XIANG ZHU - University of Georgia

SUBCELLULAR PROTEOMIC ANALYSIS USING GELC-MS/MS APPROACH

by

XIANG ZHU

(Under the Direction of Ron Orlando)

ABSTRACT

Mass spectrometry (MS) has become a widely used analytical technique to study the

proteome of complex biological matrices. In this research, the gel based proteomic approach

(GeLC-MS) was developed and applied to solve biological problems in different organisms such

as Trypanosoma cruzi (T. cruzi) and embryonic stem cells.

A membrane proteomic analysis of the protozoan parasite T. cruzi was performed. Using

two individual membrane enrichment preparations, a total of 551 protein groups got identified

from around 80 LC-MS/MS runs. Both two preparation strategies were effectively enriching

some respective membrane proteins. The identified membrane proteins accounted for almost

40% of the protein identifications within the whole proteome, which shows great enrichment

compared to regular global analyses which only have about 5%. The most attractive result for us

is the identification of 87 trans-sialidases, 9 mucin associated surface protein (MASP), 3 mucins,

and 2 GP63 proteins. These GPI anchored surface proteins are involved in parasite survival and

cell invasions, thus could become potential vaccine targets.

A comprehensive proteome analysis of T. cruzi intracellular amastigotes was introduced.

Subcellular organelle and membrane enriched fractions as well as cytosol soluble fractions were

individually obtained and analyzed using GeLC-MS/MS approach. In addition to matching the

MS/MS spectra to the annotated proteome database, we performed a whole genome search in

order to identify additional genes potentially missed in the annotation of the T. cruzi genome. We

also utilized a hybrid identification tool (ByOnic) for the identification of unanticipated

mutations caused by different T. cruzi strains.

We also report here the application of GeLC-MS approach to resolve some protein

isoforms’ identification including trans-sialidases, GP63, etc in T. cruzi. Additionally this

technique was utilized to analyze the mouse embryonic stem cell proteome and focused on

looking for some potential protein degradation products. Our identification data has shown that

this approach is efficient and helpful for discovering the protein degradation process, which

plays essential roles in biological cellular functions and activities.

INDEX WORDS: Mass spectrometry, Proteomics, Membrane, GeLC-MS, Protein isoform,

Degradation, Trypanosoma cruzi, Embryonic stem cell


by

XIANG ZHU

B.S., University of Science and Technology of China, China, 2003

A Dissertation Submitted to the Graduate Faculty of The University of Georgia in Partial

Fulfillment of the Requirements for the Degree

DOCTOR OF PHILOSOPHY

ATHENS, GEORGIA

2011

© 2011

Xiang Zhu

All Rights Reserved


by

XIANG ZHU

Major Professor: Ron Orlando

Committee: Lance Wells

Joshua Sharp

Electronic Version Approved:

Maureen Grasso

Dean of the Graduate School

The University of Georgia

December 2011

iv

DEDICATION

This dissertation is dedicated to my grandfather, Leting Zhu, my parents, Weihan Zhu and

Xiulan Zhou, my wife, Liling Zeng, and my daughter, Julia Zhu for their unconditional love and

support.

v

ACKNOWLEDGEMENTS

First and foremost, I would like to thank my advisor, Dr. Ron Orlando, for his guidance,

patience, encouragement and kind support during these years, as well as for providing me with

excellent experiences and facilities. I feel fortunate and enjoyable to study and conduct research

under his guidance.

I would also like to thank my committee members Dr. Lance Wells and Dr. Joshua Sharp,

for their insightful and helpful discussions on my thesis.

My sincerest gratitude is also expressed to all the individuals who I have had the honor of

working with on my projects: Dr. James Atwood, Brent Weatherly, Dr. Rick Tarleton, Dr. Todd

Minning, Dr. Marshall Bern, Dr. Matt Bechard and Dr. Stephen Dalton. My appreciation also

goes to all past and present members of the Orlando group for their collaboration and help.

Finally, I would like to thank my entire family and friends for their support.

vi

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS .............................................................................................................v

CHAPTER

1 INTRODUCTION .........................................................................................................1

2 LITERATURE REVIEW ..............................................................................................5

3 MEMBRANE PROTEOMIC ANALYSIS OF THE PROTOZOAN PARASITE

TRYPANOSOMA CRUZI .............................................................................................22

4 SUBCELLULAR PROTEOMICS OF TRYPANOSOMA CRUZI INTRACELLULAR

AMASTIGOTE............................................................................................................45

5 RESOLVING PROTEIN ISOFORMS IN PROTOZOAN PARASITE

TRYPANOSOMA CRUZI USING GELC-MS/MS APPROACH ................................72

6 GELC-MS/MS ANALYSIS ON EMBRYONIC STEM CELL PROTEIN

DEGRADATION ........................................................................................................96

7 CONCLUSIONS........................................................................................................115

1

CHAPTER 1

INTRODUCTION

Mass spectrometry (MS) is a widely used analytical technique to determine molecular

mass of unknown compounds by measuring the mass-to-charge ratios (m/z) of molecular ions.

For a long time, this technique is mostly limited in the small molecules area and characterization

of biological large molecules is not desirable.1 The main reason is because the traditional

methods such as electron ionization (EI) and chemical ionization (CI) can not vaporize those

molecules without fragmenting them. The invention of soft ionization methods such as Matrix

Assisted Laser Desorption Ionization (MALDI)2 and Electrospray Ionization (ESI)

3 facilitate the

application of analyzing large molecules with MS.

Analogous to genomics which is the study of gene, proteomics is described as the large-

scale study of proteins expressed in complex matrices, such as cells, tissues, serum, etc.4,5

MS

based proteomics is widely used for protein identification, post-translation modification (PTM)

determination and quantitative analysis. Compared to mRNA analysis, proteomics is a more

accurate analytical method to reveal the real gene product expressions. This is because for many

organisms such as T. cruzi, the control of gene expression happens post-transcriptionally and the

mRNA is not always translated to proteins.6,7

The correlation between mRNA and protein levels

is becoming very poor. Herein, we applied the gel based proteomics method to investigate the

proteome of different organisms such as T. cruzi and embryonic stem cells.

In chapter 3, we performed a membrane proteomic analysis of the protozoan parasite T.

cruzi. The membrane fractions were enriched using three different preparations: sucrose cushion

2

method, detergent resistant preparation and the combination of sucrose and detergent. Our

analysis has identified an essential number of membrane proteins including those

immunodominant trans-sialidase and mucin proteins. Identified membrane proteins also show

various distributions among the preparation methods. The methods developed in this study have

been extensively applied in all the other projects.

In chapter 4, we focused our study on subcellular proteomics of intracellular amastigote

which is one of the T. cruzi mammalian stages. In the protein identification data processing,

besides matching the MS/MS spectra to the annotated proteome database, we also performed the

whole DNA search in order to identify additional genes potentially missed in the T. cruzi genome

sequencing annotations. We also utilize a hybrid identification tool (ByOnic) that can perform a

wildcard-database search strategy for the identification of unanticipated modifications and

potential mutations.8 The aim of this work was to find much more interesting gene products that

are normally expressed at low levels and less investigated before. The results derived from this

proteome analysis will largely expand the current datasets of the T. cruzi proteome and help us

better understand the parasite’s system biology.

For T. cruzi, at least 30% of this parasite’s genome is composed of multi-copy gene

families. These protein isoforms usually contain very similar sequences with some shared

peptides and regular shotgun proteomics experiments like MudPIT can't differentiate them well.

In chapter 5, we demonstrated how the GeLC-MS approach is utilized to resolve protein

isoforms based on combining shotgun proteomic results with molecular weight information and

protein grouping. Similar methods were also selected to evaluate some protein degradation

process in an embryonic stem cell system, described in chapter 6. More comprehensive studies

3

on the ES cell protein degradation products and related pathways could make valuable

contribution to the development of stem cell differentiation researches.

4

REFERENCES

(1) Domon, B.; Aebersold, R. Science 2006, 312, 212.

(2) Karas, M.; Hillenkamp, F. Anal Chem 1988, 60, 2299.

(3) Fenn, J. B.; Mann, M.; Meng, C. K.; Wong, S. F.; Whitehouse, C. M. Science

1989, 246, 64.

(4) Blackstock, W. P.; Weir, M. P. Trends Biotechnol 1999, 17, 121.

(5) Anderson, N. L.; Anderson, N. G. Electrophoresis 1998, 19, 1853.

(6) Dhingra, V.; Gupta, M.; Andacht, T.; Fu, Z. F. Int J Pharm 2005, 299, 1.

(7) Paba, J.; Ricart, C. A.; Fontes, W.; Santana, J. M.; Teixeira, A. R.; Marchese, J.;

Williamson, B.; Hunt, T.; Karger, B. L.; Sousa, M. V. J Proteome Res 2004, 3, 517.

(8) Bern, M.; Cai, Y.; Goldberg, D. Anal Chem 2007, 79, 1393.

5

CHAPTER 2

LITERATURE REVIEW

2.1 Mass Spectrometry

Mass spectrometry (MS) is a powerful analytical tool to determine molecular mass of

unknowns by measuring the mass-to-charge ratios (m/z) of gas phase molecular ions. A typical

mass spectrometer contains three major components: ion source, mass analyzer and detector. The

first step in MS analysis is to generate the gas phase analyte molecular ions. The main traditional

methods are electron ionization (EI) and chemical ionization (CI), which are commonly used for

volatile small molecules. The large, nonvolative and thermally unstable analytes such as proteins

and peptides can not be effectively vaporized without fragmentation, thus making these two

methods not applicable to analyze biomolecules. The breakthrough for structural analysis of

large biomolecules using MS occurred in 1980's with the invention of matrix-assisted laser

desorption/ionization (MALDI)1 and electrospray ionization (ESI).

