Proteomics, Metabolomics And Mass Spectrometry

Steven  S.  Gross    Department  of  Pharmacology,  x66257  

   

                                                             Clinical  and  Research  Genomics  -­‐  4/30/2014  

Plan for Today’s Lecture!

  Describe enabling mass spectrometry(MS)-based strategies & principals used for analysis of biomolecules   Show  how  MS  can  be  applied  for  to  broadly  iden<fy  proteins  in    complex  biological  mixtures  –  PROTEOMICS       Show  how  untargeted  LC-­‐MS  can  be  used  to  broadly  discover    changes  in  small  molecule  expression  in  extracts  of  cells,  <ssues    and  biofluids  -­‐  METABOLOMICS  

PROTEOME The Entire Protein Complement of a Genome A major determinant of cell phenotype

Dynamically changing with cell cycle, aging, environment, differentiation, interventions, acute insults, disease

Much more complex than one gene, one protein adage!

All proteins predicted from the genome (22K ORFs predicted in humans) + proteins arising by:

- Alternative mRNA splicing - Posttrsanslational protein processing (proteolysis and > 40 known chemical modifications)

Undefined Complexity of the Proteome

Defines cell phenotype on a moment-to-moment basis Not predicted by any other “omic” info: Genomic, Transcriptomic or Proteomic

A biomedical frontier that is largely undefined & only partly “self” determined

Elucidation is a challenge that will yield breakthrough knowledge & disease cures

METABOLOME The entire Metabolite complement of a Genome

Mass Spectrometers Weigh Molecules

Unit  of  mass  is  the  “Dalton”  (Da,  amu)  =  1/12  of    12C  -­‐  the  most  abundant  isotope  of  carbon      1  Da  =  1/Avogadro’s  number  =  1.6605  x  10-­‐24  g    Ions  are  detected  by  MS  as  m/z,  not  abs.  mass!    

Nomenclature Used to Describe Molecular Mass

Nominal Mass considers only unitary mass of ions Mono-isotopic Mass considers mass defects, e.g., H = 1.00782 Average Mass considers mass defects and isotopic abundance, e.g. 1.109% 13C vs 12C; 0.38% 15N 0.0036 vs 14N

A!3C = 36!2O = 32!1N = 14!7H = 7! 89! (-18)! 71 !

Residue Masses for Amino Acids

Enabling Breakthroughs for Protein Identification by Mass Spectrometry

Soft ionization techniques enabled very large molecules"to be studied without excess fragmentation""MALDI – matrix-assisted laser desorption ionization""ESI – electrospray ionization"

"Establishment of protein sequence databases "(from translation of the genome) ""Development of powerful search engines for databases "

MALDI!

ESI!

Mass Spectrometry Can be Used To Inform on Protein Structure

 Confirm identity of a predicted protein   Identify an unknown protein – peptide fingerprinting   Identify an unknown protein - fragmentation

pattern of one or more component peptides   Specify posttranslational protein modifications and sites   Elucidate protein binding partners in a coprecipitate - learn the composition of multi-protein assemblies  Quantify differential protein expression

Protein mass spectrometry may be: Top-down - Analyzing intact proteins Bottom-up – Analyzing peptides in a protein digest

C381H856N107O114S6!monoisotopic mass = 8676.15!

Sample Preparation for Bottom-up Mass Spectrometry

Enzymatic Digest and

Fractionation

Sample Preparation for Bottom-up Mass Spectrometry

Silver-stained 2-D Gel of Soluble Yeast Proteins - Gygi et al., PNAS 2000  

Single Stage MS

MS

Trypsin-Digested Protein Spectrum

To Determine Peptide Mass, Must Know Charge: Isotopic Resolution of a Singly-Charged Peptide

m/z=1285.508 (mass of ion/charge=1285.508/1)

mass of ion=1285.508

Isotopic Resolution of a Doubly-Charged Peptide

m/z=785.865 (z=2)

M = (m x z) – z m = 1571.73 - 2

mass of uncharged peptide = 1569.73

Protein Identification from Peptide Maps

What Does the Search Engine Do?!

