More on urinary metabolomics in autism research

The -omics. Y'know all those new-fangled disciplines which have sprung up to describe how sciences look at genes, bacteria, etc. We used to call it plain old scientific analysis, but now depending on what your sample medium or technology or your target species is, its been rebranded and repackaged as an -omic.

Shepherdess @ Wikipedia  

I've talked about a few of the -omics quite a bit on this blog and their relationship to systems biology; ranging from microbiomics (studying bacteria) to epigenomics (chemical modifications of the genome) to metallomics (metals affecting cellular functions). I've even invented a new -omic: psychobacteriomics.

Indeed in my other life I'm currently helping out on an article talking about one of the 'next big' -omic things: lipidomics. I'm just waiting for the science of Paulomics to emerge and discover just what makes me tick.

Anyhow, the reason for the -omics chatter is due to my stumbling across an interesting couple of papers from Patrick Emond and colleagues* and by the same authorship group, Sylvie Marcel and colleagues** and their description of findings based on the science of metabolomics. Aside from the metabolomics link (which ties into some of my own research interest or at least that of the people I work with) I was always going to be interested in these papers because of some of the authorship group on the papers and their work on a favourite autism assessment schedule of mine***.

So, metabolomics - think low molecular weight metabolites and where we look for them (blood, saliva, urine, CSF) - and how there may be good reason for looking at group differences and similarities across different peoples and different conditions. I might point out that this is not the first time urinary metabolomics - the sample medium described - has been discussed on this blog as per the Ming findings and Yap findings with autism in mind. I want to also direct you to the Yang findings on the appliance of metabolomics to schizophrenia which were really rather interesting and are still crying out for independent replication.

The Emond paper briefly:

• The tools of the trade were gas chromatography - mass spectrometry (GC-MS), which were applied to urine samples received from 26 children diagnosed with an autism spectrum disorder (ASD) and compared with 24 asymptomatic controls.
• Analysis of the samples was followed by some nifty statistics to help identify any potential discriminating metabolites between the groups and bingo, a few compounds of interest were reported on.
• Results: "The relative concentrations of the succinate and glycolate were higher" in the autism group. 
• But, "hippurate, 3-hydroxyphenylacetate, vanillylhydracrylate, 3-hydroxyhippurate, 4-hydroxyphenyl-2-hydroxyacetate, 1H-indole-3-acetate, phosphate, palmitate, stearate, and 3-methyladipate were lower" in the autism group.

The Mavel paper also:

• A slightly different analytical technique based on nuclear magnetic resonance spectroscopy (NMR) applied to urine samples from 30 children with ASD and 28 controls. Indeed quite a specific type of NMR was done (Heteronuclear Single Quantum Coherence, HSQC) which aids in metabolite identification. I'm not sure if the participant groups from this paper overlapped with that of the Emond paper or not.
• Similar statistics and modelling to that in the GC-MS paper were used on the data obtained and differentiation data were presented across the groups.
• Results: findings for a few compounds intersected those reported in the Emond paper (i.e. succinate reported to be present in higher quantities alongside β-alanine, glycine and taurine). Other compounds were reported as being lower: "creatine and 3-methylhistidine concentrations were lower in autistic children than in controls".
• More than that however were the details of the methodology used, and how 2D HSQC NMR might be applied to further larger studies in the autism research field. 

As a bit of a cop-out, I'm not going to go through each compound with a fine-toothed comb. OK, perhaps a little more explanation of some of those metabolites might be in order and in particular the role that gut bacteria may very well have had on them.

Hippurate for example, is an interesting metabolite given its proposed links to all things gut bacterial. Indeed I note on that very interesting paper by Andrew Clayton on gut bacteria and the aromatic amino acids potentially with autism in mind, hippurate and its precursor benzoic acid were mentioned in one of the rat models discussed. That the autism group seemed to show generally lower levels of hippuric acid (hippurate) might potentially imply some involvement of the amino acid glycine (which conjugates with benzoic acid) although I am of course, just speculating. That being said, the glycine finding in the Mavel paper (being higher) might suggest that there is more going on here.

I'll also just mention that the higher levels of urinary taurine were also noted in the Yap paper**** too. Oh and some wondering about whether lower urinary creatine levels might also be tied into the lower urinary creatinine levels that we reported on a few years back (paper is here in case your interested).

I note that in the Emond paper, the authors talk quite a bit about how you treat the urine samples prior to analysis might affect what results you get. Again, it's not something that I really want to get into. Indeed just before you completely switch off, although I know a little bit about sample prep when it comes to things like SPE, I freely admit that the process of oximation (for derivatisation) is a different language for me. I will however say that basing results of ion mass with only one (sometimes no) decimal place, is a short-coming as compared to the power of accurate mass via Time-of-Flight (ToF) spectrometry for example when it comes to authoritative compound assignment, but that's not a criticism.

I am genuinely intrigued over the potential of the -omics when it comes to conditions like autism (sorry, the autisms). The two French papers are another step into that brave new world bearing in mind that chromatographic methods***** also need to be allied with strong detection technology****** (open-access). Assuming science can also start to sort out some of those phenotypes which make up the autism spectrum, this area of work promises so much in terms of insights into pathology and the development of objective diagnostic markers.

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* Emond P. et al. GC-MS-based urine metabolic profiling of autism spectrum disorders. Anal Bioanal Chem. April 2013.

** Mavel S. et al. 1H–13C NMR-based urine metabolic profiling in autism spectrum disorders. Talanta. 2013; 114: 95-102.

*** Barthélémy C. et al. Validation of the Revised Behavior Summarized Evaluation Scale. J Autism Dev Disord. 1997; 27: 139-53.

**** Yap IK. et al. Urinary metabolic phenotyping differentiates children with autism from their unaffected siblings and age-matched controls. J Proteome Res. 2010; 9: 2996-3004.

***** Zurawicz E. et al. Chromatographic methods in the study of autism. Biomed Chromatogr. April 2013.

****** Wood AG. et al. Mass spectrometry as a tool for studying autism spectrum disorder. Journal of Molecular Psychiatry 2013; 1: 6.

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Emond P, Mavel S, Aïdoud N, Nadal-Desbarats L, Montigny F, Bonnet-Brilhault F, Barthélémy C, Merten M, Sarda P, Laumonnier F, Vourc'h P, Blasco H, & Andres CR (2013). GC-MS-based urine metabolic profiling of autism spectrum disorders. Analytical and bioanalytical chemistry PMID: 23571465



Mavel, S., Nadal-Desbarats, L., Blasco, H., Bonnet-Brilhault, F., Barthélémy, C., Montigny, F., Sarda, P., Laumonnier, F., Vourc′h, P., Andres, C., & Emond, P. (2013). 1H–13C NMR-based urine metabolic profiling in autism spectrum disorders Talanta, 114, 95-102 DOI: 10.1016/j.talanta.2013.03.064