Introduction to proteomics, techniques to study proteomics such as protein electrophoresis, chromatography and mass spectrometry and protein database analysis, case studies derived from scientific literature including comparisons between healthy and diseased tissues, new approaches to analyse metabolic pathways, comprehensive analysis of protein-protein interactions in different cell types.

1. Metabolomics, Substrate
channeling, Targeted and
Untargeted Metabolomics
Dr. Shiny C Thomas, Department of Biosciences, ADBU

2. Definitions and Background
Metabolome = the total metabolite pool
- All low molecular weight (MW < 1000 Da) organic molecules in a
sample such as a leaf, fruit, or tuber.
Peptides
Oligonucleotides
Sugars
Nucleosides
Organic acids
Ketones
Aldehydes
Amines
Amino acids
Lipids
Steroids
Alkaloids
Drugs (xenobiotics)

3. Metabolomics = high-throughput analysis of metabolites
Definitions and Background
Metabolomics is the simultaneous ('multiparallel') measurement of the
levels of a large number of cellular metabolites (typically several hundred).
Many of these are not identified (i.e. are just peaks in a profile).

4. Metabolomics = high-throughput analysis of metabolites
Definitions and Background
Metabolomics analysis is like a snapshot, showing which compounds are
present and at what relative levels at a specific time point.
More generally, metabolomics refers to a holistic analytical approach to
metabolism that is not guided by specific hypotheses. Instead, metabolomics
sets out to determine how (in principle, all) metabolite levels respond to
genetic or environmental changes and, from the data, to generate new
hypotheses.

5. Definitions and Background

6. Definitions and Background
Fluxomics = A branch of metabolomics that measures the
turnover of metabolites in pathways using labeled
isotopes such as 13C.
- New technology, just beginning to be utilized
- Instead of being a snapshot of metabolism, it is like a movie

7. Definitions and Background
History and Development
Metabolic profiling is not new. Profiling for clinical detection of human disease using
blood and urine samples has been carried out for Centuries.
This urine wheel was
published in 1506 by
Ullrich Pinder, in his book
Epiphanie Medicorum.
The wheel describes the
possible colors, smells
and tastes of urine, and
uses them to diagnose
disease.
Nicholson, J. K. & Lindon, J. C. Nature
455, 1054–1056 (2008).

8. Definitions and Background
History and Development
Advanced chromatographic separation techniques were developed in the late
1960’s.
Linus Pauling published “Quantitative Analysis of Urine Vapor and Breath by Gas-
Liquid Partition Chromatography” in 1971
Chuck Sweeley at MSU helped pioneer metabolic profiling using gas chromatography/
mass spectrometry (GC-MS)
Gates SC, Sweeley CC (1978) Quantitative metabolic profiling based on
gas chromatography. Clin Chem 24:1663-73. Quantitative metabolic
profiles of volatilizable components of human biological fluids, e.g.
urinary organic acids, were established using GC/MS. Data were
processed by computer and statistical methods for analyzing metabolic
profiles were developed. [Note that all the elements of metabolic
profiling are here.]

9. Definitions and Background
History and Development
Plant metabolic biochemists (e.g. Lothar Willmitzer) were among other early
leaders in the field.
Metabolomics is expanding to catch up with other multiparallel analytical
techniques (transcriptomics, proteomics) but remains far less developed and less
accessible.

10. Definitions and Background
Plant Metabolome Size
It is estimated that all plant species contain 90,000 - 200,000 compounds.
Each individual plant species contains about 5,000 – 30,000 compounds.
e.g. ~ 5,000 in Arabidopsis
The plant metabolome is much larger than that of yeast, where there are far
fewer metabolites than genes or proteins (<600 metabolites vs. 6000 genes).
The size of the plant metabolome reflects the vast array of plant secondary
compounds. This makes metabolic profiling in plants much harder than in other
organisms.

11. Definitions and Background
Metabolomics compared to Genomics, Transcriptomics, and Proteomics
Differences between metabolomics and the other multiparallel approaches:
(a) Conceptual: 1 GENE → 1 mRNA → 1 Protein → Many Metabolites
(and conversely: Many proteins → 1 Metabolite)
There is no direct relationship between metabolite and gene in the way there is
between genes and mRNAs and proteins. A single gene does not specify the level
of a single metabolite, i.e. its pool size (although it may determine whether the
metabolite is present or absent).
Rather, as MCA teaches, the level of a metabolite is determined by the activities of
all the enzymes of all the pathways that involve that metabolite, and by effectors
that act on these enzymes. In practice, therefore, metabolite levels change
according to developmental, physiological, and pathological states.
Biological variance in metabolite levels (i.e., the variation between genetically
identical plants grown in the same conditions) is accordingly large – about 10× the
analytical variability – and limits the resolution of metabolomics.

