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ArticleFebruary 11, 2011

Hierarchical Alignment and Full Resolution Pattern Recognition of 2D NMR Spectra: Application to Nematode Chemical Ecology
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Analytical Chemistry

Cite this: Anal. Chem. 2011, 83, 5, 1649–1657

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https://doi.org/10.1021/ac102724x

Published February 11, 2011

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Abstract

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Nuclear magnetic resonance (NMR) is the most widely used nondestructive technique in analytical chemistry. In recent years, it has been applied to metabolic profiling due to its high reproducibility, capacity for relative and absolute quantification, atomic resolution, and ability to detect a broad range of compounds in an untargeted manner. While one-dimensional (1D) ¹H NMR experiments are popular in metabolic profiling due to their simplicity and fast acquisition times, two-dimensional (2D) NMR spectra offer increased spectral resolution as well as atomic correlations, which aid in the assignment of known small molecules and the structural elucidation of novel compounds. Given the small number of statistical analysis methods for 2D NMR spectra, we developed a new approach for the analysis, information recovery, and display of 2D NMR spectral data. We present a native 2D peak alignment algorithm we term HATS, for hierarchical alignment of two-dimensional spectra, enabling pattern recognition (PR) using full-resolution spectra. Principle component analysis (PCA) and partial least squares (PLS) regression of full resolution total correlation spectroscopy (TOCSY) spectra greatly aid the assignment and interpretation of statistical pattern recognition results by producing back-scaled loading plots that look like traditional TOCSY spectra but incorporate qualitative and quantitative biological information of the resonances. The HATS-PR methodology is demonstrated here using multiple 2D TOCSY spectra of the exudates from two nematode species: Pristionchus pacificus and Panagrellus redivivus. We show the utility of this integrated approach with the rapid, semiautomated assignment of small molecules differentiating the two species and the identification of spectral regions suggesting the presence of species-specific compounds. These results demonstrate that the combination of 2D NMR spectra with full-resolution statistical analysis provides a platform for chemical and biological studies in cellular biochemistry, metabolomics, and chemical ecology.

Subjects

⊥ Author Present Address

Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, UK SW7 2AZ.

¶ Author Present Address

United States Department of Agriculture, Agricultural Research Service.

Nuclear magnetic resonance (NMR) is a powerful and almost universal detector due to its ability to analyze essentially all types of molecules at atomic resolution. Recently, it has been applied extensively to metabolic profiling in studies of human and animal biofluids, (1, 2) cellular biochemistry, (3, 4) and nonmammalian chemical ecology. (5-8) While one-dimensional (1D) ¹H NMR spectra have been used in the majority of NMR-based metabolic profiling studies, resonance overlap and lack of structural correlations can limit the utility of the 1D approach. In contrast, powerful multidimensional NMR methods are routinely used in structural biology and natural products studies (9) but have been used much less frequently for metabolomics. Two dimensional (2D) homonuclear (such as total correlation spectroscopy (TOCSY) and correlation spectroscopy (COSY)) and heteronuclear (such as heteronuclear single quantum coherence (HSQC) and heteronuclear multiple-bond correlation (HMBC)) spectra offer increased dispersion of signals in two dimensions that can overcome the problem of signal overlap, which often limits the use of 1D ¹H NMR spectra. Additionally, the information provided by these 2D experiments through specific atomic correlations can assist in the identification and assignment of known molecules and the structural elucidation of unknown small molecules that may be significant to the underlying biology. The presence of novel, uncharacterized small molecules in complex biological mixtures is a frequent occurrence, especially in nonmammalian metabolomics where a priori knowledge of the metabolome is often incomplete. Even when complex biological mixtures are composed mainly of known metabolites, the assignment of a large number of spectral peaks is a significant complication for metabolic profiling. The use of 2D correlation spectra such as TOCSY simplify the use of semiautomated assignment platforms such as MetaboMiner (10) and COLMAR. (11-13)

While it is clear that metabolic profiling would benefit from more extensive use of 2D NMR spectra, preprocessing methods such as signal alignment and visualization tools for multiple 2D spectra have lagged behind similar methods for 1D spectra. The importance of signal alignment for statistical analysis of spectral data sets is widely appreciated by the metabolomics community for both NMR (14, 15) and mass spectrometry. (16, 17) The development of 1D alignment methods has allowed these spectra to be analyzed with increasing accuracy and resolution. However, the relative paucity of 2D spectral alignment (18) and analysis methods has meant that metabolomics studies making use of 2D spectra have relied on low-resolution bins or crosspeak integrals to avoid the problem of peak shift. (19-21) While often quite successful in mitigating the effects of chemical shift variation, the use of integrals or bins for pattern recognition causes significant loss of valuable information from spectral resolution, complicating subsequent model interpretation and assignment of relevant metabolites. Here, we present a method for hierarchical alignment of sets of 2D NMR spectra, thereby enabling pattern recognition using full-resolution data; we call this overall approach HATS-PR for hierarchical alignment of two-dimensional spectra-pattern recognition. We demonstrate that statistical representations of full-resolution spectra such as scaled principle component analysis (PCA) loadings enhance the interpretation of metabolic variation in the data set and assist in the identification and assignment of statistically relevant metabolites.

We have applied HATS-PR to an experimental comparison of the exudates (i.e., exometabolomes) from two nematode species, Pristionchus pacificus and Panagrellus redivivus. Nematode chemical ecology, especially applied to the model organism Caenorhabditis elegans, has become the subject of a number of recent studies in development, (22-25) aggregation behavior, (26) mating behavior, (27, 28) aging, (29) chemical ecology, (7, 26) and functional genomics. (6, 29, 30, 31) The discovery and structural elucidation of a class of pheromones called ascarosides has expanded the domain of C. elegans research well into chemical ecology. (32) In a combination of chemical ecology and genetics, Pungaliya et al. used comparative 2D NMR spectral analysis to discover new ascarosides and their biological roles in C. elegans. (27) By applying standard statistical pattern recognition methods to 2D NMR spectra, we demonstrate the utility of HATS-PR to distinguish and characterize chemical differences between two different nematode species.

Experimental Section

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Collection of P. pacificus and P. redivivus Exudates

P. pacificus and P. redivivus strains were obtained from the Sternberg laboratory at Caltech.

P. pacificus

Ten nematode growth medium (NGM) plates seeded with Escherichia coli (OP50 strain) were inoculated with worms and incubated at room temperature for 3 days. Worms were washed off the plates with 25 mL of S-complete medium and transferred to a 250 mL Erlenmeyer flask containing 3% (0.75 g) E. coli (HB101 strain). Worms were incubated at 22 °C for 3 days and then transferred to a 2 L Erlenmeyer flask containing 250 mL of S-complete medium and 3% (7.5 g) HB101. Once the majority of worms were adults with eggs, sucrose flotation was used to remove bacteria and other life-stage worms. Adults with eggs were then incubated for 48 h at 22 °C at 250 rpm without food to allow the adult worms to lay the eggs and the eggs to hatch. J2 P. pacificus were separated from adult worms using a 20 μm nylon filter (Sefar Inc.) to obtain a semisynchronous culture. Semisynchronized J2 P. pacificus (2.5 million) were grown to adult stage on S-complete medium supplemented with 3% HB101 and incubated in a shaker at 250 rpm and 22 °C. Worms from this culture were then put onto a sucrose gradient to remove bacteria. Clean P. pacificus were placed in M9 buffer for 30 min to clear their gut. Worms were washed three times with double-distilled water (ddH₂0) and then incubated for 1 h in ddH₂0 with a density of ∼30 000 worms/mL to collect worm exudates. Worm exudates were filtered with a 0.2 μm nylon syringe filter and stored at −80 °C for further analysis.

P. redivivus

P. redivivus were cultivated similarly to P. pacificus and transferred to liquid culture. A mixed population of P. redivivus with worm density of 10 000 worms/mL was incubated in 250 mL of S-complete medium supplemented with 3% HB101. P. redivivus were cultured for 12 days, and 3% HB101 was added every third day. After 12 days of incubation, the worms were separated from bacteria via sucrose flotation. The P. pacificus protocol described above was also used to collect P. redivivus exudates.

Sample Fractionation

P. pacificus and P. redivivus exudates were each fractionated by C18 solid phase extraction using a Varian Mega BE C18, which has a 5 mL void volume. A 50 mL syringe was attached to the column using a rubber adapter. Teflon tape was placed around the rubber stopper so that rubber pieces would not contaminate the solution. Approximately constant pressure was applied to the column by gently pressing the syringe. Each C18 column was first washed with 50 mL of 90% methanol, followed by 50 mL of ddH₂0 for equilibration. P. pacificus and P. redivivus exudates were then added to individual columns, and the flow-through was collected. The columns were washed with 25 mL of ddH₂0 and then eluted with 25 mL of 50% methanol and 25 mL of 90% methanol. All samples were collected in glass vials with Teflon caps and stored at −80 °C.

NMR Spectroscopy

The flow-through fractions were lyophilized, dissolved in 500 μL of 99.9% D₂O (Cambridge Isotope Laboratories), and put into 5 mm NMR tubes. The 50% MeOH fractions were lyophilized, dissolved in 110 μL of 99.95% methanol-d₄ (Cambridge Isotope Laboratories), and put into 2.5 mm NMR tubes. 1D ¹H and 2D TOCSY spectra were collected on a Bruker Avance II spectrometer operating at 600.23 MHz using a 5 mm TXI cryoprobe. NMR data were collected with spectral widths of 11 ppm at a sample temperature of about 300 K. 1D ¹H spectra were collected using a simple pulse-acquire sequence with presaturation of residual water. TOCSY spectra were collected with the DIPSI-2 mixing sequence (33) with water presaturation and 60 or 90 ms mixing times for the 50% MeOH or flow-through fractions, respectively. TOCSY data were collected with 2048 and 256 complex data points in the direct and indirect dimensions, respectively. TOCSY data were zero filled to 2048 F2 and 1024 F1 data points, Fourier transformed, phased, and baseline corrected in Topspin 2.0 (Bruker) before being transferred to Matlab 2009b for alignment and pattern recognition analysis. As an additional application to a more complex mixture, TOCSY spectra were acquired from human urine (details provided in Supporting Information), and data were processed similarly to the worm exudate spectra.

Hierarchical Alignment of Two-Dimensional Spectra

The HATS alignment method presented here makes use of a guide tree to structure the alignment process. While many existing 1D alignment methods have attempted to align each spectrum in a data set to a single consensus spectrum, the quality of these “star alignments” can suffer if there is wide chemical diversity between spectra, as any individual spectrum chosen as a star may not include the full complement of metabolites present in the other data sets. The use of guide trees was a significant advance for multiple sequence alignment in sequence bioinformatics, and methods such as CLUSTAL-W (34) have enjoyed widespread popularity in nucleic acid and protein sequence alignment. We suggest that the use of a guide tree will be especially important in the alignment of 2D spectra as correlation spectra are often collected when significant metabolic diversity is expected and the assumptions underlying star alignments may not be appropriate.

The UPGMA agglomerative hierarchical clustering method (35) was used to construct the initial guide tree for the subsequent pairwise alignment of spectral groups. We have used the squared distance of Pearson’s correlation coefficient as a metric for clustering (eqs 1 and 2), and the resulting dendrogram specifies the order in which alignment of the spectra proceeds.

(1)

(2)Here, r_mn is the correlation coefficient between the reshaped vectors (1 × F1·F2) of spectra S_m and S_n with mean values S̅_m and S̅_n and standard deviations σ_{s_m} and σ_{s_n} and d_mn is a distance version of the correlation value. At each branch point in the dendrogram, the set of spectra specified by the branches are aligned. For the alignment illustrated in Figure 1, the two red spectra are aligned first, followed by the two blue spectra; then, the two blue spectra are aligned with the black spectrum, and finally, the two red spectra are aligned with the three blue and black spectra. If there are multiple spectra in an alignment group, positional adjustments are made equally to all spectra in the group.

Figure 1. Overall strategy of hierarchical alignment of two-dimensional spectra (HATS). Before alignment, the HATS algorithm produces a guide tree by the UPGMA agglomerative hierarchical clustering algorithm from a matrix of pairwise correlation distances. This guide tree is used to structure the alignment process. At each intersection of branches in the guide tree, the spectra specified by the two branches are aligned globally and locally. For the alignment tree shown here, the two red spectra are aligned first, followed by the two blue spectra. The two blue spectra are then aligned with the black spectrum, and finally, the three blue and black spectra are aligned with the red spectra.

The alignment at each branch point of the guide tree consists of two parts: a global alignment in which all spectral data points are shifted equally and a local alignment in which specific crosspeak regions are shifted individually. The global alignment step seeks to correct defects such as miscalibration or referencing, which produce chemical shift differences across the entire spectrum. One of the groups of spectra is passed over the other group as a mask in a point-wise manner in both F1 and F2 dimensions, and at each positional step x and y, the alignment quality is expressed as r_xy, the mean correlation between groups (eq 3). Here, a is the number of spectra in group 1, and b is the number in group 2, and r_mn is the correlation coefficient between the reshaped vectors of spectrum m in group 1 and spectrum n in group 2. Spectra in the mask group are shifted to the points m and n producing the maximal mean correlation which is identified by a simple gradient ascent algorithm.

(3)

While global alignment can resolve factors affecting all resonances equally, it is not capable of resolving differences in specific resonances caused by conditions such as pH, temperature, metal ion concentration, and osmolality. To resolve local chemical shift variation, crosspeak regions must be aligned individually. To identify segments of the spectrum corresponding to crosspeak regions, a local noise surface is calculated for each spectrum. (36) Initial spectral segments are identified as regions within 0.04 ppm of a peak, defined as a local maximum with intensity at least 10× the noise value at that point. Each segment is then labeled with a unique integer value, and these initial segments are expanded by iterated minimum and maximum filtering. Min/max filtering has the effect of growing the segments until they encounter another spectral segment or until they reach a maximum frequency range of approximately 0.2 ppm (Figure 1S, Supporting Information). This procedure creates spectral segments that are bounded by nearby resonances yet avoid close cropping of crosspeak lineshapes. Each crosspeak segment is then aligned individually by maximizing the mean cross-correlation of crosspeak segments between alignment groups in the same way as the global alignment but treating each crosspeak segment independently (eq 3, where r_xy is the line shape vector for a single crosspeak region rather than the entire spectrum).

Full Resolution TOCSY Pattern Recognition

Following alignment, HATS-PR makes use of principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) for full resolution spectral pattern recognition. PCA is an unsupervised dimensionality reduction technique that is widely used in metabolomics. PCA transforms a data set of N observations with P features into a set of principal components that are orthogonal linear combinations of features accounting for as much of the variation in the original data set as possible. Each principal component consists of N scores and P loadings, where scores identify relationships between observations (here, TOCSY spectra) and loadings represent the pattern of features (here, 2D NMR chemical shifts) underlying the relationships identified by scores. We used the NIPALS algorithm (37) to identify the first five principal components of the mean-centered and univariate scaled flow-though (N = 10) and 50% MeOH (N = 8) TOCSY spectra. While PCA avoids the problem of overfitting by taking an unsupervised approach to pattern recognition, it is not optimized for class separation. Partial least-squares methods such as PLS-DA are often the supervised method of choice for the analysis of metabolic differentials. We apply SIMPLS (38) PLS-DA to the data set to help clarify the metabolic differential identified by PCA while avoiding overfitting by the use of 4-fold cross-validation and reporting performance statistics for the PLS-DA model.

