Evaluation of diversity among common beans (Phaseolus vulgaris L.) from two centers of domestication using 'omics' technologies

<p>Abstract</p> <p>Background</p> <p>Genetic diversity among wild accessions and cultivars of common bean (<it>Phaseolus vulgaris </it>L.) has been characterized using plant morphology, seed protein allozymes, random amplified polymorphic DNA, restriction fragment length polymorphisms, DNA sequence analysis, chloroplast DNA, and microsatellite markers. Yet, little is known about whether these traits, which distinguish among genetically distinct types of common bean, can be evaluated using omics technologies.</p> <p>Results</p> <p>Three 'omics' approaches: transcriptomics, proteomics, and metabolomics were used to qualitatively evaluate the diversity of common bean from two Centers of Domestication (COD). All three approaches were able to classify common bean according to their COD using unsupervised analyses; these findings are consistent with the hypothesis that differences exist in gene transcription, protein expression, and synthesis and metabolism of small molecules among common bean cultivars representative of different COD. Metabolomic analyses of multiple cultivars within two common bean gene pools revealed cultivar differences in small molecules that were of sufficient magnitude to allow identification of unique cultivar fingerprints.</p> <p>Conclusions</p> <p>Given the high-throughput and low cost of each of these 'omics' platforms, significant opportunities exist for their use in the rapid identification of traits of agronomic and nutritional importance as well as to characterize genetic diversity.</p

Label-free detection of C-reactive protein using an electrochemical DNA immunoassay

A label-free electrochemical immunoassay that combines DNA-directed immobilization (DDI) with electrochemical impedance spectroscopy (EIS) on microwire sensors is reported for the detection of C-reactive protein (CRP). CRP is an acute-phase protein that is strongly correlated with systemic inflammation. Since inflammation plays a role in pathogenesis of cardiovascular diseases, CRP can be used to predict the likelihood of coronary events. To demonstrate the new chemistry, 25-μm Au electrodes were modified with single strand DNA (ssDNA) and exposed to a solution containing complementary ssDNA conjugated to monoclonal anti-CRP. The charge-transfer resistance of the [Fe(CN)6]3−/4− redox couple was used to determine the CRP concentration after binding. A stepwise increase in the charge transfer resistance was observed using EIS for each modification step, ssDNA, ssDNA-anti-CRP hybridization and the final CRP capture. Cyclic voltammetry (CV) was used to verify the EIS results, and showed an increase in peak potential splitting in a similar stepwise manner for each modification step. Finally, fluorescence microscopy was used to confirm the DNA hybridization and CRP binding. Standard addition of CRP revealed that EIS could be used to detect CRP at clinically relevant levels in serum samples. This new form of electrochemical DNA immunoassay (eDI) has significant potential as a simple, label-free sensor for proteins in microfluidic devices. Keywords: Electrochemical impedance spectroscopy (EIS), C-reactive protein, Gold microwire electrode, DNA-directed immobilization, Label-free detectio

Metabolite Profiling of a Diverse Collection of Wheat Lines Using Ultraperformance Liquid Chromatography Coupled with Time-of-Flight Mass Spectrometry

<div><p>Genetic differences among major types of wheat are well characterized; however, little is known about how these distinctions affect the small molecule profile of the wheat seed. Ethanol/water (65% v/v) extracts of seed from 45 wheat lines representing 3 genetically distinct classes, tetraploid durum (<em>Triticum turgidum</em> subspecies <em>durum</em>) (DW) and hexaploid hard and soft bread wheat (<em>T. aestivum</em> subspecies <em>aestivum</em>) (BW) were subjected to ultraperformance liquid chromatography coupled with time-of-flight mass spectrometry (UPLC-TOF-MS). Discriminant analyses distinguished DW from BW with 100% accuracy due to differences in expression of nonpolar and polar ions, with differences attributed to sterol lipids/fatty acids and phospholipids/glycerolipids, respectively. Hard versus soft BW was distinguished with 100% accuracy by polar ions, with differences attributed to heterocyclic amines and polyketides versus phospholipid ions, respectively. This work provides a foundation for identification of metabolite profiles associated with desirable agronomic and human health traits and for assessing how environmental factors impact these characteristics.</p> </div

Pedigree information for 45 wheat lines evaluated.