12. Definitions and Background
Metabolomics compared to Genomics, Transcriptomics, and Proteomics
Differences between metabolomics and the other multiparallel approaches:
(b) Chemical:
Unlike nucleic acids and proteins, metabolites have a vast range of chemical
structures and properties. Their molecular weights span two orders of magnitude
(30–3000 Da).
Therefore no single extraction or analysis method works for all metabolites.
(Unlike DNA sequencing, microarrays, MS analysis of proteins – all are general
methods.)
(c) Dyamic:
Many metabolite levels change with half times of minutes or seconds – far
faster than nucleic acids or proteins. Thus valuable information is lost if
sampling times are too far apart. Also drastic artifactual changes can occur in
short intervals between harvest and extraction; this adds to biological variance.

13. Definitions and Background
The Power of Metabolomics
The dependence of protein expression
on mRNA levels, in linear coordinates.
PMID: 1718905
Metabolomics analysis can powerfully complement transcriptomics and proteomics.
Metabolomes are a step nearer actual function.
Transcriptomes or proteomes are very inadequate monitors of cell function because
there is no simple relationship between mRNA or protein levels and metabolism.
Thus changes in mRNA level or protein
level in mutants or transgenics are
usually not closely linked to changes in
metabolic function or phenotype as a
whole.
Part of the reason for this is the non-
linear relation between mRNA and
protein levels (see graph) and the
typically hyperbolic relation between
enzyme level and in vivo flux rate (see
MCA class). Another cause is the high
level of functional redundancy in plant
metabolism – i.e. parallel or alternative
pathways for the same process.

14. Definitions and Background
The Power of Metabolomics
Silent Knockout Mutations.
~90% of Arabidopsis knockout mutations are silent – i.e. have no visible phenotype
and so provide no clues to gene function. (The search for some sort of visible
phenotype therefore often becomes desperate.) The situation in yeast is similar –
up to 85% of yeast genes are not needed for survival.
When there is little or no change in growth rate (visible phenotype) of a knockout
mutant, the pool sizes of metabolites have altered so as to compensate for the
effect of the mutation, leaving metabolic fluxes are unchanged. Thus – intuitively –
mutations that are silent when scored for metabolic fluxes or growth rate (growth
rate is the sum of all metabolic fluxes) should have obvious effects on metabolite
levels. There is a firm theoretical basis for this in MCA.

15. Definitions and Background
The Power of Metabolomics
Example.
In the Chloroplast 2010 project
(phenotype analysis of knockouts of
Arabidopsis genes encoding predicted
chloroplast proteins):
Various knockouts
showed essentially
normal growth and
color but highly
abnormal free
amino acid profiles,
e.g. At1g50770
(‘Aminotransferase-
like’)

16. Metabolic Profiling Methods
Sample Preparation
Metabolites are typically extracted in aqueous or methanolic media, then fractionated
into lipophilic and polar phases that are then analyzed separately. Further fractionation
of each phase may follow to split metabolites into classes prior to analysis.
No single extraction procedure works for all metabolites because conditions that
stabilize one type of compound will destroy other types or interfere with their analysis.
Therefore the extraction protocol has to be tailored to the metabolites to be profiled.

17. Metabolic Profiling Methods
Sample Preparation
• In practice, these considerations mean that metabolic profiling
is often confined to fairly stable compounds that can be
extracted together.
• These include major primary metabolites (sugars, sugar
phosphates, amino acids, and organic acids) and certain
secondary metabolites (e.g., phenylpropanoids, alkaloids).
• The most comprehensive profiling can cover several
hundred such compounds, many of which are
unidentified.
• Many crucial metabolites, particularly minor or
unstable ones, are currently being missed in
metabolomics analyses.

18. Networking metabolites and diseases
• Biological systems are increasingly viewed and analyzed as highly
complex networks of interlinked macromolecules and
metabolites.
• Network analysis has been applied to interactome maps of
protein–protein, protein–DNA, and protein–RNA interactions as
well as transcriptional, metabolic, and genetic data.
• Such network views of biological systems should facilitate the
detection of nonlinear long-range effects of perturbations, for
example, by mutations, and help identification of unanticipated
indirect causal connections.

