Metabolomics & Metabolite Atlases

Ben Bowen

Pathway Tools

Workshop2010

Dealing With the Unknown

Acknowledgements Trent NorthenRichard Baran

Wolfgang Reindl

Do Yup Lee

Jane Tanamachi

Jill BanfieldCurt Fisher

Paul Wilmes

US Department of Energy BER Genome Sciences Program

Sample independent: suitable for

unsequenced organisms and communities

AGILENT 6520 QTOF

HPLC (C18; hilic)

MS/MSMetabolite ‘features’

&Quantification

C18NEG/255.22807/3.39329/Hexadecanoic acid;C18NEG/255.22862/4.89002/Hexadecanoic acid;C18NEG/248.8424/1.47135/24-Dibromophenol;C18NEG/112.98576/27.34079/Acetylenedicarboxylate;C18NEG/270.82471/1.34821/C18NEG/168.88735/1.29241/

metabolite solvent

extraction

LC-MS/MS Workflow

How a data point becomes a compound

From Feature to Formula

From Formula to Compound

Annotation of

Metabolite Atlases

Photo: John Waterbury, Woods Hole Oceanographic Institute (DOE)

• Selection of features• Pure Spectra• Isotopic pattern fitting• Stable Isotope Labeling

• Exact Match to MS/MS Spectra• Partial Match to MS/MS Spectra• Exchangable hydrogen• Retention time• Authentic standards• Other (NMR & Synthesis)

• Define feature in database• Sample Metadata• Extraction methods• LC/MS methods• mz@rt annotations

Systems biology depends on accurate modelsAnalysis of MetaCyc shows many unique formulas are shown in only a few reactions or pathways

• Models provide a framework to prove or disprove observations.

• Highlight gaps in annotations when new compounds are discovered

Pathway Specific MarkersOrSparsity of Knowledge

Using inexact mass for formula ID

Isotopic Pattern FittingC & N Isotopic Labels

Reduce Degeneracy About m/z value

Mass and Degeneracy are Correlated

Heuristically Filtered

Brute Force Method

CONTROL

Na15NO3

NaH13CO3

Large-scale formula determination using stable isotopic labeling

Baran et. al. Untargeted metabolite profiling of Synechococcus sp. PCC 7002 reveals a large fraction of unexpected metabolites (Analytical Chemistry 2010)

PROBLEM: Difficult to ID many metabolites give low coverage of authentic standards

Approach: Stable isotope labeling (SIL) for direct empirical formula determination

Less Degeneracy Isn’t Better

We Prefer to Work With Unique Chemical Formulae

Heuristically Filtered OnlyHeuristically Filtered + SIL

Unfiltered + SIL

Noise & Isotopic Patterns

Initial focus is on Synechococcus sp a simple yet important model system

1. Photosynthetic bacteria2. Small genome (3299

ORFs)3. ~fast growing and easy to

grow4. No metabolite

background (salt media)5. Adaptable: 0-2M salt, T up

to 45C

Simple systemFor method development

Widely distributed and globally important in carbon cycling

Benefits of Using SIL

• Are the signals being measured biological?

• What type of ion is the signal?

• Has this signal been seen before?

• What compound(s) is it?• What else in the sample

behaves like that compound?

Global Profiling

StandardsSIL

Stable isotope labeling

Control

13C

15N[15N]NaNO3

[13C]NaHCO3

Stable isotope labeling

m/z

RT

Non-biological features dominate

• Manually curated

• Computationally Identified

• Sets are constructed by grouping features by retention time

Results

~100 distinct metabolites detected 82 assigned chemical formulas

74 unique 45 outside of Syn7002Cyc 24 outside of MetaCyc or KEGG

54 identified or putatively identified metabolites Using authentic standards or

MS/MS

Most dominant biological features

Formula MetabolitePeak height Formula matches in

Cell extract Media extract7002 MetaCyc KEGG(+) (-) (+) (-)

(Glucosylglycerol) 452242 658300 1 2 2

Glutamate 228714 44229 3 9 10

(Hexos(amine)-based oligomer) 184691 90745 0 0 0

(Hexos(amine)-based oligomer) 174581 152126 0 0 0

(Glucosylglycerate) 39066 163000 0 2 1

19819 83700 2 26 29

(NNN-trimethylhistidine) 69974 2444 0 1 1

C9H18O8C5H9NO4C25H40N2O18C25H40N2O18C9H16O9C12H22O11 (2Hexoses-H2O)

C9H15N3O2

Putative hexose(amine)-based trisaccharide:

