Cryptogenic Disease and 'Omics Profiling

An interesting and exciting case report will be published in the March edition of Genetics in Medicine (Worthey et al., Genet Med. 2011 Mar, 13(3):255-262). This Brief Report describes the case of a 6-year old boy in Wisconsin suffering from multiple intestinal fistulas. Despite comprehensive clinical evaluations and a battery of tests, the boy's physicians were unable to arrive at a definitive diagnosis and were baffled at a syndrome that had not been seen before. Clinical management was limited, making it difficult for the boy to eat solid food. After over 100 surgeries the boy only grew sicker and the physicians were at a loss. It was at this point that they carried out whole-exome sequencing. It was discovered that the boy had a mutation in his XIAP gene, a mutation not previously associated with the boy's condition but that had been linked with another pathology which was curable by bone-marrow transplantation. The team performed a bone-marrow transplant (which they would not have considered without the sequencing) and saved the boy's life.

This report, as well as a couple of other recent examples from the literature, raises the question of the role of genomics, proteomics, and metabolomics in initiating treatment regimens for cryptogenic disease. Clearly, this is a striking example of the clinical benefit of sequencing a patient with a poorly understood syndrome. Here, sequencing led to treatment insights that would have been otherwise difficult to arrive at. In the context of this particular case study, it is unclear that proteomic and metabolomic profiling would have offered much clinical benefit. Although metabollite profiling may be useful in diagnosing diseases for which biomarkers have been elucidated, would metabolomics be useful to investigate rare cryptogenic cases such as those described here? At this time, it is difficult to imagine learning much interesting information by doing "metabolite profiling" on a single individual without appropriate controls and statistics. But perhaps this is something that may change with the evolution of our understanding of the human metabolome and the development of much more advanced metabolite databases. For now, metabolomics might be better suited for providing pathobiological insight when it comes to dealing with rare cryptogenic disease.

Metabolomics review in Clinical Biochemistry

Applications of liquid chromatography coupled to mass spectrometry-based metabolomics in clinical chemistry and toxicology: A review.
Roux A, Lison D, Junot C, Heilier JF.

metaXCMS: Second-Order Analysis of Untargeted Metabolomics Data

Abstract: Mass spectrometry-based untargeted metabolomics often results in the observation of hundreds to thousands of features that are differentially regulated between sample classes. A major challenge in interpreting the data is distinguishing metabolites that are causally associated with the phenotype of interest from those that are unrelated but altered in downstream pathways as an effect. To facilitate this distinction, here we describe new software called metaXCMS for performing second-order (“meta”) analysis of untargeted metabolomics data from multiple sample groups representing different models of the same phenotype. While the original version of XCMS was designed for the direct comparison of two sample groups, metaXCMS enables meta-analysis of an unlimited number of sample classes to facilitate prioritization of the data and increase the probability of identifying metabolites causally related to the phenotype of interest. metaXCMS is used to import XCMS results that are subsequently filtered, realigned, and ultimately compared to identify shared metabolites that are up- or down-regulated across all sample groups. We demonstrate the software’s utility by identifying histamine as a metabolite that is commonly altered in three different models of pain. metaXCMS is freely available at http://metlin.scripps.edu/metaxcms/.

The full article is available here.

Hans Rosling shows the best stats you've ever seen

Beautiful visualizations of statistics by Hans Rosling.
Might be inspiring for visualizations of metabolomics time series data ...
There is also the documentary "The Joy of Stats" on BBC in December.

Combined genome-wide association mapping with metabolomics

Interesting article in PLoS Genetics:

The Complex Genetic Architecture of the Metabolome


Association between more than 200,000 single-nucleotide variants across the genome and levels of 327 metabolites in 96 strains of Arabidopsis thaliana showed that only 23–30% of the variation in cellular metabolite levels was associated with specific sites in the genome.

The Untargeted Metabolomics Workflow

During the past 2 years, the methodology that I have employed to make metabolite identifications using an untargeted metabolomics workflow has evolved. In 2008, not only were metabolite databases smaller, but they also did not have some of the advanced functionality that is available today. For example, searching for sodium and potassium adducts required manually calculating masses from the observed m/z values. We have come a long way with improvements in both metabolomics software and databases facilitating metabolite identification. Major databases emerging as the key players for untargeted studies are HMDB, Lipid Maps, and METLIN. Each, in my opinion, have their own advantages. I would like to start this blog by surveying which databases metabolomics investigators utilize the most frequently and why. HMDB, for example, provides so-called MetaboCards in which fundamental biological facts are introduced for queried molecules. This information can be particularly useful in filtering putative hits for metabolites that may not be relevant to the sample type being analyzed, such as a hit for a plant metabolite from bacterial cell results. Another new function that has been recently incorporated into METLIN is the ability to search fragment ions from MS/MS data. With this function, it is now possible to do MS/MS on all features of interest in a dataset prior to querying databases to potentially reduce false-negative hits. A few years ago, the workflow of identifying metabolites in a global MS-based study offered little room for creativity. I am certain today, however, that investigators are taking advantage of the various new database functionality in a multitude of innovative ways. I hope that by discussing and exchanging ideas about our untargeted workflows we can learn new ways to facilitate what I still would classify as the rate-limiting step in metabolomics, metabolite identification. So what process do you use to make metabolite identifications? How do you prioritize your feature lists? Do you search all the databases on the web, or do you refine yourself to an in-house library? I look forward to reading about your different points of view!

A short history of XCMS

XCMS is an open-source, platform-independent R-package that was developed to perform untargeted metabolite profiling with LC/MS. XCMS reads and processes LC/MS data stored in netcdf , mzXML, mzData and mzML files. It provides method for peak picking, non-linear retention time alignment, visualization, relative quantization and statistics. XCMS is capable of simultaneously preprocessing, analyzing, and visualizing the raw data from hundreds of samples.  The original XCMS paper published in 2006 was cited more than 270 times (Google Scholar, 11/12/2010).
Colin Smith initially developed XCMS in 2004.  In 2008 Steffen Neumann and Ralf Tautenhahn joined the development team and later in 2009 Paul Benton. Today, XCMS contains two methods for LC/MS feature detection and a method for peak detection in single high-res spectra (FTICR, MALDI, DIMS). Two different non-linear retention time correction methods are available, and  two methods to group  LC/MS features.   A separate method is implemented to align single high-res spectra using a moving-window technique. Mass spectra, TICs, EICs and EIC overlays, 3D LC/MS surface plots and boxplots can be generated by XCMS.  Methods to read and preprocess MS/MS data are available. XCMS can make use of multicore processors, as well as MPI or SNOW clusters to speed up the data processing.  XCMS is now widely used for untargeted metabolomics, metabolic profiling and  biomarker discovery.

 Spring    2004: Added methods for reading and displaying raw data from NetCDF files (Colin)
Summer    2004: Developed methods for kernel density peak grouping and LOESS retention time alignment (Colin)
Fall      2004: Developed matched filter peak picker, EIC generation (Colin)
March     2005: Checked into Bioconductor SVN repository (Colin)
December  2005: mzXML, mzData import added (Colin)
April     2007: centWave peak detection added (Ralf)
November  2007: Reading of MS/MS spectra added (Steffen)
January   2008: single spectra alignment method added (Steffen)
July      2008: Multiprocessor peak picking added via MPI (Ralf)
March     2009: OBI-Warp retention time alignment added (Steffen, Ralf)
April     2009: group nearest alignment method added (Steffen, Ralf)
June      2010: gap filler/stitch method added (Paul)
September 2010: 64 bit support added (Steffen, Ralf)