Metabolomics is an upgrading branch in omics technology ,today and has been used with other omics technology.

1. Dr Soniya Kasliwal

2. Metabolomics
Metabolomics is the study of cells by measuring profiles of all, or a large number, of
their metabolites.
Metabolomics was originally proposed as a method of functional genomics but its
utility extends well beyond that—it is useful whenever an assessment of changes in
metabolite levels is important.
Metabolomics is used for comparing mutants, assessing responses to
environmental stress , studying global effects of genetic manipulation , comparing
different growth stages , toxicology , drug discovery, nutrition , cancer, diabetes and
natural product discovery .
Metabolite profiling, Whether targeting specific metabolite classes or
untargeted, can also be applied as a tool in systems biology , where metabolite
snapshots are used to study cellular dynamics through mathematical models

4. Approaches used in metabolomics
There are three major approaches used in metabolomics studies:
(i) Targeted analysis
(ii) Metabolite profiling
(iii) Metabolic fingerprinting
TARGETEDANALYSIS
Targeted analysis is the most developed analytical approach in metabolomics. It is used to
measure the concentration of a limited number of known metabolites precisely.
METABOLIC FINGERPRINTING
Metabolic fingerprinting considers total profile, or fingerprint, as a unique pattern
characterizing a snapshot of the metabolism in a particular cell line or tissue
METABOLITE PROFILING
Metabolite profiling is the measurement of the levels of a metabolite set in a sample.
Metabolite profiling is a well-established activity in biomedical sciences, used routinely
on biological fluids as a form of diagnosis that aids in characterizing the health state of
a patient.

6. Liu and Locasale.,2017

8. Metabolite extraction
• The first requirement for the extraction protocol is comprehensiveness,
i.e. the extract should represent as large number of the cellular
metabolites as possible.
• The other prime requirement is that enzymatic activity be halted for the
duration of the extraction process to prevent possible degradation or
inter-conversion of metabolites.
• For small-molecule metabolites, protein precipitation or liquid–liquid
extraction are the most commonly used methods. Polar organic solvents
such as methanol, acetonitrile, or isopropanol are used to extract mostly
polar metabolites, whereas relatively non-polar solvents such as hexane,
chloroform, or methyl tertiary butyl ether, or combinations of polar and
non-polar solvents, are used to extract lipids .
• Occasionally, acid is added to the extraction solvent to preserve the
stability of certain compounds, such as acyl-coenzyme A (CoA)
compounds.

10. Nuclear magnetic resonance (NMR)
• NMR is well-suited to
metabolomics studies as it
can uniquely identify and
simultaneously quantify a
wide range of organic
compounds in the
micromolar range.
• NMR has been extensively
used for metabolite
fingerprinting, profiling and
metabolic flux analysis.
• low sensitivity

11. Mass spectrometry
• MS involves first ionizing the molecules (i.e., adding a positive or negative charge
to them) and then moving these molecules through electric fields where they
are eventually analyzed. At each time point, the data are recorded as mass
spectra comprising the mass-to-charge ratio (m/z) for each intact ion and
corresponding intensity. Each ion has a retention time (RT) and mass spectrum,
the values of which are dependent on the instrumental setup.

12. • Due to its high sensitivity and wide range of covered metabolites
• MS can be used to analyse biological samples either directly via direct-injection MS
or following chromatographic or electrophoretic separation.
• Direct-injection MS, especially when using a high-resolution mass spectrometer,
provides a very rapid technique to analyse large number of metabolites, and
therefore is extensively used for metabolic fingerprinting and metabolite profiling.
• direct infusion also has drawbacks, mostly due to a phenomenon known as co-
suppression
Two commonly used approaches for MS analysis are multiple reaction monitoring
(MRM) and high-resolution MS (HRMS). MRM experiments are usually conducted
on a triple- quadrupole mass spectrometer . The first quadrupole filters ions
(parent ion) within a defined molecular weight (usually with a resolution of 1
atomic mass unit). The second quadrupole fragments the molecules that have
been selected and the third quadrupole selects characteristic fragments.
Therefore, before any data acquisition, the parent and fragment ions must be
defined and the optimized energy for the fragmentation and RT for each
metabolite is needed.

13. Gas chromatography – mass spectrometry (GC -MS)
• The most mature technology for
rapid metabolite profiling is gas
chromatography coupled with
electron impact (EI) quadrupole
or time-of flight (TOF) MS (GC–
MS).
• A significant advantage of GC–MS
with EI ionization is the availability
of many searchable mass spectral
libraries.
• 2005 NIST/EPA/NIH Mass Spectral
Library
http://www.nist.gov/srd/nist1.ht
m) containing 190825 spectra
• 7th
edition of the Wiley Registry of
Mass Spectral Data
(http://www.wiley.com), which
contains 338 000 EI mass spectra
(or over 460 000 EI mass spectra
combined with NIST library)