Pattern recognition of NMR spectra was done in Matlab 2009b following Probabilistic Quotient Normalization (39) to account for random dilution effects and alignment using the HATS method for the TOCSY spectra and the RSPA (15) algorithm for the 1D ¹H spectra. Each of the N 2048 × 1024 TOCSY data matrices was reshaped to a 1 × 2 097 152 vector before the application of PCA or PLS. This is a necessary step because it creates an N × P matrix, where P = F1·F2 data points on which pattern recognition algorithms can operate. To visualize the resulting 1 × P loadings, the loadings vectors for each principal component were reshaped back into a 2048 × 1024 data matrix. Contours were defined by multiplying the univariate scaled loadings by the standard deviation along the third dimension of the original TOCSY stack, a procedure known as back-scaling. Contour colors were then defined by the loading values themselves divided by the maximum loading coefficient to produce a range of −1 to +1. This strategy produces loading spectra which have crosspeak lineshapes similar to traditional TOCSY spectra but include sign and intensity information related to the relationships between spectra specified by the corresponding PCA scores. PLS-DA results are presented in a similar manner, but instead of specifying scores and loadings, only the predictive feature vector is shown, along with performance statistics for the model. This 2D NMR unfolding and refolding approach has recently been applied successfully to HSQC spectra and provides a simple methodology to apply statistical pattern recognition approaches to 2D NMR spectra. (40, 41)

Semi-Automated Assignment of TOCSY Loadings

To assign the compounds represented in our back-scaled TOCSY loadings plots, we applied COLMAR query (11) to correlated chemical shifts present in the loadings spectra. When COLMAR query identified a match to a compound reference spectrum present in the BMRB, (42, 43) HMDB, (44) or MMCD (45) databases, the reference TOCSY spectrum for the suggested compound was downloaded from the database web servers, processed in Topspin 2.0, and overlaid onto an experimental spectrum for confirmation. We considered the compound to be assigned if all reference chemical shifts and experimental chemical shifts differ by less than 0.02 ppm and all through-bond correlations (TOCSY crosspeaks) in the experimental spin-system were accounted for by the reference spectrum. It should be emphasized that for this study we did not verify assignments by spiking natural samples with authentic commercial or synthetic standards, a common practice for final verification of compound identity by NMR.

Results

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Previous work from our group and others has shown that C. elegans releases a large number of ascarosides that have been shown to have multiple functions. (22-25, 27, 28, 32) These ascarosides typically partition into the 50% MeOH fraction of a C18 solid phase extraction. We have also shown that C. elegans releases an abundance of small polar molecules including amino acids, sugars, and organic acids, many of which elute from a C18 column in the hydrophilic flow-through fraction. (7) Here, we analyzed 2D TOCSY NMR data of the flow-through and 50% MeOH C18 elution fractions from P. redivivus and P. pacificus exudates in order to demonstrate our new methods and to show the utility of this approach in metabolomics and natural product comparative studies. In order to demonstrate the general applicability of our approach, we have also applied it to the alignment of 2D TOCSY spectra from human urine (Figure 1S, Supporting Information).

Alignment of TOCSY Spectra

Despite the high reproducibility of NMR spectra, resonance variation, sometimes referred to as positional noise, resulting from slight differences in sample conditions hinder comparative studies and complicate the interpretation of statistical analyses such as univariate regression and significance testing, principal component analysis (PCA), and partial least-squares (PLS). Previous research with 1D ¹H NMR spectra has shown that peak shift can result in both false positives and negatives when attempting to identify metabolite biomarkers. (46) While binning is often used to reduce positional variation, loss of high-resolution information complicates metabolite assignment and limits the added value of 2D experiments. The HATS alignment methodology presented here compensates for the effects of resonance variation, thereby increasing the accuracy and interpretability of comparative 2D NMR analyses.

The HATS-PR approach to eliminating positional variation is to first cluster all of the spectra through a guide tree, as shown in Figure 2A. The problem of positional variation in our nematode TOCSY spectra is easily seen in the expansion of the anomeric region of the sugars from the flow-through fractions (Figure 2B,C). In the unaligned data (Figure 2B), local differences in resonance frequency of the anomeric protons affect multiple spectra of both P. redivivus and P. pacificus samples and result in a general “blurriness” that significantly impacts subsequent statistical analyses. Removing this variation is clearly desirable, but the close proximity (∼0.03 ppm) and similar patterns and lineshapes of the P. pacificus glucose and P. redivivus trehalose crosspeaks raise the possibility of inappropriate alignment of the glucose to the trehalose crosspeaks. The use of the HATS-PR guide tree (Figure 2A) helps avoid this possibility by aligning the most similar before the less similar spectra. Using this approach, the glucose and trehalose resonances are not compared for alignment until they have been well aligned separately. As shown in Figure 2C, this results in the correction of positional variation within both glucose and trehalose crosspeaks and rejection of the inappropriate alignment of the two sugars.

Figure 2. Alignment results for *P. redivivus* (Pre) and *P. pacificus* (Ppa) TOCSY spectra of flow-through fractions from C18 solid phase extractions. (A) The guide tree produced by HATS groups spectra from the same species, resulting in intraspecies alignment before interspecies alignment. The utility of a guide tree-based alignment is exemplified by the anomeric crosspeaks of glucose, sucrose, and trehalose in the TOCSY spectra (B, C). By aligning spectra with similar composition first, false positive alignments of nearby but unrelated crosspeaks, such as the anomeric crosspeaks shown above, are avoided.

P. redivivus and P. pacificus Release Different Polar Molecules

Next, using the aligned TOCSY spectra (Figure 3A) of C18 flow-through fractions, we conducted PCA and PLS analysis. The PCA scores show clear, unambiguous separation of the P. redivivus spectra from the P. pacificus spectra in the first principal component (Figure 3B). This separation allows the PC1 loadings to be interpreted as a pattern of TOCSY crosspeaks differentiating the two species (Figure 4). The back-scaling procedure outlined above produces contours reflecting the crosspeak lineshapes in the original spectra but colored according to the loading coefficients. As the P. redivivus and P. pacificus PC1 scores have opposite signs, we represent features with greater intensity in the P. redivivus spectra in red (loadings between 0 and 1) and with greater P. pacificus intensity in blue (loadings between −1 and 0). The PLS predictors also agree with the PC1 loadings (Figure 2S, Supporting Information). The assignment of the crosspeaks in the PC1 loadings spectrum enables the identification of compounds unique to each species as well as quantitative differences between compound levels.

To interpret this spectral information in a chemical context, it is necessary to assign NMR resonances to their molecules (Table 1S, Supporting Information). While assignment has traditionally been one of the more difficult steps in metabolomic analysis due to the large chemical complexity in most crude biological matrices, the establishment of reference spectra databases for common metabolites such as the BMRB, (41-43) HMDB, (44) and MMCD (45) has facilitated the development of database searching algorithms to match sets of correlated chemical shifts to a compound ID. For example, PRIMe SpinAssign, (47, 48) NMRshifDB, (49) MetaboMiner, (10) and COLMAR (12) are freely available web utilities that offer automated database matching capabilities to aid the assignment process. The assignments shown in Figure 4 were generated by a combination of COLMAR query (11) to identify possible matches and manual overlay of reference spectra from the BMRB to confirm identifications, as described in the Experimental Section. While correlated chemical shifts were identified manually for input into COLMAR query, the back-scaled loadings matrix produced by HATS-PR should be compatible with spectral decomposition methods such as DemixC (13) and local peak clustering. (50)

Figure 3. (A) Overlay of *P. redivivus* (Pre) and *P. pacificus* (Ppa) flow-through TOCSY spectra and (B) scores plot from PCA of the flow-through TOCSY data set. *P. redivivus* and *P. pacificus* spectra are clearly differentiated by PC1, which allows the loadings of PC1 to be interpreted as relative quantitative spectral differences between the species.

Figure 4. Spectral back-projection of PC1 loadings. Here, contours are defined by the back-projected intensities, while colors are defined by the unit variance-scaled loading coefficients. Crosspeaks with positive loading coefficients (red) represent compounds overexpressed by *P. redivivus* relative to *P. pacificus*. Blue crosspeaks represent negative loading coefficients indicating *P. pacificus* overexpressed compounds. Assignment of crosspeaks in the back-projected loadings places the chemical information contained in the TOCSY spectra in an immediate biological context. Abbreviations: AIB, 3-aminoisobutyrate; Ala, alanine; Asn, asparagine; Asp, aspartate; Glu, glutamate; NC-Glu, N-carbamylglutamate; Pre Unk1, *P. redivivus* unknown 1; Pre Unk2, *P. redivivus* unknown 2; Pro, l-proline; Thr, threonine.

P. redivivus and P. pacificus Appear to Produce Different Ascarosides

C. elegans releases at least 10 different ascarosides important in the nematode’s chemical ecology, (32) and methods to efficiently compare other species for ascaroside-like compounds would be useful. The most diagnostic NMR signatures for ascarosides are typically resonance peaks from anomeric, methyl, and methylene protons (Figure 5) present in the eluate of C18 SPE chromatography matrix with 50% MeOH C18. As with the flow-through fraction, PC1 separates the P. redivivus and P. pacificus 50% MeOH fraction spectra (Figure 3S, Supporting Information). The PC1 loadings from the 50% MeOH fractions show numerous crosspeaks in the ascaroside signature region. These peaks are difficult to resolve using 1D ¹H NMR, and loadings from pattern recognition on the 1D spectra are difficult to interpret (Figure 4S, Supporting Information). Interestingly, both P. redivivus and P. pacificus show signals representing ascaroside-like compounds; however, differences in chemical shift and signal intensity suggest different sets of ascarosides produced by these species. We tried database matching the 50% MeOH, as we did with the flow-through fractions, but we were unsuccessful in finding any matches. Identification of these compounds is in progress using traditional approaches and will be reported elsewhere.

Figure 5. Representative spectra and PC1 loadings for *P. redivivus* and *P. pacificus* 50% MeOH fractions. The region selected is useful for ascaroside differentiation, as crosspeaks here indicate correlations from the methylene protons on the ascaroside side chain to protons near the terminal functional groups. PC1 loadings suggest that *P. redivivus* and *P. pacificus* produce different mixtures of ascaroside-like compounds.

Discussion and Conclusions

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We have presented an efficient method to align, statistically analyze, and identify compounds in 2D NMR spectra. Although we demonstrated the approach with TOCSY spectra, all of the algorithms used for alignment, PCA, and PLS-DA are general and should work without modification for other types of 2D NMR data sets. In addition to problems with alignment, which we have addressed in this study, the increased acquisition time per sample relative to 1D experiments remains a barrier to adoption of 2D NMR methods in metabolic profiling. However, many groups are developing rapid methods for 2D data acquisition (51-53) and designing more sensitive probes, (54-56) making 2D NMR analysis of relatively large numbers of samples increasingly feasible.

The additional structural information provided by 2D NMR methods is often helpful for structural characterization of known metabolites and is necessary for assignment of unknown or novel compounds. Our use of COLMAR query for assignment of compounds present in multiple TOCSY spectra constitutes a proof-of-principle demonstration of how metabolomic analysis can be streamlined by integrating multi-2D statistical pattern recognition with informatics tools for compound assignment. While not applied in this study, we suggest that the application of automated spectral deconvolution methods, which isolate signals arising from single molecules, such as local clustering (50) and DemixC, (13) to back-scaled 2D NMR loadings matrices will result in further streamlining of metabolomic data analysis. While automated assignment is currently restricted to compounds for which reference data is present in spectral databases, progress is being made on methods to identify probable chemical structures directly from chemical shift calculations. (57) The polar metabolites present in the flow-through fraction of the nematode exudate C18 SPE extractions were particularly well suited for semiautomated assignment by COLMAR query as the reference databases contain large numbers of sugars, amino acids, small organic acids, and other common biological metabolites that tend to partition in the aqueous phase.

It is common practice with 2D NMR to overlay spectra with different colors, as shown for example in Figure 3A. There are several advantages that HATS-PR has oversimple spectral overlay. First, with spectral overlay, it is very difficult or impossible to see quantitative changes in a particular compound. Using HATS-PR, quantitative information is clearly represented by changes in the color of the crosspeaks in the loadings plots. Second, using overlays is not particularly amenable to evaluating statistical replicates and comparing multiple conditions. However, with HATS-PR, like any PCA or PLS analysis, statistical replicates enhance the analysis and are easily visualized through the corresponding scores plots and regression statistics. Additionally, quantitative covariance between 2D NMR crosspeaks may provide a means by which to identify connectivity in metabolites with multiple spin systems.

In principle, it is possible to do PCA analysis on 2D NMR data without alignment. However, alignment of the spectra aids in the interpretation of individual compounds by improving the quality of the pattern recognition analysis. Pattern recognition on unaligned 1D spectra often results in line shapes resembling first derivatives rather than Lorentzian line shapes. (15) In 2D NMR spectra, misalignment often results in the duplication of peaks (Figure 5S, Supporting Information) in the back-scaled loadings or attenuation of loading coefficients (Figure 6S, Supporting Information) due to peak position differences and can result in compound misidentification. Despite making small changes in the chemical shifts of the crosspeaks, alignment of crosspeak lineshapes to their maximum correlation results in more interpretable loadings for both the flow-though and 50% MeOH fraction, as shown by supplemental Figures S4 and S5, Supporting Information. HATS-PR represents, to our knowledge, the first usage of a guide tree in a spectral alignment method, which removes the requirement that all compounds be present in a single spectrum selected as the alignment reference. We believe guide trees will also be generally useful for peak alignment in 1D ¹H NMR, chromatographic profiles, and LC-MS spectra as well.

HATS-PR is a relatively simple way to chemically discriminate two (or more) biological states, such as treated vs untreated, diseased vs normal, one species vs another, etc. Using existing database search algorithms and rapidly growing NMR metabolomic databases, it is straightforward using HATS-PR not only to compare biological states but also to identify and assign the relevant compounds that differentiate these states. In this study, we compared exudates of two nematode species. The samples were prepared under identical conditions, yet revealed surprisingly large differences, particularly in the polar flow-through fractions. In previous work, we found that C. elegans produces at least 40 common, mostly polar metabolites, (7) and we expected to find a similar group of compounds in P. redivivus and P. pacificus. HATS-PR very efficiently showed us that the two species produce significantly different sets of polar small molecules, including sugars. P. redivivus in general releases a less diverse set of polar molecules but produces large amounts of the disaccharide trehalose and their exudates also contain a large quantity of sucrose, yet no glucose was observed. While P. pacificus releases a very diverse set of polar small molecules including glucose, the two disaccharides sucrose and trehalose were not detected. All of the nematodes for this study were separated from their bacterial food using a sucrose gradient, so it is possible that some or all of the sucrose detected in P. redivivus originates from the sample preparation. However, we find a comparable amount of trehalose in the same samples, and more importantly, we find no sucrose in P. pacificus, which were prepared with an identical sucrose gradient.

Despite the fact that spectra collected on the flow-through fractions of P. redivivus were much simpler than those from P. pacificus, the TOCSY spectra of 50% MeOH fractions from the same worm preparations exhibited similar levels of complexity but also some clear differences. Although, on the basis of the data presented here, we are unable to determine unambiguously whether these fractions contain ascarosides, the spectral signatures of the 50% MeOH fractions for both species suggest the presence of complex ascaroside mixtures, similar to those found in C. elegans. The identification and assignment of these compounds requires additional work and additional NMR spectroscopic and mass spectrometric analyses and is beyond the scope of this study. However, it is clear that HATS-PR is a powerful method to compare small molecules from one organism to another and as such provides a significant bridge between metabolomics and natural products analysis. As our use of HATS-PR for alignment of a set of urine spectra demonstrates, the method is broadly applicable and can successfully align arrays of 2D spectra even in situations where sample variation leads to significant chemical shift differences between spectra. We expect that this work will contribute significantly to informatics approaches to metabolic profiling as well as to biological and structural characterization of metabolic phenotypes in both mammalian and nonmammalian models.

Supporting Information

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Additional information as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org.