2

Matrix Assisted Laser Desorption/Ionization (MALDI)

In 1985, MALDI was firstly termed by Franz Hillenkamp, Michael Karas and their

colleagues.1 They found that with a pulsed 266 nm laser, the amino acids could be easily ionized.

The breakthrough of this technique came in 1987 when Koichi Tanaka and his co-workers of

Shimadzu Corp applied this soft method to ionize a 35KDa protein with the proper laser

wavelength and matrix.3 For sample preparation, the analyte was firstly mixed with matrix

molecules. The matrix compounds are usually having low molecular weight, acidic and can

absorb the laser irradiation at applied wavelength.4,5

Matrix molecules protect the analytes from

6

strong laser irradiation and transfer part of the charge to them, causing the analyte co-

evaporation and ionization. Most of the molecular ions produced through MALDI are singly

charged.

Electrospray Ionization (ESI)

Another soft ionization technique developed for large biomolecules is electrospray

ionization (ESI), introduced by John Bennett Fenn and coworkers.2 In this technique, a strong

electric field is imposed on a liquid containing the analyte flowing through a capillary. At the

end of spray tip, highly charged droplets were produced due to charge accumulation. The liquid

changes the shape to a "Taylor cone", which can hold more charges than a sphere.6-8

With the

evaporation of solvents, the droplet size is shrunk and become unstable due to high charge

density. After it reaches the Rayleigh limit, the droplets are broken apart and form Coulomb

fission. There are several advantages for ESI in the application of MS. First, this ionization

method can produce multiply charged ions, making high molecular weight ions possible to be

detected at relatively low mass-to-charge ratio range. Secondly, ESI can be easily coupled to on-

line high performance liquid chromatography (HPLC) system or electrophoresis.9,10

Mass Analyzers

Mass spectrometers usually consist of three major components: an ion source, a mass

analyzer and an ion detector. Among them the mass analyzer plays critical roles for separating

ions based on their m/z ratios through electric or magnetic field. There are several different types

of mass analyzers; most widely used are quadrupole, time-of-flight (TOF), ion trap and Fourier

transform ion cyclotron resonance (FTICR). Each mass analyzer has its own advantages and

limitations. Choosing proper mass analyzer in different projects should be based on the

7

individual application purpose. In the following paragraphs, we will briefly discuss the working

mechanisms for some commonly used mass analyzers.

Quadrupole Mass Analyzer

This type of mass analyzer is composed of four parallel cylindrical rods. Opposite rod

pair is connected electrically. Fixed direct current (DC) and alternating radio frequency (RF)

potentials are applied to these pair of rods, generating the oscillating electric field. During

analysis, the Ions move between the four parallel rods. Only ions with a selected m/z value will

have a stable trajectory in the oscillating electric field. Those ions can pass through the

quadrupole and successfully reach the detector for a given RF/DC ratio. Other unstable ions will

collide with the rods and get disappeared. The mass spectrum is generated by continuously

altering the RF and DC voltages to scan a range of m/z values. The quadrupole has a mass

accuracy of 0.1~1Da and unit mass resolution.11

The sensitivity of this mass analyzer is in

moderate range. One of the most popular instruments using quadrupole as mass analyzer is triple

quadrupole spectrometer (QQQ).12

In this instrument, the first quadrupole Q1 is used as a mass

filter to select parent ions. Q2 has the function of collision cell and fragment ions using collision

induced dissociation (CID). The third Q3 quadrupole is applied to filter fragment ions. The major

scan modes and application for this instrument is the capability of performing precursor ion scan,

neutral loss scan and multiple reaction monitoring (MRM) scan.

Time-of-Flight (TOF) Mass Analyzer

In TOF mass spectrometer, the ion's m/z value is determined by measuring the flight

time. Ions are accelerated by a fixed strength of electric field (2-25 kV). During acceleration, all

the ions travel through the same distance by the same force, thus they obtain the same kinetic

energy. The ions were selected following the equation (zU=KE=1

/2

m/v2 ), where U is the

8

strength of the electric field and contains constant. The velocity of an ion is inversely

proportional to the square-root of its m/z value. Therefore, larger m/z ions need more time to fly.

Typical TOF instruments can have a mass accuracy in the tens of ppm. The sensitivity of this

mass spectrometer is very high because all ions are transmitted to the detector. The traditional

TOF has a low resolution, which is only around 500 units.13

In recent years, there are two major

techniques largely increase the TOF's resolution. The first one is "Delayed Extraction".14

In this

method, the applied accelerating voltage is postponed some short time delay after the laser pulse.

Ions with greater initial kinetic energy have a higher velocity and are closer to the extraction

electrode before the accelerating voltage is applied. After a certain time, the delayed extraction

pulse is added to compensate for the spread in kinetic energies. Finally, the ions with the same

m/z will reach the detector at the same time. The resolution can also be improved by a

reflectron.15,16

The reflectron is an electrostatic field which reflects the ions towards the detector.

The ions with higher initial kinetic energy penetrate deeper into the electrostatic reflectron and

spend a longer time to reach the detector. On the other hand, lower kinetic ions of the same m/z

will flight a shorter distance. Finally, ions of same m/z will arrive the detector at the same time.

Besides that, reflectron increases the flight path length in a given length of flight tube. Current

TOF instrument applying these techniques can achieve a resolving power of more than 10,000.

Quadrupole Ion Trap Mass Analyzer

Quadrupole ion trap is the three dimensional analogue of a quadrupole mass analyzer.

This device contains three electrodes with hyperbolic surfaces: two endcap electrode and one

ring electrode. DC and main RF electric fields are applied on the electrodes to trap the ions. By

adjusting the RF and DC voltage at the electrodes, ions can be excited, become unstable and

ejected out for detection when their resonance frequency matches the resonance applied to the

9

trap.17

The mass spectrum can be obtained by scanning the fields at which ions are ejected from

the trap to the detector. Ion traps are typically very sensitive since they accumulate the ions in the

trap before doing mass separation. The other advantage of ion trap is the availability of doing

multi-stage tandem mass spectrometry by operating sequential analysis in time. However

traditional ion trap has limited resolution, low ion-trapping capacity, and space-charge effects

due to limited size. The development of linear ion trap analyzer (LTQ) has provided a higher

trapping capacity by using two dimensional quadrupole field instead of a 3D field. The mass

accuracy, sensitivity and resolution are all largely improved with this new technique.18-20

Fourier Transform Ion Cyclotron Resonance (FTICR)

A very high mass accuracy and resolution can be achieved by FTICR. FTICR mass

spectrometers use high magnetic fields under ultra-high vacuum to trap the ions and cyclotron

resonance to excite and detect ions.21

The extremely high mass accuracy makes FTICR trustable

to determine the molecular composition based on accurate mass since most elements have mass

defects.22

Combination of LTQ and FTICR are able to perform the isolation and fragmentation of

ions outside FT. In this way, the precursor ion mass is scan with high accurate FTMS, but the

fragment ion masses can be acquired using the fast ion trap scan.23

The limitation of FTICR scan

is the relatively lower sensitivity due to the slow scan rate. Another drawback is the significant

high cost of the instrument and maintenance.

2.2 Proteomics

Analogous to genomics which is the study of gene, proteomics is described as the large-

scale study of proteins expressed in complex matrices, such as cells, tissues, serum, etc.24,25

Besides protein sequence identification, proteomics is also targeted at other areas such as post-

translational modification (PTM) determination, modification site mapping, quantitative analysis

10

of protein expression, protein-protein and protein-carbohydrate interactions etc. The major tools

used in proteomic analysis are the combination of mass spectrometry, advanced separation

techniques and bioinformatic data processing methodologies. In general, there are two primary

strategies used in MS-based proteomics: top-down and bottom-up proteomics.26

For top-down

proteomics, the intact protein is directly fragmented in the gas-phase followed by MS analysis. In

bottom-up proteomics, the protein mixtures undergo proteolytic digestion into peptides prior to

being analyzed by MS.

Top-down Proteomics

In top-down proteomics approach intact proteins are ionized and subjected to gas phase

fragmentation in the mass spectrometer. The major advantages of top-down proteomics are the

high protein sequence coverage and the possibility to detect all PTMs.27

In addition, it doesn't

require the protein digestion step which is time-consuming. This technique also has some

limitations compared to bottom-up proteomics. First, the top-down approach can't obtain

satisfied results of intact proteins larger than 50 kDa. Second, the analysis of intact proteins

generally requires FTICR to provide high resolution and mass accuracy measurements, and the

cost is very expensive. Third, the protein dissociation mechanism is still not well understood and

corresponding powerful bioinformatic tools are quite limited.28,29

For large scale high-throughput

proteomics, top-down approach may not be a good choice at current status.30

Bottom-up Proteomics

Bottom-up proteomics is the most widely used analytical approach to perform large scale

proteome identification and quantitation. In this method, the protein analytes are firstly

proteolytic digested into peptides which are further analyzed by MS. The obtained peptide

information is then assembled into protein sequences for identification purpose. Generally, there

11

are two approaches for bottom-up protein identification: peptide mass fingerprinting (PMF) and

tandem mass spectrometry (MS/MS).31

MALDI-TOF is usually utilized for PMF analysis. In this method, a list of experimental

peptide mass is generated from mass spectrum of the peptide mixture. The measured masses are

then compared with the in-silico theoretical peptide masses from the protein database. The

results are statistically analyzed to make the proper identification. Typical PMF requires less

complex protein mixtures, so separation of the protein mixtures before analysis is essential. The

most commonly used technique is two dimensional gel electrophoresis (2DGE) where proteins

are separated in one dimension by their isoelectric point and molecular weight in the second

dimension.32-34

The second approach in bottom-up proteomics is using tandem mass spectrometry. This

is also the one we choose in our proteomic analysis. The prominent feature of this method is the

ability of elucidating the peptide sequence by fragment ions. The most common method of

fragmentation is called collision induced dissociation (CID). Selected precursor ions are collided

with inert gas such as helium or nitrogen to generate fragment ions. Fragmentation of the peptide

occurs at three locations on the backbone. After fragmentation, if the charge is retained on the N-

terminal part of the peptide, the ion is named as a, b, or c fragment ion. Ions containing C-

terminal fragments are then defined as x, y, z ions. Regular tryptic digested peptides with CID

fragmentation mostly result in b and y ions. MS/MS based bottom-up proteomics is usually

applied to study a complex biological system which requires effective fractionation separations.

