathic pulmonary fibrosis. Lung cancers were all of non-small-cell histology and first developed in native lungs. Three patients had bronchogenic carcinoma at the time of surgery. The remaining 3 patients were diagnosed between 280 and 1,982 days post-transplantation. Of the 6 patients, 4 presented with a rapid course suggestive of an infectious process. The 1- and 2-year survival rates after diagnosis were 33% and 17%, respectively.

CONCLUSION

Lung transplant recipients are at risk for harboring or developing bronchogenic carcinoma in their native lungs. Rapid progression to locally advanced or metastatic disease commonly occurs, at times mimicking an infection. Bronchogenic carcinoma should be considered in the differential diagnosis of pleuroparenchymal processes involving the native lung.