Amrit Singh

mixOmics: An R package for ‘omics feature selection and multiple data integration

The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.

MicroRNA expression in response to controlled exposure to diesel exhaust: attenuation by the antioxidant N-acetylcysteine in a randomized crossover study.

Background: Adverse health effects associated with diesel exhaust (DE) are thought to be mediated in part by oxidative stress, but the detailed mechanisms are largely unknown. MicroRNAs (miRNAs) regulate gene expression post-transcriptionally and may respond to exposures such as DE. Objectives: We profiled peripheral blood cellular miRNAs in participants with mild asthma who were exposed to controlled DE with and without antioxidant supplementation. Methods: Thirteen participants with asthma underwent controlled inhalation of filtered air and DE in a double-blinded, randomized crossover study of three conditions: a) DE plus placebo (DEP), b) filtered air plus placebo (FAP), or c) DE with N-acetylcysteine supplementation (DEN). Total cellular RNA was extracted from blood drawn before exposure and 6 hr after exposure for miRNA profiling by the NanoString nCounter assay. MiRNAs significantly associated with DEP exposure and a predicted target [nuclear factor (erythroid-derived 2)-like 2 (NRF2)] as well as antioxidant enzyme genes were assessed by reverse transcription–quantitative polymerase chain reaction (RT-qPCR) for validation, and we also assessed the ability of N-acetylcysteine supplementation to block the effect of DE on these specific miRNAs. 8-hydroxy-2´-deoxyguanosine (8-OHdG) was measured in plasma as a systemic oxidative stress marker. Results: Expression of miR-21, miR-30e, miR-215, and miR-144 was significantly associated with DEP. The change in miR-144 was validated by RT-qPCR. NRF2 and its downstream antioxidant genes [glutamate cysteine ligase catalytic subunit (GCLC) and NAD(P)H:quinone oxidoreductase 1 (NQO1)] were negatively associated with miR-144 levels. Increases in miR-144 and miR-21 were associated with plasma 8-hydroxydeoxyguanosine 8-OHdG level and were blunted by antioxidant (i.e, DEN). Conclusions: Systemic miRNAs with plausible biological function are altered by acute moderate-dose DE exposure. Oxidative stress appears to mediate DE-associated changes in miR-144.

Decreased miR-192 expression in peripheral blood of asthmatic individuals undergoing an allergen inhalation challenge.

BackgroundMicroRNAs are small non-coding RNAs that regulate gene expression at the post-transcriptional level. While they have been implicated in various diseases, the profile changes in allergen inhalation challenge are not clarified in human. We aimed to evaluate changes in the microRNA profiles in the peripheral blood of asthmatic subjects undergoing allergen inhalation challenge.ResultsSeven mild asthmatic subjects participated in the allergen inhalation challenge. In addition, four healthy control subjects (HCs) were recruited. MicroRNA profiles in peripheral blood samples (pre-challenge and 2 hours post-challenge) were measured by the NanoString nCounter assay to determine changes in miRNA levels as these asthmatic subjects underwent an allergen inhalation challenge. One common miRNA, miR-192, was significantly expressed in both comparisons; HCs vs. pre-challenge and pre- vs. post-challenge, showing that miR-192 was significantly under-expressed in asthmatics compared to HCs and decreased in post-challenge at an FDR of 1%. Cell-specific statistical deconvolution attributed miR-192 expression in whole blood to PBMCs. MiR-192 was technically validated using real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR) showing that the level in asthmatics (pre-challenge) was significantly lower than HCs and that post-challenge was significantly lower than pre-challenge. The normalized relative miR-192 expression quantified using RT-qPCR specific to PBMCs was also validated. Ontology enrichment and canonical pathway analyses for target genes suggested several functions and pathways involved in immune response and cell cycle.ConclusionsThe miRNA profile in peripheral blood was altered after allergen inhalation challenge. Change in miR-192 levels may be implicated in asthma mechanisms. These results suggest that allergen inhalation challenge is a suitable method to characterize peripheral miRNA profiles and potentially elucidate the mechanism of human asthma.

A Regulatory T-Cell Gene Signature Is a Specific and Sensitive Biomarker to Identify Children With New-Onset Type 1 Diabetes

Type 1 diabetes (T1D) is caused by immune-mediated destruction of insulin-producing β-cells. Insufficient control of autoreactive T cells by regulatory T cells (Tregs) is believed to contribute to disease pathogenesis, but changes in Treg function are difficult to quantify because of the lack of Treg-exclusive markers in humans and the complexity of functional experiments. We established a new way to track Tregs by using a gene signature that discriminates between Tregs and conventional T cells regardless of their activation states. The resulting 31-gene panel was validated with the NanoString nCounter platform and then measured in sorted CD4+CD25hiCD127lo Tregs from children with T1D and age-matched control subjects. By using biomarker discovery analysis, we found that expression of a combination of six genes, including TNFRSF1B (CD120b) and FOXP3, was significantly different between Tregs from subjects with new-onset T1D and control subjects, resulting in a sensitive (mean ± SD 0.86 ± 0.14) and specific (0.78 ± 0.18) biomarker algorithm. Thus, although the proportion of Tregs in peripheral blood is similar between children with T1D and control subjects, significant changes in gene expression can be detected early in disease process. These findings provide new insight into the mechanisms underlying the failure to control autoimmunity in T1D and might lead to a biomarker test to monitor Tregs throughout disease progression.

