May 15, 2016 (Vol. 36, No. 10)

MaryAnn Labant

Genes, Transcripts, Proteins—All Have Come into Their “-Ome”

Metabolomics, the comprehensive evaluation of the products of cellular processes, can provide new findings and insight in a vast array of diseases and dysfunctions. Though promising, metabolomics lacks the standing of genomics or proteomics. It is, in a manner of speaking, the new kid on the “omics” block.

Even though metabolomics is still an emerging discipline, at least some quarters are giving it a warm welcome. For example, metabolomics is being advanced by the Common Fund, an initiative of the National Institutes of Health (NIH). The Common Fund has established six national metabolomics cores. In addition, individual agencies within NIH, such as the National Institute of Environmental Health Sciences (NIEHS), are releasing solicitations focused on growing more detailed metabolomics programs.

Whether metabolomic studies are undertaken with or without public support, they share certain characteristics and challenges. Untargeted or broad-spectrum studies are used for hypotheses generation, whereas targeted studies probe specific compounds or pathways. Reproducibility is a major challenge in the field; many studies cannot be reproduced in larger cohorts. Carefully defined guidance and standard operating procedures for sample collection and processing are needed.

While these challenges are being addressed, researchers are patiently amassing metabolomic insights in several areas, such as retinal diseases, neurodegenerative diseases, and autoimmune diseases. In addition, metabolomic sleuths are availing themselves of a growing selection of investigative tools.


The retina is responsible for capturing images from the visual field. Retinitis pigmentosa, which refers to a group of inherited diseases that cause retinal degeneration, causes a gradual decline in vision because retinal photoreceptor cells (rods and cones) die. Images on the left are courtesy of the National Eye Institute, NIH; image on the right is courtesy of Robert Fariss, Ph.D., and Ann Milam, Ph.D., National Eye Institute, NIH.

A Metabolomic Eye on Retinal Degeneration

The retina has one of the highest metabolic activities of any tissue in the body and is composed of multiple cell types. This fact suggests that metabolomics might be helpful in understanding retinal degeneration. At least, that’s what occurred to Ellen Weiss, Ph.D., a professor of cell biology and physiology at the University of North Carolina School of Medicine at Chapel Hill. To explore this possibility, Dr. Weiss began collaborating with Susan Sumner, Ph.D., director of systems and translational sciences at RTI International.

Retinal degeneration is often studied through the use of genetic-mouse models that mimic the disease in humans. In the model used by Dr. Weiss, cells with a disease-causing mutation are the major light-sensing cells that degenerate during the disease. Individuals with the same or a similar genetic mutation will initially lose dim-light vision then, ultimately, bright-light vision and color vision.

Wild-type and mutant phenotypes, as well as dark- and light-raised animals, were compared, since retinal degeneration is exacerbated by light in this genetic model. Retinas were collected as early as day 18, prior to symptomatic disease, and analyzed. Although data analysis is ongoing, distinct differences have emerged between the phenotypes as well as between dark- and light-raised animals.

“There is a clear increase in oxidative stress in both light-raised groups but to a larger extent in the mutant phenotype,” reports Dr. Weiss. “There are global changes in metabolites that suggest mitochondrial dysfunction, and dramatic changes in lipid profiles. Now we need to understand how these metabolites are involved in this eye disease and the relevance of these perturbations.”

For example, the glial cells in the retina that upregulate a number of proteins in response to stress to attempt to save the retina are as likely as the light-receptive neurons to undergo metabolic changes.

“One of the challenges in metabolomics studies is assigning the signals that represent the metabolites or compounds in the samples,” notes Dr. Sumner. “Signals may be ‘unknown unknowns,’ compounds that have never been identified before, or ‘known unknowns,’ compounds that are known but that have not yet been assigned in the biological matrix.”

Internal and external libraries, such as the Human Metabolome Dictionary, are used to match signals. Whether or not a match exists, fragmentation patterns are used to characterize the metabolite, and when possible a standard is obtained to confirm identity. To assist with this process, the NIH Common Fund supports Metabolite Standard Synthesis Cores (MSSCs). RTI International holds an MSSC contract in addition to being a NIH-designated metabolomics core.