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Author Information

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Corresponding Author
- Arthur S. Edison - †Department of Biochemistry and Molecular Biology and ‡National High Magnetic Field Laboratory, University of Florida, Gainesville, Florida 32610-0245, United States; Boyce Thompson Institute, Cornell University, Ithaca, New York 14853, United States; Email: [email protected]
Authors
- Steven L. Robinette - †Department of Biochemistry and Molecular Biology and ‡National High Magnetic Field Laboratory, University of Florida, Gainesville, Florida 32610-0245, United States; Boyce Thompson Institute, Cornell University, Ithaca, New York 14853, United States
- Ramadan Ajredini - †Department of Biochemistry and Molecular Biology and ‡National High Magnetic Field Laboratory, University of Florida, Gainesville, Florida 32610-0245, United States; Boyce Thompson Institute, Cornell University, Ithaca, New York 14853, United States
- Hasan Rasheed - †Department of Biochemistry and Molecular Biology and ‡National High Magnetic Field Laboratory, University of Florida, Gainesville, Florida 32610-0245, United States; Boyce Thompson Institute, Cornell University, Ithaca, New York 14853, United States
- Abdulrahman Zeinomar - †Department of Biochemistry and Molecular Biology and ‡National High Magnetic Field Laboratory, University of Florida, Gainesville, Florida 32610-0245, United States; Boyce Thompson Institute, Cornell University, Ithaca, New York 14853, United States
- Frank C. Schroeder - Boyce Thompson Institute, Cornell University, Ithaca, New York 14853, United States
- Aaron T. Dossey - †Department of Biochemistry and Molecular Biology and ‡National High Magnetic Field Laboratory, University of Florida, Gainesville, Florida 32610-0245, United States; Boyce Thompson Institute, Cornell University, Ithaca, New York 14853, United States

Acknowledgment

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S.L.R. acknowledges financial support from the Barry M. Goldwater Foundation and from the Howard Hughes Medical Institute Science for Life program at UF. We thank James R. Rocca, Paul Sternberg, and Andrea Choe for helpful discussions. Burt Singer and Greg Stupp kindly read the manuscript and provided us with stimulating discussions. Nematode NMR data were collected in the University of Florida AMRIS Facility, which is part of the NSF-funded National High Magnetic Field Laboratory. Human urine NMR data were collected at Imperial College, and we are grateful to Jeremy Nicholson for the use of the NMR spectrometer. This work was supported by NIH 1R01GM085285-01and 3R01GM085285-01A1S1 to A.S.E.

References

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This article references 57 other publications.