<p>Table columns: Numbers = identifiers used within manuscript to visualize chemical separations in scatter plots and dendrograms; Wheat Line = common field identifier; Source = geographical location where grown; Class = 1 of 3 market classes: durum (DW), hard bread wheat (HBW), or soft bread wheat (SBW); Subclass = subclass within bread wheat market classes based on seed coat color and growth habit (HWW = hard white winter; HWS = hard white spring; HRW = hard red winter; HRS = hard red spring; SWW = soft white winter; SWS = soft white spring; SRW = soft red winter); Pedigree = wheat line development and breeding.</p

Metabolite profiling distinguishes between SBW subclasses with 100% accuracy.

<p>Multivariate discriminant analysis of the high-quality ion list, consisting of 935 ions in 12 wheat lines, was used to distinguish between subclasses of soft white winter (SWW) comprising soft white winter (SWW), soft red winter (SRW), and soft white spring (SWS). <b>(Panel 3A)</b> To visualize inherent clustering patterns, the scatter plot represents unsupervised analysis through the PCA model. Model fit: R2X(cum) = 48.9%, with 2 components, and Q2(cum) = 4.9%. <b>(Panel 3B)</b> To determine contributing sources of variation, the scatter plot represents supervised analysis of the OPLS-DA model. Subclasses demonstrate complete separation, and the propensity of wheat lines to localize near lines of similar growth habit, as observed with hard bread wheat lines, was observed in soft bread wheat lines: the divergence of SRW and SWW from a common parent cluster indicates chemical similarity. Model fit: R2Y(cum) = 99.1%, Q2Y(cum) = 64.9%. <b>(Panel 3C-Inset)</b> The misclassification table for the OPLS-DA model indicates that 100% of wheat lines (12 out of 12 lines) were correctly classified, with low probability (p = 7.20E−05) of random table generation as assessed by Fisher’s Exact Probability. <b>(Panel 3C)</b> To visualize the misclassification rate, the dendrogram was constructed using single linkage hierarchical clustering and sorted by size. Two main clusters comprise 1) SWS and 2) the 2 winter habit subclasses, with cluster 2 branching into 2A, comprising SRW lines, and 2B, comprising SWW lines, suggesting that SBW subclasses have unique chemical profiles.</p

Metabolite profiling distinguishes between genetically distinct wheat classes with 100% accuracy.

<p>Multivariate discriminant analysis of the high-quality ion list, consisting of 935 ions in 45 wheat lines, was used to distinguish between wheat classes of differing ploidy levels: tetraploid durum wheat (DW) vs. hexaploid bread wheat, which comprises hard (HBW) and soft (SBW) bread wheat. Each point represents a single observation (e.g. each wheat line). <b>(Panel 1A)</b> To visualize inherent clustering patterns, the scatter plot represents unsupervised analysis through the PCA 3-class model. Separation of DW lines from HBW and SBW lines is observed. Model fit: R2X(cum) = 68.6%, with 7 components, and Q2(cum) = 38.9%. <b>(Panel 1B)</b> To determine contributing sources of variation, the scatter plot represents supervised analysis of the 3-class OPLS-DA model, which rotates the model plane to maximize separation due to class assignment. Near-complete separation of the 3 classes was observed. Model fit: R2Y(cum) = 93.2%, Q2Y(cum) = 71.0%. <b>(Panel 1C-Inset)</b> The misclassification table for the 3-class OPLS-DA model indicates that 100% of wheat lines (45 of 45 lines) were correctly classified, with low probability (p = 3.10E−17) of random table generation as assessed by Fisher’s Exact Probability. <b>(Panel 1C)</b> To visualize the misclassification rate, the dendrogram depicts hierarchical clustering patterns among major wheat classes using single linkage and size. Two main clusters comprise 1) DW lines and 2) all BW lines, with cluster 2 branching into 2A, comprising SBW lines, and 2B, comprising HBW lines. Node height of cluster 1 from 0 confirms the high degree of chemical distinctness seen within the DW lines evaluated in this study compared to node height of cluster 2.</p

Metabolite profiling distinguishes between HBW subclasses with >62% accuracy.