19. Diseasome and Drug-Target Network
• Recently, Goh et al. (1) constructed a ‘‘diseasome’’ network in
which two diseases are linked to each other if they share at least
one gene, in which mutations are associated with both diseases.
• In the resulting network, related disease families cluster tightly
together, thus phenotypically defining functional modules.
• Importantly, for the first time this study applied concepts from
network biology to human diseases, thus opening the door for
discovering causal relationships between disregulated networks
and resulting ailments.

20. • Yilderim et al. linked drugs to protein targets in a drug–target
network, which could then be overlaid with the diseasome
network.
• One notable finding was the recent trend toward the
development of new compounds directly targeted at disease gene
products, whereas previous drugs, often found by trial and error,
appear to target proteins only indirectly related to the actual
disease molecular mechanisms.

21. • An important question that remains in this emerging field of
network analysis consists of investigating the extent to which
directly targeting the product of mutated genes is an efficient
approach or whether targeting network properties instead, and
thereby accounting for indirect nonlinear effects of system
perturbations by drugs, may prove more fruitful.
• However, to answer such questions it is important to have a good
understanding of the various influences that can lead to diseases.

22. Metabolic Connections
• One group of diseases that was very poorly connected
in the original diseasome network was the family of
metabolic diseases.
• In this issue of PNAS, Lee et al. (3) hypothesize that
metabolic diseases may instead be connected via
metabolites and common reactions.
To investigate this hypothesis Lee et al. first constructed a
metabolic network from data available in two manually
curated databases detailing well known metabolic
reactions, the involved metabolites, and catalyzing
enzymes.

23. • In addition, gene–disease associations were identified
by using the Online Mendelian Inheritance in Man
(OMIM) database (www.ncbi.nlm.nih.gov/sites/
entrez?dbomim & itooltoolbar).
• In a last step, a metabolic disease network (MDN) was
constructed by connecting two diseases if their
associated genes are linked in the metabolic network by
a common metabolite or metabolites used in a common
reaction.

24. • Metabolites are not only linked by common reactions,
but on a larger scale by coupled fluxes within a
metabolic network, which may also influence disease
phenotypes.
• An increase in the concentration of one metabolite may
increase several fluxes across reaction pathways that use
this compound, which may lead to diverse phenotypes
and distinct diseases.

25. • The fluxes within the metabolic network are calculated
by using the Flux Coupling Finder method described by
Nikolaev et al. (4) and Burgard et al. , which is based on
the assumption that pools of metabolites are
conserved.
• To functionally validate the network, co-expression
correlations are measured for genes linked by adjacent
reactions and those linked by fluxes.
• Interestingly, the average co-expression correlation for
flux-coupled genes (0.31) is higher than that for genes
simply catalyzing adjacent reactions (0.24) (compared
with 0.10 for all gene pairs in the network).

26. • If the links between diseases identified in the MDN are
functionally and causally relevant it should be expected
that linked diseases occur more frequently in the same
individual.
• To test this hypothesis, Lee et al. (3) measured the co-
occurrence of diseases in patients by using detailed
Medicare information of 13 million patients and 32
million hospital visits within a 3-year period.
• A comorbidity index was computed to measure the
degree to which one disease will increase the likelihood
of a second disease in the same patient.

27. • The average comorbidity for all genes is 0.0008 (Pearson
correlation coefficient), which increases 3-fold to 0.0027
when disease pairs that are metabolically linked are
analyzed, which is highly statistically significant (P 108).
• When diseases are analyzed that are directionally
coupled by a flux (see ref. 3 for details), the correlation
increases to 0.0062.
• Thus, whereas 17% of all diseases in the network show
significant comorbidity, this fraction nearly doubles to
31% for metabolically linked diseases.
• Further analysis reveals that comorbidity effects can be
detected up to three links (metabolites, reactions) apart
from each other with statistical significance, but not
farther away.

28. • Lee et al. (3) next investigated whether metabolic
diseases are better linked through the metabolic
network than they are in the previously described
gene–disease network.
• When purely metabolic diseases are considered, the
comorbidity is, in fact, best predicted by metabolic links.
• Interestingly, when all diseases linked to metabolic
enzymes are considered, which involves many diseases that
are merely related to metabolic diseases through
multifunctional enzymes, the gene and metabolic networks
are nearly equally predictive of comorbidity, indicating that
as a general approach information from many different
biological dimensions should be integrated to identify the
most relevant connections.