1
Nicholson, J. K.; Wilson, I. D. Prog. Nucl. Magn. Reson. Spectrosc. 1989, 21, 449– 501
Google Scholar
There is no corresponding record for this reference.
2
Nicholson, J. K.; Connelly, J.; Lindon, J. C.; Holmes, E. Nat. Rev. Drug Discovery 2002, 1, 153– 161
Google Scholar
2
Innovation: Metabonomics: a platform for studying drug toxicity and gene function
Nicholson, Jeremy K.; Connelly, John; Lindon, John C.; Holmes, Elaine
Nature Reviews Drug Discovery (2002), 1 (2), 153-161CODEN: NRDDAG ISSN:. (Nature Publishing Group)
A review. The later that a mol. or mol. class is lost from the drug development pipeline, the higher the financial cost. Minimizing attrition is therefore one of the most important aims of a pharmaceutical discovery program. Novel technologies that increase the probability of making the right choice early save resources, and promote safety, efficacy and profitability. Metabonomics is a systems approach for studying in vivo metabolic profiles, which promises to provide information on drug toxicity, disease processes and gene function at several stages in the discovery- and-development process.
3
Behrends, V.; Ebbels, T. M.; Williams, H. D.; Bundy, J. G. Appl. Environ. Microbiol. 2009, 75, 2453– 2463
Google Scholar
There is no corresponding record for this reference.
4
Schroeder, F. C.; Gibson, D. M.; Churchill, A. C.; Sojikul, P.; Wursthorn, E. J.; Krasnoff, S. B.; Clardy, J. Angew. Chem., Int. Ed. Engl. 2007, 46, 901– 904
Google Scholar
There is no corresponding record for this reference.
5
Bundy, J. G.; Sidhu, J. K.; Rana, F.; Spurgeon, D. J.; Svendsen, C.; Wren, J. F.; Sturzenbaum, S. R.; Morgan, A. J.; Kille, P. BMC Biol. 2008, 6, 25
Google Scholar
5
'Systems toxicology' approach identifies coordinated metabolic responses to copper in a terrestrial non-model invertebrate, the earthworm Lumbricus rubellus
Bundy Jacob G; Sidhu Jasmin K; Rana Faisal; Spurgeon David J; Svendsen Claus; Wren Jodie F; Sturzenbaum Stephen R; Morgan A John; Kille Peter
BMC biology (2008), 6 (), 25 ISSN:.
BACKGROUND: New methods are needed for research into non-model organisms, to monitor the effects of toxic disruption at both the molecular and functional organism level. We exposed earthworms (Lumbricus rubellus Hoffmeister) to sub-lethal levels of copper (10-480 mg/kg soil) for 70 days as a real-world situation, and monitored both molecular (cDNA transcript microarrays and nuclear magnetic resonance-based metabolic profiling: metabolomics) and ecological/functional endpoints (reproduction rate and weight change, which have direct relevance to population-level impacts). RESULTS: Both of the molecular endpoints, metabolomics and transcriptomics, were highly sensitive, with clear copper-induced differences even at levels below those that caused a reduction in reproductive parameters. The microarray and metabolomic data provided evidence that the copper exposure led to a disruption of energy metabolism: transcripts of enzymes from oxidative phosphorylation were significantly over-represented, and increases in transcripts of carbohydrate metabolising enzymes (maltase-glucoamylase, mannosidase) had corresponding decreases in small-molecule metabolites (glucose, mannose). Treating both enzymes and metabolites as functional cohorts led to clear inferences about changes in energetic metabolism (carbohydrate use and oxidative phosphorylation), which would not have been possible by taking a 'biomarker' approach to data analysis. CONCLUSION: Multiple post-genomic techniques can be combined to provide mechanistic information about the toxic effects of chemical contaminants, even for non-model organisms with few additional mechanistic toxicological data. With 70-day no-observed-effect and lowest-observed-effect concentrations (NOEC and LOEC) of 10 and 40 mg kg-1 for metabolomic and microarray profiles, copper is shown to interfere with energy metabolism in an important soil organism at an ecologically and functionally relevant level.
6
Blaise, B. J.; Giacomotto, J.; Elena, B.; Dumas, M. E.; Toulhoat, P.; Segalat, L.; Emsley, L. Proc. Natl. Acad. Sci. U.S.A. 2007, 104, 19808– 19812
Google Scholar
6
Metabotyping of Caenorhabditis elegans reveals latent phenotypes
Blaise, Benjamin J.; Giacomotto, Jean; Elena, Benedicte; Dumas, Marc-Emmanuel; Toulhoat, Pierre; Segalat, Laurent; Emsley, Lyndon
Proceedings of the National Academy of Sciences of the United States of America (2007), 104 (50), 19808-19812CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
Assigning functions to every gene in a living organism is the next challenge for functional genomics. In fact, 85-90% of the 19,000 genes of the nematode Caenorhabditis elegans genome do not produce any visible phenotype when inactivated, which hampers detg. their function, esp. when they do not belong to previously characterized gene families. We used 1H high-resoln. magic angle spinning NMR spectroscopy (1H HRMAS-NMR) to reveal the latent phenotype assocd. to superoxide dismutase (sod-1) and catalase (ctl-1) C. elegans mutations, both involved in the elimination of radical oxidative species. These two silent mutations are significantly discriminated from the wild-type strain and from each other. We identify a metabotype significantly assocd. with these mutations involving a general redn. of fatty acyl resonances from triglycerides, unsatd. lipids being known targets of free radicals. This work opens up perspectives for the use of 1H HRMAS-NMR as a mol. phenotyping device for model organisms. Because it is amenable to high throughput and is shown to be highly informative, this approach may rapidly lead to a functional and integrated metabonomic mapping of the C. elegans genome at the systems biol. level.
7
Kaplan, F.; Badri, D. V.; Zachariah, C.; Ajredini, R.; Sandoval, F. J.; Roje, S.; Levine, L. H.; Zhang, F.; Robinette, S. L.; Alborn, H. T.; Zhao, W.; Stadler, M.; Nimalendran, R.; Dossey, A. T.; Bruschweiler, R.; Vivanco, J. M.; Edison, A. S. J. Chem. Ecol. 2009, 35, 878– 892
Google Scholar
7
Bacterial Attraction and Quorum Sensing Inhibition in Caenorhabditis elegans Exudates
Kaplan, Fatma; Badri, Dayakar V.; Zachariah, Cherian; Ajredini, Ramadan; Sandoval, Francisco J.; Roje, Sanja; Levine, Lanfang H.; Zhang, Fengli; Robinette, Steven L.; Alborn, Hans T.; Zhao, Wei; Stadler, Michael; Nimalendran, Rathika; Dossey, Aaron T.; Brueschweiler, Rafael; Vivanco, Jorge M.; Edison, Arthur S.
Journal of Chemical Ecology (2009), 35 (8), 878-892CODEN: JCECD8; ISSN:0098-0331. (Springer)
Caenorhabditis elegans, a bacterivorous nematode, lives in complex rotting fruit, soil, and compost environments, and chem. interactions are required for mating, monitoring population d., recognition of food, avoidance of pathogenic microbes, and other essential ecol. functions. Despite being one of the best-studied model organisms in biol., relatively little is known about the signals that C. elegans uses to interact chem. with its environment or as defense. C. elegans exudates were analyzed by using several anal. methods and found to contain 36 common metabolites that include org. acids, amino acids, and sugars, all in relatively high abundance. Furthermore, the concns. of amino acids in the exudates were dependent on developmental stage. The C. elegans exudates were tested for bacterial chemotaxis using Pseudomonas putida (KT2440), a plant growth promoting rhizobacterium, Pseudomonas aeruginosa (PAO1), a soil bacterium pathogenic to C. elegans, and Escherichia coli (OP50), a non-motile bacterium tested as a control. The C. elegans exudates attracted the two Pseudomonas species, but had no detectable antibacterial activity against P. aeruginosa. To our surprise, the exudates of young adult and adult life stages of C. elegans exudates inhibited quorum sensing in the reporter system based on the LuxR bacterial quorum sensing (QS) system, which regulates bacterial virulence and other factors in Vibrio fischeri. We were able to fractionate the QS inhibition and bacterial chemotaxis activities, thus demonstrating that these activities are chem. distinct. Our results demonstrate that C. elegans can attract its bacterial food and has the potential of partially regulating the virulence of bacterial pathogens by inhibiting specific QS systems.
8
Bundy, J. G.; Spurgeon, D. J.; Svendsen, C.; Hankard, P. K.; Osborn, D.; Lindon, J. C.; Nicholson, J. K. FEBS Lett. 2002, 521, 115– 120
Google Scholar
There is no corresponding record for this reference.
9
Rae, R.; Iatsenko, I.; Witte, H.; Sommer, R. J. Environ. Microbiol. 2010, 12, 3007– 3021
Google Scholar
There is no corresponding record for this reference.
10
Xia, J.; Bjorndahl, T. C.; Tang, P.; Wishart, D. S. BMC Bioinformatics 2008, 9, 507
Google Scholar
There is no corresponding record for this reference.
11
Robinette, S. L.; Zhang, F.; Bruschweiler-Li, L.; Bruschweiler, R. Anal. Chem. 2008, 80, 3606– 3611
Google Scholar
There is no corresponding record for this reference.
12
Zhang, F.; Robinette, S. L.; Bruschweiler-Li, L.; Bruschweiler, R. Magn. Reson. Chem. 2009, 47 (Suppl 1) S118– 122
Google Scholar
There is no corresponding record for this reference.
13
Zhang, F.; Dossey, A. T.; Zachariah, C.; Edison, A. S.; Bruschweiler, R. Anal. Chem. 2007, 79, 7748– 7752
Google Scholar
There is no corresponding record for this reference.
14
Lee, G. C.; Woodruff, D. L. Anal. Chim. Acta 2004, 513, 413– 416
Google Scholar
There is no corresponding record for this reference.
15
Veselkov, K. A.; Lindon, J. C.; Ebbels, T. M.; Crockford, D.; Volynkin, V. V.; Holmes, E.; Davies, D. B.; Nicholson, J. K. Anal. Chem. 2009, 81, 56– 66
Google Scholar
15
Recursive Segment-Wise Peak Alignment of Biological 1H NMR Spectra for Improved Metabolic Biomarker Recovery
Veselkov, Kirill A.; Lindon, John C.; Ebbels, Timothy M. D.; Crockford, Derek; Volynkin, Vladimir V.; Holmes, Elaine; Davies, David B.; Nicholson, Jeremy K.
Analytical Chemistry (Washington, DC, United States) (2009), 81 (1), 56-66CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
Chem. shift variation in small-mol. 1H NMR signals of biofluids complicates biomarker information recovery in metabonomic studies when using multivariate statistical and pattern recognition tools. Current peak realignment methods are generally time-consuming or align major peaks at the expense of minor peak shift accuracy. The authors present a novel recursive segment-wise peak alignment (RSPA) method to reduce variability in peak positions across the multiple 1H NMR spectra used in metabonomic studies. The method refines a segmentation of ref. and test spectra in a top-down fashion, sequentially subdividing the initial larger segments, as required, to improve the local spectral alignment. The authors also describe a general procedure that allows robust comparison of realignment quality of various available methods for a range of peak intensities. The RSPA method is illustrated with respect to 140 1H NMR rat urine spectra from a caloric restriction study and is compared with several other widely used peak alignment methods. The authors demonstrate the superior performance of the RSPA alignment over a wide range of peaks and its capacity to enhance interpretability and robustness of multivariate statistical tools. The approach is widely applicable for NMR-based metabolic studies and is potentially suitable for many other types of data sets such as chromatog. profiles and MS data.
16
Wong, J. W. H.; Cagney, G.; Cartwright, H. M. Bioinf. 2005, 21, 2088– 2090
Google Scholar
16
SpecAlign-processing and alignment of mass spectra datasets
Wong, Jason W. H.; Cagney, Gerard; Cartwright, Hugh M.
Bioinformatics (2005), 21 (9), 2088-2090CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
Pre-processing of chromatog. profile or mass spectral data is an important aspect of many types of proteomics and biomarker discovery expts. Here we present a graphical computational tool, SpecAlign, that enables simultaneous visualization and manipulation of multiple datasets. SpecAlign not only provides all common processing functions, but also uniquely implements an algorithm that enables the complete alignment of each mass spectrum within a loaded dataset. We demonstrate its utility by aligning two datasets each contg. six spectra; one set was acquired prior to instrument calibration and the other following calibration.
17
Smith, C. A.; Want, E. J.; O’Maille, G.; Abagyan, R.; Siuzdak, G. Anal. Chem. 2006, 78, 779– 787
Google Scholar
17
XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification
Smith, Colin A.; Want, Elizabeth J.; O'Maille, Grace; Abagyan, Ruben; Siuzdak, Gary
Analytical Chemistry (2006), 78 (3), 779-787CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity detn., and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biol. origin. Here we introduce an LC/MS-based data anal. approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal stds., the method dynamically identifies hundreds of endogenous metabolites for use as stds., calcg. a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.
18
Zheng, M.; Lu, P.; Liu, Y.; Pease, J.; Usuka, J.; Liao, G.; Peltz, G. Bioinformatics 2007, 23, 2926– 2933
Google Scholar
There is no corresponding record for this reference.
19
Lewis, I. A.; Schommer, S. C.; Markley, J. L. Magn. Reson. Chem. 2009, 47 (Suppl 1) S123– 126
Google Scholar
There is no corresponding record for this reference.
20
Ludwig, C.; Ward, D. G.; Martin, A.; Viant, M. R.; Ismail, T.; Johnson, P. J.; Wakelam, M. J.; Gunther, U. L. Magn. Reson. Chem. 2009, 47 (Suppl 1) S68– 73
Google Scholar
There is no corresponding record for this reference.
21
Van, Q. N.; Issaq, H. J.; Jiang, Q.; Li, Q.; Muschik, G. M.; Waybright, T. J.; Lou, H.; Dean, M.; Uitto, J.; Veenstra, T. D. J Proteome Res. 2008, 7, 630– 639
Google Scholar
21
Comparison of 1D and 2D NMR Spectroscopy for Metabolic Profiling
Van, Que N.; Issaq, Haleem J.; Jiang, Qiujie; Li, Qiaoli; Muschik, Gary M.; Waybright, Timothy J.; Lou, Hong; Dean, Michael; Uitto, Jouni; Veenstra, Timothy D.
Journal of Proteome Research (2008), 7 (2), 630-639CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
High-resoln., liq. state NMR spectroscopy is a popular platform for metabolic profiling because the technique is nondestructive, quant., reproducible, and the spectra contain a wealth of biochem. information. Because of the large dynamic range of metabolite concns. in biofluids, statistical analyses of one-dimensional (1D) proton NMR data tend to be biased toward selecting changes in more abundant metabolites. Although two-dimensional (2D) proton-proton expts. can alleviate spectral crowding, they have been mainly used for structural detn. In this study, 2D total correlation spectroscopy NMR was used to compare the global metabolic profiles of urine obtained from wild-type and Abcc6-knockout mice. The 2D data were compared to an improved 1D expt. in which signal contributions from macromols. and the urea peak have been spectroscopically removed for more accurate quantitation of low-abundance metabolites. Although statistical models from both 1D and 2D data could differentiate samples acquired from the two groups of mice, only the 2D spectra allowed the characterization of statistically relevant changes in the low-abundance metabolites. While acquisition of the 2D data require more time, the data obtained resulted in a more meaningful and comprehensive metabolic profile, aided in metabolite identifications, and minimized ambiguities in peak assignments.
22
Butcher, R. A.; Ragains, J. R.; Clardy, J. Org. Lett. 2009, 11, 3100– 3103
Google Scholar
There is no corresponding record for this reference.
23
Butcher, R. A.; Ragains, J. R.; Kim, E.; Clardy, J. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 14288– 14292
Google Scholar
There is no corresponding record for this reference.
24
Butcher, R. A.; Fujita, M.; Schroeder, F. C.; Clardy, J. Nat. Chem. Biol. 2007, 3, 420– 422
Google Scholar
24
Small-molecule pheromones that control dauer development in Caenorhabditis elegans
Butcher, Rebecca A.; Fujita, Masaki; Schroeder, Frank C.; Clardy, Jon
Nature Chemical Biology (2007), 3 (7), 420-422CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
In response to high population d. or low food supply, the nematode Caenorhabditis elegans enters an alternative larval stage, known as the dauer, that can withstand adverse conditions for prolonged periods. C. elegans senses its population d. through a small-mol. signal, traditionally called the dauer pheromone, that it secretes into its surroundings. Here we show that the dauer pheromone consists of several structurally related ascarosides-derivs. of the dideoxysugar ascarylose-and that 2 of these ascarosides (1 and 2) are roughly 2 orders of magnitude more potent at inducing dauer formation than a previously reported dauer pheromone component (3) and constitute a physiol. relevant signal. The identification of dauer pheromone components 1 and 2 will facilitate the identification of target receptors and downstream signaling proteins.
25
Jeong, P. Y.; Jung, M.; Yim, Y. H.; Kim, H.; Park, M.; Hong, E.; Lee, W.; Kim, Y. H.; Kim, K.; Paik, Y. K. Nature 2005, 433, 541– 545
Google Scholar
25
Chemical structure and biological activity of the Caenorhabditis elegans dauer-inducing pheromone
Jeong, Pan-Young; Jung, Mankil; Yim, Yong-Hyeon; Kim, Heekyeong; Park, Moonsoo; Hong, Eunmi; Lee, Weontae; Kim, Young Hwan; Kim, Kun; Paik, Young-Ki
Nature (London, United Kingdom) (2005), 433 (7025), 541-545CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
Pheromones are cell type-specific signals used for communication between individuals of the same species. When faced with overcrowding or starvation, Caenorhabditis elegans secrete the pheromone daumone, which facilitates communication between individuals for adaptation to adverse environmental stimuli. Daumone signals C. elegans to enter the dauer stage, an enduring and non-ageing stage of the nematode life cycle with distinctive adaptive features and extended life. Because daumone is a key regulator of chemosensory processes in development and ageing, the chem. identification of daumone is important for elucidating features of the daumone-mediated signalling pathway. Here we report the isolation of natural daumone from C. elegans by large-scale purifn., as well as the total chem. synthesis of daumone. We present the stereospecific chem. structure of purified daumone, a fatty acid deriv. We demonstrate that both natural and chem. synthesized daumones equally induce dauer larva formation in C. elegans (N2 strain) and certain dauer mutants, and also result in competition between food and daumone. These results should help to elucidate the daumone-mediated signalling pathway, which might in turn influence ageing and obesity research and the development of antinematodal drugs.
26
Macosko, E. Z.; Pokala, N.; Feinberg, E. H.; Chalasani, S. H.; Butcher, R. A.; Clardy, J.; Bargmann, C. I. Nature 2009, 458, 1171– 1175
Google Scholar
There is no corresponding record for this reference.
27
Pungaliya, C.; Srinivasan, J.; Fox, B. W.; Malik, R. U.; Ludewig, A. H.; Sternberg, P. W.; Schroeder, F. C. Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 7708– 7713
Google Scholar
There is no corresponding record for this reference.
28
Srinivasan, J.; Kaplan, F.; Ajredini, R.; Zachariah, C.; Alborn, H. T.; Teal, P. E.; Malik, R. U.; Edison, A. S.; Sternberg, P. W.; Schroeder, F. C. Nature 2008, 454, 1115– 1118
Google Scholar
There is no corresponding record for this reference.
29
Fuchs, S.; Bundy, J. G.; Davies, S. K.; Viney, J. M.; Swire, J. S.; Leroi, A. M. BMC Biol. 2010, 8, 14
Google Scholar
29
A metabolic signature of long life in Caenorhabditis elegans
Fuchs Silke; Bundy Jacob G; Davies Sarah K; Viney Jonathan M; Swire Jonathan S; Leroi Armand M
BMC biology (2010), 8 (), 14 ISSN:.