Peripheral blood gene expression changes during allergen inhalation challenge in atopic asthmatic individuals.

Objectives. (1) To investigate the effects of globin mRNA depletion in detecting differential gene expression in peripheral blood and (2) to investigate changes in peripheral blood gene expression in atopic asthmatic individuals undergoing allergen inhalation challenge. Methods. Asthmatic subjects (20–60 years of age, with stable, mild allergic asthma, n = 9) underwent allergen inhalation challenges. All had an early asthmatic response of ≥20% fall in forced expiratory volume in 1 second. Blood was collected immediately prior to and 2 hours after allergen challenge using PAXgene tubes (n = 4) and EDTA tubes (n = 5). Aliquots of the PAXgene blood samples were subjected to globin reduction (PAX-GR). Transcriptome analysis was performed using Affymetrix GeneChip® Human Gene 1.0 ST arrays. Data were preprocessed using factor analysis for robust microarray summarization and analyzed using linear models for microarrays. Pathway analyses were performed using Ingenuity Pathway Analysis. Results. Globin reduction uncovered probe sets of lower abundance. However, it significantly reduced the ability to detect differentially expressed genes (DEGs) when compared to non-globin-reduced PAXgene samples (PAX-NGR). Combined transcriptional analysis of four PAX-NGR and five EDTA sample pairs identified 1595 DEGs associated with allergen inhalation challenge (false discovery rate ≤ 5%), with the top-ranked network of perturbed biological functions consisting of inflammatory response, cellular movement, and immune cell trafficking. Conclusions. While we have demonstrated a diminished ability to detect DEGs after globin reduction, we have nevertheless identified significant changes in the peripheral blood transcriptome of people with mild asthma 2 hours after allergen inhalation challenge.

Plasma proteomics can discriminate isolated early from dual responses in asthmatic individuals undergoing an allergen inhalation challenge.

This proteomics study was designed to determine the utility of iTRAQ MALDI‐TOF/TOF technology to compare plasma samples from carefully phenotyped mild, atopic asthma subjects undergoing allergen inhalation challenge.

Gene-Metabolite Expression in Blood Can Discriminate Allergen-Induced Isolated Early from Dual Asthmatic Responses

Some asthmatic individuals undergoing allergen inhalation challenge develop an isolated early response whereas others develop a dual response (early plus late response). In the present study we have used transcriptomics (microarrays) and metabolomics (mass spectrometry) of peripheral blood to identify molecular patterns that can discriminate allergen-induced isolated early from dual asthmatic responses. Peripheral blood was obtained prior to (pre-) and 2 hours post allergen inhalation challenge from 33 study participants. In an initial cohort of 14 participants, complete blood counts indicated significant differences in neutrophil and lymphocyte counts at pre-challenge between early and dual responders. At post-challenge, significant genes (ALOX15, FADS2 and LPCAT2) and metabolites (lysolipids) were enriched in lipid metabolism pathways. Enzymes encoding for these genes are involved in membrane biogenesis and metabolism of fatty acids into pro-inflammatory and anti-inflammatory mediators. Correlation analysis indicated a strong negative correlation between ALOX15, FADS2, and IL5RA expression with 2-arachidonoylglycerophosphocholine levels in dual responders. However, measuring arachidonic acid and docosahexaenoic acid levels in a validation cohort of 19 participants indicated that the free form of DHA (nmoles/µg of protein) was significantly (p = 0.03) different between early and dual responders after allergen challenge. Collectively these results may suggest an imbalance in lipid metabolism which dictates pro- (anti-) inflammatory and pro-resolving mechanisms. Future studies with larger sample sizes may reveal novel mechanisms and therapeutic targets of the late phase asthmatic response.