Mitochondrial Dysfunction in Alzheimer’s Disease

Alzheimer’s disease (AD) is difficult to diagnose early due to its asymptomatic phase; accurate diagnosis occurs only in postmortem brain tissue. To evaluate familial AD, a rare inherited form of the disease, the laboratory of Eugenia Trushina, Ph.D., associate professor of neurology and associate professor of pharmacology at the Mayo Clinic, uses mouse models to study the disease’s early molecular mechanisms.

Synaptic loss underlies cognitive dysfunction. The length of neurons dictates that mitochondria move within the cell to provide energy at the site of the synapses. An initial finding was that very early on mitochondrial trafficking was affected reducing energy supply to synapses and distant parts of the cell.

During energy production, the major mitochondrial metabolite is ATP, but the organelle also produces many other metabolites, molecules that are implicated in many pathways. One can assume that changes in energy utilization, production, and delivery are associated with some disturbance.

“Our goal,” explains Dr. Trushina, “was to get a proof of concept that we could detect in the blood of AD patients early changes of mitochondria dysfunction or other changes that could be informative of the disease over time.”

A Mayo Clinic aging study involves a cohort of patients, from healthy to those with mild cognitive impairment (MCI) through AD. Patients undergo an annual battery of tests including cognitive function along with blood and cerebrospinal fluid sampling. Metabolic signatures in plasma and cerebrospinal fluid of normal versus various disease stages were compared, and affected mitochondrial and lipid pathways identified in MCI patients that progressed to AD.

“Last year we published on a new compound that goes through the blood/brain barrier, gets into mitochondria, and very specifically, partially inhibits mitochondrial complex I activity, making the cell resistant to oxidative damage,” details Dr. Trushina. “The compound was able to either prevent or slow the disease in the animal familial models.

“Treatment not only reduced levels of amyloid plaques and phosphorylated tau, it also restored mitochondrial transport in neurons. Now we have additional compounds undergoing investigation for safety in humans, and target selectivity and engagement.”

“Mitochondria play a huge role in every aspect of our lives,” Dr. Trushina continues. “The discovery seems counterintuitive, but if mitochondria function is at the heart of AD, it may provide insight into the major sporadic form of the disease.”


Distinguishing Types of Asthma

In children, asthma generally manifests as allergy-induced asthma, or allergic asthma. And allergic asthma has commonalities with allergic dermatitis/eczema, food allergies, and allergic rhinitis. In adults, asthma is more heterogeneous, and distinct and varied subpopulations emerge. Some have nonallergic asthma; some have adult-onset asthma; and some have obesity-, occupational-, or exercise-induced asthma.

Adult asthmatics may have markers of TH2 high verus TH2 low asthma (T helper 2 cell cytokines) and they may respond to various triggers—environmental antigens, occupational antigens, irritants such as perfumes and chlorine, and seasonal allergens. Exercise, too, can trigger asthma.

One measure that can phenotype asthmatics is nitric oxide, an exhaled breath biomarker. Nitric oxide is a smooth muscle relaxant, vasodilator, and bronchodilator that can have anti-inflammatory properties. There is a wide range of values in asthmatics, and a number of values are needed to understand the trend in a particular patient. L-arginine is the amino acid that produces nitric oxide when converted to L-citrulline, a nonessential amino acid.

According to Nicholas Kenyon, M.D., a pulmonary and critical care specialist who is co-director of the University of California, Davis Asthma Network (UCAN), some metabolomic studies suggest that there is a state of L-arginine depletion during asthma attacks or in severe asthma suggesting a lack of substrate to produce nitric oxide. Dr. Kenyon is conducting clinical work on L-arginine supplementation in a double-blind cross-over  intervention trial of L-arginine versus placebo. The 50-subject study in severe asthmatics should be concluded in early 2017.

Many new biologic therapies are coming to market to treat asthma; it will be challenging to determine which advanced therapy to provide to which patient. Therapeutics mostly target severe asthma populations and are for patients with evidence of higher numbers of eosinophils in the blood and lung, which include anti-IL-5 and (soon) anti-IL-13, among others.


Tools Development

Mass spectrometry (MS) is the gold standard in metabolomics and lipidomics. But there is a limit to what accurate mass and resolution can achieve. For example, neither isobaric nor isomeric species are resolvable solely by MS. New orthogonal analytical tools will allow more confident identifications.