Excreted metabolites

Formula MetabolitePeak height Formula matches in

Cell extract Media extract7002 MetaCyc KEGG(+) (-) (+) (-)

Phenylalanine 12860 8878 24417 8259 1 4 4

(Alanine) 3987 7325 2479 1500 4 7 8

Isoleucine 1200 1301 4427 1532 2 8 11

Leucine 2089 1992 4093 1707 2 8 11

Tryptophan 1778 2264 929 1 2 7

Methionine 950 1 5 4

Valine 600 1 8 10

Methyluridine 220 570 0 0 2

Methylguanosine 350 140 0 3 1

Methyladenosine 310 0 1 2

C9H11NO2C3H7NO2C6H13NO2C6H13NO2C11H12N2O2C5H11NO2S

C5H11NO2C10H14N2O6C11H15N5O5C11H15N5O4

Histidine-betaine derivatives

NH

N

N

OH

O

HSNH

N

N

OH

O

HONH

N

N

OH

O

Previously only to attributed to non-yeast-fungi and Actinomycetales bacteria

Culture purity validated by PCR of markers of ribosomal RNA and sequencing

Lysine biosynthesis V (Syn7002Cyc)

Lysine biosynthesis VI (Syn7002Cyc)

N2-acetyllysine

Analyze selected features by MS/MS

Target features at specificm/z & r.t.

MS/MS structural confirmation

• Commercial Standards

• Metlin

• Massbank

• Collaborating to expand the number of authentic standards (Siuzdak, Mukhopadhyay) and make these publically available.

De novo MS/MS analysis

5-methyluridine

Proton Painting

CiHjOkNxPySz Ci (HNj1HEX

j2) OkNxPySz

j=j1+j2

Chemical properties in addition to m/z

decyldimethylammoniopropane sulfonate Glycylglycine

Lipids from microbial communities

• Unlabeled

• 15N labeled

• 2H labeled (exchangeable)

• Sample independent

Resolve Isomers of lysolipids

Pure-Spectra Includes Ca2+ & Fe2+ Adducts

Absolute abundance of L-PE features is much higher in a “friable” sample.

AB Muck DS2

AB Muck Friable

Relative abundance of various PEs changes with development stage.

Moving from features to formulas to metabolites is challenging

Time (sec)

m/z 205.097

C11H12N2O2

Chemical formula determination

Structural analysis

Retention Time Correlation

Afte

r 12

Obs

erva

tions

Store retention time correlations

SIL Automatic Annotation

Test the fit for all possible formulas for common

ionization mechanisms

Label Purity and Percent Incorporation are Parameters

Correlation and mass defect analysis

C2H4

200 400 600 800-0.4

-0.3

-0.2

-0.1

0

Nominal Mass

Kend

rick

Mas

s D

efec

t

650 700 750 800

-0.32

-0.3

-0.28

-0.26

Nominal Mass

Ken

dric

k M

ass

Def

ect

0 50 100 1500

1

2

3

4

x 1012

G(

) 28 28.02 28.04 28.060

2

4

6

8

10

12x 1011

G(

)

C2H4

Autocorrelation Spectra of unprocessed data

Find the dominant mass differences in data

H2O

Modular Metabolome

13.99 14 14.01 14.02 14.03 14.04 14.05 14.060

0.01

0.02

0.03

0.04

0.05

0.06

m/z lag,

Corre

latio

n, G

()

Estimate the likelihood of all possible chemical differences

How can you know that this is CH2?

What can be resolved

0.98 0.99 1 1.01 1.02 1.03 1.04 1.050

0.2

0.4

0.6

0.8

1

G(

)

-3 -2 -1 0 1 2 3x 10-3

0

0.2

0.4

0.6

0.8

1

*

G(

)

Mass of an electron shown for scale

Time and Mass Correlation

Neutron: Zero Time Correlation

H2O: Mixture of: Zero Time and Negative Time Correlation

C2H4: Positive Time Correlation

Relate back to features

16.94 16.96 16.98 17 17.02 17.04 17.06 17.08 17.1 17.120

0.005

0.01

0.015

0.02

0.025

0.03

0.035

m/z lag,

Cor

rela

tion,

G(

)

Microbial Metabolite Atlases

600 800 1000 1200 1400 1600 1800 2000 2200 24000

2

4

6

x 105

retention time (sec)

inte

nsity

900 1000 11000

1

2

3

4

5

6

x 105

retention time (sec)

inte

nsity

0 500 1000 1500 2000500

1000

1500

2000

2500

m/z

rete

ntio

n tim

e (s

ec)

From Features to

Pure Spectra

Within one experiment: 1000s of features from 100s of metabolites

The End