This is achieved mainly through two approaches: gel-free and gel-based analyses.

Gel-free Approach

12

Gel-free approach or sometimes referred as shotgun proteomics is a method that utilizes

peptide separation before MS/MS analysis.35,36

The protein mixtures are directly in-solution

digested, the resulting tryptic peptides are further separated by multi-dimensional high

performance liquid chromatography and analyzed by ESI-MS/MS.37

The multi-dimensional

peptide separation can be varied based on different physicochemical properties.38

For example,

reverse phase liquid chromatography (RPLC) is the most popular one, which separates peptides

by hydrophobicity. Strong cation exchange (SCX) is known to separate peptides by charge and

size exclusion chromatography (SEC) is based on molecular size difference. Moreover, the

orthogonal combination of two or more coupled chromatographic approaches has been applied to

separate complex peptide mixtures. Multidimensional protein identification technology

(MudPIT)39

is one of the most famous gel-free proteomic technique, where SCX functions as the

first dimensional separation and RPLC provide the second separation before introduced into MS.

In recent years, this promising technique has been widely used in many applications and proven

to extensively increase the dynamic range of identifications.40-42

Gel-based Approach (GeLC-MS)

In bottom-up proteomics, reducing the sample complexity is an important factor for

detecting larger dynamic range of products. Compared to gel-free technique that performs all the

separation at peptide level, the GeLC approach43-47

we introduced here initially separates the

proteins by 1D gel electrophoresis. Proteins in the excised gel bands are then subsequently

reduced, alkylated, and in gel digested. Generated peptides were extracted and separated through

an on-line RPLC system before analyzed by MS/MS. There are several advantages using this

strategy. First, the separation at protein level can isolate some low abundant proteins from the

high abundant ones. This significantly increase the dynamic range of the analysis and helpful to

13

identify new gene products. Second, the gel based method is highly compatible with detergent

and denature agents. This is particularly important for samples that have poor solubility during

gel-free analysis. Most of the salts, which interfere with ESI mass spectrometry, are also easily

washed out from the gel matrix. Third, gels can be stored for quite a long time without changing

the analysis results. In addition, we have shown in our analysis that the GeLC-MS approach can

facilitate to resolve protein isoforms and detect possible protein degradation process. However it

still has some limitations in this technique, for example the relatively poor peptide yield, the risk

of contaminating analytes with keratins or other contaminants in the gel processing steps and

lower reproducibility compared to gel-free approach.

Data Analysis

Assigning hundreds of thousands of MS/MS spectra to peptide sequences is another

important step in high-throughput bottom-up proteomics. This task is usually fulfilled by

bioinformatic data analysis strategies. The most commonly used method is through database

searching programs, such as SEQUEST, Mascot and X!Tandem.48-50

These programs compare

the experimental spectra (both parent ion mass and MS/MS spectrum) with the in-silico

predicted spectra of peptides from the protein database. A score (Xcorr value for SEQUEST and

Mowse score for Mascot) is then assigned for candidate peptides to represent the similarity

between the experimental and the theoretical data, and therefore becomes the primary

discriminating factor for separating correct from false positive identifications. Although these

methods are powerful for general peptide mapping, they still have limitations in the identification

of modified peptides. Allowing multiple modifications in database search will largely slow down

the running process, and it can't effectively identify the unexpectedly modified peptides. De novo

sequencing based programs such as PEAKS, DenovoX, etc can better handle the unexpected

http://dict.cn/strategies

14

modification problem.51

It is also almost the only way to identify unknown species which don't

have public protein databases. While this technique usually requires more complete

fragmentation information and better spectrum qualities, thus less sensitive for unmodified

peptides than database searching. Nowadays, some hybrid approaches combine small amount of

de novo sequencing and database searching. Those strategies are applied to provide a more

sensitive searching and having the ability to resolve unexpected modifications and mutations as

well. In our trypanosoma cruzi intracellular amastigote study (chapter 4), we utilize one of these

approaches ByOnic52

to search PTMs and mutations.

Subcellular Proteomics

One of the major challenges in proteomics is to achieve comprehensive analysis and

applicable of detecting low abundant proteins. Most eukaryote cells express a large number of

genes, for example the number of expressed genes in a mammalian cell can be more than

10,000.53

Because of this, a lot of low abundant genes are inevitably hidden by those high

abundant proteins. In regular whole cell proteomic analysis, it's impossible to detect the entire

proteome, and the identification are more focused on those high abundant expressed genes.

However, a lot of low abundant proteins are expressed in specific subcellular localizations

although they only exist in low copy numbers. Thus a combination of organelle subcelluar

enrichment and proteomics becomes essential for comprehensive analysis, especially with a

purpose of detecting particularly low abundant organelle proteins.54,55

The most commonly used

and effective subcellular fractionation method is through differential centrifugation. The working

mechanism of this technique is based on the different density of various organelles. The

fractionation can be achieved either through centrifugation with different speed or density

gradient centrifugation.56,57

Both methods can generate several fractions enriched with specific

15

organelles. According to the density from light to heavy, those fractions are mainly contained

with 1) nucleus; 2) heavy mitochondria, cytoskeletal networks; 3) light mitochondria,

peroxisomes and lysosomes; 4) endoplasmic reticulum (ER) and endosomes; 5) golgi apparatus,

microsomes and plasma membranes; 6) cytosol. There are several other techniques besides

centrifugation are often applied for fractionation enrichment. For example, free-flow

electrophoresis can be used to isolate plasma membrane vesicles, detergent-resistant membranes,

and mitochondria based on electrical charge effects.54

Ligand affinity for immunoisolation has

been applied to purify synaptic vesicles and caveolae, etc.58-61

Among all the subcellular organelle fractions, the cell surface membranes have attracted

the most interest in proteomic studies. It consists of lipid bilayer with membrane embedded and

associated proteins. The major role of the membranes is to provide a physical barrier between the

cell and its environment. The membrane proteins carry out many important biological functions

and get involved in a variety of cellular processes, including cell-cell interactions, ion

transportation, and signal transduction, etc. Membrane proteins also have great potential in drug

discovery. Currently, almost 70% of all known pharmaceutical drug targets are with membrane

proteins.62

There is also growing interest in the use of disease specific cell surface proteins as the

target of therapeutic monoclonal antibodies. The membrane proteins are usually categorized into

several different ones. Integral membrane proteins are amphipathic and permanently attached to

the membrane. Without the assistance of detergent, they are not easily released from the lipid

bilayer. Peripheral membrane or membrane associated proteins are temporarily attached either to

the lipid bilayer or to integral proteins by non-covalent interactions. The loosely bound

interaction can be broken by high pH or high salt solutions. Another important membrane protein

on the cell surface is glycosylphosphatidylinositol (GPI)-anchored proteins. They are attached to

16

the cell surface through a glycolipid linker. The regions containing them are defined as "lipid

rafts".63-65

Although proteomics has gained numerous progresses in the analysis of soluble

proteins in recent years, studies of membrane proteins have been largely lagged behind.66,67

This

is mainly because 1) membrane proteins are usually in low abundance; 2) their hydrophobic

domains make the protein solubilization process difficult; 3) the detergent and denature agents

used for solubilization interfere with digestion and MS analysis. In recent years, improved

subcellular fractionation and enrichments as well as refined solubilization, modern MS

techniques have facilitated the membrane proteomic studies.68-72

The first large scale membrane

proteomics was conducted by Yate's group.40

In their research, an enriched yeast membrane

fraction is analyzed by MudPIT technique. 131 integral membrane proteins were identified, with

three or more predicted transmembrane domains from the 1,484 total identified yeast proteins.

Various membrane proteomic analyses have been performed in other different organisms to

understand specific biological questions.

In chapter 3 and 4, we are going to introduce our investigation of subcellular and

membrane proteome of trypanosoma cruzi, in which those membrane proteins play critical roles

for parasite invasion and survival from host immune response.

17

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(3) Tanaka, K.; Waki, H.; Ido, Y.; Akita, S.; Yoshida, Y.; Yoshida, T.; Matsuo, T.

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(14) Brown, R. S.; Lennon, J. J. Analytical Chemistry 1995, 67, 1998.

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(16) Kaufmann, R.; Chaurand, P.; Kirsch, D.; Spengler, B. Rapid Commun Mass

Spectrom 1996, 10, 1199.

(17) Stafford, G., Jr. J Am Soc Mass Spectrom 2002, 13, 589.

18

(18) Hager, J. W.; Le Blanc, J. C. J Chromatogr A 2003, 1020, 3.

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22

CHAPTER 3

MEMBRANE PROTEOMIC ANALYSIS OF THE PROTOZOAN PARASITE

TRYPANOSOMA CRUZI1

______________________________________________________________________ 1 Xiang Zhu, Brent Weatherly, Marshall Bern, James A. Atwood III, T.A. Minning, R.L.

Tarleton, Ron Orlando. To be submitted to Journal of Proteome Research.