<p>Multivariate discriminant analysis of the high-quality ion list, consisting of 935 ions in 27 wheat lines, was used to distinguish between subclasses of hard bread wheat (HBW) comprising hard white winter (HWW), hard white spring (HWS), hard red winter (HRW), and hard red spring (HRS). <b>(Panel 2A)</b> To visualize inherent clustering patterns, the scatter plot depicts unsupervised analysis through the PCA model. Model fit: R2X(cum) = 40.3%, with 3 components, and Q2(cum) = 10.8%. <b>(Panel 2B)</b> To determine contributing sources of variation, the scatter plot represents supervised analysis of the OPLS-DA model. Near-complete separation of subclasses was observed. Model fit: R2Y(cum) = 36.6%, Q2Y(cum) = 17.3%. <b>(Panel 2C-Inset)</b> The misclassification table for the OPLS-DA model indicates that approximately 63% of wheat lines (17 out of 27 lines) were correctly classified, with low probability (p = 1.40E−05) of random table generation as assessed by Fisher’s Exact Probability. <b>(Panel 2C)</b> To visualize the misclassification rate, the dendrogram was constructed using single linkage hierarchical clustering and sorted by size. Two main clusters comprise 1) HRS and 2) the other 3 subclasses, which do not cluster by subclass, indicating a high degree of chemical homogeneity and therefore resistance to clustering by hierarchical methods between HBW subclasses.</p

Discriminatory ions of differential polarity determine separation of tetraploid DW from hexaploid BW lines.

<p>Multivariate analysis was extended to identify influential ions responsible for the separation between classes. <b>(Panel 4A)</b> The supervised OPLS-DA model was created to compare all durum lines to all bread wheat lines, and an S-plot was constructed by plotting modeled correlation in the first predictive principal component against modeled correlation from the first predictive component (t1). Upper right and lower left regions of S-plots contain candidate biomarkers with both high reliability and high magnitude; discriminatory ions (n = 36) chosen from these regions are enlarged X3. <b>(Panel 4B)</b> To determine the statistical reliability of the ions chosen in <b>Panel 4A</b>, jack-knifed confidence intervals (JKCI) were created on the magnitude of covariance in the first component for the 36 ions and sorted in ascending order based on expression in durum wheat; ions with JKCI including 0 were excluded from further analysis (indicated by red bars in <b>Panel 4B</b> and red icons in <b>Panel 4A</b>), resulting in n = 35 ions responsible for the separation of durum wheat from bread wheat. <b>(Panel 4C)</b> Of the 31 ions to which tentative compound identities and empirical formulas could be assigned by the METLIN: Metabolite and Tandem MS Database, the tentative identity with the smallest accurate mass error was assigned to 1 of 8 lipid classes according to the Lipid Maps classification system: 1) fatty acyls (FA), 2) glycerolipids (GL), 3) glycerophospholipids (GP), 4) sphingolipids (SP), 5) sterol lipids (ST), 6) prenol lipids (PR), 7) saccharolipids (SL), 8) polyketides (PK), or NC for the 4 ions in which tentative identities could not be matched to the ion m/z. Of the 19 ions overexpressed in durum wheat (DW) compared to bread wheat (BW), 62.5% were tentatively identified as nonpolar lipids, while 74% of the 16 ions overexpressed in BW were tentatively identified as polar lipids, suggesting differences in lipid biosynthetic pathways within the two species.</p