BACKGROUND: Many Caenorhabditis elegans mutations increase longevity and much evidence suggests that they do so at least partly via changes in metabolism. However, up until now there has been no systematic investigation of how the metabolic networks of long-lived mutants differ from those of normal worms. Metabolomic technologies, that permit the analysis of many untargeted metabolites in parallel, now make this possible. Here we use one of these, 1H nuclear magnetic resonance spectroscopy, to investigate what makes long-lived worms metabolically distinctive. RESULTS: We examined three classes of long-lived worms: dauer larvae, adult Insulin/IGF-1 signalling (IIS)-defective mutants, and a translation-defective mutant. Surprisingly, these ostensibly different long-lived worms share a common metabolic signature, dominated by shifts in carbohydrate and amino acid metabolism. In addition the dauer larvae, uniquely, had elevated levels of modified amino acids (hydroxyproline and phosphoserine). We interrogated existing gene expression data in order to integrate functional (metabolite-level) changes with transcriptional changes at a pathway level. CONCLUSIONS: The observed metabolic responses could be explained to a large degree by upregulation of gluconeogenesis and the glyoxylate shunt as well as changes in amino acid catabolism. These responses point to new possible mechanisms of longevity assurance in worms. The metabolic changes observed in dauer larvae can be explained by the existence of high levels of autophagy leading to recycling of cellular components.See associated minireview: http://jbiol.com/content/9/1/7.
30
Blaise, B. J.; Giacomotto, J.; Triba, M. N.; Toulhoat, P.; Piotto, M.; Emsley, L.; Segalat, L.; Dumas, M. E.; Elena, B. J. Proteome Res. 2009, 8, 2542– 2550
Google Scholar
30
Metabolic Profiling Strategy of Caenorhabditis elegans by Whole-Organism Nuclear Magnetic Resonance
Blaise, Benjamin J.; Giacomotto, Jean; Triba, Mohamed N.; Toulhoat, Pierre; Piotto, Martial; Emsley, Lyndon; Segalat, Laurent; Dumas, Marc-Emmanuel; Elena, Benedicte
Journal of Proteome Research (2009), 8 (5), 2542-2550CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
In this study, the authors present a methodol. for metabotyping of C. elegans using 1H high resoln. magic angle spinning (HRMAS) whole-organism NMR. The authors demonstrate and characterize the robustness of their metabolic phenotyping method, discriminating wild-type N2 from mutant sod-1(tm776) animals, with the latter being an otherwise silent mutation, and the authors identify and quantify several confounding effects to establish guidelines to ensure optimal quality of the raw data across time and space. The authors monitor the sample stability under exptl. conditions and examine variations arising from effects that can potentially confuse the biol. interpretation or prevent the automation of the protocol, including sample culture (breeding of the worms by two biologists), sample prepn. (freezing), NMR acquisition (acquisition by different spectroscopists, acquisition in different facilities), and the effect of the age of the animals. When working with intact model organisms, some of these exogenous effects are shown to be significant and therefore require control through exptl. design and sample randomization.
31
Atherton, H. J.; Jones, O. A.; Malik, S.; Miska, E. A.; Griffin, J. L. FEBS Lett. 2008, 582, 1661– 1666
Google Scholar
There is no corresponding record for this reference.
32
Edison, A. S. Curr. Opin. Neurobiol. 2009, 19, 378– 388
Google Scholar
There is no corresponding record for this reference.
33
Shaka, A. J.; Lee, C. J.; Pines, A. J. Magn. Reson. 1988, 77, 274– 293
Google Scholar
There is no corresponding record for this reference.
34
Thompson, J. D.; Higgins, D. G.; Gibson, T. J. Nucleic Acids Res. 1994, 22, 4673– 4680
Google Scholar
There is no corresponding record for this reference.
35
Sneath, P. H. A.; Sokal, R. R. Numerical Taxonomy; Freeman: San Francisco, CA, 1973.
Google Scholar
There is no corresponding record for this reference.
36
Koradi, R.; Billeter, M.; Engeli, M.; Guntert, P.; Wuthrich, K. J. Magn. Reson. 1998, 135, 288– 297
Google Scholar
There is no corresponding record for this reference.
37
Wold, S.; Esbensen, K.; Geladi, P. Chemom. Intell. Lab. Syst. 1987, 2, 37– 52
Google Scholar
37
Principal component analysis
Wold, Svante; Esbensen, Kim; Geladi, Paul
Chemometrics and Intelligent Laboratory Systems (1987), 2 (1-3), 37-52CODEN: CILSEN; ISSN:0169-7439.
A review with 46 refs. Principal component anal. and its application in chem. anal. are discussed.
38
Dejong, S. Chemom. Intell. Lab. Syst. 1993, 18, 251– 263
Google Scholar
38
SIMPLS: an alternative approach to partial least squares regression
de Jong, Sijmen
Chemometrics and Intelligent Laboratory Systems (1993), 18 (3), 251-63CODEN: CILSEN; ISSN:0169-7439.
A novel algorithm for partial least squares (PLS) regression, SIMPLS, is proposed which calcs. the PLS factors directly as linear combinations of the original variables. The PLS factors are detd. such as to maximize a covariance criterion, while obeying certain orthogonality and normalization restrictions. This approach follows that of other traditional multivariate methods. The construction of deflated data matrixes as in the nonlinear iterative partial least squares (NIPALS)-PLS algorithm is avoided. For univariate y SIMPLS is equiv. to PLS1 and closely related to existing bidiagonalization algorithms. This follows from an anal. of PLS1 regression in terms of Krylov sequences. For multivariate Y there is a slight difference between the SIMPLS approach and NIPALS-PLS2. In practice the SIMPLS algorithm appears to be fast and easy to interpret as it does not involve a breakdown of the data sets.
39
Dieterle, F.; Ross, A.; Schlotterbeck, G.; Senn, H. Anal. Chem. 2006, 78, 4281– 4290
Google Scholar
There is no corresponding record for this reference.
40
Hedenstrom, M.; Wiklund-Lindstrom, S.; Oman, T.; Lu, F. C.; Gerber, L.; Schatz, P.; Sundberg, B.; Ralph, J. Mol. Plant 2009, 2, 933– 942
Google Scholar
There is no corresponding record for this reference.
41
Hedenstrom, M.; Wiklund, S.; Sundberg, B.; Edlund, U. Chemom. Intell. Lab. Syst. 2008, 92, 110– 117
Google Scholar
There is no corresponding record for this reference.
42
Markley, J. L.; Anderson, M. E.; Cui, Q.; Eghbalnia, H. R.; Lewis, I. A.; Hegeman, A. D.; Li, J.; Schulte, C. F.; Sussman, M. R.; Westler, W. M.; Ulrich, E. L.; Zolnai, Z. Pac. Symp. Biocomput. 2007, 157– 168
Google Scholar
42
New bioinformatics resources for metabolomics
Markley, John L.; Anderson, Mark E.; Cui, Qiu; Eghbalnia, Hamid R.; Lewis, Ian A.; Hegeman, Adrian D.; Li, Jing; Schulte, Christopher F.; Sussman, Michael R.; Westler, William M.; Ulrich, Eldon L.; Zolnai, Zsolt
Pacific Symposium on Biocomputing 2007, Maui, HI, United States, Jan. 3-7, 2007 (2007), (), 157-168CODEN: 69KYN2 ISSN:. (World Scientific Publishing Co. Pte. Ltd.)
We recently developed two databases and a lab. information system as resources for the metabolomics community. These tools are freely available and are intended to ease data anal. in both MS and NMR based metabolomics studies. The first database is a metabolomics extension to the BioMagResBank (BMRB, http://www.bmrb.wisc.edu), which currently contains exptl. spectral data on over 270 pure compds. Each small mol. entry consists of five or six one- and two-dimensional NMR data sets, along with information about the source of the compd., soln. conditions, data collection protocol and the NMR pulse sequences. Users have free access to peak lists, spectra, and original time-domain data. The BMRB database can be queried by name, monoisotopic mass and chem. shift. We are currently developing a deposition tool that will enable people in the community to add their own data to this resource. Our second database, the Madison Metabolomics Consortium Database (MMCD, available from http://mmcd.nmrfam.wisc.edu/), is a hub for information on over 10,000 metabolites. These data were collected from a variety of sites with an emphasis on metabolites found in Arabidopsis. The MMC database supports extensive search functions and allows users to make bulk queries using exptl. MS and/or NMR data. In addn. to these databases, we have developed a new module for the Sesame lab. information management system (http://www.sesame.wisc.edu) that captures all of the exptl. protocols, background information, and exptl. data assocd. with metabolomics samples. Sesame was designed to help coordinate research efforts in labs. with high sample throughput and multiple investigators and to track all of the actions that have taken place in a particular study.
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Ulrich, E. L.; Akutsu, H.; Doreleijers, J. F.; Harano, Y.; Ioannidis, Y. E.; Lin, J.; Livny, M.; Mading, S.; Maziuk, D.; Miller, Z.; Nakatani, E.; Schulte, C. F.; Tolmie, D. E.; Kent Wenger, R.; Yao, H.; Markley, J. L. Nucleic Acids Res. 2008, 36, D402– 408
Google Scholar
43
BioMagResBank
Ulrich, Eldon L.; Akutsu, Hideo; Doreleijers, Jurgen F.; Harano, Yoko; Ioannidis, Yannis E.; Lin, Jundong; Livny, Miron; Mading, Steve; Maziuk, Dimitri; Miller, Zachary; Nakatani, Eiichi; Schulte, Christopher F.; Tolmie, David E.; Kent Wenger, R.; Yao, Hongyang; Markley, John L.
Nucleic Acids Research (2008), 36 (Database Iss), D402-D408CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
The BioMagResBank (BMRB: www.bmrb.wisc.edu) is a repository for exptl. and derived data gathered from NMR (NMR) spectroscopic studies of biol. mols. BMRB is a partner in the Worldwide Protein Data Bank (wwPDB). The BMRB archive consists of four main data depositories: (i) quant. NMR spectral parameters for proteins, peptides, nucleic acids, carbohydrates and ligands or cofactors (assigned chem. shifts, coupling consts. and peak lists) and derived data (relaxation parameters, residual dipolar couplings, hydrogen exchange rates, pKa values, etc.), (ii) databases for NMR restraints processed from original author depositions available from the Protein Data Bank, (iii) time-domain (raw) spectral data from NMR expts. used to assign spectral resonances and det. the structures of biol. macromols. and (iv) a database of one- and two-dimensional 1H and 13C one- and two-dimensional NMR spectra for over 250 metabolites. The BMRB website provides free access to all of these data. BMRB has tools for querying the archive and retrieving information and an ftp site (ftp.bmrb.wisc.edu) where data in the archive can be downloaded in bulk. Two BMRB mirror sites exist: one at the PDBj, Protein Research Institute, Osaka University, Osaka, Japan (bmrb.protein.osaka-u.ac.jp) and the other at CERM, University of Florence, Florence, Italy (bmrb.postgenomicnmr.net/). The site at Osaka also accepts and processes data depositions.
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Wishart, D. S.; Tzur, D.; Knox, C.; Eisner, R.; Guo, A. C.; Young, N.; Cheng, D.; Jewell, K.; Arndt, D.; Sawhney, S.; Fung, C.; Nikolai, L.; Lewis, M.; Coutouly, M. A.; Forsythe, I.; Tang, P.; Shrivastava, S.; Jeroncic, K.; Stothard, P.; Amegbey, G.; Block, D.; Hau, D. D.; Wagner, J.; Miniaci, J.; Clements, M.; Gebremedhin, M.; Guo, N.; Zhang, Y.; Duggan, G. E.; Macinnis, G. D.; Weljie, A. M.; Dowlatabadi, R.; Bamforth, F.; Clive, D.; Greiner, R.; Li, L.; Marrie, T.; Sykes, B. D.; Vogel, H. J.; Querengesser, L. Nucleic Acids Res. 2007, 35, D521– 526
Google Scholar
44
HMDB: the Human Metabolome Database
Wishart, David S.; Tzur, Dan; Knox, Craig; Eisner, Roman; Guo, An Chi; Young, Nelson; Cheng, Dean; Jewell, Kevin; Arndt, David; Sawhney, Summit; Fung, Chris; Nikolai, Lisa; Lewis, Mike; Coutouly, Marie-Aude; Forsythe, Ian; Tang, Peter; Shrivastava, Savita; Jeroncic, Kevin; Stothard, Paul; Amegbey, Godwin; Block, David; Hau, David. D.; Wagner, James; Miniaci, Jessica; Clements, Melisa; Gebremedhin, Mulu; Guo, Natalie; Zhang, Ying; Duggan, Gavin E.; MacInnis, Glen D.; Weljie, Alim M.; Dowlatabadi, Reza; Bamforth, Fiona; Clive, Derrick; Greiner, Russ; Li, Liang; Marrie, Tom; Sykes, Brian D.; Vogel, Hans J.; Querengesser, Lori
Nucleic Acids Research (2007), 35 (Database Iss), D521-D526CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
The Human Metabolome Database (HMDB) is currently the most complete and comprehensive curated collection of human metabolite and human metab. data in the world. It contains records for more than 2180 endogenous metabolites with information gathered from thousands of books, journal articles and electronic databases. In addn. to its comprehensive literature-derived data, the HMDB also contains an extensive collection of exptl. metabolite concn. data compiled from hundreds of mass spectra (MS) and NMR metabolomic analyses performed on urine, blood and cerebrospinal fluid samples. This is further supplemented with thousands of NMR and MS spectra collected on purified, ref. metabolites. Each metabolite entry in the HMDB contains an av. of 90 sep. data fields including a comprehensive compd. description, names and synonyms, structural information, physico-chem. data, ref. NMR and MS spectra, biofluid concns., disease assocns., pathway information, enzyme data, gene sequence data, SNP and mutation data as well as extensive links to images, refs. and other public databases. Extensive searching, relational querying and data browsing tools are also provided. The HMDB is designed to address the broad needs of biochemists, clin. chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community.
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Cui, Q.; Lewis, I. A.; Hegeman, A. D.; Anderson, M. E.; Li, J.; Schulte, C. F.; Westler, W. M.; Eghbalnia, H. R.; Sussman, M. R.; Markley, J. L. Nat. Biotechnol. 2008, 26, 162– 164
Google Scholar
45
Metabolite identification via the Madison Metabolomics Consortium Database
Cui, Qiu; Lewis, Ian A.; Hegeman, Adrian D.; Anderson, Mark E.; Li, Jing; Schulte, Christopher F.; Westler, William M.; Eghbalnia, Hamid R.; Sussman, Michael R.; Markley, John L.
Nature Biotechnology (2008), 26 (2), 162-164CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)
There is no expanded citation for this reference.
46
Dumas, M. E.; Maibaum, E. C.; Teague, C.; Ueshima, H.; Zhou, B. F.; Lindon, J. C.; Nicholson, J. K.; Stamler, J.; Elliott, P.; Chan, Q.; Holmes, E. Anal. Chem. 2006, 78, 2199– 2208
Google Scholar
There is no corresponding record for this reference.
47
Akiyama, K.; Chikayama, E.; Yuasa, H.; Shimada, Y.; Tohge, T.; Shinozaki, K.; Hirai, M. Y.; Sakurai, T.; Kikuchi, J.; Saito, K. In Silico Biol. 2008, 8, 339– 345
Google Scholar
47
PRIMe: A Web site that assembles tools for metabolomics and transcriptomics
Akiyama, Kenji; Chikayama, Eisuke; Yuasa, Hiroaki; Shimada, Yukihisa; Tohge, Takayuki; Shinozaki, Kazuo; Hirai, Masami Yokota; Sakurai, Tetsuya; Kikuchi, Jun; Saito, Kazuki
In Silico Biology (2008), 8 (3,4), 339-345CODEN: ISBIFC; ISSN:1386-6338. (IOS Press)
PRIMe (http://prime.psc.riken.jp/), the Platform for RIKEN Metabolomics, is a Web site that has been designed and implemented to support research and anal. workflows ranging from metabolome to transcriptome anal. The site provides access to a growing collection of standardized measurements of metabolites obtained by using NMR, GC-MS, LC-MS, and CE-MS, and metabolomics tools that support related analyses (SpinAssign for the identification of metabolites by means of NMR, KNApSAcK for searches within metabolite databases). In addn., the transcriptomics tools provide Correlated Gene Search, and Cluster Cutting for the anal. of mRNA expression. Use of the tools and database can contribute to the anal. of biol. events at the levels of metabolites and gene expression, and we describe one example of such an anal. for Arabidopsis thaliana using the batch-learning self-organizing map (BL-SOM), which is provided via the Web site.
48
Chikayama, E.; Sekiyama, Y.; Okamoto, M.; Nakanishi, Y.; Tsuboi, Y.; Akiyama, K.; Saito, K.; Shinozaki, K.; Kikuchi, J. Anal. Chem. 2010, 82, 1653– 1658
Google Scholar
48
Statistical Indices for Simultaneous Large-Scale Metabolite Detections for a Single NMR Spectrum
Chikayama, Eisuke; Sekiyama, Yasuyo; Okamoto, Mami; Nakanishi, Yumiko; Tsuboi, Yuuri; Akiyama, Kenji; Saito, Kazuki; Shinozaki, Kazuo; Kikuchi, Jun
Analytical Chemistry (Washington, DC, United States) (2010), 82 (5), 1653-1658CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
NMR-based metabolomics has become a practical and anal. methodol. for discovering novel genes, biomarkers, metabolic phenotypes, and dynamic cell behaviors in organisms. Recent developments in NMR-based metabolomics, however, have not concd. on improvements of comprehensiveness in terms of simultaneous large-scale metabolite detections. To resolve this, we have devised and implemented a statistical index, the SpinAssign p-value, in NMR-based metabolomics for large-scale metabolite annotation and publicized this information. It enables simultaneous annotation of more than 200 candidate metabolites from the single 13C-HSQC (heteronuclear single quantum coherence) NMR spectrum of a single sample of cell ext.
49
Steinbeck, C.; Kuhn, S. Phytochemistry 2004, 65, 2711– 2717
Google Scholar
There is no corresponding record for this reference.
50
Robinette, S. L.; Veselkov, K. A.; Bohus, E.; Coen, M.; Keun, H. C.; Ebbels, T. M.; Beckonert, O.; Holmes, E. C.; Lindon, J. C.; Nicholson, J. K. Anal. Chem. 2009, 81, 6581– 6589
Google Scholar
There is no corresponding record for this reference.
51
Shrot, Y.; Frydman, L. J. Chem. Phys. 2008, 128, 052209
Google Scholar
There is no corresponding record for this reference.
52
Sommer, R. J.; Tautz, D. Nature 1993, 361, 448– 450
Google Scholar
There is no corresponding record for this reference.
53
Freeman, R.; Kupce, E. J. Biomol. NMR 2003, 27, 101– 113
Google Scholar
There is no corresponding record for this reference.
54
Zhang, F. L.; Bruschweiler, R. J. Am. Chem. Soc. 2004, 126, 13180– 13181
Google Scholar
There is no corresponding record for this reference.
55
Brey, W. W.; Edison, A. S.; Nast, R. E.; Rocca, J. R.; Saha, S.; Withers, R. S. J. Magn. Reson. 2006, 179, 290– 293
Google Scholar
There is no corresponding record for this reference.
56
Olson, D. L.; Peck, T. L.; Webb, A. G.; Magin, R. L.; Sweedler, J. V. Science 1995, 270, 1967– 1970
Google Scholar
56
High-resolution microcoil 1H-NMR for mass-limited, nanoliter-volume samples
Olson, Dean L.; Peck, Timothy L.; Webb, Andrew G.; Magin, Richard L.; Sweedler, Jonathan V.
Science (Washington, D. C.) (1995), 270 (5244), 1967-70CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
High-resoln., 1H NMR spectra of 5-nL samples were obtained with much higher mass sensitivity [signal-to-noise ratio (S/N) per μmol] than with traditional methods. Arginine and sucrose show a mean sensitivity enhancement of 130 compared to 278-μL samples run in a 5-mm tube in a conventional, com. probe. This can reduce data acquisition time by a factor of >16,000 or reduce the needed sample mass by a factor of ∼130. A linewidth of 0.6 Hz was achieved on a 300-MHz spectrometer by matching the magnetic susceptibility of the medium that surrounds the detection cell to that of the Cu coil. For sucrose, the limit of detection (defined at S/N = 3) was 19 ng (56 p-mol) for a 1-min data acquisition. This technique should prove useful with mass-limited samples and for use as a detector in capillary sepns.
57
Kuhn, S.; Egert, B.; Neumann, S.; Steinbeck, C. BMC Bioinf. 2008, 9, 400
Google Scholar
57
Building blocks for automated elucidation of metabolites: machine learning methods for NMR prediction
Kuhn Stefan; Egert Bjorn; Neumann Steffen; Steinbeck Christoph
BMC bioinformatics (2008), 9 (), 400 ISSN:.
BACKGROUND: Current efforts in Metabolomics, such as the Human Metabolome Project, collect structures of biological metabolites as well as data for their characterisation, such as spectra for identification of substances and measurements of their concentration. Still, only a fraction of existing metabolites and their spectral fingerprints are known. Computer-Assisted Structure Elucidation (CASE) of biological metabolites will be an important tool to leverage this lack of knowledge. Indispensable for CASE are modules to predict spectra for hypothetical structures. This paper evaluates different statistical and machine learning methods to perform predictions of proton NMR spectra based on data from our open database NMRShiftDB. RESULTS: A mean absolute error of 0.18 ppm was achieved for the prediction of proton NMR shifts ranging from 0 to 11 ppm. Random forest, J48 decision tree and support vector machines achieved similar overall errors. HOSE codes being a notably simple method achieved a comparatively good result of 0.17 ppm mean absolute error. CONCLUSION: NMR prediction methods applied in the course of this work delivered precise predictions which can serve as a building block for Computer-Assisted Structure Elucidation for biological metabolites.