23

ABSTRACT

The protozoan parasite Trypanosoma cruzi (T. cruzi) is the causative agent of Chagas’ disease,

which affects 16-18 million people and kills an estimated 50,000 people annually in Latin

American countries. The T. cruzi cell surface membrane proteins including trans-sialidase,

mucin-associated surface proteins (MASP) and gp63 proteins play important roles for parasite’s

host cell entry and immune escape. The trans-sialidase epitopes are also proven to dominate the

CD8+ T-cell response and thus are potential vaccine candidates. While these T. cruzi membrane

proteins are of critical importance, there were limited proteomic studies specifically targeting

them. Herein, the membrane enriched fractions were isolated from T. cruzi CL-Brenner strain

trypomastigotes using two protocols and characterized using bottom-up proteomics

methodology. There were a total of 551 protein groups identified from ~80 MS/MS runs. Both

preparation strategies were effectively enriching some respective membrane proteins. The most

attractive result for us is the identification of 87 trans-sialidases, 9 mucin associated surface

protein (MASP), 3 mucins, and 2 GP63 proteins. These GPI anchored surface proteins are

involved in parasite survival and cell invasions, thus could become potential vaccine targets.

24

INTRODUCTION

The protozoan parasite Trypanosoma cruzi (T. cruzi) is the causative agent of Chagas’ disease,

which is a chronic illness causing congestive heart failure and sudden death in the world. It

affects 16-18 million people and kills an estimated 50,000 people annually in Latin American

countries.1-3

Right now this disease has also been spread out in the U.S and at least 50,000 to

100,000 people are infected as well. More than 8 billion $ were lost regarding to the Chagas’

disease each year.4 T. cruzi has a complex life cycle, with four different life stages cycling

between the mammalian host and insect vectors. Metacyclic trypomastigotes are infective forms

living in the hindgut of the insect vectors such as triatomine bugs. The infection is initiated when

the blood-feeding insect vectors deposit their feces containing metacyclic trypomastigotes onto

the wounded mammalian skins. After they enter the infected cells around the wound, metacyclic

trypomastigotes differentiate into the amastigotes that reside in the host cell cytoplasm. After

many times of binary fission, a large number of amastigotes are produced in the host cells. Then

these amastigotes transform to the other infective flagellated trypomastigotes, which burst out

from the host cells and circulate in the blood stream to invade other cells throughout the human

bodies. Some of the trypomastigotes are ingested by the insect vectors during their blood meal

and differentiated into epimastigotes. The epimastigotes replicate in the vector midgut and

finally convert into metacyclic trypomastigotes thus finishing the life cycle. Currently diagnosis

of T. cruzi infection is very difficult and treatment is limited to chemotherapeutics, which are

highly toxic and exhibit many dangerous side effects, no effective vaccines have been developed

yet.

Membrane proteins that coat the parasite surfaces usually play very important roles in host cell

entry and immune evasion. Proteomic studies on these membrane proteins will help understand

25

the nature of parasites invasion and survival mechanisms and could explore the way for vaccine

development. In recent years several membrane proteomic studies have been done on some

parasite organisms causing important diseases. For example Sanders studied the raft-like

membranes of mature Plasmodium falciparum, a major protozoan parasite causing human

malaria.5 In Braschi’s recent paper, proteomic analysis was utilized to study surface membranes

of the blood fluke Schistosoma mansoni, which induce Schistosomiasis disease.6,7

Trypanosoma

brucei, the other dangerous trypanosoma parasite causing trypanosomiasis (or sleeping sickness)

in Africa has also been investigated using proteomic methods for their surface membranes by

Bridges and several other groups.8-10

Although with the significant importance, there have been

very limited proteomic studies specifically targeting these membrane protein expressions in T.

cruzi.11

Previous proteomic studies on T. cruzi were more focused on whole cell analysis and

comparative protein expressions on four developmental stages. Those global proteomic analyses

inevitably missed a large number of membrane proteins since the soluble proteins are dominated

in the identifications because of their relatively high abundance. While as we mentioned above,

with the increasing urgent need for development of vaccines and biomedical therapeutics, the

proteomic study of surface membrane proteome should attract much more concerns. In fact this

area has been underrepresented and lagged behind. Compared to the soluble proteins, membrane

proteins are usually of low abundance, high hydrophobicity and basic isoelectric points, thus

making the isolation and identification to be a challenging task.

In this research, we focused on the enrichment of membrane protein preparation and identify the

membrane proteins using bottom-up proteomics methodologies. We described two preparation

methods to enrich the membrane fractions from the whole cell lysates. The first method is based

on the sucrose cushion theory. Using sucrose cushion many soluble proteins and cytoskeleton

26

proteins are depleted, hence largely enrich the membrane fractions. In parallel the most

important surface membrane proteins such as trans-sialidase and mucins are known to be

glycosylphosphatidylinositol (GPI) anchored proteins. Previous results have shown these GPI

anchored proteins are enriched in cholesterol and sphingolipid lipid rafts membrane domains,

which are resistant to the non-ionic detergent at low temperatures.12-14

We adopted this idea and

introduced triton X-100 in the cellular lysates during preparation in order to isolate more GPI

anchored proteins like trans-sialidase, etc. The prepared membrane fractions were separated

using 1D-SDS-PAGE gel followed by in gel digestion. Generated peptides were then separated

by reverse phase liquid chromatography and analyzed by tandem mass spectrometry on both a

linear ion trap (LTQ) and hybrid linear ion trap Fourier transform (LTQ-FT) mass spectrometers.

Peak lists were searched using Mascot algorithm and protein identifications were selected below

a 1% peptide false discovery rate using the ProValT algorithm.15

Our analysis has identified an

essential number of membrane proteins including those immunodominant trans-sialidase and

mucin proteins. Identified membrane proteins also show various distributions between the two

preparation methods as expected.

MATERIALS AND METHODS

Parasite Preparation and Cell Lysis

The CL-Brenner lab strain of trypomastigotes were grown in monolayers of Vero cells (ATCC

no. CCL-81) in RPMI supplemented with 5% horse serum as previously described.16

Emergent

trypomastigotes were harvested daily and examined by light microscopy to determine the

percentages of trypomastigotes. The parasite cells (5 x 108) were harvested by centrifugation at

3,000 x g for 15 min at room temperature, washed three times with ice-cold PBS buffer, and

subjected to fractionation.

27

Membrane Preparation using Sucrose Cushion

Approximately 5 x 108 T. cruzi trypomastigote cells were suspended in 3 mL of ice-cold lysis

buffer (10 mM HEPES, 1 mM EDTA, pH 7.2) containing protease inhibitors. After 15 min

incubation at 4 C, cells were homogenized by 25 strokes of a 7 mL Dounce homogenizer. An

equal amount of sucrose buffer (10 mM HEPES, 1 mM EDTA, 500 mM sucrose, pH 7.2) was

added with additional 25 strokes of homogenizer. Cellular debris and unbroken cells were

removed as pellets after centrifugation at 6,000 g for 15 min at 4 C. The supernatant was

collected and centrifuged at 150,000 g for 1 hour at 4 C. Supernatant was removed and the crude

pellet membrane was incubated in 100 mM sodium carbonate solution (pH 11.3) for 15 min at

4 C. After incubation, the membrane pellet was collected by centrifuging at 150,000 g for 1 hour

at 4 C.

Lipid Raft Membrane Preparation using Non-ionic Detergent

Approximately 5 x 108 T. cruzi trypomastigote cells were suspended in 3 mL of ice-cold lysis

buffer (10 mM HEPES, 1 mM EDTA, pH 7.2) containing protease inhibitors. An equal volume

of 1% (w/v) Triton X-100 solution was mixed with the lysis buffer. After 50 strokes of

homogenizer, the homogenate was centrifuged for 15 min at 6,000 g at 4 C, pelleting the cellular

debris and unbroken cells. The supernatant was collected and centrifuged at 150,000 g for 1 hour

at 4 C. Crude membrane pellet was resuspended with 1% (w/v) Triton X-100 solution at 4 C and

incubated for 30 min. Mixed solution was centrifuged at 150,000 g for 1 hour at 4 C. The

supernatant was removed completely, leaving the pellet for gel separation.

1-D Gel Electrophoresis and in-gel Digestion

Crude membrane pellets from both preparations were resuspended in 20 l Laemmli buffer

(Sigma-Aldrich) and boiled at 80 C for 15 min. Solublized proteins were separated by 1-D SDS-

28

PAGE using NuPAGE 4-12% Bis-Tris (Invitrogen) gradient gels at 150 V for 2 hours. Gel lanes

from both preparations were washed twice in ddH2O for 15 min and then cut into ~20 slices.

Proteins were reduced by incubating the gel bands in 10 mM DTT/100 mM Ambic (ammonium

bicarbonate) solution at 56 C for 1 h. Then the proteins were carboxyamidomethylated with 55

mM iodoacetamide/100 mM Ambic for 1 h at room temperature in the dark. Enzymatic digestion

were performed by adding sequencing grade porcine trypsin (1:50, Promega, Madison, WI) and

incubated at 37 C overnight. The tryptic peptides were extracted three times with 200 l of

ACN/water (1:1) solution. Combined extracts were completely dried in speed vacuum,

resuspended in 50 l of 0.1% formic acid and then stored at -20 C, before analysis by MS.

LC-MS/MS Analysis

The resulting peptides were analyzed on both LTQ and LTQ-FT interfaced directly to an Agilent

1100 quaternary pump (Agilent Technologies, Palo Alto, CA). The mobile phase A and B were

H2O/0.1% formic acid and ACN/0.1% formic acid, respectively. The digested peptides were

pressure loaded for 1 h onto a PicoFrit 11 cm x 50 m column (New Objective, Woburn, MA)

packed with 8 cm length, 5 m diameter C18 beads. The peptides were desalted for 10 min with

0.1% formic acid in water and then were eluted from the C18 column into the mass

spectrometers during a 90 min linear gradient from 5 to 60% B at a flow rate of 200 nl/min. Top

9 abundant precursor ions were selected to be fragmented acquiring MS/MS spectra from each

full MS scan with a repeat count of 1and repeat duration of 5 s. Dynamic exclusion was enabled

for 200 s. In full mass scan, LTQ was set as centroid mode and LTQ-FT was in profile mode. For

the MS/MS scan both were in centroid mode. Generated Raw tandem mass spectra were

converted into mzXML format and then into PKL format using ReAdW followed by

29

mzMXL2Other.17

The peak lists were then searched using Mascot 1.9 (Matrix Science, Boston,

MA).

