Reporter and Curator: Dr. Sudipta Saha, Ph.D.
Molecular biomarkers could detect biochemical changes associated with disease processes. The key metabolites have become an important part for improving the diagnosis, prognosis, and therapy of diseases. Because of the chemical diversity and dynamic concentration range, the analysis of metabolites remains a challenge. Assessment of fluctuations on the levels of endogenous metabolites by advanced NMR spectroscopy technique combined with multivariate statistics, the so-called metabolomics approach, has proved to be exquisitely valuable in human disease diagnosis. Because of its ability to detect a large number of metabolites in intact biological samples with isotope labeling of metabolites using nuclei such as H, C, N, and P, NMR has emerged as one of the most powerful analytical techniques in metabolomics and has dramatically improved the ability to identify low concentration metabolites and trace important metabolic pathways. Multivariate statistical methods or pattern recognition programs have been developed to handle the acquired data and to search for the discriminating features from biosample sets. Furthermore, the combination of NMR with pattern recognition methods has proven highly effective at identifying unknown metabolites that correlate with changes in genotype or phenotype. The research and clinical results achieved through NMR investigations during the first 13 years of the 21st century illustrate areas where this technology can be best translated into clinical practice.
In the last decade, proteomics and metabolomics have contributed substantially to our understanding of cardiovascular diseases. The unbiased assessment of pathophysiological processes without a priori assumptions complements other molecular biology techniques that are currently used in a reductionist approach. A discrete biological function is very rarely attributed to a single molecule; more often it is the combined input of many proteins. In contrast to the reductionist approach, in which molecules are studied individually, “omics” platforms allow the study of more complex interactions in biological systems. Combining proteomics and metabolomics to quantify changes in metabolites and their corresponding enzymes will advance our understanding of pathophysiological mechanisms and aid the identification of novel biomarkers for cardiovascular disease.
Marginal deficiency of vitamin B-6 is common among segments of the population worldwide. Because pyridoxal 5′-phosphate serves as a coenzyme in the metabolism of amino acids, carbohydrates, organic acids, and neurotransmitters, as well as in aspects of one-carbon metabolism, vitamin B-6 deficiency could have many effects. NMR spectral features of selected metabolites indicated that vitamin B-6 restriction significantly increased the ratios of glutamine/glutamate and 2-oxoglutarate/glutamate and tended to increase concentrations of acetate, pyruvate, and trimethylamine-N-oxide. Tandem MS showed significantly greater plasma proline after vitamin B-6 restriction, but there were no effects on the profile of 14 other amino acids and 45 acylcarnitines. These findings demonstrate that marginal vitamin B-6 deficiency has widespread metabolic perturbations and illustrate the utility of metabolomics in evaluating complex effects of altered vitamin B-6 intake.
Hepatocellular carcinoma is one of the most common malignancies worldwide, and it has a poor prognosis due to its rapid development and early metastasis. An understanding of tumor metabolism would be helpful for the clinical diagnosis and therapy of hepatocellular carcinoma. To investigate the metabolic features of hepatocellular carcinoma, a non-targeted metabolic profiling strategy based on liquid chromatography-mass spectrometry was performed. The results revealed multiple metabolic changes in the tumor, and the principal changes included elevated glycolysis, inhibition of the tricarboxylic acid cycle, accelerated gluconeogenesis and β-oxidation for energy supply and down-regulated Δ-12 desaturase. Furthermore, increased levels of anti-oxidative molecules, such as glutathione, and decreased levels of inflammatory-related polyunsaturated fatty acids and the phospholipase A2 enzyme were also observed. The differential metabolites found in the tissue were tested in serum samples from the chronic hepatitis, cirrhosis and hepatocellular carcinoma patients. The combination of betaine and propionylcarnitine was confirmed to have a good diagnostic potential to distinguish hepatocellular carcinoma from chronic hepatitis and cirrhosis. External validation of cirrhosis and hepatocellular carcinoma serum samples further shows the combination biomarker is useful for hepatocellular carcinoma diagnosis.
Current diagnostic techniques have increased the detection of prostate cancer; however, these tools inadequately stratify patients to minimize mortality. Recent studies have identified a biochemical signature of prostate cancer metastasis, including increased sarcosine abundance. Prostate tumors had significantly altered metabolite profiles compared to cancer-free prostate tissues, including biochemicals associated with cell growth, energetics, stress, and loss of prostate-specific biochemistry. Many metabolites were further associated with clinical findings of aggressive disease. Aggressiveness-associated metabolites stratified prostate tumor tissues with high abundances of compounds associated with normal prostate function (e.g., citrate and polyamines) from more clinically advanced prostate tumors. These aggressive prostate tumors were further subdivided by abundance profiles of metabolites including NAD+ and kynurenine. When added to multiparametric nomograms, metabolites improved prediction of organ confinement and 5-year recurrence. These findings support and extend earlier metabolomic studies in prostate cancer and studies where metabolic enzymes have been associated with carcinogenesis and/or outcome. Furthermore, it suggests that panels of analytes may be valuable to translate metabolomic findings to clinically useful diagnostic tests.