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Haiyan Liu, Fariba Tayyari, Arthur S. Edison, Zhihua Su, Liwei Gu. NMR-based metabolomics reveals urinary metabolome modifications in female Sprague–Dawley rats by cranberry procyanidins. The Journal of Nutritional Biochemistry 2016, 34 , 136-145. https://doi.org/10.1016/j.jnutbio.2016.05.007
Joachim Møllesøe Vinther, Sileshi Gizachew Wubshet, Dan Staerk. NMR‐based Metabolomics and Hyphenated NMR Techniques: A Perfect Match in Natural Products Research. 2015, 63-74. https://doi.org/10.1002/9781118930717.ch7
Chaevien S. Clendinen, Gregory S. Stupp, Ramadan Ajredini, Brittany Lee-McMullen, Chris Beecher, Arthur S. Edison. An overview of methods using 13C for improved compound identification in metabolomics and natural products. Frontiers in Plant Science 2015, 6 https://doi.org/10.3389/fpls.2015.00611
Chang‐Mo Kang, Jin Seong Hyeon, So Ra Kim, Eun Kyeong Lee, Hyun Jin Yun, Sun Young Kim, Young Kee Chae. Application of NMR Spectroscopy to Assessment of Radiation Dose and Time Lapse. Bulletin of the Korean Chemical Society 2015, 36 (5) , 1328-1334. https://doi.org/10.1002/bkcs.10252
Constanze Kuhlisch, Georg Pohnert. Metabolomics in chemical ecology. Natural Product Reports 2015, 32 (7) , 937-955. https://doi.org/10.1039/C5NP00003C
Nadine Bohni, Emerson F. Queiroz, Jean‐Luc Wolfender. On‐line and At‐line Liquid Chromatography Nuclear Magnetic Resonance and Related Micro‐Nuclear Magnetic Resonance Methods in Natural Product Analysis. 2014, 1-31. https://doi.org/10.1002/9780470027318.a9916
Fanny Gaillard, Philippe Potin. Proteomics and Metabolomics of Marine Organisms: Current Strategies and Knowledge. 2014, 457-472. https://doi.org/10.1002/9783527681501.ch21
Marc–Emmanuel Dumas, James Kinross, Jeremy K. Nicholson. Metabolic Phenotyping and Systems Biology Approaches to Understanding Metabolic Syndrome and Fatty Liver Disease. Gastroenterology 2014, 146 (1) , 46-62. https://doi.org/10.1053/j.gastro.2013.11.001
Young Kee Chae, Seol Hyun Kim, Youn‐Ki Nam. Application of Two‐Dimensional NMR Spectroscopy to Metabotyping Laboratory Escherichia coli Strains. Chemistry & Biodiversity 2013, 10 (10) , 1816-1827. https://doi.org/10.1002/cbdv.201300016
Trung Vu, Kris Laukens. Getting Your Peaks in Line: A Review of Alignment Methods for NMR Spectral Data. Metabolites 2013, 3 (2) , 259-276. https://doi.org/10.3390/metabo3020259
Que N. Van. Current NMR Strategies for Biomarker Discovery. 2013, 87-117. https://doi.org/10.1016/B978-0-12-394446-7.00006-6
J.-L. Wolfender, N. Bohni, K. Ndjoko-Ioset, A.S. Edison. Advanced Spectroscopic Detectors for Identification and Quantification. 2013, 349-384. https://doi.org/10.1016/B978-0-12-415807-8.00016-X
Que N. Van. Current NMR strategies for biomarker discovery. 2013, 103-131. https://doi.org/10.1016/B978-0-12-818607-7.00007-4
Yong-Jie Yu, Hai-Long Wu, Jing-Fang Niu, Juan Zhao, Yuan-Na Li, Chao Kang, Ru-Qin Yu. A novel chromatographic peak alignment method coupled with trilinear decomposition for three dimensional chromatographic data analysis to obtain the second-order advantage. The Analyst 2013, 138 (2) , 627-634. https://doi.org/10.1039/C2AN35931F
Young Kee Chae, Seol Hyun Kim, James J. Ellinger, John L. Markley. Tracing Metabolite Footsteps of Escherichia coli Along the Time Course of Recombinant Protein Expression by Two-Dimensional NMR Spectroscopy. Bulletin of the Korean Chemical Society 2012, 33 (12) , 4041-4046. https://doi.org/10.5012/bkcs.2012.33.12.4041
Andrea Choe, Tatsuji Chuman, Stephan H. von Reuss, Aaron T. Dossey, Joshua J. Yim, Ramadan Ajredini, Adam A. Kolawa, Fatma Kaplan, Hans T. Alborn, Peter E. A. Teal, Frank C. Schroeder, Paul W. Sternberg, Arthur S. Edison. Sex-specific mating pheromones in the nematode Panagrellus redivivus. Proceedings of the National Academy of Sciences 2012, 109 (51) , 20949-20954. https://doi.org/10.1073/pnas.1218302109
Woo Young Kang, Seol Hyun Kim, Young Kee Chae. Stress adaptation of Saccharomyces cerevisiae as monitored via metabolites using two-dimensional NMR spectroscopy. FEMS Yeast Research 2012, 12 (5) , 608-616. https://doi.org/10.1111/j.1567-1364.2012.00811.x
Bo Zhang, Robert Powers. Analysis of Bacterial Biofilms Using NMR-Based Metabolomics. Future Medicinal Chemistry 2012, 4 (10) , 1273-1306. https://doi.org/10.4155/fmc.12.59
Stephen T. Deyrup, Laura E. Eckman, Patrick H. McCarthy, Scott R. Smedley, Jerrold Meinwald, Frank C. Schroeder. 2D NMR-spectroscopic screening reveals polyketides in ladybugs. Proceedings of the National Academy of Sciences 2011, 108 (24) , 9753-9758. https://doi.org/10.1073/pnas.1107020108
Bernd Schneider. Chemical Ecology. 1996, 451-466. https://doi.org/10.1002/9780470034590.emrstm1341

