More effort — more results: recent advances in integrative ‘omics’ data analysis

The development of ‘omics’ technologies has progressed to address complex biological questions that underlie various plant functions thereby producing copious amounts of data. The need to assimilate large amounts of data into biologically meaningful interpretations has necessitated the development of statistical methods to integrate multidimensional information. Throughout this review, we provide examples of recent outcomes of ‘omics’ data integration together with an overview of available statistical methods and tools

Serum Metabolomics Reveals Distinct Profiles during Ischemia and Reperfusion in a Porcine Model of Myocardial Ischemia–Reperfusion

Acute myocardial infarction (MI) is one of the leading causes of death worldwide. Early identification of ischemia and establishing reperfusion remain cornerstones in the treatment of MI, as mortality and morbidity can be significantly reduced by establishing reperfusion to the affected areas. The aim of the current study was to investigate the metabolomic changes in the serum in a swine model of MI induced by ischemia and reperfusion (I/R) injury, and to identify circulating metabolomic biomarkers for myocardial injury at different phases. Female Yucatan minipigs were subjected to 60 min of ischemia followed by reperfusion, and serum samples were collected at baseline, 60 min of ischemia, 4 h of reperfusion, and 24 h of reperfusion. Circulating metabolites were analyzed using an untargeted metabolomic approach. A bioinformatic approach revealed that serum metabolites show distinct profiles during ischemia and during early and late reperfusion. Some notable changes during ischemia include accumulation of metabolites that indicate impaired mitochondrial function and N-terminally modified amino acids. Changes in branched-chain amino-acid metabolites were noted during early reperfusion, while bile acid pathway derivatives and intermediates predominated in the late reperfusion phases. This indicates a potential for such an approach toward identification of the distinct phases of ischemia and reperfusion in clinical situations

Canonical correlation analysis maximizes the correlation between the linear combination of the cell wall polysaccharides in the glycan array and the fiber properties.

<p>In this figure, given a linear combination of <i>X</i> variables: <i>U₁</i> = <i>f</i>_1×1+ <i>f</i>_2×2+ …+<i>f</i>_p<i>X</i>_p and a linear combination of <i>Y</i> variables: <i>V₁</i> = <i>g</i>₁<i>Y</i>₁+ <i>g</i>₂<i>Y</i>₂+ …+<i>g</i>_q<i>Y</i>_q, the first canonical correlation is the maximum correlation coefficient between <i>U₁</i> and <i>V₁</i>, for all <i>U₁</i> and <i>V₁.</i></p

Summary statistics of the five possible multiple regression models.

<p>Summary statistics of the five possible multiple regression models.</p

Graphical representation of the variables selected by sPLS on the first two dimensions predicts specific cell wall polysaccharides linked to the fiber properties.

<p>The coordinates of each variable are obtained by computing the correlation between the latent variable vectors and the original dataset. The selected variables are then projected onto correlation circles where highly correlated variables cluster together. These graphics help to identify association between the two datasets. The correlation between two variables is positive if the angle is sharp cos(α)>0, negative if the angle is obtuse cos(θ)<0, and null if the vectors are perpendicular cos(β)∼0.</p

Understanding the Relationship between Cotton Fiber Properties and Non-Cellulosic Cell Wall Polysaccharides

<div><p>A detailed knowledge of cell wall heterogeneity and complexity is crucial for understanding plant growth and development. One key challenge is to establish links between polysaccharide-rich cell walls and their phenotypic characteristics. It is of particular interest for some plant material, like cotton fibers, which are of both biological and industrial importance. To this end, we attempted to study cotton fiber characteristics together with glycan arrays using regression based approaches. Taking advantage of the comprehensive microarray polymer profiling technique (CoMPP), 32 cotton lines from different cotton species were studied. The glycan array was generated by sequential extraction of cell wall polysaccharides from mature cotton fibers and screening samples against eleven extensively characterized cell wall probes. Also, phenotypic characteristics of cotton fibers such as length, strength, elongation and micronaire were measured. The relationship between the two datasets was established in an integrative manner using linear regression methods. In the conducted analysis, we demonstrated the usefulness of regression based approaches in establishing a relationship between glycan measurements and phenotypic traits. In addition, the analysis also identified specific polysaccharides which may play a major role during fiber development for the final fiber characteristics. Three different regression methods identified a negative correlation between micronaire and the xyloglucan and homogalacturonan probes. Moreover, homogalacturonan and callose were shown to be significant predictors for fiber length. The role of these polysaccharides was already pointed out in previous cell wall elongation studies. Additional relationships were predicted for fiber strength and elongation which will need further experimental validation.</p></div

Multivariate tests of significance for the canonical functions.

<p>Multivariate tests of significance for the canonical functions.</p

Redundancy analysis of dependent and independent variates for the first canonical function.

<p>Redundancy analysis of dependent and independent variates for the first canonical function.</p