29. • Together, all these findings support the initial
hypothesis that metabolic diseases are linked by
metabolic networks.
• Practically, alteration of one metabolite or one reaction
can have numerous repercussions in the network, each
of which can manifest as different diseases that
frequently occur together in affected patients.

30. Perspective:
• The challenges associated with this type of analysis, is
very likely the imperfect information about underlying
networks and linkages, which in this case include missing
disease–gene associations and incompletely defined
metabolic networks.
• When considering a more global picture, network
analysis is restricted by still very incomplete knowledge
about, for example, information fluxes in the protein
interactome network, which are mediated by protein
interactions and enzyme– substrate relationships, and
many other network dimensions that are too numerous
to list.

31. • A second limitation, although necessary for a first
analysis of this kind, is the restriction to one dimension
of the biological system (metabolic reactions), whereas
in vivo effects on many different levels act together to
yield a given phenotype.
• Thus, most importantly, this work by Lee et al. (3)
defines a program for, and constitutes an important step
toward, linking data from diverse areas of systems
biology.

32. • Data gained by metabolic profiling, mapping of
enzyme–substrate and interactome networks, and many
other activities need to be combined into a single high-
dimensional systems network model, which can then be
used to explore network effects of disease causing
genetic or environmental alterations.
• Construction of such models, however, will require
much more comprehensive data for nearly all aspects of
biology and may even necessitate the development of
novel mathematical and statistical tools to deal with
them

33. • Ultimately, it should be expected that this type of
integrated network analysis will profoundly alter our
view of biological systems, our understanding of the
way mutations lead to disease phenotypes, and how these
insights are used in drug discovery.
• Exciting times lie ahead of us.

34. Substrate channeling
• Substrate channeling is a process of direct transfer of the product
of an enzyme to another proximate enzyme or cell as its substrate
without equilibration with the bulk phase.
• Such phenomena occur only when a distance between two entities
are close enough but not when the distance between cascade
entities is far away.
• As a result, reaction rates among cascade entities may be
accelerated greatly.
• For example, adjacent different active sites can be located on
either separate domains in a multifunctional enzyme, separate
subunits in a multi-enzyme complex, or separate enzymes that are
spatially close enough.

35. • Natural multifunctional enzyme complexes have been discovered
in both primary and secondary pathways.
• In cases of primary pathways, substrate channeling has been
reported in the:
• glycolysis pathway the Calvin cycle,
• the tricarboxylic acid cycle in the mitochondrial matrix ,
• the oxidative pentose phosphate pathway,
• the gluconeogenesis pathway,
• the heme biosynthetic pathway,
• fatty acid oxidation, cellulose biosynthesis,
• amino acid synthesis,
• carboxysome, proteasome, and so on.

36. • For effective synthesis of specific natural products (secondary
metabolites) and prevention of primary metabolic interference,
numerous secondary metabolites have been synthesized through
enzyme complexes associated with substrate channeling effects,
such as ;
• isoprenoids, alkaloids, phenylpropanoids, flavonoids, and
cyanogenic glucosides.
• Two general mechanisms for substrate channeling have been
proposed.
• One is a direct transfer mechanism (i.e., perfect channeling)
that the intermediate of the first enzyme is passed directly to
the second enzyme or cell without diffusion to the bulk phase.
• In these cases, intermediates between complexes are difficult
to detect.

37. • The other mechanism is often referred to as a proximity
mechanism or leaky channelling,
• which is operative for any cascade reaction where the
second receptive enzyme or cell is locally close to the first
enzyme.
• For example, they can be bound at high densities on a surface
(e.g., cellular membrane or solid substrate) or be loosely
associated in large aggregates.
• The intermediate dissociated from the first enzyme has a high
probability of being captured by the adjacent second receptor
(Fig. 1B).
• In these cases, the intermediate may be detected in the bulk
phase.

38. • Therefore, whether substrate channeling occurs or not should be
kinetically determined with high caution (Spivey and Ovádi,
1999).
• In both mechanisms, diffusion of the intermediate into the bulk
fluid is impeded by the juxtaposition of active sites and/or by
steric hindrance (Geck and Kirsch, 1999; Spivey and Merz, 1989).
• Several kinetic methods have been used to determine occurrence
of substrate channelling, including transient-time analysis,
isotope dilution or enrichment, competing reaction method,
enzyme buffer kinetics, and transient-state kinetics.