Database Searching: MS

Information from Peptide Map Expts

• • • • • • • •

Incubate gel band with trypsin, extract tryptic

peptides, desalt

Peptide ion masses determined by MS

Protein Identification by In-gel Trypsinolysis + MS

54 kDa

45 kDa

Excise band

MKKCTILVVASLLLVNSLLPGYGQNKIIQA QRNLNELCYNEGNDNKLYHVLNSKNGKIYN RNTVNRLLPMLRRKKNEKKNEKIERNNKLK QPPPPPNPNDPPPPNPNDPPPPNPNDPPPP

NPNDPPPPNANDPPPPNANDPAPPNANDPA PPNANDPAPPNANDPAPPNANDPAPPNAND PAPPNANDPPPPNPNDPAPPQGNNNPQPQP

RPQPQPQPQPQPQPQPQPQPRPQPQPQPGG NNNNKNNNNDDSYIPSAEKILEFVKQIRDS ITEEWSQCNVTCGSGIRVRKRKGSNKKAED LTLEDIDTEICKMDKCSSIFNIVSNSLGFV

ILLVLVFFN

Compare observed peptide ion masses with

peptide database

Tandem Mass Spectrometry (MS/MS) Provides More Confident Protein ID

MS/MS

Tandem Mass Spectrometry (MS/MS) !

Peptide Fragmentation

-HN-CH-CO-NH-CH-CO-NH-

Ri CH-R’

bi

yn-i yn-i-1

bi+1 R”

i+1

i+1 ai

xn-i

ci

zn-i

Peptide Fragmentation Peptide: S-G-F-L-E-E-D-E-L-K

MW ion ion MW 88 b1 S GFLEEDELK y9 1079

145 b2 SG FLEEDELK y8 1022 292 b3 SGF LEEDELK y7 875 405 b4 SGFL EEDELK y6 762 534 b5 SGFLE EDELK y5 633 663 b6 SGFLEE DELK y4 504 778 b7 SGFLEED ELK y3 389 907 b8 SGFLEEDE LK y2 260

1020 b9 SGFLEEDEL K y1 147

Peptide Fragmentation

K 1166

L 1020

E 907

D 778

E 663

E 534

L 405

F 292

G 145

S 88 b ions

100

0 250 500 750 1000 m/z

% In

tens

ity

147 260 389 504 633 762 875 1022 1080 1166 y ions y6

y7

y2 y3 y4

y5

y8 y9

b3

b5 b6 b7 b8 b9

b4

Digest with Specific Protease

Trypsin (K, R; not followed by P) Chymotrypsin (F, W, Y, L, M) Lys-C (K) Arg-C (R) Asp-N (D, N-terminal) V8-bicarb (E) V8-biphosph (E, D) {CNBr (M)}

Digest with Specific Protease

Why trypsin?

High specificity (K or R, not followed by P)

Acetylated form commercially available (acetylation lessens autodigestion)

Autolysis peaks are great internal calibrants

(842.509 and 2212.11)

Protein Identification by MS/MS

Input: •  Mass of the parent ion, and pattern

match to the MS/MS spectrum Output: •  Amino-acid sequence of the peptide

and protein of origin

Database Searching: MS/MS

De Novo Peptide Sequencing

100

0 250 500 750 1000 m/z

% In

tens

ity

E L F

KL

SGF G

E D E

L E

E D E L

De Novo Interpretation

Amino-Acid Residual MW Amino-Acid Residual MW A Alanine 71.03712 M Methionine 131.04049 C Cysteine 103.00919 N Asparagine 114.04293 D Aspartic acid 115.02695 P Proline 97.05277 E Glutamic acid 129.04260 Q Glutamine 128.05858 F Phenylalanine 147.06842 R Arginine 156.10112 G Glycine 57.02147 S Serine 87.03203

H Histidine 137.05891 T Threonine 101.04768 I Isoleucine 113.08407 V Valine 99.06842 K Lysine 128.09497 W Tryptophan 186.07932 L Leucine 113.08407 Y Tyrosine 163.06333

Common Posttranslation Protein Modifications

Analysis of Protein Complexes: Mitochondrial Proteins Resolved by 2D Native Blue-Gels

Ion Trap MS

HPLC-Chip/MS

interface

HPLC-Chip (not to scale)

NanoLC

Nanospray tip, tip assembly & fittings

Nano LC Column

Enrichment column, capillaries, fittings, frits HV ESI contact

RF tag

HPLC-Chip

HPLC or Capillary Electrophoresis Coupled to MS: High-Res Peptide Separation with “Chip-Column”

General Strategy for Proteome Analysis

- Sample Preparation (sub-proteome?) - Protein Separation (2-DE, Capillary LC) - Protein Identification (MALDI-TOF, MS/MS) - Specify Nature and Position of Modifications (analyze protein fragmentation by MS)

Quantifying Absolute and Differential Protein Expression by Mass Spectrometry

-­‐  Stable  Isotope  Labeling  by  Amino  acids  in  Cell  

culture  (SILAC)  

