A metabolic network map of arabidopsis thaliana’s citric acid cycle.

Wouldn’t it be great if doctors could make use of new biomarker research as rapidly as it was confirmed in the lab? A recent review published in the International Journal of Molecular Science makes the case for using information technology to translate bountiful multi-omic biomarker data into deep analyses for use by doctors within clinical practices.1 The review suggests that in prior years, the primary barrier between metablomic data and clinical practice was a deficit of proven hypotheses linking metabolites with functional outcomes in patients—but not any more.

Mature hypotheses within metabolic science are fully fleshed out and are ready to go in the clinic, provided that researchers and doctors have access to the right software suites which can access ‘omic data sets and run analyses for the purpose of clinical use. The review states that while integrating metablomic data into clinical practice has a large data load and requires advanced information technology, the possibilities for biomarker-driven clinical practice are simply too enticing to miss out on.

As an example of an application which would benefit the most from clinical integration of metablomic data, the review examined how doctors typically diagnose inborn errors of metabolism in newborns. For those who aren’t familiar, inborn errors of metabolism are enzyme deficits which cause bottlenecks in metabolism, disrupting biological activity.2 Traditional diagnosis of inborn errors of metabolism relies on individually characterized biomarkers which must be specifically tested for after identifying downstream pathologies like jaundice.3 This method of diagnosis is problematic because it means that the patient must be ill—sometimes seriously—before the inborn error of metabolism can be tracked down and compensated for with enzyme therapy or dietary restrictions. Given that inborn errors of metabolism are lifelong conditions that can be fatal to newborns before they are noticed, the traditional method of diagnosis is far from ideal4

Harnessing metablomic knowledge

Part of the historical disconnect between diagnosis of inborn errors of metabolism and metablomic knowledge is the logistical difficulty of biomarker testing. Before high throughput laboratory technology was the standard, few clinical practices had access to robust biomarker testing facilities. This is no longer the case; most hospitals can easily run massive metablomic panels using hardware that they have on hand.

The metabolic researchers of yesteryear understood that high-throughput biomarker processing and analysis would open the door to clinical use of their research as soon as the hardware and software requirements were commonplace, and so they continued to amass biomarker data even when the vast majority of it wouldn’t be usable in the clinic.5 It took roughly twenty years for hardware to catch up to their hard-fought metablomic data, and even now clinical practices may not be equipped with the powerful software necessary to crunch metablomic information.

For a clinic to make use of metablomic data in the context of diagnosis, they need to have the following capabilities:

  • Access to metablomic databases and also databases-in-progress
  • Laboratory hardware to process patient samples
  • Laboratory software to represent the data from processed patient samples
  • Clinical software to integrate the data from processed patient samples into the patient database
  • Laboratory software to represent working metablomic models of metabolic disorders or variations in metabolic activity
  • Laboratory software to compare data from processed patient samples with the spectrum of metablomic models
  • Clinical insight into downstream pathology or consequences of each metablomic model
  • Clinical addition of anonymized patient pathology data into metablomic databases to improve the body of knowledge about each metabolic profile

In essence, the current generation of clinical practice will become integrated with laboratory harvesting of metablomic data via software in a personalized though stereotyped fashion.6

New challenges for clinicians and experimentalists alike

Researchers may be accustomed to the scale of metablomic data sets, but clinicians are likely to be blindsided by the vast number of variables and the evidence supporting the relevancy of each one. Likewise, researchers may have difficulty integrating downstream pathology information into their metablomic models, as proteomic explanations for pathologies may be lacking. Both camps will have to handle vastly more data and different types of data than they’re used to.

For researchers, making use of data coming back to them from the clinic will require retooling their information technology capacities. Unlike purely metabolic data, metablomic researchers will have to grapple with areas outside of their wheel house, including proteomics and physiology. Rather than dabble in these massive fields, researchers will form collaborations with clinicians and other researchers in which data can be shared and analyzed collectively. This data exchange will be multidirectional and extremely vibrant; metablomics data will flow from the laboratory to the clinic, then pathology data will flow from the clinic to the metablomics researchers, who will disburse some of the data to specialists, who can then respond to both them and the clinical researchers upon further elucidation.

It goes without saying that this kind of vibrant exchange of data harvesting, data analysis and cooperative hypothesizing simply isn’t possible without an abundance of information technology resources which must play nicely together and be accessible by all parties. Traditional ELNs will be outclassed by the scale of metablomic data sets as well as the constantly fluctuating flow of information, and won’t be able to live up to the task. Thankfully, there is a powerful information technology platform that will be capable of becoming the nexus of metablomic data exchange from the laboratory to the clinic and back.   

Science Cloud is the data sharing, analysis, hypothesis collaboration and experimental planning software of the future’s laboratory-clinical complex. Using Science Cloud, you’ll be able to process metablomic data sets, share them with collaborators, and work together to improve human health outcomes. Contact us today to find out how you can use Science Cloud to start contributing to the future of clinical metablomics.

  1.  “Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations.” September 2016, https://www.ncbi.nlm.nih.gov/pubmed/27649151
  2.  “Mass Spectrometry and Stable Isotopes in Nutritional and Pediatric Research: Inborn Errors of Metabolism.” 2017, https://books.google.com/books?hl=en&lr=&id=HkQIDgAAQBAJ&oi=fnd&pg=PA258&dq=inborn+errors+of+metabolism&ots=4QHpfeg8Iw&sig=8igtPfBC7k_iJkPST1FgO5Megug#v=onepage&q=inborn%20errors%20of%20metabolism&f=false
  3. “Inborn Errors of Metabolism in Infancy: A Guide to Diagnosis.” December 1998, http://pediatrics.aappublications.org/content/102/6/e69.short
  4.  “Inborn Errors of Metabolism That Cause Sudden Infant Death: A Systematic Review with Implications for Population Neonatal Screening Programmes.” June 2016, http://www.karger.com/Article/Abstract/443874
  5. “Diagnosis of Inborn Errors of Metabolism from Blood Spots by Acylcarnitines and Amino Acids Profiling Using Automated Electrospray Tandem Mass Spectrometry.” May 1995, http://www.nature.com/pr/journal/v38/n3/abs/pr1995184a.html
  6. “A Next Generation Multiscale View of Inborn Errors of Metabolism.” January 2016, http://www.sciencedirect.com/science/article/pii/S1550413115006130