39. Metabolic engineering
• Metabolic engineering is the directed improvement of cellular
properties through the modification of specific biochemical
reactions or the introduction of new ones by recombinant DNA
technology (Bailey, 1991; Stephanopoulos, 1999).
• To achieve high production rates and high product titers, fluxes of
enzymatic reactions in cascade pathways are required to not only
limit the accumulation of intermediates, especially toxic ones, but
also keep levels of labile metabolites very low .

40. • Two classic strategies for balancing pathway fluxes are:
• (i) modulating expression levels of individual enzymes (e.g.,
through manipulating promoter strengths, ribosome binding
site strengths, plasmid copy number, or tunable intergenic
regions controlling mRNA processing) and
• (ii) improving turnover activities of rate-limiting enzymes
by directed evolution or isolated from other sources.
• By mimicking natural enzyme complexes for substrate channeling,
Keasling and his coworkers (Dueber et al., 2009) introduced a
third strategies — co-localization of cascade enzymes linked by
synthetic protein scaffolds.

41. • The optimization of three mevalonate biosynthetic enzymes for
the formation of a synthetic complex achieved 77-fold
improvement in product titer with low enzyme expression so that
it reduced bioenergetic load.

42. Comparison Between Metabolic Target Analysis and Metabolic Profiling
• Metabolic target analysis and metabolic profiling are two techniques in the field of
metabolomics that generate profiles of the metabolites existing within a biological sample.
• These techniques are important for the research and development of new antimicrobials,
diagnostic tests, therapeutic interventions, probiotics, and vaccines.
• The applications of metabolic analyses continue to increase, leading both metabolic target
analysis and metabolic profiling to be of growing importance.
• Both types of analysis can be used as tools for identifying new biomarkers of disease,
highlighting toxicologic and pathologic changes, as well as for uncovering the underlying
mechanisms of biological systems, all of which are important for opening up new avenues
for the development of preventions, diagnoses, and treatments for a number of diseases.
Below we discuss the differences between the two approaches.

43. Metabolic target analysis
• In metabolic target analysis, known metabolites are profiled.
• The approach is based on the analyses of targeted quantitative MS and NMR, however,
newer analytical methods such as CE-MS are also being incorporated into the method.
• Often, targeted analysis involves multiple stable isotope-labeled standards being added into
the samples to be tested before the steps of extraction and derivatization.
• This is to control for analyte loss that occurs during sample processing and also compensate
for the effects of ionization-suppression.
• Chemical solvents are used to extract aliquots of the sample which are then derivatized for
the specific chemical properties of the cluster of analytes.
• This process of analysis is known as processing the samples in a modular format.
• Amino acids, organic acids, and nucleotides, etc., that are being looked for within the
sample call for an addition of cognate stable isotope standards to the sample.

44. • An advantage of the targeted method is that it is quantitatively precise.
• A drawback of the method is that it is limited in its breadth of analysis as it can only
analyze a certain number of metabolites at a time.
• One area in which this type of analysis has seen particular success is in the survey of
metabolic fuel, and profiling energy-yielding metabolic pathways.

45. Metabolic profiling
• Metabolic profiling is an untargeted approach where NMR and High-Resolution MS
techniques are combined to simultaneously analyze the metabolites and proteins within a
biological sample.
• It can analyze as many metabolites that exist in a given sample, and the method is usually
used in the identification of new biomarkers, the characterization of metabolic pathways, or
the comparison between two clinical or biological states.
• Commonly, the chemical identity of the NMR or MS-resolved peaks that are generated by the
analyses are unknown before the testing begins, and further analysis is required to
distinguish the molecules causing these peaks.
• An advantage of the method is that it is an excellent tool for generating comprehensive
metabolic profiles of a wide range of samples.
• However, the technique is fairly low in sensitivity, and deconvolution and normalization of
complex spectra provide challenges.

46. Summary of differences between metabolic target analysis and metabolic profiling
• On the one hand, metabolic profiling is a semi-quantitative method of detecting a wide
range of metabolites.
• It acquires data without a priori knowledge of the metabolites of interest and can be used
to generate a research hypothesis.
• It is a middle-in strategy that can be used to find the “needle in the haystack”.
• On the other hand, targeted analysis can be used for hypothesis testing or systems biology
modeling.
• It can generate absolute quantification using isotopic internal standards and is a bottom-up
strategy.
• These limitations generally prevent the method from analyzing any more than the top 100
most abundant molecules in a sample, which means it may miss out on those low in
concentration.
• This is a problem because metabolites in high abundance are generally those shared
between populations and situations, whereas those low in abundance often represent key
differences that may have clinical significance

