Metabolomic Fingerprinting: Challenges and Opportunities

Kosmides, Alyssa K.; Kamisoglu, Kubra; Calvano, Steve E.; Corbett, Siobhan; Androulakis, Ioannis P.

doi:10.1615/critrevbiomedeng.2013007736

Cited by 121 publications

(104 citation statements)

References 92 publications

(206 reference statements)

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“…Some of the least frequently used samples include cerebrospinal fluid, colostrum, semen, adipose tissue, kidney and kidney perfusate, feces, amniotic fluid, bile and liver (Table 3). The relatively low number of papers reporting data on tissue metabolomics likely reflects the challenges and costs of animal culling especially for larger livestock, sample collection, and the need to rapidly perform metabolic quenching via liquid nitrogen (immediately after surgery or necropsy) to obtain useful tissue samples for metabolite analysis [73; 74]. …”

Section: Resultsmentioning

confidence: 99%

Livestock metabolomics and the livestock metabolome: A systematic review

Goldansaz¹,

Guo²,

Sajed³

et al. 2017

PLoS ONE

244

236

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Metabolomics uses advanced analytical chemistry techniques to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids. The ability to rapidly detect and quantify hundreds or even thousands of metabolites within a single sample is helping scientists paint a far more complete picture of system-wide metabolism and biology. Metabolomics is also allowing researchers to focus on measuring the end-products of complex, hard-to-decipher genetic, epigenetic and environmental interactions. As a result, metabolomics has become an increasingly popular “omics” approach to assist with the robust phenotypic characterization of humans, crop plants and model organisms. Indeed, metabolomics is now routinely used in biomedical, nutritional and crop research. It is also being increasingly used in livestock research and livestock monitoring. The purpose of this systematic review is to quantitatively and objectively summarize the current status of livestock metabolomics and to identify emerging trends, preferred technologies and important gaps in the field. In conducting this review we also critically assessed the applications of livestock metabolomics in key areas such as animal health assessment, disease diagnosis, bioproduct characterization and biomarker discovery for highly desirable economic traits (i.e., feed efficiency, growth potential and milk production). A secondary goal of this critical review was to compile data on the known composition of the livestock metabolome (for 5 of the most common livestock species namely cattle, sheep, goats, horses and pigs). These data have been made available through an open access, comprehensive livestock metabolome database (LMDB, available at http://www.lmdb.ca). The LMDB should enable livestock researchers and producers to conduct more targeted metabolomic studies and to identify where further metabolome coverage is needed.

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Section: Resultsmentioning

confidence: 99%

Livestock metabolomics and the livestock metabolome: A systematic review

Goldansaz¹,

Guo²,

Sajed³

et al. 2017

PLoS ONE

244

236

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show abstract

“…Moreover, the clinical and biological characteristics of the patients (such as age, sex, weight, diet or chemistry information) might affect the spectral composition of the samples as well as the outcome of interest adding potential discriminant sources of data [15]. Hence, considering the nature of the metabolomic data and the possible usefulness of additional sources of data, we propose to extend our previous work by adding 2D rules (i.e., two distinct conditions of variables) in order to integrate the interactions between the variables.…”

Section: Introductionmentioning

confidence: 99%

Rule-Mining for the Early Prediction of Chronic Kidney Disease Based on Metabolomics and Multi-Source Data

Luck

Bertho

Bateson³

et al. 2016

PLoS ONE

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1H Nuclear Magnetic Resonance (NMR)-based metabolic profiling is very promising for the diagnostic of the stages of chronic kidney disease (CKD). Because of the high dimension of NMR spectra datasets and the complex mixture of metabolites in biological samples, the identification of discriminant biomarkers of a disease is challenging. None of the widely used chemometric methods in NMR metabolomics performs a local exhaustive exploration of the data. We developed a descriptive and easily understandable approach that searches for discriminant local phenomena using an original exhaustive rule-mining algorithm in order to predict two groups of patients: 1) patients having low to mild CKD stages with no renal failure and 2) patients having moderate to established CKD stages with renal failure. Our predictive algorithm explores the m-dimensional variable space to capture the local overdensities of the two groups of patients under the form of easily interpretable rules. Afterwards, a L2-penalized logistic regression on the discriminant rules was used to build predictive models of the CKD stages. We explored a complex multi-source dataset that included the clinical, demographic, clinical chemistry, renal pathology and urine metabolomic data of a cohort of 110 patients. Given this multi-source dataset and the complex nature of metabolomic data, we analyzed 1- and 2-dimensional rules in order to integrate the information carried by the interactions between the variables. The results indicated that our local algorithm is a valuable analytical method for the precise characterization of multivariate CKD stage profiles and as efficient as the classical global model using chi2 variable section with an approximately 70% of good classification level. The resulting predictive models predominantly identify urinary metabolites (such as 3-hydroxyisovalerate, carnitine, citrate, dimethylsulfone, creatinine and N-methylnicotinamide) as relevant variables indicating that CKD significantly affects the urinary metabolome. In addition, the simple knowledge of the concentration of urinary metabolites classifies the CKD stage of the patients correctly.

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“…In‐silico models are increasingly used to elucidate the effect of drugs on metabolic phenotypes before moving on to in vivo testing. Gut microbiota associated with specific diseases can be better understood with the help of metabolomics, which can contribute to drug development . Despite all these challenges, metabolomics along with the other “omics” facilitates the development of more efficient and precise biomarkers important for the understanding of disease progression and drug development.…”

Section: Discussionmentioning

confidence: 99%

Metabolomics toward personalized medicine

Jacob¹,

Lopata

Dasouki³

et al. 2017

Mass Spectrometry Reviews

247

174

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Metabolomics, which is the metabolites profiling in biological matrices, is a key tool for biomarker discovery and personalized medicine and has great potential to elucidate the ultimate product of the genomic processes. Over the last decade, metabolomics studies have identified several relevant biomarkers involved in complex clinical phenotypes using diverse biological systems. Most diseases result in signature metabolic profiles that reflect the sums of external and internal cellular activities. Metabolomics has a major role in clinical practice as it represents >95% of the workload in clinical laboratories worldwide. Many of these metabolites require different analytical platforms, such as Nuclear Magnetic Resonance (NMR), Mass Spectrometry (MS), and Ultra Performance Liquid Chromatography (UPLC), while many clinically relevant metabolites are still not routinely amenable to detection using currently available assays. Combining metabolomics with genomics, transcriptomics, and proteomics studies will result in a significantly improved understanding of the disease mechanisms and the pathophysiology of the target clinical phenotype. This comprehensive approach will represent a major step forward toward providing precision medical care, in which individual is accounted for variability in genes, environment, and personal lifestyle. In this review, we compare and evaluate the metabolomics strategies and studies that focus on the discovery of biomarkers that have "personalized" diagnostic, prognostic, and therapeutic value, validated for monitoring disease progression and responses to various management regimens.

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Metabolomic Fingerprinting: Challenges and Opportunities

Cited by 121 publications

References 92 publications

Livestock metabolomics and the livestock metabolome: A systematic review

Livestock metabolomics and the livestock metabolome: A systematic review

Rule-Mining for the Early Prediction of Chronic Kidney Disease Based on Metabolomics and Multi-Source Data

Metabolomics toward personalized medicine

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