Source References:
http://www.ncbi.nlm.nih.gov/pubmed/23828598
http://www.ncbi.nlm.nih.gov/pubmed/23827455
http://www.ncbi.nlm.nih.gov/pubmed/23776431
http://www.ncbi.nlm.nih.gov/pubmed/23824744
http://www.ncbi.nlm.nih.gov/pubmed/23824564
Published related articles on this open access online scientific journal:
World of Metabolites: Lawrence Berkeley National Laboratory developed Imaging Technique for their Capturing
Aviva Lev-Ari, PhD, RN 06/13/2013
Metabolite Identification Combining Genetic and Metabolic Information: Genetic association links unknown metabolites to functionally related genes
Aviva Lev-Ari, PhD, RN 10/22/2012
Metabolomics: its applications in food and nutrition research
Dr. Sudipta Saha, Ph.D., RN 05/12/2013
Increased Cardiovascular Risk: Intestinal Microbial Metabolism
Aviva Lev-Ari, PhD, RN 05/07/2013
Late Onset of Alzheimer’s Disease and One-carbon Metabolism
Dr. Sudipta Saha, Ph.D., RN 05/06/2013
https://pharmaceuticalintelligence.com/2013/05/06/alzheimers-disease-and-one-carbon-metabolism/
Importance of Omega-3 Fatty Acids in Reducing Cardiovascular Disease
Dr. Sudipta Saha, Ph.D., RN 04/29/2013
Mitochondrial Metabolism and Cardiac Function
Larry H Bernstein, MD, FACP, RN 04/14/2013
https://pharmaceuticalintelligence.com/2013/04/14/mitochondrial-metabolism-and-cardiac-function/
How Methionine Imbalance with Sulfur-Insufficiency Leads to Hyperhomocysteinemia
Larry H Bernstein, MD, FACP, RN 04/04/2013
https://pharmaceuticalintelligence.com/2013/04/04/sulfur-deficiency-and-hyperhomocusteinemia/
Ca2+ Signaling: Transcriptional Control
Larry H Bernstein, MD, FACP, RN 03/06/2013
https://pharmaceuticalintelligence.com/2013/03/06/ca2-signaling-transcriptional-control/
Calcium (Ca) supplementation (>1400 mg/day): Higher Death Rates from all Causes and Cardiovascular Disease in Women
Aviva Lev-Ari, PhD, RN 02/19/2013
A Second Look at the Transthyretin Nutrition Inflammatory Conundrum
Larry H Bernstein, MD, FACP, RN 12/03/2013
Pancreatic Cell News: Beta cell dysfunction attributed to saturated non-esterified fatty acid palmitate
Aviva Lev-Ari, PhD, RN 11/27/2012
Metabolic drivers in aggressive brain tumors
Prabodh Kandala, PhD, RN 11/11/2012
https://pharmaceuticalintelligence.com/2012/11/11/metabolic-drivers-in-aggressive-brain-tumors/
Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets
Larry H Bernstein, MD, FACP, RN 10/22/2012
Expanding the Genetic Alphabet and Linking the Genome to the Metabolome
Larry H Bernstein, MD, FACP, RN 09/24/2012
Risks of Hypoglycemia in Diabetics with CKD
Larry H Bernstein, MD, FACP, RN 08/01/2012
https://pharmaceuticalintelligence.com/2012/08/01/risks-of-hypoglycemia-in-diabetics-with-ckd/
Nitric Oxide in bone metabolism
Aviral Vatsa, PhD, MBBS, RN 07/16/2012
https://pharmaceuticalintelligence.com/2012/07/16/nitric-oxide-in-bone-metabolism/
2012pharmaceutical
Aashir Awan, Phd
Alan F. Kaul, PharmD., MS, MBA, FCCP
alexcrystal6
amnondanzig1724
anamikasarkar
apreconasia
aviralvatsa
David Orchard-Webb, PhD
danutdaagmailcom
Demet Sag, Ph.D., CRA, GCP
Dror Nir
evelinacohn
Gail S Thornton
Irina Robu
jdpmd
kellyperlman
Ed Kislauskis
larryhbern
lmulligan13gmailcom
marzankhan
megbaker58
pkandala
Rosalind Codrington, PhD
ritusaxena
Rick Mandahl
sjwilliamspa
stuartlpbi
Dr. Sudipta Saha
tildabarliya
zraviv06
zs22
Dr. Saha, for the HCC and prostate cancer studies was the metabolomic profile of the tumor or patient’s plasma analyzed? As I recall early metabolomic studies of patient plasma had showed differences between normal and cancer patients for tumors such as breast and colon, however it seemed it was hard to use these techniques to provide predictive value because of reduced sensitivity and specificity. How has the sensitivity/specificity of the metabolomic analyses improved over time? Or ha specificity improved as a result of increased patient sampling? I also recall there were a multitude of issues regarding sample handling across different investigator sites which confounded the utulization of metabolomics as a predictive/prognostic test.