52. Secondary metabolite in Biological system
• Secondary metabolites are organic compounds that are
not directly involved in the normal growth, development,
or reproduction of an organism.
Unlike primary metabolites, absence of secondary
metabolites does not result in immediate death, but rather in
long term impairment of the organism's survivability,
fecundity, or aesthetics, or perhaps in no significant change at
all.
• Secondary metabolites are often restricted to
a narrow set of species within a phylogenetic group.
• Secondary metabolites often play an important role in
plant defense against herbivory and other interspecies
defenses. Humans use secondary metabolites as
medicines, flavorings, and recreational drugs.[1]

53. • Secondary metabolites aid a plant in important functions
such as protection, competition, and species
interactions, but are not necessary for survival.
• One important defining quality of secondary metabolites
is their specificity.
• Usually, secondary metabolites are specific to an
individual species.
• Research also shows that secondary metabolic can affect
different species in varying ways.
• In the same forest, four separate species of
arboreal marsupial folivores reacted differently to a
secondary metabolite in eucalypts.

54. • This shows that differing types of secondary metabolites
can be the split between two herbivore ecological
niches.
• Additionally, certain species evolve to resist plant
secondary metabolites and even use them for their own
benefit.
For example, monarch butterflies have evolved to be able
to eat milkweed (Asclepias) despite the toxic
secondary metabolite it contains.
This ability additionally allows the butterfly and caterpillar
to be toxic to other predators due to the high concentration
of secondary metabolites consumed.

55. Human health implications
• Most polyphenol nutraceuticals from plant origin must
undergo intestinal transformations, by microbiota and
enterocyte enzymes, in order to be absorbed at enterocyte
and colonocyte levels.
• This gives rise to diverse beneficial effects in the
consumer, including a vast array of protective effects
against viruses, bacteria, and
protozoan parasites.
• Secondary metabolites also have a strong impact on the
food humans eat.
• Some researchers believe that certain secondary
metabolite volatiles are responsible for human food
preferences that may be evolutionarily based in
nutritional food.

56. • This area of interest has not been thoroughly
researched, but has interesting implications for
human preference.
• Many secondary metabolites aid the plant in gaining
essential nutrients, such as nitrogen.
For example, legumes use flavonoids to signal a symbiotic
relationship with nitrogen fixing fungi (rhizobium)
to increase their nitrogen uptake.
• Therefore, many plants that utilize secondary
metabolites are high in nutrients and advantageous for
human consumption.

57. Categories
• Most of the secondary metabolites of interest to
humankind fit into categories which classify secondary
metabolites based on their biosynthetic origin.
• Since secondary metabolites are often created by
modified primary metabolite synthases, or "borrow"
substrates of primary metabolite origin, these
categories should not be interpreted as saying that all
molecules in the category are secondary metabolites
(for example the steroid category), but rather that there
are secondary metabolites in these categories.

58. Small "small molecules"
Alkaloids (usually a small, heavily derivated amino acid):
• Hyoscyamine, present in Datura stramonium
• Atropine, present in Atropa belladonna, Deadly
nightshade
• Cocaine, present in Erythroxylum coca the Coca plant
• Scopolamine, present in the Solanaceae (nightshade)
plant family
• Codeine and Morphine, present in Papaver somniferum,
the opium poppy
• Tetrodotoxin, a microbial product in Fugu and some
salamanders
• Vincristine & Vinblastine, mitotic inhibitors found in the
Rosy Periwinkle

59. Terpenoids (come from semiterpene oligomerization):
• Azadirachtin, (Neem tree)
• Artemisinin, present in Artemisia annua Chinese
wormwood
• tetrahydrocannabinol, present in cannabis
Steroids (Terpenes with a particular ring structure)
Saponins (plant steroids, often glycosylated)

60. Flavonoids (or bioflavonoids) (from the Latin word flavus
meaning yellow, their color in nature) are a class of plant
and fungus secondary metabolites):
• isoflavanoids & neoflavanoids, flavone, flavanones
• Glycosides (heavily modified sugar molecules):
• Nojirimycin
• Glucosinolates
Natural phenols:
• Resveratrol
• Phenazines:
• Pyocyanin
• Phenazine1carboxylic acid (and derivatives)
• Biphenyls and dibenzofurans are phytoalexins of the
Pyrinae