Database Search and Validation

Two databases were built for mascot search. Firstly search was against the normal sequence

database consisting of 23,095 T. cruzi protein sequences provided by Trypanosoma cruzi

Sequencing Consortium (TSK-TSC). A random database was constructed by reversing the

sequence in the normal database and was used to establish accurate scoring thresholds or normal

database protein identification. The Parameters are listed below. Only fully tryptic peptide

matches were considered with 4 maximum missed cleavages. Fixed modification was set as

carbamidomethyl due to carboxyamidomethylation (+57 Da) and variable modification was

chosen as oxidation (+16 Da) when the peptide contained Methionine. For LTQ the peptide

tolerance was 1000 ppm and average experimental mass value was adopted. LTQ-FT’s peptide

tolerance was 50 ppm and the mass value was chosen as monoisotopic. MS/MS tolerances for

both instruments were 0.6 Da. Peptide matches were extracted from the normal and random

database search results. Statistical validation of protein identification using clustered peptides

was based on an in-house developed software program ProValT, as implemented in ProteoIQ

(BioInquire, LLC, Athens, GA)

Annotations

TMHMM 2.018

was used to predict the transmembrane spanning domains. Subcellular

localization of membrane proteins were annotated by Gene Ontology and confirmed with

literature references.

RESULTS AND DISCUSSION

Membrane Protein Preparation

30

The CL-Brenner lab strain of trypomastigote life stage was utilized for this study. The reason we

chose trypomastigote instead of other developmental stages was because it is the infective form

that invade host cells and verified to express more surface membrane proteins that play important

roles in immune responses.19

Since current T. cruzi genome20

database is constructed using CL-

Brenner strain, so in order to get more accurate and comprehensive identification results for

membrane proteome, we did our study with this lab strain. In our initial strategy for enriching the

membrane fractions, we utilized the well-known sucrose cushion method. Previous studies in our

group have shown that cytoskeletal proteins such as alpha tubulin, beta tubulin and some other

soluble proteins like heat shock proteins usually dominate the identification from the whole cell

analysis. Compared to these proteins most membrane proteins are in low abundance and also

either embedded in or attached to the lipid bilayer membranes making them difficult to be

isolated and detected. The sucrose cushion has been shown to be a simple and effective way for

membrane enrichment. Sucrose solution density varies from different concentrations, so at

certain concentrations the whole packed membrane fractions can be pelleted down using ultra-

centrifugation while leaving the smaller soluble proteins remained in the solution. To enrich

further the integral membrane proteins and GPI anchored proteins, the crude membrane pellets

were treated with high pH carbonate solution, which removed some loosely bounded membrane

associated proteins. In our analysis, identification of trans-sialidase and several other surface

membrane proteins will attract more of our interest since they are widely presented on the

parasite surface and claimed to be potential targets for vaccine development. Unlike integral

membrane proteins spanning across lipid bilayers, they are attached to the plasma membrane via

a C-terminal glycosylphosphatidylinositol (GPI) anchor. Recent studies indicated that those GPI

anchored proteins usually reside on some specific membrane domains, which are called “lipid

31

rafts”.10,14,21-25

The rafts are mainly composed of sphingolipid and cholesterol. Sphingolipid

contains long, largely saturated acyl chains allowing them to pack tightly together and form a

liquid-ordered state. This rigid tight domain structure has been found to be resistant to some non-

ionic detergent such as Triton X-100 at low temperatures. While membranes besides the “lipid

raft” regions will be disrupted by the detergent and release the embedded proteins. Based on this

information, we introduced Triton X-100 in our second preparation at 4 C trying to enrich and

observe more GPI anchored proteins like trans-sialidase and mucins, etc. Proteins from both

method fractions were separated by 1-D SDS-PAGE gel electrophoresis. After separation the gel

lanes were sliced into small fractions for each and then these fractions were subjected to in-gel

trypsin digestion. We applied two mass spectrometers to analyze the tryptic peptides. LTQ ion

trap was first used since it has very high sensitivity thus could identify some low abundant

membrane proteins. We also ran all of our samples in LTQ-FT, which offers very high mass

accuracy and resolution. Some weak identification from LTQ got believed to be true with the

additional spectra confirmation by LTQ-FT. To reduce the possibility of false positive

identification, we searched the data against both normal and random database and set the protein

false discovery rate (FDR) as 1% during clustering peptides.

Protein Identification

There were total of 551 protein groups identified at a maximum 1% protein FDR. Among them

419 protein groups were identified in the sucrose cushion preparation and the detergent

preparation resulted in the identification of 398 protein groups with 266 shared proteins. Besides

319 soluble proteins and 22 microtubular proteins we found quite amount of membrane proteins

in our identification results including 69 integral membrane proteins, 40 membrane associated

proteins and 101 GPI anchored proteins. Thus the combined membrane fractions account for

32

38% within the whole identification, which shows great enrichment compared to all previous

global analysis. Viewing from the top 40 protein groups, although some regular high abundant

proteins like beta tubulin, alpha tubulin and heat shock protein 70 (HSP70) were still present, but

there were 14 membrane proteins including 13 trans-sialidase and 1 ATPase beta subunit were

identified. Among them trans-sialidase (8114.t00003) is the third most abundant protein, and

trans-sialidases (7202.t00003, 5412.t00001, 8498.t00001) are respectively identified as the 8th,

9th, 10th top abundant proteins. While in Atwood’s whole trypomastigote proteome study, the

most abundant trans-sialidase is only ranked as No 284, and there were only 8 trans-sialidase

proteins among the top 400 groups. These comparisons clearly indicate after membrane

extraction, the membrane proteins especially the GPI anchored membrane proteins got largely

enriched and some of the very low abundant membrane proteins could now be detected under

current conditions. This enrichment is also supported by the fact that several high abundant

cytosolic soluble proteins identified in the whole trypomastigote proteome were highly depleted

in our preparation methods. Those absent proteins include the 9th most abundant protein

NADH:flavin oxidoreductase/NADH oxidase, the 12th most abundant protein tyrosine

aminotransferase, the 13th top protein glutamate dehydrogenase and other 8 proteins in top 30

identifications in the trypomastigote proteome.

Membrane Protein Identification and Distribution

Among the total 551 proteins, 210 of them were membrane proteins. Classified by sub-cellular

localization (Figure 3.1) 101 membrane proteins were annotated as GPI anchored proteins, which

include 87 trans-sialidase, 9 mucin associated surface protein (MASP), 2 gp63 protein, and 3

mucins proteins. According to the literature searches, the mucins have never been identified

using proteomics method before although in the T. cruzi genome the mucins family ie encoded

33

by a large number of genes and pseudogenes. The reason for this is because these proteins are all

highly glycosylated and the post-translational modifications complicated the detection in the

proteome. In our membrane preparation these immunodominant surface proteins almost all

double the number of identification compared to the whole cell analysis. Besides these GPI

anchored proteins, there were another 17 protein groups annotated as plasma membrane proteins.

Most of them are P type ATPase with the function as ATP binding and ion channels. The

membrane proteins identified in the organelles are mainly localized within the mitochondria (23

proteins), endoplasmic reticulum (ER) (5 proteins), golgi (14 proteins), nucleus (4 proteins) and

some others (12 proteins). For example ADP/ATP carrier protein 1 is an integral mitochondria

membrane protein mediating the exchange of ADP for ATP generated in the mitochondrial

matrix. Oligosaccharyl transferase is found as the ER membrane protein that plays important role

for transferring Glc3Man9GlcNAc2 from dolichol to nascent protein. There were 34 hypothetic

protein also thought as integral membrane proteins since they contained transmembrane

spanning domains predicted using TMHMM 2.0. Table 3.1 shows the transmembrane domain of

all identified integral membrane proteins.

Distribution of Membrane Proteins in Two Methods

As we described two different methods were used to enrich the membrane fractions. Sucrose

method with carbonate washing should produce more membrane proteins while the Triton X-100

treated method was expected to identify more GPI anchored proteins. This trend could be

verified from our identification results. Using the sucrose cushion method, we were able to

identify 128 membrane proteins while the detergent resistant protocol for isolation of lipid raft

associated proteins (GPI anchored) yielded 81 membrane protein identifications. While the

sucrose cushion resulted in higher membrane proteome coverage, the detergent resistant method

34

resulted in a significant enrichment of GPI anchored cell surface proteins; noted by an almost 5

fold increase in the spectral counts for identified GPI anchored proteins trans-sialidases (Figure

3.2). One of the key factors for sucrose method enrichment is that sucrose cushion can highly

deplete the largely abundant cytoskeletal proteins like beta tubulin and alpha tubulin. These two

proteins were only ranked as 13th and 25th according to the sucrose method protein score. But

they could not be removed only using the detergent treated method and they became the most

two abundant protein groups with a 25 fold (beta tubulin) and 21 fold (alpha tubulin) spectra

counts increase. Glyceraldehyde 3-phosphate dehydrogenase could be considered as another

indicator for the abundance change of cytoskeletal proteins. This cytosolic glycolytic enzyme has

been reported as expression on some different cell surface, which seems unlikely for most

cytosolic proteins. This is because it could bind to the cytoskeletal microtubules. So when we

deplete the cytoskeletal proteins using sucrose cushion method this cytosleletal-associated

glycolytic protein also got removed. On the other hand the non-ionic detergent treatment could

not deplete it. Reflected on the identification result, using detergent method glyceraldehyde 3-

phosphate dehydrogenase ranked as the 20th, while it dropped to the 410th at sucrose cushion

method. Depletion of the highly expressed cytoskeletal proteins increases the possibility to

identify many low abundant membrane proteins, so the number of identified membrane proteins

using the sucrose cushion method is much more than detergent method. Meanwhile the relative

abundance of each membrane protein can be compared using spectra counts. The membrane

protein spectra counts especially the GPI anchored ones got a lot of difference between these two

preparation methods. As we expected the detergent resistant GPI anchored proteins got more

enriched with the treatment of Triton X-100. The possible reason is that some major expressed

GPI anchored proteins were still remained on the lipid raft when some others got cleaved by the

35

parasite expressed enzyme phosphatidylinositol phospholipase C (PI-PLC) during cell culture

and preparation. The cleaved GPI anchored proteins will be together removed with some integral

membrane proteins under detergent treatment. As a result this process enriched the major GPI

anchored proteins which got more spectra counts for identification but also reduce the number of

identification for whole membrane proteins.