14. Liquid chromatography- mass spectrometry (LC -MS)
• LC–MS is being increasingly used in
metabolomics applications due to
its high sensitivity and a range in
analyte polarity and molecular mass
wider than GC–MS. Liquid
chromatography, and specifically
high-performance liquid
chromatography (HPLC), is a mature
technique that combines high
resolution and analytical flexibility.
• LC–MS has one advantage over GC–
MS, in that, there is largely no need
for chemical derivatization of
metabolites (which is required for
analysis of non-volatile compounds
by GC–MS) soft ionization methods
like electrospray (ESI)

15. Capillary electrophoresis –mass spectrometry (CE-MS)
CE–MS provides several important
advantages over other separation
techniques. It has very high resolving
power with plate numbers of 100
000–1,000,000, very small sample
requirement with average injection
volume ranging from 1 to 20 nl, and
short analysis time. CE has been used
for both targeted and non-targeted
analysis of metabolites , including
analysis of inorganic ions, organic
acids , amino acids ,nucleotides and
nucleosides , vitamins , thiols,
carbohydrates and peptides .

17. BIOINFORMATICS FOR METABOLOMICS
• Data and information
• Management, raw analytical data processing, metabolomics

19. AMDIS (automated mass spectral deconvolution and identification system,
http://chemdata.nist.gov/ mass-spc/amdis/) software that utilizes well described
algorithms, has proven to be extremely useful in processing of the GC–MS data , but
its applicability to LC–MS or CE–MS is somewhat limited.
ESI–LC–MS data can be processed using component detection algorithm (CODA) or
‘windowed mass selection method’ (WMSM)
MZmine (http://mzmine.sourceforge.net/ index.shtml), a platform-independent
software for processing of the LC–MS data in metabolomics and proteomics
applications

20. Metabolic pathway databases and pathway viewers
• KEGG (http://www.genome.ad.jp/kegg/)
• MetaCyc (http://metacyc.org/),
• AraCyc (http://www.Arabidopsis.org/tools/aracyc/)
• MapMan (http://gabi.rzpd.de/projects/MapMan/)
• KaPPA-View (http://kpv.kazusa.or.jp/kappa-view/)
The data model for plant metabolomics experiments
• ArMet (http://www.armet.org/)
Functional genomics databases
• MetNet (http://metnet.vrac.iastate.edu/)
• DOME (http://medicago.vbi.vt.edu).
DOME is intended for the management of metabolomic, proteomic and transcriptomic data
.DOME also incorporates a suite of tools for statistical data analysis and data visualization
(e.g. BROME, standing for BRowser for OMEs).

23. Analysis of a Metabolomics Experiment
• Nevertheless, it is not humanly possible to process the entirety of the data from
intuition alone. Computational tools are needed for further analysis. Software for
feature extraction often include additional data analysis functions, such as
principal components analysis and hierarchical clustering, and numerous
statistical tests and data visualization plots to identify the largest-changing
features and specific signatures in the data.
• Pathway-enrichment analyses, which are commonly used in gene expression
analysis, can also be used with metabolomics data to identify affected metabolic
pathways.
• For example, in a recent study pathway-enrichment analysis of metabolomics data
showed that the methionine cycle was altered before other parts of the network
were affected.
• Further, network mappings can contribute to systems-level analysis on integration
with other ‘omics’ data sets.

24. • One commonly used network mapping tool is Cytoscape and the metabolism
plugin for Cytoscape, called Metscape .
• There are other software that employ the Cytoscape platform, such as MetaMapR
and GAM, which may generate metabolic networks based on enzymatic
transformations, metabolite structural similarity, mass spectral similarity, or
empirical associations.
• The recently developed software PIUMet facilitates unknown metabolite
identification by network integration of untargeted metabolomics.

25. Recent biological insights obtained from metabolomics

26. • Metabolomics has also been widely used in drug discovery and drug action. An
extensive drug database, DrugBank (http://www.drugbank.ca/), now exists. Over
8000 drug entries are recorded in this database and related information, such as
known drug metabolism and known or proposed drug targets, is also provided.
• Semi-targeted metabolomic analyses have led to a deeper understanding of the
etiology of aging, cardiometabolic disease, and cancer.
• Meanwhile, metabolomics is helping researchers gain new knowledge of drug
action. For example, through a metabolomics study Ser et al. showed that 5-
fluorouracil (5-FU), a commonly used chemotherapy drug, caused overproduction
of the nucleotide deoxyuridine, which was also measured in 5-FU-treated
mouse serum. The overflow of this nucleotide could potentially be used as a
biomarker for positive response to 5-FU treatment.

27. • Semi-targeted metabolomic analyses have also provided insights into human
population genetics and the origins of metabolic traits.
CASE STUDY
• Genome-wide association studies (GWASs) for variation in metabolite levels were
conducted on human blood from 2820 individuals. Surprisingly, numerous genetic
loci associated with blood metabolite concentrations were discovered and the
locations of these loci, for the most part, were in proximity to a gene encoding a
metabolic enzyme involved in the production or consumption of the given
metabolite. This analysis provided evidence that some of the metabolome
variation observed across individuals may be directly encoded in the genome.
Such studies may be valuable for the understanding of personalized nutrition,
which is still in its infant stages

28. Concluding Remarks: Challenges and Future Directions in Metabolomics