DIABLO - an integrative, multi-omics, multivariate method for multi-group classification

Systems biology approaches, leveraging multi-omics measurements, are needed to capture the complexity of biological networks while identifying the key molecular drivers of disease mechanisms. We present DIABLO, a novel integrative method to identify multi-omics biomarker panels that can discriminate between multiple phenotypic groups. In the multi-omics analyses of simulated and real-world datasets, DIABLO resulted in superior biological enrichment compared to other integrative methods, and achieved comparable predictive performance with existing multi-step classification schemes. DIABLO is a versatile approach that will benefit a diverse range of research areas, where multiple high dimensional datasets are available for the same set of specimens. DIABLO is implemented along with tools for model selection, and validation, as well as graphical outputs to assist in the interpretation of these integrative analyses (http://mixomics.org/).Rapid advances in technology have led to a wealth of large-scale molecular omics datasets. Integrating such data offers an unprecedented opportunity to assess molecular interactions at multiple functional levels and provide a more comprehensive understanding of the biological pathways involved in different diseases subgroups. However, multiple omics data integration is a challenging task due to the heterogeneity in the different platforms used. There is a need to address the complex and correlated nature of different data-types, in order to identify a robust and reliable multi-omics signature that can predict a phenotype of interest. We introduce a novel multivariate dimension reduction method for multiple omics integration, classification and identification of a multi-omics molecular signature. DIABLO - Data Integration Analysis for Biomarker discovery using a Latent component method for Omics studies, models the correlation structure between omics datasets, resulting in an improved ability to associate biomarkers across multiple functional levels to phenotypes of interest. We demonstrate the capabilities of DIABLO using simulated data and studies of breast cancer and asthma, integrating up to four types of omics datasets to identify relevant biomarkers, while still retaining competitive classification and predictive performance compared to existing methods. Our statistical integrative framework can benefit a diverse range of research areas with varying types of study designs, as well as enabling module-based analyses. Importantly, graphical outputs of our method assist in the interpretation of such complex analyses and provide significant biological insights.

Th17/Treg ratio derived using DNA methylation analysis discriminates allergen-induced early from dual asthmatic responses

Background Atopic allergic asthmatic individuals experience acute bronchoconstriction (early response) upon allergen exposure. Several hours after the initial exposure, some individuals exhibit a chronic late phase (dual responders, DRs) whereas others do not (early responders, ERs). The purpose of this study is to determine changes in Th17 and regulatory T (Treg) cell numbers and their associated gene expression profiles in whole blood between allergen-induced ERs and DRs. Methods 14 participants with mild, atopic asthma (8 ERs and 6 DRs) underwent a cat allergen inhalation challenge as part of the AllerGen Clinical Investigator Collaborative. Whole blood was collected immediately prior to challenge (pre) and 2 hours post-challenge. DNA methylation analysis was used to measure the frequency of Th17, Treg, B and T cells (Epiontis, Germany). Whole blood transcriptome profiling was performed using Affymetrix GeneChip ® Human Gene 1.0 ST Arrays. Statistical analysis was performed using R. Results Sum of the T cell and B cell frequencies obtained using the methylation assays strongly correlated (r = 0.95) with the lymphocyte frequency obtained using a hematolyzer. Allergen inhalation did not significantly (p>0.05) change Th17, Treg, B and T cell counts between ERs and DRs. However, the Th17/Treg ratio was significantly (p=0.03) different between ERs and DRs post challenge. 199 genes positively correlated with Th17 cells at an FDR of 5%. 463 genes positively correlated with Treg cells at an FDR of 5%. Th17 genes were inversely correlated with Treg genes. Conclusions Th17/Treg ratio derived using DNA methylation analysis discriminates allergen-induced early from dual asthmatic responses. The inverse correlation between Th17 genes and Treg genes may be indicative of the inflammatory or suppressive phenotypes of these cells.

Transcriptional changes of Blood eosinophils After Methacholine Inhalation challenge in Asthmatics

Background Methacholine challenge is commonly used within the asthma diagnostic algorithm. Methacholine challenge has recently been shown to induce airway remodelling in asthma via bronchoconstriction, without additional airway inflammation. We evaluated the effect of methacholine-induced bronchoconstriction on the peripheral whole-blood transcriptome. Methods Fourteen males with adult-onset, occupational asthma, 26–77 years of age, underwent methacholine inhalation challenges. The concentration of methacholine eliciting a ≥20% fall in FEV1 (PC20) was determined. Blood was collected immediately prior to and two hours after challenge. Complete blood counts and leukocyte differentials were obtained. Transcriptome analysis was performed using Affymetrix GeneChip® Human Gene 1.0 ST arrays. Data were analyzed using robust LIMMA and SAM. The cell-specific Significance Analysis of Microarrays (csSAM) algorithm was used to deconvolute the gene expression data according to cell type. Results Microarray pathway analysis indicated that inflammatory processes were differentially affected. CsSAM identified 1,559 transcripts differentially expressed (all down-regulated) between pre- and post-methacholine in eosinophils at a false discovery cutoff of 10%. Notable changes included the GOLGA5 and METTL2B genes and the protein ubiquitination and CCR3 pathways. Conclusions We demonstrated significant changes in the peripheral blood eosinophil-specific transcriptome of asthmatics two hours after methacholine challenge. CCR3 and protein ubiquitination pathways are both significantly down-regulated.