Important Protein Families

T. cruzi trypomastigote is the life stage that circulates in the host blood stream and performs the

cell invasion function. During this process the host immune system will respond to them

immediately and rely on some antigen-specific T cells and antibodies to kill the pathogens. One

of the major strategies for T. cruzi to escape the host immune response is that they can express

several large members of surface antigen proteins. Trans-sialidase is one of the most important

surface protein families for T. cruzi. This large protein family is encoded by more than 1300

genes. T. cruzi is unable to synthesize sialic acid itself so it relies on trans-sialidase to transfer

the sialic acid from host sialoglycoconjugates onto terminal galactose residues on its surface

mucin molecules. The sialiation of surface glycoproteins prevents complement activation and

increases the infectivity. Thus the trans-sialidase genes are critical for parasite survival and

potentially to be the vaccine target. Recent studies have reported that only a small set of trans-

sialidase proteins possess enzymatic activity. Expressing together with those effective trans-

sialidase enzymes the large number of non-enzymatic family members could deflect the immune

response from the real enzymatic targets and counteract the T cell responses by providing their

altered peptides. Within the significant importance, while the identification of these protein

families is always difficult and challenging because typically proteins from the same family have

similar structure, function and peptide sequence. For example many identified trans-sialidase

36

shared some high frequently identified peptides like FAGVGGGALWPVSQQGQNQR,

HQWQPIYGSTPVTPTGSWETGK and LLGLSYDEK, etc. Because of these very similar

expression and shared sequences, it’s difficult to differentiate between them unless we find out

some unique peptides. In our identification we identified 87 trans-sialidase and among them

there are 43 defined as unique ones because they have the unique peptides only expressed in one

protein and not in all other 86 trans-sialidases. In our proteomic identification, some trans-

sialidase protein could even be recognized with 6 or 7 unique peptides. Although several of them

only get one unique peptide, while they are the unique ones in the whole almost 1300 trans-

sialidase genes from the database so they are certainly uniquely identified with high confidence.

In addition to trans-sialidase protein families, several other membrane proteins such as mucins,

mucin-associated surface proteins (MASPs), and gp63 proteins have also shown to be targets of

CD8+ T cells and thus to be important for study.

26 Mucins are highly O-glycosylated mucin-like

glycoproteins expressed on cell surface through GPI anchor. The dense oligosaccharides coating

can protect the parasite from immune response and is also involved in the host cell invasion

process. Mucin-like glycoprotein (7726.t00002), mucin TcMUCII (5957.t00036 and

7195.t00017) have been identified. To our knowledge, this is the first experimental evidence to

identify these mucin proteins using proteomic methods. At the same time we found 9 mucin

associated surface proteins (MASP). Unlike the trans-sialidase the identification of mucins and

MASP are all belonged to single peptide match and only detected in sucrose method. This result

suggests the true expression level for mucins and MASP may not be as high abundant as trans-

sialidase although their gene families are also large. The other possibility is because the high

dense glycosylation make them undesirable to be detected by regular shotgun proteomics without

deglycosylation steps. Two surface GP63 proteins were also identified in sucrose cushion

37

fraction within one 5 peptide matches (7158.t00002) and the other single peptide match

(7383.t00011). Besides these immunodominant surface membrane proteins, the enzymes that

participate the mucins O-glycosylation pathways are another important membrane protein groups

for T. cruzi, which are UDP-Gal or UDP-GlcNAc-dependent glycosyltransferase. In T. cruzi the

glycosyltransferase transfer the N-acetylglucosamine (GlcNAc) from an UDP-GlcNAc precursor

molecule to the Thr/Ser residues in the mucins protein core. While in other vertebrate mucins,

transfer of the N-acetylgalactosamine (GalNAc) is often found. Because of the complexity of the

mucins familiy in structure and sequence, the parasite needs multiple GlcNAc-transferase to get

involved in the O-glycosylation pathways. There were totally six protein groups identified from

this family.

CONCLUSION

Cell surface membrane proteins play critical roles for T. cruzi host cell invasion mechanisms.

Previous proteomic studies didn't provide enough information for these important genes due to

the sample preparation strategies. In this study, we provided two membrane enrichment

methodologies and applied gel based bottom-up proteomics to analyze the membrane proteome

of the mammalian stage, trypomastigote. Compared to previous whole cell analysis, large

amount of membrane proteins have been identified. 210 out of 551 identifications are membrane

proteins, including several important immunodominant gene families: 87 trans-sialidase, 9 mucin

associated surface protein (MASP), 2 gp63 protein, and 3 mucins proteins. The two enrichment

methods can also provide effective functions. The sucrose cushion yielded more integral

membrane proteins, while the detergent resistant method was proven to be more efficient for

some GPI anchored proteins. Those membrane enrichment methods were successfully applied

for followed studies shown in Chapter 4 and 5.

38

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41

Table 3.1. Identified proteins with transmembrane spanning domains (TMSD), the proteins were

ranked by their relative abundance. The numbers of TMSD were predicted by TMHMM 2.018

Gene ID Gene Name

Number

of

TMSD

Tc00.1047053511289.70

ADP,ATP carrier protein 1, mitochondrial precursor,

putative [8647.t00007] 3

Tc00.1047053506211.160

ADP,ATP carrier protein 1, mitochondrial precursor,

putative [6853.t00016] 3

Tc00.1047053508461.570 Gim5A protein, putative [7739.t00057] 1

Tc00.1047053503829.80 hypothetical protein, conserved [4773.t00008] 4

Tc00.1047053505163.80 oligosaccharyl transferase subunit, putative [5150.t00008] 8

Tc00.1047053509551.30

mitochondrial phosphate transporter, putative

[5738.t00003] 1




Tc00.1047053506295.130 prohibitin, putative [6889.t00013] 1

Tc00.1047053505763.19 P-type H+-ATPase, putative [6697.t00002] 3

Tc00.1047053507811.60

vesicle-associated membrane protein, putative

[7479.t00006] 1

Tc00.1047053508767.10 surface protein TolT [7864.t00001] 1

Tc00.1047053510773.20

vacuolar-type proton translocating pyrophosphatase 1,

putative [8493.t00002] 16

Tc00.1047053506297.240

pretranslocation protein, alpha subunit, putative

[6890.t00024] 9

Tc00.1047053506581.10

dolichyl-phosphate beta-D-mannosyltransferase precursor,

putative [7007.t00001] 1



Tc00.1047053509601.70

vacuolar proton translocating ATPase subunit A, putative

[8148.t00007] 6



Tc00.1047053506401.170 vacuolar-type Ca2+-ATPase, putative [6930.t00017] 8

Tc00.1047053511517.37 reticulon domain protein, putative [6139.t00023] 3

Tc00.1047053509671.90

COP-coated vesicle membrane protein gp25L precursor,

putative [8166.t00009] 2


Tc00.1047053508175.70 lanosterol synthase, putative [7625.t00007] 1


Tc00.1047053506971.20 surface protease GP63, putative [7158.t00002] 1



42


Tc00.1047053506925.530 ABC transporter, putative [7143.t00053] 3

Tc00.1047053509777.70

calcium-translocating P-type ATPase, putative

[8200.t00007] 3


Tc00.1047053507611.280 cytochrome c oxidase subunit IX, putative [7402.t00028] 1

Tc00.1047053508817.130 carbonic anhydrase-like protein, putative [7883.t00013] 1


Tc00.1047053429257.20 fatty acid desaturase, putative [1807.t00002] 5

Tc00.1047053504109.200

retrotransposon hot spot (RHS) protein, putative

[4913.t00020] 2



Tc00.1047053471901.20 SNARE protein, putative [3923.t00002] 1

Tc00.1047053509157.60

1-acyl-sn-glycerol-3-phosphate acyltransferase, putative

[8017.t00006] 1

Tc00.1047053507795.50 syntaxin, putative [7473.t00005] 1


Tc00.1047053510659.250 phospholipase A2-like protein, putative [8457.t00025] 2



Tc00.1047053506355.20 receptor-type adenylate cyclase, putative [6911.t00002] 1






Tc00.1047053507559.110 surface protease GP63, putative [7383.t00011] 3

Tc00.1047053405737.14 hypothetical protein [511.t00003] 1

Tc00.1047053506275.50 Golgi SNARE protein-like, putative [6881.t00005] 1


Tc00.1047053507895.100

hypothetical protein, conserved (pseudogene)

[7517.t00010]|TRUNCATED PRODUCT 1



Tc00.1047053508059.40 syntaxin, putative [5539.t00004] 1


Tc00.1047053507465.10 receptor-type adenylate cyclase, putative [7348.t00001] 2

Tc00.1047053506999.90 reiske iron-sulfur protein precursor, putative [7167.t00009] 1




43

Figure 3.1. The membrane protein subcellular distribution was categorized. The plasma

membrane proteins contain the largest portion including those GPI anchored trans-sialidase, etc.