61. Big "small molecules", produced by large,
modular, "molecular factories“
Polyketides:
Erythromycin
Lovastatin and other statins
Discodermolide
Aflatoxin B1
Avermectins
Nystatin
Rifamycin

62. Fatty acid synthase products :
FR900848
U106305
phloroglucinols
Nonribosomal peptides:
Vancomycin
Ramoplanin
Teicoplanin
Gramicidin
Bacitracin
Ciclosporin
Ribosomally synthesized and post
translationally
modified peptides:

63. Thiostrepton
Hybrids of the above three:
Epothilone
Polyphenols
Non"
small molecules" DNA,
RNA, ribosome, or polysaccharide "classical"
biopolymers
Ribosomal peptides:
MicrocinJ25

64. What’s the difference between metabonomics and metabolomics?
The distinction is mainly philosophical, rather than technical.
❖ Metabonomics broadly aims to measure the global, dynamic
metabolic response of living systems to biological stimuli or
genetic manipulation.
The focus is on understanding systemic change through time
in complex multi cellular systems.
❖ Metabolomics seeks an analytical description of complex
biological samples, and aims to characterize and quantify all the
small molecules in such a sample.
In practice, the terms are often used interchangeably, and
the analytical and modelling procedures are the same.

65. How did modern-day metabonomics begin?
• There were two, largely independent, starting points.
• The first was metabolic-control analysis, a mathematical method
developed in the 1960s for modelling metabolism in cells.
• This required metabolite concentrations to be quantified, and so
methods were developed to do this — often using gas
chromatography (GC) or GC coupled to mass spectrometry (MS).
The second contributing factor was the development of nuclear
magnetic resonance (NMR) spectroscopy.
By the mid-1980s, NMR was sensitive enough to identify
metabolites in unmodified biological fluids.
This led to the discovery that altered metabolite profiles are
caused by certain diseases or by adverse side effects to drugs.

66. • MS techniques were also developed for profiling biological
fluids.
• But metabonomics really took off with the realization that
pattern-recognition methods (also known as chemometrics or
multi variate statistical analysis) could help to interpret the complex
data sets that result from these studies.
Metabonomics of yore. This urine
wheel was published in 1506 by Ullrich
Pinder, in his book Epiphanie
Medicorum. It describes the possible
colours, smells and tastes of urine,
and uses them to diagnose disease.

67. How does this approach fit in with systems biology?
• It provides a ‘top-down’, integrated view of biochemistry in
complex organisms, as opposed to the traditional ‘bottom-up’
approach that investigates the network of inter actions
between genes, proteins and metabolites in individual cell
types.
• A problem with systems biology is that each level of
biological organization and control — genomics, gene
expression, protein expression and metabolism — operates
on a markedly different timescale from the
others, making it difficult to find causal linkages.

68. • Moreover, environmental and lifestyle factors greatly
influence metabolism, making it difficult to disentangle their
effects from gene-related outcomes.
• Environmental influences on gene expression also make it hard
to interpret genomic data, for example to predict an individual’s
susceptibility to diseases.
• Metabonomics cuts through these problems by monitoring the
global outcome of all the influencing factors, without making
assumptions about the effect of any single contribution to that
outcome.
• Yet in so doing, the individual contributions can be teased out.

69. What analytical techniques are used for metabonomics?
• Usually NMR spectroscopy and MS. NMR is generally used to detect
hydrogen atoms in metabolites.
In a typical biological-fluid sample, all hydrogen-containing
molecules in the sample — including nearly all metabolites — will give
an 1H NMR spectrum, as long as they are present in concentrations
above the detection limit.
The NMR spectrum of a biological fluid is therefore the
superposition of the spectra of all of the metabolites in the fluid (Fig.)

70. • An advantage of NMR is that the biological fluid doesn’t require any
physical or chemical treatment prior to the analysis.
• MS studies, on the other hand, usually require the metabolites to
be separated from the biological fluid before detection, typically by
using high-performance liquid chromatography (or modern
variants).
• Alternatively, the metabolites can be chemically modified to make
them more volatile, so that GC–MS can be used.

71. Fig: The discovery process in metabonomics.
Metabonomics analyses the metabolites in
biological fluids to determine the metabolic
response of an organism to a physiological
stimulus.
a, A typical procedure might start with
the NMR spectrum of a biological fluid, which
contains signals from hundreds of
metabolites.
b, The individual spectra for each metabolite
are identified.