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Analytical Chemistry

Cite this: Anal. Chem. 2011, 83, 5, 1649–1657

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Abstract
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Figure 1
Figure 1. Overall strategy of hierarchical alignment of two-dimensional spectra (HATS). Before alignment, the HATS algorithm produces a guide tree by the UPGMA agglomerative hierarchical clustering algorithm from a matrix of pairwise correlation distances. This guide tree is used to structure the alignment process. At each intersection of branches in the guide tree, the spectra specified by the two branches are aligned globally and locally. For the alignment tree shown here, the two red spectra are aligned first, followed by the two blue spectra. The two blue spectra are then aligned with the black spectrum, and finally, the three blue and black spectra are aligned with the red spectra.
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Figure 2
Figure 2. Alignment results for P. redivivus (Pre) and P. pacificus (Ppa) TOCSY spectra of flow-through fractions from C18 solid phase extractions. (A) The guide tree produced by HATS groups spectra from the same species, resulting in intraspecies alignment before interspecies alignment. The utility of a guide tree-based alignment is exemplified by the anomeric crosspeaks of glucose, sucrose, and trehalose in the TOCSY spectra (B, C). By aligning spectra with similar composition first, false positive alignments of nearby but unrelated crosspeaks, such as the anomeric crosspeaks shown above, are avoided.
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Figure 3
Figure 3. (A) Overlay of P. redivivus (Pre) and P. pacificus (Ppa) flow-through TOCSY spectra and (B) scores plot from PCA of the flow-through TOCSY data set. P. redivivus and P. pacificus spectra are clearly differentiated by PC1, which allows the loadings of PC1 to be interpreted as relative quantitative spectral differences between the species.
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Figure 4
Figure 4. Spectral back-projection of PC1 loadings. Here, contours are defined by the back-projected intensities, while colors are defined by the unit variance-scaled loading coefficients. Crosspeaks with positive loading coefficients (red) represent compounds overexpressed by P. redivivus relative to P. pacificus. Blue crosspeaks represent negative loading coefficients indicating P. pacificus overexpressed compounds. Assignment of crosspeaks in the back-projected loadings places the chemical information contained in the TOCSY spectra in an immediate biological context. Abbreviations: AIB, 3-aminoisobutyrate; Ala, alanine; Asn, asparagine; Asp, aspartate; Glu, glutamate; NC-Glu, N-carbamylglutamate; Pre Unk1, P. redivivus unknown 1; Pre Unk2, P. redivivus unknown 2; Pro, l-proline; Thr, threonine.
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Figure 5
Figure 5. Representative spectra and PC1 loadings for P. redivivus and P. pacificus 50% MeOH fractions. The region selected is useful for ascaroside differentiation, as crosspeaks here indicate correlations from the methylene protons on the ascaroside side chain to protons near the terminal functional groups. PC1 loadings suggest that P. redivivus and P. pacificus produce different mixtures of ascaroside-like compounds.
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References
This article references 57 other publications.
1. 1
  Nicholson, J. K.; Wilson, I. D. Prog. Nucl. Magn. Reson. Spectrosc. 1989, 21, 449– 501
  There is no corresponding record for this reference.
2. 2
  Nicholson, J. K.; Connelly, J.; Lindon, J. C.; Holmes, E. Nat. Rev. Drug Discovery 2002, 1, 153– 161
  2
  Innovation: Metabonomics: a platform for studying drug toxicity and gene function
  Nicholson, Jeremy K.; Connelly, John; Lindon, John C.; Holmes, Elaine
  Nature Reviews Drug Discovery (2002), 1 (2), 153-161CODEN: NRDDAG ISSN:. (Nature Publishing Group)
  A review. The later that a mol. or mol. class is lost from the drug development pipeline, the higher the financial cost. Minimizing attrition is therefore one of the most important aims of a pharmaceutical discovery program. Novel technologies that increase the probability of making the right choice early save resources, and promote safety, efficacy and profitability. Metabonomics is a systems approach for studying in vivo metabolic profiles, which promises to provide information on drug toxicity, disease processes and gene function at several stages in the discovery- and-development process.
3. 3
  Behrends, V.; Ebbels, T. M.; Williams, H. D.; Bundy, J. G. Appl. Environ. Microbiol. 2009, 75, 2453– 2463
  There is no corresponding record for this reference.
4. 4
  Schroeder, F. C.; Gibson, D. M.; Churchill, A. C.; Sojikul, P.; Wursthorn, E. J.; Krasnoff, S. B.; Clardy, J. Angew. Chem., Int. Ed. Engl. 2007, 46, 901– 904
  There is no corresponding record for this reference.
5. 5
  Bundy, J. G.; Sidhu, J. K.; Rana, F.; Spurgeon, D. J.; Svendsen, C.; Wren, J. F.; Sturzenbaum, S. R.; Morgan, A. J.; Kille, P. BMC Biol. 2008, 6, 25
  5
  'Systems toxicology' approach identifies coordinated metabolic responses to copper in a terrestrial non-model invertebrate, the earthworm Lumbricus rubellus
  Bundy Jacob G; Sidhu Jasmin K; Rana Faisal; Spurgeon David J; Svendsen Claus; Wren Jodie F; Sturzenbaum Stephen R; Morgan A John; Kille Peter
  BMC biology (2008), 6 (), 25 ISSN:.
  BACKGROUND: New methods are needed for research into non-model organisms, to monitor the effects of toxic disruption at both the molecular and functional organism level. We exposed earthworms (Lumbricus rubellus Hoffmeister) to sub-lethal levels of copper (10-480 mg/kg soil) for 70 days as a real-world situation, and monitored both molecular (cDNA transcript microarrays and nuclear magnetic resonance-based metabolic profiling: metabolomics) and ecological/functional endpoints (reproduction rate and weight change, which have direct relevance to population-level impacts). RESULTS: Both of the molecular endpoints, metabolomics and transcriptomics, were highly sensitive, with clear copper-induced differences even at levels below those that caused a reduction in reproductive parameters. The microarray and metabolomic data provided evidence that the copper exposure led to a disruption of energy metabolism: transcripts of enzymes from oxidative phosphorylation were significantly over-represented, and increases in transcripts of carbohydrate metabolising enzymes (maltase-glucoamylase, mannosidase) had corresponding decreases in small-molecule metabolites (glucose, mannose). Treating both enzymes and metabolites as functional cohorts led to clear inferences about changes in energetic metabolism (carbohydrate use and oxidative phosphorylation), which would not have been possible by taking a 'biomarker' approach to data analysis. CONCLUSION: Multiple post-genomic techniques can be combined to provide mechanistic information about the toxic effects of chemical contaminants, even for non-model organisms with few additional mechanistic toxicological data. With 70-day no-observed-effect and lowest-observed-effect concentrations (NOEC and LOEC) of 10 and 40 mg kg-1 for metabolomic and microarray profiles, copper is shown to interfere with energy metabolism in an important soil organism at an ecologically and functionally relevant level.
6. 6
  Blaise, B. J.; Giacomotto, J.; Elena, B.; Dumas, M. E.; Toulhoat, P.; Segalat, L.; Emsley, L. Proc. Natl. Acad. Sci. U.S.A. 2007, 104, 19808– 19812
  6
  Metabotyping of Caenorhabditis elegans reveals latent phenotypes
  Blaise, Benjamin J.; Giacomotto, Jean; Elena, Benedicte; Dumas, Marc-Emmanuel; Toulhoat, Pierre; Segalat, Laurent; Emsley, Lyndon
  Proceedings of the National Academy of Sciences of the United States of America (2007), 104 (50), 19808-19812CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
  Assigning functions to every gene in a living organism is the next challenge for functional genomics. In fact, 85-90% of the 19,000 genes of the nematode Caenorhabditis elegans genome do not produce any visible phenotype when inactivated, which hampers detg. their function, esp. when they do not belong to previously characterized gene families. We used 1H high-resoln. magic angle spinning NMR spectroscopy (1H HRMAS-NMR) to reveal the latent phenotype assocd. to superoxide dismutase (sod-1) and catalase (ctl-1) C. elegans mutations, both involved in the elimination of radical oxidative species. These two silent mutations are significantly discriminated from the wild-type strain and from each other. We identify a metabotype significantly assocd. with these mutations involving a general redn. of fatty acyl resonances from triglycerides, unsatd. lipids being known targets of free radicals. This work opens up perspectives for the use of 1H HRMAS-NMR as a mol. phenotyping device for model organisms. Because it is amenable to high throughput and is shown to be highly informative, this approach may rapidly lead to a functional and integrated metabonomic mapping of the C. elegans genome at the systems biol. level.
7. 7
  Kaplan, F.; Badri, D. V.; Zachariah, C.; Ajredini, R.; Sandoval, F. J.; Roje, S.; Levine, L. H.; Zhang, F.; Robinette, S. L.; Alborn, H. T.; Zhao, W.; Stadler, M.; Nimalendran, R.; Dossey, A. T.; Bruschweiler, R.; Vivanco, J. M.; Edison, A. S. J. Chem. Ecol. 2009, 35, 878– 892
  7
  Bacterial Attraction and Quorum Sensing Inhibition in Caenorhabditis elegans Exudates
  Kaplan, Fatma; Badri, Dayakar V.; Zachariah, Cherian; Ajredini, Ramadan; Sandoval, Francisco J.; Roje, Sanja; Levine, Lanfang H.; Zhang, Fengli; Robinette, Steven L.; Alborn, Hans T.; Zhao, Wei; Stadler, Michael; Nimalendran, Rathika; Dossey, Aaron T.; Brueschweiler, Rafael; Vivanco, Jorge M.; Edison, Arthur S.
  Journal of Chemical Ecology (2009), 35 (8), 878-892CODEN: JCECD8; ISSN:0098-0331. (Springer)
  Caenorhabditis elegans, a bacterivorous nematode, lives in complex rotting fruit, soil, and compost environments, and chem. interactions are required for mating, monitoring population d., recognition of food, avoidance of pathogenic microbes, and other essential ecol. functions. Despite being one of the best-studied model organisms in biol., relatively little is known about the signals that C. elegans uses to interact chem. with its environment or as defense. C. elegans exudates were analyzed by using several anal. methods and found to contain 36 common metabolites that include org. acids, amino acids, and sugars, all in relatively high abundance. Furthermore, the concns. of amino acids in the exudates were dependent on developmental stage. The C. elegans exudates were tested for bacterial chemotaxis using Pseudomonas putida (KT2440), a plant growth promoting rhizobacterium, Pseudomonas aeruginosa (PAO1), a soil bacterium pathogenic to C. elegans, and Escherichia coli (OP50), a non-motile bacterium tested as a control. The C. elegans exudates attracted the two Pseudomonas species, but had no detectable antibacterial activity against P. aeruginosa. To our surprise, the exudates of young adult and adult life stages of C. elegans exudates inhibited quorum sensing in the reporter system based on the LuxR bacterial quorum sensing (QS) system, which regulates bacterial virulence and other factors in Vibrio fischeri. We were able to fractionate the QS inhibition and bacterial chemotaxis activities, thus demonstrating that these activities are chem. distinct. Our results demonstrate that C. elegans can attract its bacterial food and has the potential of partially regulating the virulence of bacterial pathogens by inhibiting specific QS systems.
8. 8
  Bundy, J. G.; Spurgeon, D. J.; Svendsen, C.; Hankard, P. K.; Osborn, D.; Lindon, J. C.; Nicholson, J. K. FEBS Lett. 2002, 521, 115– 120
  There is no corresponding record for this reference.
9. 9
  Rae, R.; Iatsenko, I.; Witte, H.; Sommer, R. J. Environ. Microbiol. 2010, 12, 3007– 3021
  There is no corresponding record for this reference.
10. 10
  Xia, J.; Bjorndahl, T. C.; Tang, P.; Wishart, D. S. BMC Bioinformatics 2008, 9, 507
  There is no corresponding record for this reference.
11. 11
  Robinette, S. L.; Zhang, F.; Bruschweiler-Li, L.; Bruschweiler, R. Anal. Chem. 2008, 80, 3606– 3611
  There is no corresponding record for this reference.
12. 12
  Zhang, F.; Robinette, S. L.; Bruschweiler-Li, L.; Bruschweiler, R. Magn. Reson. Chem. 2009, 47 (Suppl 1) S118– 122
  There is no corresponding record for this reference.
13. 13
  Zhang, F.; Dossey, A. T.; Zachariah, C.; Edison, A. S.; Bruschweiler, R. Anal. Chem. 2007, 79, 7748– 7752
  There is no corresponding record for this reference.
14. 14
  Lee, G. C.; Woodruff, D. L. Anal. Chim. Acta 2004, 513, 413– 416
  There is no corresponding record for this reference.
15. 15
  Veselkov, K. A.; Lindon, J. C.; Ebbels, T. M.; Crockford, D.; Volynkin, V. V.; Holmes, E.; Davies, D. B.; Nicholson, J. K. Anal. Chem. 2009, 81, 56– 66
  15
  Recursive Segment-Wise Peak Alignment of Biological 1H NMR Spectra for Improved Metabolic Biomarker Recovery
  Veselkov, Kirill A.; Lindon, John C.; Ebbels, Timothy M. D.; Crockford, Derek; Volynkin, Vladimir V.; Holmes, Elaine; Davies, David B.; Nicholson, Jeremy K.
  Analytical Chemistry (Washington, DC, United States) (2009), 81 (1), 56-66CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
  Chem. shift variation in small-mol. 1H NMR signals of biofluids complicates biomarker information recovery in metabonomic studies when using multivariate statistical and pattern recognition tools. Current peak realignment methods are generally time-consuming or align major peaks at the expense of minor peak shift accuracy. The authors present a novel recursive segment-wise peak alignment (RSPA) method to reduce variability in peak positions across the multiple 1H NMR spectra used in metabonomic studies. The method refines a segmentation of ref. and test spectra in a top-down fashion, sequentially subdividing the initial larger segments, as required, to improve the local spectral alignment. The authors also describe a general procedure that allows robust comparison of realignment quality of various available methods for a range of peak intensities. The RSPA method is illustrated with respect to 140 1H NMR rat urine spectra from a caloric restriction study and is compared with several other widely used peak alignment methods. The authors demonstrate the superior performance of the RSPA alignment over a wide range of peaks and its capacity to enhance interpretability and robustness of multivariate statistical tools. The approach is widely applicable for NMR-based metabolic studies and is potentially suitable for many other types of data sets such as chromatog. profiles and MS data.
16. 16
  Wong, J. W. H.; Cagney, G.; Cartwright, H. M. Bioinf. 2005, 21, 2088– 2090
  16
  SpecAlign-processing and alignment of mass spectra datasets
  Wong, Jason W. H.; Cagney, Gerard; Cartwright, Hugh M.
  Bioinformatics (2005), 21 (9), 2088-2090CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
  Pre-processing of chromatog. profile or mass spectral data is an important aspect of many types of proteomics and biomarker discovery expts. Here we present a graphical computational tool, SpecAlign, that enables simultaneous visualization and manipulation of multiple datasets. SpecAlign not only provides all common processing functions, but also uniquely implements an algorithm that enables the complete alignment of each mass spectrum within a loaded dataset. We demonstrate its utility by aligning two datasets each contg. six spectra; one set was acquired prior to instrument calibration and the other following calibration.
17. 17
  Smith, C. A.; Want, E. J.; O’Maille, G.; Abagyan, R.; Siuzdak, G. Anal. Chem. 2006, 78, 779– 787
  17
  XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification
  Smith, Colin A.; Want, Elizabeth J.; O'Maille, Grace; Abagyan, Ruben; Siuzdak, Gary
  Analytical Chemistry (2006), 78 (3), 779-787CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
  Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity detn., and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biol. origin. Here we introduce an LC/MS-based data anal. approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal stds., the method dynamically identifies hundreds of endogenous metabolites for use as stds., calcg. a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.
18. 18
  Zheng, M.; Lu, P.; Liu, Y.; Pease, J.; Usuka, J.; Liao, G.; Peltz, G. Bioinformatics 2007, 23, 2926– 2933
  There is no corresponding record for this reference.
19. 19
  Lewis, I. A.; Schommer, S. C.; Markley, J. L. Magn. Reson. Chem. 2009, 47 (Suppl 1) S123– 126
  There is no corresponding record for this reference.
20. 20
  Ludwig, C.; Ward, D. G.; Martin, A.; Viant, M. R.; Ismail, T.; Johnson, P. J.; Wakelam, M. J.; Gunther, U. L. Magn. Reson. Chem. 2009, 47 (Suppl 1) S68– 73
  There is no corresponding record for this reference.
21. 21
  Van, Q. N.; Issaq, H. J.; Jiang, Q.; Li, Q.; Muschik, G. M.; Waybright, T. J.; Lou, H.; Dean, M.; Uitto, J.; Veenstra, T. D. J Proteome Res. 2008, 7, 630– 639
  21
  Comparison of 1D and 2D NMR Spectroscopy for Metabolic Profiling
  Van, Que N.; Issaq, Haleem J.; Jiang, Qiujie; Li, Qiaoli; Muschik, Gary M.; Waybright, Timothy J.; Lou, Hong; Dean, Michael; Uitto, Jouni; Veenstra, Timothy D.
  Journal of Proteome Research (2008), 7 (2), 630-639CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
  High-resoln., liq. state NMR spectroscopy is a popular platform for metabolic profiling because the technique is nondestructive, quant., reproducible, and the spectra contain a wealth of biochem. information. Because of the large dynamic range of metabolite concns. in biofluids, statistical analyses of one-dimensional (1D) proton NMR data tend to be biased toward selecting changes in more abundant metabolites. Although two-dimensional (2D) proton-proton expts. can alleviate spectral crowding, they have been mainly used for structural detn. In this study, 2D total correlation spectroscopy NMR was used to compare the global metabolic profiles of urine obtained from wild-type and Abcc6-knockout mice. The 2D data were compared to an improved 1D expt. in which signal contributions from macromols. and the urea peak have been spectroscopically removed for more accurate quantitation of low-abundance metabolites. Although statistical models from both 1D and 2D data could differentiate samples acquired from the two groups of mice, only the 2D spectra allowed the characterization of statistically relevant changes in the low-abundance metabolites. While acquisition of the 2D data require more time, the data obtained resulted in a more meaningful and comprehensive metabolic profile, aided in metabolite identifications, and minimized ambiguities in peak assignments.
22. 22
  Butcher, R. A.; Ragains, J. R.; Clardy, J. Org. Lett. 2009, 11, 3100– 3103
  There is no corresponding record for this reference.
23. 23
  Butcher, R. A.; Ragains, J. R.; Kim, E.; Clardy, J. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 14288– 14292
  There is no corresponding record for this reference.
24. 24
  Butcher, R. A.; Fujita, M.; Schroeder, F. C.; Clardy, J. Nat. Chem. Biol. 2007, 3, 420– 422
  24
  Small-molecule pheromones that control dauer development in Caenorhabditis elegans
  Butcher, Rebecca A.; Fujita, Masaki; Schroeder, Frank C.; Clardy, Jon
  Nature Chemical Biology (2007), 3 (7), 420-422CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
  In response to high population d. or low food supply, the nematode Caenorhabditis elegans enters an alternative larval stage, known as the dauer, that can withstand adverse conditions for prolonged periods. C. elegans senses its population d. through a small-mol. signal, traditionally called the dauer pheromone, that it secretes into its surroundings. Here we show that the dauer pheromone consists of several structurally related ascarosides-derivs. of the dideoxysugar ascarylose-and that 2 of these ascarosides (1 and 2) are roughly 2 orders of magnitude more potent at inducing dauer formation than a previously reported dauer pheromone component (3) and constitute a physiol. relevant signal. The identification of dauer pheromone components 1 and 2 will facilitate the identification of target receptors and downstream signaling proteins.
25. 25
  Jeong, P. Y.; Jung, M.; Yim, Y. H.; Kim, H.; Park, M.; Hong, E.; Lee, W.; Kim, Y. H.; Kim, K.; Paik, Y. K. Nature 2005, 433, 541– 545
  25
  Chemical structure and biological activity of the Caenorhabditis elegans dauer-inducing pheromone
  Jeong, Pan-Young; Jung, Mankil; Yim, Yong-Hyeon; Kim, Heekyeong; Park, Moonsoo; Hong, Eunmi; Lee, Weontae; Kim, Young Hwan; Kim, Kun; Paik, Young-Ki
  Nature (London, United Kingdom) (2005), 433 (7025), 541-545CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  Pheromones are cell type-specific signals used for communication between individuals of the same species. When faced with overcrowding or starvation, Caenorhabditis elegans secrete the pheromone daumone, which facilitates communication between individuals for adaptation to adverse environmental stimuli. Daumone signals C. elegans to enter the dauer stage, an enduring and non-ageing stage of the nematode life cycle with distinctive adaptive features and extended life. Because daumone is a key regulator of chemosensory processes in development and ageing, the chem. identification of daumone is important for elucidating features of the daumone-mediated signalling pathway. Here we report the isolation of natural daumone from C. elegans by large-scale purifn., as well as the total chem. synthesis of daumone. We present the stereospecific chem. structure of purified daumone, a fatty acid deriv. We demonstrate that both natural and chem. synthesized daumones equally induce dauer larva formation in C. elegans (N2 strain) and certain dauer mutants, and also result in competition between food and daumone. These results should help to elucidate the daumone-mediated signalling pathway, which might in turn influence ageing and obesity research and the development of antinematodal drugs.
26. 26
  Macosko, E. Z.; Pokala, N.; Feinberg, E. H.; Chalasani, S. H.; Butcher, R. A.; Clardy, J.; Bargmann, C. I. Nature 2009, 458, 1171– 1175
  There is no corresponding record for this reference.
27. 27
  Pungaliya, C.; Srinivasan, J.; Fox, B. W.; Malik, R. U.; Ludewig, A. H.; Sternberg, P. W.; Schroeder, F. C. Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 7708– 7713