To improve metabolomics separations before MS detection, a post-ionization separation tool, like ion mobility, which is currently used to support traditional UPLC-MS and MS imaging metabolomics protocols, becomes useful. The collision-cross section (CCS), which measures the shape of molecules, can be derived, and it can be used as an additional identification coordinate.

Other new chromatographic tools are under development, such as microflow devices and UltraPerformance Convergence Chromatography (UPC2), which uses liquid CO2 as its mobile phase, to enable new ways of separating chiral metabolites. Both UPC2 and microflow technologies have decreased solvent consumption and waste disposal while maintaining UPLC-quality performance in terms of chromatographic resolution, robustness, and reproducibility.

Informatics tools are also improving. In the latest versions of Waters’ Progenesis software, typical metabolomics identification problems are resolved by allowing interrogation of publicly available databases and scoring according to accurate mass, isotopic pattern, retention time, CCS, and either theoretical or experimental fragments.

MS imaging techniques, such as MALDI and DESI, provide spatial information about the metabolite composition in tissues. These approaches can be used to support and confirm traditional analyses without sample extraction, and they allow image generation without the use of antibodies, similar to immunohistochemistry.

“Ion-mobility tools will soon be implemented for routine use, and the use of extended CCS databases will help with metabolite identification,” comments Giuseppe Astarita Ph.D., principal scientist, Waters. “More applications of ambient ionization MS will emerge, and they will allow direct-sampling analyses at atmospheric pressure with little or no sample preparation, generating real-time molecular fingerprints that can be used to discriminate among phenotypes.”


Waters is developing metabolomics applications that use multivariate statistical methods to highlight compounds of interest. Typically these applications combine separation procedures, accomplished by means of liquid chromatography or gas chromatography (LC or GC), with detection methods that rely on mass spectrometry (MS). To support the identification, quantification, and analysis of LC-MS data, the company provides bioinformatics software. For example, Progenesis QI software can interrogate publicly available databases and process information about isotopic patterns, retention times, and collision cross-sections.

Microflow Technology

Microflow technology offers sensitivity and robustness. For example, at the Proteomics and Metabolomics Facility, Colorado State University, peptide analysis was typically performed using nanoflow chromatography; however, nanoflow chromatography is slow and technically challenging. Moving to microflow offered significant improvements in robustness and ease-of-use and resulted in improved chromatography without sacrificing sensitivity.

Conversely, small molecule applications were typically performed with analytical-scale chromatography. While this flow regime is extremely robust and fast, it can sometimes be limited in sensitivity. Moving to microflow offered significant improvements in sensitivity, 5- to 10-fold depending on the compound, without sacrificing robustness.

But broad-scale microflow adoption is hampered by a lack of available column chemistries and legacy HPLC or UPLC infrastructure that is not conducive to low-flow operation.

“We utilize microflow technology on all of our tandem quadrupole instruments for targeted quantitative assays,” says Jessica Prenni, Ph.D., director, Proteomics and Metabolomics Facility, Colorado State University. “All of our peptide quantitation is exclusively performed with microflow technology, and many of our small molecule assays. Application examples include endocannabinoids, bile acids and plant phytohormone panels.”

Compound annotation and comparability and transparency in data processing and reporting is a challenge in metabolomics research. Multiple groups are actively working on developing new tools and strategies; common best practices need to be adopted.

The continued growth of open-source spectral databases and new tools for spectral prediction from compound databases will dramatically impact the ability for metabolomics to result in novel discoveries. The move to a systems-level understanding through the combination of various omics data also will have a huge influence and be enabled by the continued development of open-source and user-friendly pathway-analysis tools.


The Colorado State University Proteomics and Metabolomics Facility utilizes microflow technology on all of its tandem-quadrupole instruments for targeted quantitative assays. Peptide quantitation is exclusively performed with microflow technology, along with many small molecule assays. This image shows a microscale separations device, the Waters iKey, being inserted into a mass spectrometer (MS). According to Colorado State scientists, the iKey/MS system performs at least as well as traditional nanospray systems in terms of sampling efficiency, ionization efficiency, and chromatographic performance.

























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