-­‐  Isotope  Coded  Affinity  Tag  MS  (ICAT)    -­‐        Label-­‐free  QuanRficaRon    -­‐        Fluorescence  MulRplex  2-­‐DE    

SILAC

Stable Isotope Labeling by Amino acids in Cell Culture

ICAT

Isotope Coded Affinity Tag MS

METABOLOMICS

Metabolites, not Genes or Proteins Define a Cell’s Phenotype

Untargeted Profiling of Metabolites in Cell Extracts & Biofluids

Targeted £  Look for known metabolites, one-at-a-time £  Absolute quantification, based on comparison with standards

Untargeted £  Quantify known and unknown metabolites, en-masse £  Metabolites (aka “features”) defined by LC RT/accurate mass

pairs £  Relative quantification, based on ion count abundance £  Differentially-expressed features established by ANOVA £  Molecular identities based on comparison with Stds & database

QTOF for Metabolite Profiling £   Accurate  mass  (<1  ppm)  to  enable  unequivocal  assignment    of  formulae  &  MS/MS    £   Robust  high-­‐resoluRon  chromatographic  systems  and  soZware  for          reproducible  specificaRon  of  “features”  with  broadly  differing  biophysical            properRes  (i.e.,  all  cellular  metabolites  in  the  range  of  50  -­‐1000  Da  m/z)    

£   Wide  dynamic  range  and  low  fmole  detecRon  to  broadly  cover  feature  space    £   Use  searchable  small  molecule  database  for  feature  IdenRficaRon  (METLIN)    

£   StaRsRcal  soZware  for  relaRve  quanRficaRon  of  “features”  and  group  changes    

£   Standards  and  MS/MS-­‐based  fragmentaRon  to  annotate  the              growing  METLIN  database  with  RT/accurate  mass  pairs  for              confident  molecular  idenRficaRon  of  features  in  high-­‐throughput  

UHD  QTOF  

Raw  MS  TOF  data  (RT,  m/z,  Abundance)    

Feature  Alignment  (  RT,  Mass)  

Metabolite  ID,STD  RT  match  DB  search    and  fragmentaRon  confirmaRon      

Molecular  Feature  ExtracRon  

Chemometrics  profiling  &MulRvariate  Analysis    

)  

Pathway  interpretaRon  

Recursive  Analysis  (Find  by  ions)  

Untargeted Metabolite Profiling Data Acquisition/Analysis Workflow

≈850  features  have  a  METLIN  database  match      

Taurine Carnitine Proline Arginine Uric acid

Betaine Valine Guanosine Lysine Citrulline

Creatinine Tryptophan Ornithine LysoPE(18:0) LysoPC(18:0)

C Extracted ion counts showing peaks for 56 repeated quantifications (as overlays) for some exemplary molecules

A B Profile plot for 56 repeated injections of a human serum sample: normalized ion intensities for 615 metabolites quantified in all samples shows a flat

run-to-run distribution

Overlay of chromatograms for 56 repeat analyses of a human serum sample showing reproducible quant. of total ion counts

Nor

mal

ized

Inte

nsity

Val

ues

-20

-10

0

20

10

-

-

-

-

-

10 20 30 40 50 Injection Number Retention Time (min)

Ion

Inte

nsity

1 2 3 4 5 6 8 7 9 10 11 12 13 14 16 15 17 18 14 16 15 17 18

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1 1.1 1.2 1.3 1.4 1.5 -

- - - - - - - - - - -

- - - -

x 107

Repeated Metabolite Profiling of a Single Human Plasma

!"#$%&$'()$*(+,$($-.//,0,)',1!"#$%&$'()$*(+,$($-.//,0,)',1Screened

Well

GA-I

Screened

Well

GA-I

Not Screened

Affected

GA-I

The above siblings have the same genetic defect, Glutaric Acidemia, type I (GA-I). Unlike the flanking sisters, the severly-afflicted middle child did not benefit from early screening, diagnosis and treatment. (From Dr. Robert E. Grier, Detroit MC).

