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Detection and interpretation of metabolite-transcript coresponses using combined profiling data.

Redestig H, Costa IG - Bioinformatics (2011)

Bottom Line: Our aim was to predict pathway comemberships between metabolites and genes based on their coresponses to applied stress.Therefore, we propose a supervised method that applies pathway information to summarize similarity statistics to a consensus statistic that is more informative than any of the single measures.Using four combined profiling datasets, we show that comembership between metabolites and genes can be predicted for numerous KEGG pathways; this opens opportunities for the detection of transcriptionally regulated pathways and novel metabolically related genes.

View Article: PubMed Central - PubMed

Affiliation: RIKEN Plant Science Center, Yokohama, Japan. henning@psc.riken.jp

ABSTRACT

Motivation: Studying the interplay between gene expression and metabolite levels can yield important information on the physiology of stress responses and adaptation strategies. Performing transcriptomics and metabolomics in parallel during time-series experiments represents a systematic way to gain such information. Several combined profiling datasets have been added to the public domain and they form a valuable resource for hypothesis generating studies. Unfortunately, detecting coresponses between transcript levels and metabolite abundances is non-trivial: they cannot be assumed to overlap directly with underlying biochemical pathways and they may be subject to time delays and obscured by considerable noise.

Results: Our aim was to predict pathway comemberships between metabolites and genes based on their coresponses to applied stress. We found that in the presence of strong noise and time-shifted responses, a hidden Markov model-based similarity outperforms the simpler Pearson correlation but performs comparably or worse in their absence. Therefore, we propose a supervised method that applies pathway information to summarize similarity statistics to a consensus statistic that is more informative than any of the single measures. Using four combined profiling datasets, we show that comembership between metabolites and genes can be predicted for numerous KEGG pathways; this opens opportunities for the detection of transcriptionally regulated pathways and novel metabolically related genes.

Availability: A command-line software tool is available at http://www.cin.ufpe.br/~igcf/Metabolites.

Contact: henning@psc.riken.jp; igcf@cin.ufpe.br

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Related in: MedlinePlus

Three examples of MT coresponse patterns seen in the glucosinolate (Gls) synthesis pathway during sulfur deficiency [data from (Hirai et al., 2005)]. (a) ATST5a catalyzes a late step in the synthesis of Indolylmethyl-Gls and its transcript is positively correlated with the reaction product. (b) CYP7F1 and MAM3 catalyzes aliphatic Gls synthesis from methionine that appear anticorrelated with isoleucine, also a precursor for aliphatic Gls via a pathway that shares enzymes with the methionine → aliphatic Gls pathway. (c) SUR1 catalyzes a later step in aliphatic Gls synthesis and appear correlated with methylthiobutyl Gls at a positive time lag.
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Figure 1: Three examples of MT coresponse patterns seen in the glucosinolate (Gls) synthesis pathway during sulfur deficiency [data from (Hirai et al., 2005)]. (a) ATST5a catalyzes a late step in the synthesis of Indolylmethyl-Gls and its transcript is positively correlated with the reaction product. (b) CYP7F1 and MAM3 catalyzes aliphatic Gls synthesis from methionine that appear anticorrelated with isoleucine, also a precursor for aliphatic Gls via a pathway that shares enzymes with the methionine → aliphatic Gls pathway. (c) SUR1 catalyzes a later step in aliphatic Gls synthesis and appear correlated with methylthiobutyl Gls at a positive time lag.

Mentions: With these considerations in mind, it becomes clear that MT correlations may take many forms exhibiting various degrees of noise, time lags and conditionality on the studied response (see Fig. 1 for examples). Therefore, to detect such coresponses using combined profiling data, methods that can handle these issues and deliver interpretable results are needed.Fig. 1.


Detection and interpretation of metabolite-transcript coresponses using combined profiling data.

Redestig H, Costa IG - Bioinformatics (2011)

Three examples of MT coresponse patterns seen in the glucosinolate (Gls) synthesis pathway during sulfur deficiency [data from (Hirai et al., 2005)]. (a) ATST5a catalyzes a late step in the synthesis of Indolylmethyl-Gls and its transcript is positively correlated with the reaction product. (b) CYP7F1 and MAM3 catalyzes aliphatic Gls synthesis from methionine that appear anticorrelated with isoleucine, also a precursor for aliphatic Gls via a pathway that shares enzymes with the methionine → aliphatic Gls pathway. (c) SUR1 catalyzes a later step in aliphatic Gls synthesis and appear correlated with methylthiobutyl Gls at a positive time lag.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117345&req=5

Figure 1: Three examples of MT coresponse patterns seen in the glucosinolate (Gls) synthesis pathway during sulfur deficiency [data from (Hirai et al., 2005)]. (a) ATST5a catalyzes a late step in the synthesis of Indolylmethyl-Gls and its transcript is positively correlated with the reaction product. (b) CYP7F1 and MAM3 catalyzes aliphatic Gls synthesis from methionine that appear anticorrelated with isoleucine, also a precursor for aliphatic Gls via a pathway that shares enzymes with the methionine → aliphatic Gls pathway. (c) SUR1 catalyzes a later step in aliphatic Gls synthesis and appear correlated with methylthiobutyl Gls at a positive time lag.
Mentions: With these considerations in mind, it becomes clear that MT correlations may take many forms exhibiting various degrees of noise, time lags and conditionality on the studied response (see Fig. 1 for examples). Therefore, to detect such coresponses using combined profiling data, methods that can handle these issues and deliver interpretable results are needed.Fig. 1.

Bottom Line: Our aim was to predict pathway comemberships between metabolites and genes based on their coresponses to applied stress.Therefore, we propose a supervised method that applies pathway information to summarize similarity statistics to a consensus statistic that is more informative than any of the single measures.Using four combined profiling datasets, we show that comembership between metabolites and genes can be predicted for numerous KEGG pathways; this opens opportunities for the detection of transcriptionally regulated pathways and novel metabolically related genes.

View Article: PubMed Central - PubMed

Affiliation: RIKEN Plant Science Center, Yokohama, Japan. henning@psc.riken.jp

ABSTRACT

Motivation: Studying the interplay between gene expression and metabolite levels can yield important information on the physiology of stress responses and adaptation strategies. Performing transcriptomics and metabolomics in parallel during time-series experiments represents a systematic way to gain such information. Several combined profiling datasets have been added to the public domain and they form a valuable resource for hypothesis generating studies. Unfortunately, detecting coresponses between transcript levels and metabolite abundances is non-trivial: they cannot be assumed to overlap directly with underlying biochemical pathways and they may be subject to time delays and obscured by considerable noise.

Results: Our aim was to predict pathway comemberships between metabolites and genes based on their coresponses to applied stress. We found that in the presence of strong noise and time-shifted responses, a hidden Markov model-based similarity outperforms the simpler Pearson correlation but performs comparably or worse in their absence. Therefore, we propose a supervised method that applies pathway information to summarize similarity statistics to a consensus statistic that is more informative than any of the single measures. Using four combined profiling datasets, we show that comembership between metabolites and genes can be predicted for numerous KEGG pathways; this opens opportunities for the detection of transcriptionally regulated pathways and novel metabolically related genes.

Availability: A command-line software tool is available at http://www.cin.ufpe.br/~igcf/Metabolites.

Contact: henning@psc.riken.jp; igcf@cin.ufpe.br

Show MeSH
Related in: MedlinePlus