Other organelle membranes also contain their corresponding products. There were 33

hypothetical proteins also categorized as membrane proteins since they have been predicted to

have transmembrane domains.

44

Figure 3.2. The annotated trans-sialidase (TS) distribution was compared within the two

membrane preparation methods. It was shown that the sucrose cushion method has identified

more TS proteins. But for some major TS (close to the left of X-Axis), the spectra count from

detergent resistant method is about 5 times than the one in sucrose method. This indicates some

GPI anchored proteins are more enriched with the detergent resistant preparations.

Annotated TS (Detergent vs. Sucrose)

0

50

100

150

200

250

300

350

400

Tc0

0.1

047053509495.3

0

Tc0

0.1

047053506923.1

0

Tc0

0.1

047053508857.3

0

Tc0

0.1

047053506975.8

0

Tc0

0.1

047053503993.1

0

Tc0

0.1

047053509817.5

0

Tc0

0.1

047053506841.2

0

Tc0

0.1

047053506961.2

5

Tc0

0.1

047053506577.8

0

Tc0

0.1

047053511451.8

0

Tc0

0.1

047053507687.1

0

Tc0

0.1

047053508903.1

0

Tc0

0.1

047053507953.1

00

Tc0

0.1

047053509785.5

0

Tc0

0.1

047053470827.2

0

Tc0

0.1

047053509333.1

0

Tc0

0.1

047053506975.9

0

Tc0

0.1

047053508717.6

0

Tc0

0.1

047053503861.4

0

Tc0

0.1

047053503907.1

0

Tc0

0.1

047053507069.1

60

Tc0

0.1

047053510483.2

10

Tc0

0.1

047053510483.2

50

Tc0

0.1

047053507237.1

0

Tc0

0.1

047053506217.4

0

Tc0

0.1

047053509753.2

70

Tc0

0.1

047053509905.1

70

Tc0

0.1

047053504081.3

90

Tc0

0.1

047053506911.3

0

Tc0

0.1

047053506813.1

90

Tc0

0.1

047053511771.4

0

Tc0

0.1

047053511839.4

0

Tc0

0.1

047053511643.4

0

Tc0

0.1

047053424171.1

0

Annotated TS

detergent Spectral Count

sucrose Spectral Count

45

CHAPTER 4

SUBCELLULAR PROTEOMICS OF TRYPANOSOMA CRUZI INTRACELLULAR

AMASTIGOTE1

______________________________________________________________________ 1 Xiang Zhu, Brent Weatherly, Marshall Bern, James A. Atwood III, T.A. Minning, R.L.

Tarleton, Ron Orlando. To be submitted to Journal of Proteome Research.

46

ABSTRACT

The protozoan parasite Trypanosoma cruzi (T. cruzi) is the etiologic agent of Chagas’ disease,

which is a chronic illness causing congestive heart failure and sudden death. Among the

parasite’s four life stages, amastigote is a replicative stage which resides in the infected host cells

and is a primary target of the host immune-response. Due to the difficulty of isolation and

purification, very few proteomic analyses have been performed on the intracellular amastigotes.

This results in a lack of understanding concerning the parasite’s invasion and survival

mechanism, along with delaying the development of potential vaccines and drugs. Here we

introduce our recent comprehensive proteome analysis of T. cruzi intracellular amastigotes.

Subcellular organelle and membrane enriched fractions as well as cytosol soluble fractions were

individually obtained and analyzed using GeLC-MS/MS approach. In addition to matching the

MS/MS spectra to the annotated proteome database, we performed a whole genome search in

order to identify additional genes potentially missed in the annotation of the T. cruzi genome. We

also utilized a hybrid identification tool (ByOnic) for the identification of unanticipated

mutations caused by different T. cruzi strains. Our results have given us many newly identified

gene products; a lot of them are from ORFs and mutation search. Further, our analysis has

provided valuable information for T. cruzi proteome and help us better understand the parasite’s

biology.

47

INTRODUCTION

The protozoan kinetoplastid parasite Trypanosoma cruzi (T. cruzi) is the causative agent of

Chagas’ disease, which is a chronic illness causing congestive heart failure and sudden death in

the world. It affects 16-18 million people and kills an estimated 50,000 people annually in Latin

American countries.1-3

T. cruzi has a complex life cycle, with four different life stages

transmitting between the mammalian hosts and insect vectors. Metacyclic trypomastigotes are

infective forms that develop in the hindgut of the insect vectors such as triatomine bugs. The

infection is initiated when the blood-feeding insect vectors deposit their feces containing

metacyclic trypomastigotes onto the wounded mammalian skins. After they invade the host cells

around the wound, metacyclic trypomastigotes differentiate into the replicative aflagellated

amastigotes which reside in the host cell cytoplasm. After many rounds of binary fission, large

quantities of amastigotes are produced in the host cells. Later on these amastigotes transform to

the other infective flagellated trypomastigotes, which burst out from the host cells and circulate

in the blood stream to infect other cells throughout the mammalian bodies. Some of the

trypomastigotes are ingested by the insect vectors during their blood meal and converted into

epimastigotes. The epimastigotes replicate in the vector midgut and finally differentiate into

metacyclic trypomastigote thus finishing the life cycle.

The genome sequencing of T. cruzi has been completed recently using a hybrid CL Brenner

strain.4 However like other trypanosomatid parasites, T. cruzi usually regulates the gene

expression mostly post-transcriptionally, which results in the poor correlation between mRNA

and protein levels.5,6

Consequently proteomics becomes attractive for exploring the differential

gene expression through various life stages and to find out novel gene products especially some

potential drug targets and vaccine candidates.

48

Recently several studies targeting the T. cruzi proteome have been reported.7-19

Based on these

studies, many important functional proteins and some stage specific markers have been

identified; however most of these studies were performing the analysis using the whole cell

lysates without any enrichment. These approaches inevitably missed a lot of low abundant gene

products, some of which may play important functional roles for parasite infection and survival.

At the same time we found most of these proteomic studies were focused on those relatively

easily obtained stages such as epimastigotes and trypomastigotes. Researches on another

important human stage amastigotes are quite limitied due to the difficult isolation and

purification steps. Although many valuable information obtained from this stage could be highly

related to the parasite intracellular survival and host cell invasion, only a few papers reported the

proteome of amastigotes, and all of them are obtaining the cells by inducing the trypomastigotes

under low pH conditions which mimic the intracellular environment of amastigote forms.15,17,18

Since it’s not the real amastigote, so it may not express all important protein groups as the

intracullar one does. So current proteome datasets of amastigotes are quite insufficient, more

comprehensive analysis need to be carried out to discover previously underestimated gene

products.

In this paper we are going to report the subcellular fractionated proteomic analysis of the

important amastigote life stage. Unlike all previous experiments, we used the intracellular

amastigotes released from infected vero cells and analyzed the protein expression using enriched

subcellular organelle and membrane preparations. In the protein identification data processing,

besides matching the MS/MS spectra to the annotated proteome database, we also performed the

whole DNA database search in order to identify additional genes potentially missed in the T.

cruzi genome sequencing annotations. We also utilized a hybrid identification tool (ByOnic)20

49

that can perform a wildcard-database search strategy for the identification of unanticipated

modifications and potential mutations. This is very important because the T. cruzi strain we are

investigating is a native strain isolated in Brazil, while the genome sequencing of this organism

was performed on the laboratory CL-Brenner strain.4 Consequently, we anticipate that many of

the genes will differ by multiple point mutations and amino acid substitutions. We expect that

these will limit the utility of traditional database search routines, and thus we incorporated the

wild-card search strategy (ByOnic) into our dataflow in addition to traditional database

searching. The aim of this work was to find much more interesting gene products that are

normally expressed at low levels and less investigated before. The results derived from this

proteome analysis will largely expand the current datasets of the T. cruzi proteome and help us

better understand the parasite’s system biology.


Cell Culture

Monolayers of vero cells (ATCC no. CCL-81) in RPMI supplemented with 5% horse serum

were infected with Brazil strain T. cruzi trypomastigotes as previously described.21

Extracellular

trypomastigotes were washed from the flasks every other day. After 7 days post infection

cultures were examined by light microscopy to determine the percentages of extracellular

amastigotes and trypomastigotes. When the extracellular parasites were greater than 95%,

amastigotes parasites were harvested by centrifugation at 300 x g for 10 min at room

temperature. Amastigotes in the supernatants from the first spin were then pelleted by

centrifugation at 3,000 x g for 15 min at room temperature, and washed three times with ice-cold

PBS.

Plasma Membrane Preparation using Sucrose Cushion

50

Plasma membrane proteins were firstly enriched using the sucrose cushion method as previously

described with minor modifications.22

The T. cruzi intracellular amastigote cells were suspended

in 3 mL of ice-cold lysis buffer (10 mM HEPES, 1 mM EDTA, pH 7.2) containing Roche

protease inhibitor cocktail. After 15 min incubation at 4 C, cells were homogenized by 25

strokes of a 7 mL Dounce homogenizer. An equal amount of sucrose buffer (10 mM HEPES, 1

mM EDTA, 500 mM sucrose, pH 7.2) was added with additional 25 strokes of homogenizer.

Cellular debris and unbroken cells were removed as pellets after centrifugation at 6,000 x g for

15 min at 4 C. The supernatant was collected and centrifuged at 150,000 x g for 1 hour at 4 C.

Supernatant was removed and the crude pellet membrane was incubated in 100 mM sodium

carbonate solution (pH 11.3) for 15 min at 4 C. After incubation, the membrane pellet was

collected by centrifuging at 150,000 x g for 1 hour at 4 C. The supernatant was desalted through

dialysis (1000 MWCO), dried out under vacuum and collected for analysis also.