72. c, This enables the structure of
the metabolites to be determined.
d, Pattern recognition techniques can be used
to work out how the spectra of biological fluids
from individuals who have a disease differ from
those of healthy subjects. Here, ‘principal-
component analysis’ has reduced multivariate
data to a two dimensional plot.
e, Although the procedure above is
conceptually simple, this NMR spectrum
of human urine reveals how complicated the
raw data can be.

73. Applications of metabonomics: There are three broad areas that
might benefit from metabonomics. Metabolic profiling of
individuals could be used in personalized health care to work out
patients’ susceptibilities to disease or their responses to medicines,
and to tailor their lifestyles and drug therapies accordingly.

74. • Metabolic profiling of populations could allow the development of
‘molecular epidemiology’ — the ability to work out the
susceptibilities of specific groups to disease.
• This might allow metabolites to be identified as risk identifiers
(biomarkers) for diseases, with implications for health screening
programmes.
• Finally, by identifying biochemical pathways for disease,
metabonomics could uncover new targets for drug discovery.

75. Are there any drawbacks to metabonomics?
• The need to use complex data-interpretation techniques and
combinations of analytical methods isn’t ideal.
• Another problem is that the number of metabolites produced by
any given system cannot be predicted — compare this with
genome sequencing, where the number of genes is known.
• But this problem isn’t insurmountable, and is similar to that of
other fields, such as epigenetics.

76. How has metabonomics been used for drug discovery?
• Its use in evaluating drug toxicity has been comprehensively
assessed by the Consortium for Metabonomic Toxicology (COMET), a
collaboration between five pharmaceutical companies and Imperial
College London.
• COMET produced a database of NMR spectra of rodent urine and
blood serum, taken from animals that had been dosed with a range
of toxins.
• This database now forms the basis of a successful system for
predicting the liver and kidney toxicity of drug candidates.

77. • A followup project, COMET-2, is currently investigating
the detailed biochemical mechanisms of toxicity, and seeks a better
understanding of inter-subject variation in metabonomic analyses.
• It has also been demonstrated in animals that the metabolic profile
of an individual’s urine can be used to predict both how that
individual will metabolize a given drug and their susceptibility to
the side effects of that drug.
• If this principle can be applied widely in humans, it will have
enormous implications for personalized health care and in
optimizing clinical trials.

78. What insights into diseases have emerged?
• Many metabolites have been identified as flags for a variety of
diseases.
• They include markers for schizophrenia found in cerebrospinal fluid;
markers of coronary-artery occlusion
found in plasma; and even indicators of spinal-trauma-induced
infertility in men, found in seminal fluid.
• The concentrations of these metabolites often vary in response to
therapy for the disease in question.
• Furthermore, the biomarkers carry information about the sites and
mechanisms of disease.
• Metabolites have also been found that act as indicators for disease
risk, individual susceptibility, or as markers of recovery from an
illness.

79. What else has metabonomics taught us?
• It has revealed much about humans’ symbiotic relationship with
their gut flora.
• Disruption of gut microbial activity seems to be central to certain
diseases in the gut, liver, pancreas and even the brain.
• But there are thousands of species of microorganism (the micro
biome) in the human gut, and it is impossible to study each one in
isolation to work out what they do.
• Large research programmes have therefore been devised to study
the combined genetic structure of humans and their microbes —
the metagenome.
• This genetic information is invaluable, but it says nothing about the
actual activity of the microbial community, or its interactions with
the host at a physiological level.

80. • Metabonomic modelling, statistically coupled to metagenomic
analysis, has allowed possible communication networks between
species to be identified, and has also shown which metabolic
pathways are strongly influenced by which members of the
microbiome.
What will be the next big thing in metabonomics?
• We believe it will be metabolome-wide association (MWA) studies,
which identify relationships between metabolic profiles and disease
risks for both individuals and populations.
• In this approach, the metabolic profiles of thousands
of people are captured spectroscopically, and are then statistically
linked to disease risk factors such as obesity and diabetes.
• The beauty of MWA is that vast, well-curated collections
of biological-fluid samples are available from epidemiological studies
of many disease processes.
• These can be explored retrospectively for markers of disease risk.

81. • We need such indicators as part of a strategy for disease
prevention, which is essential to drive down health costs as the
world’s population increases.
• Effective prevention requires knowledge of risk, which for
most modern diseases involves unfavourable gene–environment
interactions.
• Understanding how these interactions affect metabolic regulation
and phenotype allows new testable hypotheses to be developed
about future disease risk.
• It is in this field that metabonomics might prove to be strongest
and of most value to humanity.