Benefit of Early Screening for IEMs

Limitations to Newborn Screening for IEMs

Cost – Each condition is extremely rare (1:5,000 to 1: 250,000). Too expensive to individually test for all treatable genetic diseases, even if assays were available - and they are not! Speed – Screening needs to be complete between 2 and 3 days after birth, to enable the initiation of appropriate therapy before mother and baby have left the hospital. Current approaches for IEM screening specifically target each condition and do not offer the possibility of broad coverage. Sample Availability - Babies simply don’t have enough blood to enable testing for all known IEMs using prevailing methods

The Heel Stick

Results of Expanded NBS by MS/MS: Doing the Math  

(Schulze  et  al.  Pediatrics  2003)  §  250,000  neonates  screened  for  23  IEM  

–  Overall  sensiRvity  =  100%  for  classic  forms  of  disorders  –  106  newborns  with  confirmed  metabolic  disorder  (70  required  

treatment)  

–  Prevalence  of  23  metabolic  disorders  =  1/2,400  –  Overall  specificity  =  99.67%  –  825  false  posiRves  (0.33%  false  posiRve  rate)  –  61  /106  were  judged  to  have  benefited  from  screening  and  treatment  

Beneficiaries  =  58%  of  true  posiNves  =  1/4,100  newborns  

Potential Strengths of Untargeted Metabolite Profiling for IEM Screeing

of Neonates    Coverage:  mulRplex  screening  for  known  and  unrecognized  IEMs  

Specificity:  Low  false-­‐posiRve  rate  due  to  verificaRon  by  consideraRon  of  mulRple  IEM-­‐informing  metabolites  

Sample:      Only  a  few  µl  of  plasma  needed  for  comprehensive  assays  

Speed:    Screening  can  be  completed  before  baby  and  mom  leave  hospital  for  swiZ  iniRaRon  of  therapy,  if  needed  

Cost:    Low,  aZer  iniRal  investment  in  hardware/soZware  IEM  Insights:    Possibility  to  establish  new  knowledge  regarding  

the  systems  biology  of  various  monogeneRc  diseases    

Proof-of-Principal Test of Untargeted Metabolite Profiling

for IEM Screening

In collaboration with Dr. Tila Worgall & the Metabolism Lab at Columbia Presbyterian Hospital, NYC

Tyrosinemia  Argininosuccinic  Aciduria  

Hyperprolinemia  

Babies  are  unaffected  at  birth,  but  later  poisoned  by  failure  to  effecRvely  metabolize  proteins  in  mothers  milk    A  few  days  aZer  birth,  develop  hyperammonemia,  ketoacidosis,  vomiRng,  respiratory  distress,  lethargy,  seizures  &  possibly  coma    Prevalence  of  1  in  70,000  (autosomal  recessive).    Untreated  results  in  death  –  treated  can  result  in  severe  developmental  disabiliRes    TREATMENT  –  Low  protein  diet  with  arginine  supplementaRon.    Liver  transplant,  as  for  other  Urea  Cycle  defects    

Argininosuccinate Lyase Deficiency (ASA; Urea Cycle Defect)

ASL Deficiency

ASL-­‐deficiency    Control  Heat  map,  depicRng  hierarchical  clustering  analysis  pefrormed    on  1185  masses  idenRfied  with  100%  frequency  in  at  least  one  group,  comparing  plasma  from  control  neonates  (n  =  4)  vs.  a  paRent  diagnosed  with  argininosuccinate  lyase  deficiency.    All  samples  were  analyzed  in  triplicate          

Untargeted Plasma Metabolite Profiling of Patient with Confirmed ASL Deficiency

ANP  chromatog.  +  ion  monitoring  

A  Volcano  Plot  of    argininosuccinate  lyase-­‐deficiency  vs.  control  yielded    228  features  that  were  exported  for  recursion  analysis  .    Of  these,  167  features  were  expressed  with  100%  frequency  in  at  least  one  group  and  124  /167  demonstrated  fold-­‐changes  >  2.0,  with  P    <0.05.  Right  Panel:  PCA,  performed  on  167  features  idenRfied  with  100%  frequency  aZer  recursion  analysis.    LeX  Panel:  Loadings  Plot  

ASL Deficiency Statistics

Loading  plot  (colored  by  mass)  demonstrates  that  argininosuccinate  and  citrulline  have  the  top  loadings  among  124    discriminaRng  metabolites  

Argininosuccinate  

ASL Deficiency - PCA Loading Plot

Citrulline

Loading  plot  (colored  by  mass)  demonstrates  that  argininosuccinate  and  citrulline  have  the  top  loadings  among  124    discriminaRng  metabolites  

Argininosuccinate  

ASL Deficiency - PCA Loading Plot

Citrulline

con

con

ASA

ASA

ASL Deficiency

Inosine Hydrouracil Deoxyinosine

ASA ASA ASA

con con con

Untargeted Metabolite Profiling Offers a Powerful Systems Biology Approach for

Discovering the Actions of Genes and Drugs - Likely to Revolutionize Clinical Diagnostics

CONCLUSION