The HCC is a good classical framework. Increased gluconeogenesis is coupled with “autocannabolization of LBM” and anasarca leading to “cancer cachexia”. The use of beta-oxidation suggests the emphasis of TCA cycle for tumor growth. The decreased levels of inflammatory PUFAs would be linoleic and arachidonic acid, which should result in a favorable ratio of n-3/n-6. Whether this has anything to do with the blocking of apoptosis would be interesting. Betaine and especially propionyl carnitine are of special interest. Carnitine itself is important for the heart,
The prostate cancer is progressing. NAD+ alteration is not comprehensibe without looking at the redox potential that involves NAD..NADH…NADP…NADPH.
Thank you for sharing this. I am learning how recent discoveries in the field of metabolomics is revolutionarizing how one can look into cellular mechanisms that have relevance in diseases diagnoasis and prognosis.
Dr. Saha,
1. Please conduct a search on the journal for metabolite, add them all to follow the references, as published related articles on this Open Access Online Scientific Journal
2. Pl. Address Dr. Williams points in the comment above
3. Pl. Consider to develop Three articles from this Introductory level article
3.1 Metabolomics and Cardiovascular Disorders
3.2 Metabolomics and HCC
3.3 Metabolomics and Prostate Cancer
I believe, however, that the most relevance relations will be with
3.4 Renal Cancer
3.5 Gastrointestinal
Please e-mail me status of changes I requested to last two posts.
Thank you for bringing focus to Metabolomics, this is the e-Book you are working on, Now, as content creator.
Dr. Saha,
Thank you for conducting these searches.
NOT ALL CITED ABOVE ARE RELEVANT TO THIS ARTICLE, please delete the articles that came up in your search and you do not think on a second round that they should remain. Please be judicious in your inclusion and please eliminate the too remove articles.
Always as we conduct a search, we apply CRITERIA in sorting out the search result, we do not dump lists of 70 articles out of the 1,100 articles in the Journal.
Give it a second round, please.
Reply to Dr. Williams comments and to mine on changes to past posts.
Thank you
I have checked the references and would not consider any of them unrelated. The references in sequence form the composition of the article. I think that the references should be identified with respect to their placement in the article – standard literary citation. In addition, the pubmed location is not adequate:
Title
Author
source
The writing itself is and has been quite fluid. The understanding of the subject matter, or withholding of explanatory material is a deficiency in critical evaluation of one’s message. I have a bias in metabolomics that I hold in check. I sent sufficient material to the author to become more familiar with the subject, and I think he has taken an interest.
The major focus for the last 2 decades has been the genome. A half century of monumental biochemistry was no longer mainstream. At the same time, the cost of pharmaceuticals and the use of 8-12 medications by many elderly patients (and not so elderly) with a boom in “wellness coaching”, delivered a multi-billion dollars “alternative” natural source drug industry. Some of it is well founded, and much is not held to rigorous trials.
Metabolomics can be traced back 150 years to work with oxygen and respirometry. However, the advances in spectroscopy have leaped forward only in the last 30 years. The methods were initially cumbersome, required extraction, and there had to be signatures for each analyte. You have to understand that it is necessary to pick out an analyte by position, and quantitate by peak height. There are also issues related to separation based on solubility.
Now we have to project that we have a mixture of “analytes”, and some might require derivatization. The first step is sample preparation, the next is separation, and the third is detection, the last being identification.
We aren’t finished! This is quite labor intensive. You can’t develop this to maturity without being able to put through in sequence – 100 specimens per hour.
Even given this capability, we have to understand the cellular biology. If we have no notion of how an adipocyte differs from a liver cell, or a thyroid cell differs from a pneumocyte, we can’t get very far.