  There is no corresponding record for this reference.
28. 28
  Srinivasan, J.; Kaplan, F.; Ajredini, R.; Zachariah, C.; Alborn, H. T.; Teal, P. E.; Malik, R. U.; Edison, A. S.; Sternberg, P. W.; Schroeder, F. C. Nature 2008, 454, 1115– 1118
  There is no corresponding record for this reference.
29. 29
  Fuchs, S.; Bundy, J. G.; Davies, S. K.; Viney, J. M.; Swire, J. S.; Leroi, A. M. BMC Biol. 2010, 8, 14
  29
  A metabolic signature of long life in Caenorhabditis elegans
  Fuchs Silke; Bundy Jacob G; Davies Sarah K; Viney Jonathan M; Swire Jonathan S; Leroi Armand M
  BMC biology (2010), 8 (), 14 ISSN:.
  BACKGROUND: Many Caenorhabditis elegans mutations increase longevity and much evidence suggests that they do so at least partly via changes in metabolism. However, up until now there has been no systematic investigation of how the metabolic networks of long-lived mutants differ from those of normal worms. Metabolomic technologies, that permit the analysis of many untargeted metabolites in parallel, now make this possible. Here we use one of these, 1H nuclear magnetic resonance spectroscopy, to investigate what makes long-lived worms metabolically distinctive. RESULTS: We examined three classes of long-lived worms: dauer larvae, adult Insulin/IGF-1 signalling (IIS)-defective mutants, and a translation-defective mutant. Surprisingly, these ostensibly different long-lived worms share a common metabolic signature, dominated by shifts in carbohydrate and amino acid metabolism. In addition the dauer larvae, uniquely, had elevated levels of modified amino acids (hydroxyproline and phosphoserine). We interrogated existing gene expression data in order to integrate functional (metabolite-level) changes with transcriptional changes at a pathway level. CONCLUSIONS: The observed metabolic responses could be explained to a large degree by upregulation of gluconeogenesis and the glyoxylate shunt as well as changes in amino acid catabolism. These responses point to new possible mechanisms of longevity assurance in worms. The metabolic changes observed in dauer larvae can be explained by the existence of high levels of autophagy leading to recycling of cellular components.See associated minireview: http://jbiol.com/content/9/1/7.
30. 30
  Blaise, B. J.; Giacomotto, J.; Triba, M. N.; Toulhoat, P.; Piotto, M.; Emsley, L.; Segalat, L.; Dumas, M. E.; Elena, B. J. Proteome Res. 2009, 8, 2542– 2550
  30
  Metabolic Profiling Strategy of Caenorhabditis elegans by Whole-Organism Nuclear Magnetic Resonance
  Blaise, Benjamin J.; Giacomotto, Jean; Triba, Mohamed N.; Toulhoat, Pierre; Piotto, Martial; Emsley, Lyndon; Segalat, Laurent; Dumas, Marc-Emmanuel; Elena, Benedicte
  Journal of Proteome Research (2009), 8 (5), 2542-2550CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
  In this study, the authors present a methodol. for metabotyping of C. elegans using 1H high resoln. magic angle spinning (HRMAS) whole-organism NMR. The authors demonstrate and characterize the robustness of their metabolic phenotyping method, discriminating wild-type N2 from mutant sod-1(tm776) animals, with the latter being an otherwise silent mutation, and the authors identify and quantify several confounding effects to establish guidelines to ensure optimal quality of the raw data across time and space. The authors monitor the sample stability under exptl. conditions and examine variations arising from effects that can potentially confuse the biol. interpretation or prevent the automation of the protocol, including sample culture (breeding of the worms by two biologists), sample prepn. (freezing), NMR acquisition (acquisition by different spectroscopists, acquisition in different facilities), and the effect of the age of the animals. When working with intact model organisms, some of these exogenous effects are shown to be significant and therefore require control through exptl. design and sample randomization.
31. 31
  Atherton, H. J.; Jones, O. A.; Malik, S.; Miska, E. A.; Griffin, J. L. FEBS Lett. 2008, 582, 1661– 1666
  There is no corresponding record for this reference.
32. 32
  Edison, A. S. Curr. Opin. Neurobiol. 2009, 19, 378– 388
  There is no corresponding record for this reference.
33. 33
  Shaka, A. J.; Lee, C. J.; Pines, A. J. Magn. Reson. 1988, 77, 274– 293
  There is no corresponding record for this reference.
34. 34
  Thompson, J. D.; Higgins, D. G.; Gibson, T. J. Nucleic Acids Res. 1994, 22, 4673– 4680
  There is no corresponding record for this reference.
35. 35
  Sneath, P. H. A.; Sokal, R. R. Numerical Taxonomy; Freeman: San Francisco, CA, 1973.
  There is no corresponding record for this reference.
36. 36
  Koradi, R.; Billeter, M.; Engeli, M.; Guntert, P.; Wuthrich, K. J. Magn. Reson. 1998, 135, 288– 297
  There is no corresponding record for this reference.
37. 37
  Wold, S.; Esbensen, K.; Geladi, P. Chemom. Intell. Lab. Syst. 1987, 2, 37– 52
  37
  Principal component analysis
  Wold, Svante; Esbensen, Kim; Geladi, Paul
  Chemometrics and Intelligent Laboratory Systems (1987), 2 (1-3), 37-52CODEN: CILSEN; ISSN:0169-7439.
  A review with 46 refs. Principal component anal. and its application in chem. anal. are discussed.
38. 38
  Dejong, S. Chemom. Intell. Lab. Syst. 1993, 18, 251– 263
  38
  SIMPLS: an alternative approach to partial least squares regression
  de Jong, Sijmen
  Chemometrics and Intelligent Laboratory Systems (1993), 18 (3), 251-63CODEN: CILSEN; ISSN:0169-7439.
  A novel algorithm for partial least squares (PLS) regression, SIMPLS, is proposed which calcs. the PLS factors directly as linear combinations of the original variables. The PLS factors are detd. such as to maximize a covariance criterion, while obeying certain orthogonality and normalization restrictions. This approach follows that of other traditional multivariate methods. The construction of deflated data matrixes as in the nonlinear iterative partial least squares (NIPALS)-PLS algorithm is avoided. For univariate y SIMPLS is equiv. to PLS1 and closely related to existing bidiagonalization algorithms. This follows from an anal. of PLS1 regression in terms of Krylov sequences. For multivariate Y there is a slight difference between the SIMPLS approach and NIPALS-PLS2. In practice the SIMPLS algorithm appears to be fast and easy to interpret as it does not involve a breakdown of the data sets.
39. 39
  Dieterle, F.; Ross, A.; Schlotterbeck, G.; Senn, H. Anal. Chem. 2006, 78, 4281– 4290
  There is no corresponding record for this reference.
40. 40
  Hedenstrom, M.; Wiklund-Lindstrom, S.; Oman, T.; Lu, F. C.; Gerber, L.; Schatz, P.; Sundberg, B.; Ralph, J. Mol. Plant 2009, 2, 933– 942
  There is no corresponding record for this reference.
41. 41
  Hedenstrom, M.; Wiklund, S.; Sundberg, B.; Edlund, U. Chemom. Intell. Lab. Syst. 2008, 92, 110– 117
  There is no corresponding record for this reference.
42. 42
  Markley, J. L.; Anderson, M. E.; Cui, Q.; Eghbalnia, H. R.; Lewis, I. A.; Hegeman, A. D.; Li, J.; Schulte, C. F.; Sussman, M. R.; Westler, W. M.; Ulrich, E. L.; Zolnai, Z. Pac. Symp. Biocomput. 2007, 157– 168
  42
  New bioinformatics resources for metabolomics
  Markley, John L.; Anderson, Mark E.; Cui, Qiu; Eghbalnia, Hamid R.; Lewis, Ian A.; Hegeman, Adrian D.; Li, Jing; Schulte, Christopher F.; Sussman, Michael R.; Westler, William M.; Ulrich, Eldon L.; Zolnai, Zsolt
  Pacific Symposium on Biocomputing 2007, Maui, HI, United States, Jan. 3-7, 2007 (2007), (), 157-168CODEN: 69KYN2 ISSN:. (World Scientific Publishing Co. Pte. Ltd.)
  We recently developed two databases and a lab. information system as resources for the metabolomics community. These tools are freely available and are intended to ease data anal. in both MS and NMR based metabolomics studies. The first database is a metabolomics extension to the BioMagResBank (BMRB, http://www.bmrb.wisc.edu), which currently contains exptl. spectral data on over 270 pure compds. Each small mol. entry consists of five or six one- and two-dimensional NMR data sets, along with information about the source of the compd., soln. conditions, data collection protocol and the NMR pulse sequences. Users have free access to peak lists, spectra, and original time-domain data. The BMRB database can be queried by name, monoisotopic mass and chem. shift. We are currently developing a deposition tool that will enable people in the community to add their own data to this resource. Our second database, the Madison Metabolomics Consortium Database (MMCD, available from http://mmcd.nmrfam.wisc.edu/), is a hub for information on over 10,000 metabolites. These data were collected from a variety of sites with an emphasis on metabolites found in Arabidopsis. The MMC database supports extensive search functions and allows users to make bulk queries using exptl. MS and/or NMR data. In addn. to these databases, we have developed a new module for the Sesame lab. information management system (http://www.sesame.wisc.edu) that captures all of the exptl. protocols, background information, and exptl. data assocd. with metabolomics samples. Sesame was designed to help coordinate research efforts in labs. with high sample throughput and multiple investigators and to track all of the actions that have taken place in a particular study.
43. 43
  Ulrich, E. L.; Akutsu, H.; Doreleijers, J. F.; Harano, Y.; Ioannidis, Y. E.; Lin, J.; Livny, M.; Mading, S.; Maziuk, D.; Miller, Z.; Nakatani, E.; Schulte, C. F.; Tolmie, D. E.; Kent Wenger, R.; Yao, H.; Markley, J. L. Nucleic Acids Res. 2008, 36, D402– 408
  43
  BioMagResBank
  Ulrich, Eldon L.; Akutsu, Hideo; Doreleijers, Jurgen F.; Harano, Yoko; Ioannidis, Yannis E.; Lin, Jundong; Livny, Miron; Mading, Steve; Maziuk, Dimitri; Miller, Zachary; Nakatani, Eiichi; Schulte, Christopher F.; Tolmie, David E.; Kent Wenger, R.; Yao, Hongyang; Markley, John L.
  Nucleic Acids Research (2008), 36 (Database Iss), D402-D408CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
  The BioMagResBank (BMRB: www.bmrb.wisc.edu) is a repository for exptl. and derived data gathered from NMR (NMR) spectroscopic studies of biol. mols. BMRB is a partner in the Worldwide Protein Data Bank (wwPDB). The BMRB archive consists of four main data depositories: (i) quant. NMR spectral parameters for proteins, peptides, nucleic acids, carbohydrates and ligands or cofactors (assigned chem. shifts, coupling consts. and peak lists) and derived data (relaxation parameters, residual dipolar couplings, hydrogen exchange rates, pKa values, etc.), (ii) databases for NMR restraints processed from original author depositions available from the Protein Data Bank, (iii) time-domain (raw) spectral data from NMR expts. used to assign spectral resonances and det. the structures of biol. macromols. and (iv) a database of one- and two-dimensional 1H and 13C one- and two-dimensional NMR spectra for over 250 metabolites. The BMRB website provides free access to all of these data. BMRB has tools for querying the archive and retrieving information and an ftp site (ftp.bmrb.wisc.edu) where data in the archive can be downloaded in bulk. Two BMRB mirror sites exist: one at the PDBj, Protein Research Institute, Osaka University, Osaka, Japan (bmrb.protein.osaka-u.ac.jp) and the other at CERM, University of Florence, Florence, Italy (bmrb.postgenomicnmr.net/). The site at Osaka also accepts and processes data depositions.
44. 44
  Wishart, D. S.; Tzur, D.; Knox, C.; Eisner, R.; Guo, A. C.; Young, N.; Cheng, D.; Jewell, K.; Arndt, D.; Sawhney, S.; Fung, C.; Nikolai, L.; Lewis, M.; Coutouly, M. A.; Forsythe, I.; Tang, P.; Shrivastava, S.; Jeroncic, K.; Stothard, P.; Amegbey, G.; Block, D.; Hau, D. D.; Wagner, J.; Miniaci, J.; Clements, M.; Gebremedhin, M.; Guo, N.; Zhang, Y.; Duggan, G. E.; Macinnis, G. D.; Weljie, A. M.; Dowlatabadi, R.; Bamforth, F.; Clive, D.; Greiner, R.; Li, L.; Marrie, T.; Sykes, B. D.; Vogel, H. J.; Querengesser, L. Nucleic Acids Res. 2007, 35, D521– 526
  44
  HMDB: the Human Metabolome Database
  Wishart, David S.; Tzur, Dan; Knox, Craig; Eisner, Roman; Guo, An Chi; Young, Nelson; Cheng, Dean; Jewell, Kevin; Arndt, David; Sawhney, Summit; Fung, Chris; Nikolai, Lisa; Lewis, Mike; Coutouly, Marie-Aude; Forsythe, Ian; Tang, Peter; Shrivastava, Savita; Jeroncic, Kevin; Stothard, Paul; Amegbey, Godwin; Block, David; Hau, David. D.; Wagner, James; Miniaci, Jessica; Clements, Melisa; Gebremedhin, Mulu; Guo, Natalie; Zhang, Ying; Duggan, Gavin E.; MacInnis, Glen D.; Weljie, Alim M.; Dowlatabadi, Reza; Bamforth, Fiona; Clive, Derrick; Greiner, Russ; Li, Liang; Marrie, Tom; Sykes, Brian D.; Vogel, Hans J.; Querengesser, Lori
  Nucleic Acids Research (2007), 35 (Database Iss), D521-D526CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
  The Human Metabolome Database (HMDB) is currently the most complete and comprehensive curated collection of human metabolite and human metab. data in the world. It contains records for more than 2180 endogenous metabolites with information gathered from thousands of books, journal articles and electronic databases. In addn. to its comprehensive literature-derived data, the HMDB also contains an extensive collection of exptl. metabolite concn. data compiled from hundreds of mass spectra (MS) and NMR metabolomic analyses performed on urine, blood and cerebrospinal fluid samples. This is further supplemented with thousands of NMR and MS spectra collected on purified, ref. metabolites. Each metabolite entry in the HMDB contains an av. of 90 sep. data fields including a comprehensive compd. description, names and synonyms, structural information, physico-chem. data, ref. NMR and MS spectra, biofluid concns., disease assocns., pathway information, enzyme data, gene sequence data, SNP and mutation data as well as extensive links to images, refs. and other public databases. Extensive searching, relational querying and data browsing tools are also provided. The HMDB is designed to address the broad needs of biochemists, clin. chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community.
45. 45
  Cui, Q.; Lewis, I. A.; Hegeman, A. D.; Anderson, M. E.; Li, J.; Schulte, C. F.; Westler, W. M.; Eghbalnia, H. R.; Sussman, M. R.; Markley, J. L. Nat. Biotechnol. 2008, 26, 162– 164
  45
  Metabolite identification via the Madison Metabolomics Consortium Database
  Cui, Qiu; Lewis, Ian A.; Hegeman, Adrian D.; Anderson, Mark E.; Li, Jing; Schulte, Christopher F.; Westler, William M.; Eghbalnia, Hamid R.; Sussman, Michael R.; Markley, John L.
  Nature Biotechnology (2008), 26 (2), 162-164CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)
  There is no expanded citation for this reference.
46. 46
  Dumas, M. E.; Maibaum, E. C.; Teague, C.; Ueshima, H.; Zhou, B. F.; Lindon, J. C.; Nicholson, J. K.; Stamler, J.; Elliott, P.; Chan, Q.; Holmes, E. Anal. Chem. 2006, 78, 2199– 2208
  There is no corresponding record for this reference.
47. 47
  Akiyama, K.; Chikayama, E.; Yuasa, H.; Shimada, Y.; Tohge, T.; Shinozaki, K.; Hirai, M. Y.; Sakurai, T.; Kikuchi, J.; Saito, K. In Silico Biol. 2008, 8, 339– 345
  47
  PRIMe: A Web site that assembles tools for metabolomics and transcriptomics
  Akiyama, Kenji; Chikayama, Eisuke; Yuasa, Hiroaki; Shimada, Yukihisa; Tohge, Takayuki; Shinozaki, Kazuo; Hirai, Masami Yokota; Sakurai, Tetsuya; Kikuchi, Jun; Saito, Kazuki
  In Silico Biology (2008), 8 (3,4), 339-345CODEN: ISBIFC; ISSN:1386-6338. (IOS Press)
  PRIMe (http://prime.psc.riken.jp/), the Platform for RIKEN Metabolomics, is a Web site that has been designed and implemented to support research and anal. workflows ranging from metabolome to transcriptome anal. The site provides access to a growing collection of standardized measurements of metabolites obtained by using NMR, GC-MS, LC-MS, and CE-MS, and metabolomics tools that support related analyses (SpinAssign for the identification of metabolites by means of NMR, KNApSAcK for searches within metabolite databases). In addn., the transcriptomics tools provide Correlated Gene Search, and Cluster Cutting for the anal. of mRNA expression. Use of the tools and database can contribute to the anal. of biol. events at the levels of metabolites and gene expression, and we describe one example of such an anal. for Arabidopsis thaliana using the batch-learning self-organizing map (BL-SOM), which is provided via the Web site.
48. 48
  Chikayama, E.; Sekiyama, Y.; Okamoto, M.; Nakanishi, Y.; Tsuboi, Y.; Akiyama, K.; Saito, K.; Shinozaki, K.; Kikuchi, J. Anal. Chem. 2010, 82, 1653– 1658
  48
  Statistical Indices for Simultaneous Large-Scale Metabolite Detections for a Single NMR Spectrum
  Chikayama, Eisuke; Sekiyama, Yasuyo; Okamoto, Mami; Nakanishi, Yumiko; Tsuboi, Yuuri; Akiyama, Kenji; Saito, Kazuki; Shinozaki, Kazuo; Kikuchi, Jun
  Analytical Chemistry (Washington, DC, United States) (2010), 82 (5), 1653-1658CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
  NMR-based metabolomics has become a practical and anal. methodol. for discovering novel genes, biomarkers, metabolic phenotypes, and dynamic cell behaviors in organisms. Recent developments in NMR-based metabolomics, however, have not concd. on improvements of comprehensiveness in terms of simultaneous large-scale metabolite detections. To resolve this, we have devised and implemented a statistical index, the SpinAssign p-value, in NMR-based metabolomics for large-scale metabolite annotation and publicized this information. It enables simultaneous annotation of more than 200 candidate metabolites from the single 13C-HSQC (heteronuclear single quantum coherence) NMR spectrum of a single sample of cell ext.
49. 49
  Steinbeck, C.; Kuhn, S. Phytochemistry 2004, 65, 2711– 2717
  There is no corresponding record for this reference.
50. 50
  Robinette, S. L.; Veselkov, K. A.; Bohus, E.; Coen, M.; Keun, H. C.; Ebbels, T. M.; Beckonert, O.; Holmes, E. C.; Lindon, J. C.; Nicholson, J. K. Anal. Chem. 2009, 81, 6581– 6589
  There is no corresponding record for this reference.
51. 51
  Shrot, Y.; Frydman, L. J. Chem. Phys. 2008, 128, 052209
  There is no corresponding record for this reference.
52. 52
  Sommer, R. J.; Tautz, D. Nature 1993, 361, 448– 450
  There is no corresponding record for this reference.
53. 53
  Freeman, R.; Kupce, E. J. Biomol. NMR 2003, 27, 101– 113
  There is no corresponding record for this reference.
54. 54
  Zhang, F. L.; Bruschweiler, R. J. Am. Chem. Soc. 2004, 126, 13180– 13181
  There is no corresponding record for this reference.
55. 55
  Brey, W. W.; Edison, A. S.; Nast, R. E.; Rocca, J. R.; Saha, S.; Withers, R. S. J. Magn. Reson. 2006, 179, 290– 293
  There is no corresponding record for this reference.
56. 56
  Olson, D. L.; Peck, T. L.; Webb, A. G.; Magin, R. L.; Sweedler, J. V. Science 1995, 270, 1967– 1970
  56
  High-resolution microcoil 1H-NMR for mass-limited, nanoliter-volume samples
  Olson, Dean L.; Peck, Timothy L.; Webb, Andrew G.; Magin, Richard L.; Sweedler, Jonathan V.
  Science (Washington, D. C.) (1995), 270 (5244), 1967-70CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
  High-resoln., 1H NMR spectra of 5-nL samples were obtained with much higher mass sensitivity [signal-to-noise ratio (S/N) per μmol] than with traditional methods. Arginine and sucrose show a mean sensitivity enhancement of 130 compared to 278-μL samples run in a 5-mm tube in a conventional, com. probe. This can reduce data acquisition time by a factor of >16,000 or reduce the needed sample mass by a factor of ∼130. A linewidth of 0.6 Hz was achieved on a 300-MHz spectrometer by matching the magnetic susceptibility of the medium that surrounds the detection cell to that of the Cu coil. For sucrose, the limit of detection (defined at S/N = 3) was 19 ng (56 p-mol) for a 1-min data acquisition. This technique should prove useful with mass-limited samples and for use as a detector in capillary sepns.
57. 57
  Kuhn, S.; Egert, B.; Neumann, S.; Steinbeck, C. BMC Bioinf. 2008, 9, 400
  57
  Building blocks for automated elucidation of metabolites: machine learning methods for NMR prediction
  Kuhn Stefan; Egert Bjorn; Neumann Steffen; Steinbeck Christoph
  BMC bioinformatics (2008), 9 (), 400 ISSN:.
  BACKGROUND: Current efforts in Metabolomics, such as the Human Metabolome Project, collect structures of biological metabolites as well as data for their characterisation, such as spectra for identification of substances and measurements of their concentration. Still, only a fraction of existing metabolites and their spectral fingerprints are known. Computer-Assisted Structure Elucidation (CASE) of biological metabolites will be an important tool to leverage this lack of knowledge. Indispensable for CASE are modules to predict spectra for hypothetical structures. This paper evaluates different statistical and machine learning methods to perform predictions of proton NMR spectra based on data from our open database NMRShiftDB. RESULTS: A mean absolute error of 0.18 ppm was achieved for the prediction of proton NMR shifts ranging from 0 to 11 ppm. Random forest, J48 decision tree and support vector machines achieved similar overall errors. HOSE codes being a notably simple method achieved a comparatively good result of 0.17 ppm mean absolute error. CONCLUSION: NMR prediction methods applied in the course of this work delivered precise predictions which can serve as a building block for Computer-Assisted Structure Elucidation for biological metabolites.
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Hierarchical Alignment and Full Resolution Pattern Recognition of 2D NMR Spectra: Application to Nematode Chemical EcologyClick to copy article linkArticle link copied!

Analytical Chemistry

Abstract

⊥ Author Present Address

¶ Author Present Address

Experimental Section

Collection of P. pacificus and P. redivivus Exudates

P. pacificus

P. redivivus

Sample Fractionation

NMR Spectroscopy

Hierarchical Alignment of Two-Dimensional Spectra

Figure 1

Full Resolution TOCSY Pattern Recognition

Semi-Automated Assignment of TOCSY Loadings

Results

Alignment of TOCSY Spectra

Figure 2

P. redivivus and P. pacificus Release Different Polar Molecules

Figure 3

Figure 4

P. redivivus and P. pacificus Appear to Produce Different Ascarosides

Figure 5

Discussion and Conclusions

Supporting Information

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Acknowledgment

References

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Figure 1

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Figure 3

Figure 4

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Hierarchical Alignment and Full Resolution Pattern Recognition of 2D NMR Spectra: Application to Nematode Chemical Ecology
Click to copy article linkArticle link copied!