4.1 Study subject

The FGENTCARD cohort is a Lebanese clinical cohort of patients with/without coronary artery disease (CAD) (Platt et al. 2015). Plasma samples from this cohort were profiled by ¹H NMR, to identify metabolites associated with risk factors of CAD. In this case study, we describe how the “MWASTools” package was used to identify metabolites associated with type II diabetes (T2D).

4.2 Metabonomic data

¹H NMR plasma spectra were acquired on a Bruker Avance III 600 MHz spectrometer, in a randomized design. Quality control (QC) samples, composed of identical aliquots of a representative pool of the experimental samples, were injected regularly throughout the run. Following phasing and baseline correction in TopSpin 3.2 software, the spectra were calibrated to the glucose alpha anomeric signal at δ 5.23 (Pearce et al. 2008) and aligned using recursive segment-wise peak alignment (Veselkov et al. 2009). For simplification purposes, this case study focuses on the analysis of the spectral region: δ 1.60 - 0.80

4.3 Clinical data

For each patient of the cohort, information regarding age, gender, T2D status, and body mass index (BMI) was recorded.

4.4 Load data

First we loaded the “MWASTools” package:

library(MWASTools)

We then loaded the dataset required to perform the analysis:

data("metabo_SE")
metabo_SE

## class: SummarizedExperiment 
## dim: 595 516 
## metadata(0):
## assays(1): metabolic_data
## rownames(595): 0.80006656 0.80141232 ... 1.59810224 1.599448
## rowData names(0):
## colnames(516): P1 P2 ... QC9 QC10
## colData names(5): Age Gender T2D BMI sample_type

metabo_SE is a SummarizedExperiment object, generated with the function “MWAS_SummarizedExperiment()”, and containing the following information:
- metabolic_data: matrix containing the ¹H NMR profiles (δ 1.60 - 0.80) of the experimental samples (n = 506) and the QC samples (n = 10).
- clinical_data: matrix containing clinical data (age, gender, T2D and BMI) and sample type information (i.e. experimental or QC sample).

4.5 Quality control (QC) analysis

To ensure the stability and reproducibility of the analytical run, we performed QC analysis based on principal component analysis (PCA) and coefficient of variation (CV) (sd/mean) across the QC samples (Dumas et al. 2006).

# PCA model
PCA_model = QC_PCA(metabo_SE, scale = FALSE, center = TRUE)

# Plot PCA scores (PC1 vs PC2 & PC3 vs PC4)
par(mfrow = c(1, 2))
QC_PCA_scoreplot(PCA_model, metabo_SE, main = "PC1 vs PC2")
QC_PCA_scoreplot(PCA_model, metabo_SE, px = 3, py = 4, main = "PC3 vs PC4")

In both score plots, the QC samples appear tightly clustered in the center of the Hotelling´s ellipse confirming the absence of batch effects, and ensuring the reproducibility of the analytical run.

Following QC analysis via PCA, we calculated the CVs of the NMR signals across the QC samples. Notice that CV = 0.30 and CV = 0.15 are the thresholds established by the FDA (U.S. Food and Drug Administration) for biomarker discovery and quantification, respectively.

# CV calculation
metabo_CV = QC_CV(metabo_SE, plot_hist = FALSE)

# NMR spectrum colored according to CVs
CV_spectrum = QC_CV_specNMR(metabo_SE, ref_sample = "QC1")

The results from CV analysis show that most metabolic features exhibit low CV values (99 % with CV < 0.30 and 92 % with CV < 0.15), further confirming the reproducibility of the dataset. The metabolic matrix was then CV-filtered to remove non-reproducible features:

# Filter metabolic-matrix based on a CV cut-off of 0.30
metabo_SE = CV_filter(metabo_SE, metabo_CV, CV_th = 0.3)

4.6 Metabolome-wide associations

In order to identify metabolites associated with T2D, we run logistic regression models between T2D and each NMR variable, adjusted for age, gender, and BMI. To correct the p-values for multiple-testing we used Benjamini-Hochberg (BH) correction.

# Run MWAS
MWAS_T2D = MWAS_stats(metabo_SE, disease_id = "T2D", 
    confounder_ids = c("Age", "Gender", "BMI"), assoc_method = "logistic", 
    mt_method = "BH")

MWAS_T2D is 3-column matrix, with the metabolic features (ppm values) in the rows. The columns contain the following information: estimates (i.e. beta coefficients), raw p-values and BH-corrected p-values (pFDR). These results were visualized using the function “MWAS_skylineNMR( )”.

# Visualize MWAS results
skyline = MWAS_skylineNMR(metabo_SE, MWAS_T2D, ref_sample = "QC1")

4.7 NMR metabolite assignment using STOCSY

We then used STOCSY to assign the unknown NMR signals associated with T2D. An illustrative example using δ 1.04 as driver signal is shown below.

stocsy = STOCSY_NMR(metabo_SE, ppm_query = 1.04)

The STOCSY plot shows the covariance (height) and the correlation (color) of each NMR signal with the driver signal. The most highlighted signals of the plot are two doublets at δ 1.04 and at δ 0.99, indicating that the unknown signal corresponds to valine.

4.8 Mapping metabolites of interest onto KEGG pathways

Finally, we mapped some of the metabolites of interest detected by MWAS analysis (valine “cpd:C00183” and isoleucine “cpd:C00407”) onto the KEGG pathways.

kegg_pathways = MWAS_KEGG_pathways(metabolites = c("cpd:C00183", "cpd:C00407"))
head(kegg_pathways[, c(2, 4)])

##            compound_name pathway_name                                 
## cpd:C00183 "L-Valine"    "Valine, leucine and isoleucine degradation" 
## cpd:C00183 "L-Valine"    "Valine, leucine and isoleucine biosynthesis"
## cpd:C00183 "L-Valine"    "Penicillin and cephalosporin biosynthesis"  
## cpd:C00183 "L-Valine"    "Cyanoamino acid metabolism"                 
## cpd:C00183 "L-Valine"    "Pantothenate and CoA biosynthesis"          
## cpd:C00183 "L-Valine"    "Glucosinolate biosynthesis"

This function also exported a network file that allows generating a pathway-based metabolic network in Cytoscape (Shannon et al. 2003), as shown below. In this network, human pathways are highlighted and the color of the edges indicates pathway class.