Lipid Raft Membrane Preparation using Non-ionic Detergent

Surface membrane proteins especially some GPI anchored proteins were also enriched using

detergent resistant preparations.23,24

Amastigote cells were suspended in 3 mL of ice-cold lysis

buffer (10 mM HEPES, 1 mM EDTA, 250 mM sucrose, pH 7.2) containing Roche protease

inhibitor cocktail. An equal volume of 1% (w/v) Triton X-100 solution was mixed with the lysis

buffer. After 50 strokes of homogenizer, the homogenate was centrifuged for 15 min at 6,000 x g

at 4 C, pelleting the cellular debris and unbroken cells. The supernatant was collected and

centrifuged at 150,000 x g for 1 hour at 4 C. Crude membrane pellet was resuspended with 1%

(w/v) Triton X-100 solution at 4 C and incubated for 30 min. Mixed solution was centrifuged at

150,000 x g for 1 hour at 4 C. The supernatant was removed completely and the pellet was

incubated in 100 mM sodium carbonate solution (pH 11.3) for 15 min at 4 C. After incubation,

51

the membrane pellet was collected by centrifuging at 150,000 x g for 1 hour at 4 C. The

supernatant was desalted through dialysis (1000 MWCO), dried out under vacuum and collected

for further analysis.

Subcellular Organelle Fractions Enrichment

Subcellular fractionation was performed to enrich other organelles. Briefly the amastigote cells

were suspended in 4ml of ice-cold Mannitol Lysis Buffer (400 mM Mannitol, 10mM KCl, 2mM

EDTA, 1 mM phenylmethanesulphonyl fluoride, 20 mM HEPES/KOH, pH 7.6) containing

Roche protease inhibitor cocktail. After 15 min incubation at 4 C, cells were homogenized by 25

strokes of a 7 mL Dounce homogenizer. Cellular debris and unbroken cells were removed as

pellets after centrifugation at 100 x g for 5 min at 4 C. The supernatant was centrifuged at

16,000 x g for 30 min at 4 C. Resulting pellets were collected as organelle enriched fractions and

supernatant was further centrifuged at 105,000 x g for 60 min at 4 C, final supernatant was

collected as cytosol fractions.


All dried six fractions (two membrane fractions, two membrane washes, organelle fraction and

cytosol fraction) were resuspended in 20 l Laemmli buffer (Sigma-Aldrich) and boiled at 80 C

for 15 min. Solublized proteins were separated by 1-D SDS-PAGE using NuPAGE 4-12% Bis-

Tris (Invitrogen) gradient gels at 150 V for 2 hours. Gel lanes were washed twice in ddH2O for

15 min and then cut into 20 to 30 slices. Proteins were reduced by incubating the gel bands in 10

mM DTT/100 mM Ambic (ammonium bicarbonate) solution at 56 C for 1 h. Then the proteins

were carboxyamidomethylated with 55 mM iodoacetamide/100 mM Ambic for 1 h at room

temperature in the dark. Enzymatic digestion was performed by adding sequencing grade porcine

trypsin (1:50, Promega, Madison, WI) and incubated at 37 C overnight. The tryptic peptides

52

were extracted three times with 200 l of ACN/water (1:1) solution. Combined extracts were

completely dried in speed vacuum, resuspended in 50 l of 0.1% formic acid and then stored at -

20 C, before analysis by MS.

LC-MS/MS Analysis

The peptide samples obtained from proteolytic digestion were analyzed on an Agilent 1100

capillary LC (Palo Alto, CA) interfaced directly to a LTQ linear ion trap mass spectrometer

(Thermo Fisher, San Jose, CA). Mobile phases A and B were H2O-0.1% formic acid and

acetonitrile-0.1% formic acid, respectively. The peptide samples were loaded for 50 min using

positive N2 pressure on a PicoFrit 8-cm by 50-μm column (New Objective, Woburn, MA)

packed with 5-μm-diameter C18 beads. Peptides were eluted from the column into the mass

spectrometer during a 90 min linear gradient from 5 to 60% of total solution composed of mobile

phase B at a flow rate of 200 nl min−1

. The instrument was set to acquire MS/MS spectra on the

nine most abundant precursor ions from each MS scan with a repeat count of 1 and repeat

duration of 5 s. Dynamic exclusion was enabled for 200 s. Raw tandem mass spectra were

converted into the mzXML format and then into peak lists using ReAdW software followed by

mzMXL2Other software.25

The peak lists were then searched using Mascot 2.2 (Matrix Science,

Boston, MA).

Database Searching and Protein Identification

As the first step of our data processing, a non-redundant target database was created through

combining the 11100 annotated sequences obtained from the tritrypdb

(http://tritrypdb.org/tritrypdb/) and NCBI (www.ncbi.nlm.nih.gov/). A decoy database was then

constructed by reversing the sequences in the normal database. Searches were performed against

the normal and decoy databases using the following parameters: fully tryptic enzymatic cleavage

53

with two possible missed cleavages, peptide tolerance of 800 ppm, fragment ion tolerance of 0.6

Da. Fixed modification was set as carbamidomethyl due to carboxyamidomethylation of cysteine

residues (+57 Da). Statistically significant proteins from both searches were determined at a 1%

protein false discovery rate (FDR) using the ProValT algorithm, as implemented in ProteoIQ

(BioInquire, LLC, Athens, GA).26

A subset fasta database (Database 1) was created containing

the above validated proteins passing the 1% FDR. Meanwhile, all the peak lists were searched

against the whole six-frame DNA sequences using Mascot with same parameters. Acquired data

were loaded into ProteoIQ as the target match, previously generated protein database search

result was chosen as the non-target match. Additional peptide matches were acquired only if the

target search score is higher than the non-target match score. These peptides were then clustered

to the open reading frames (ORFs). The two databases (Database 1 and ORFs database) were

combined and used to perform a wild card search using ByOnic to select unanticipated

modifications and find possible mutations. The mass tolerance parameters were the same as

Mascot search. Modifications were selected as carbamidomethyl due to

carboxyamidomethylation of cysteine residues (+57 Da), oxidation of methionine residues (+16

Da), deamidation of asparagine residues (+1 Da), Gln to Pyro-Glu (-17 Da), Glu to Pyro-Glu (-

18 Da) and any one SNP (single nucleotide polymorphism) mutation per peptide. To further

validate the identified ORFs, modified and mutated peptides, a final fasta database was

constructed combining the initially annotated sequences, the ORFs identifications from the

whole genome search, the ByOnic identified mutated sequences and 100,000 randomly

generated sequences. All the MS/MS spectra were searched again using this database with 1)

fully tryptic enzymatic cleavage and fixed modification (+57 Da) alone. 2) Fully tryptic

enzymatic cleavage, fixed modification (+57 Da), and variable modifications using oxidation of

54

methionine residues (+16 Da), deamidation of asparagine residues (+1 Da), Gln to Pyro-Glu (-17

Da) and Glu to Pyro-Glu (-18 Da). 3) Semi-tryptic enzymatic cleavage and fixed modification

(+57 Da). All the resulting search data were combined and validated using ProteoIQ. For

unmodified and fixed modified peptides (+57 Da), the numbers of random matches were

controlled below 1%, the mascot ion score thresholds for identification of a protein with 3 or

more peptides was ≥28, with 2 peptides ≥33, and a single peptide match was calculated as ≥57.

For variable modifications and semi-tryptic peptide matches, the thresholds were set even higher

to reduce false positive identifications. The minimum mascot ion score for variable modified

peptides was selected as 60 and the one for semi-tryptic peptides was set as 65.

RESULTS

Subcellular Organelle and Membrane Enrichment

In this study, we performed a subcellular fractionation analysis of the intracellular amastigotes to

identify proteins majorly enriched in organelles and plasma membranes fractions. The

differential centrifugation method was utilized for organelle enrichment after cell lysis. Through

this procedure, some heavy organelles such as mitochondria and ER enriched fractions were

obtained in the pellet of 16,000 x g. After high speed centrifugation at 105,000 x g, the

supernatant was kept as cytosol fraction. Plasma membranes as well as the Golgi apparatus were

collected using two methods (sucrose cushion and detergent resistant preparation) adopted from

another study we did recently. In order to fully investigate the subproteome, the plasma

membrane soluble fractions (supernatant from 150,000 x g centrifugation in sodium carbonate

wash solution) were also analyzed, which was claimed to contain the loosely bound membrane

associated proteins. All the six fractions were separated by 1-D SDS-PAGE gel electrophoresis

(Figure 4.1). It was indicated from the gel image that the organelle and membrane fractions

55

contained more proteins than the membrane wash and cytosol fractions. The gel lanes were

sliced into 20-30 fractions and then those fractions were subjected to in-gel trypsin digestion. All

the individual fractions were analyzed through on-line LC-MS/MS using LTQ ion trap.

Proteome Data Analysis

There were a total of 2490 proteins within 890 protein groups got identified by 50526 MS/MS

spectra in all preparation fractions. Among them the organelle enriched fractions yielded the

largest number of identifications; in the membrane and cytosol fractions we also found a

significant number of proteins. We saw relatively small number of identifications in the

membrane wash solutions and this indicated there were not many loosely bound membrane

associated proteins recovered in our preparations. The relative distribution of identified proteins

among all fractions was shown in Figure 4.2.

In order to maximize our identification data sets, we performed the database searching within

multiple steps. Initially all the MS/MS spectra were searched against a combined target database

of the annotated T. cruzi sequences with Mascot and only allowed the fixed modification of

carbamidomethyl (+57 Da). The decoy database search was performed later on and the validation

of the proteins were based on max 1% protein false discovery rate (FDR) using the ProValT

algorithm, as implemented in ProteoIQ (BioInquire, LLC, Athens, GA). There were a total of

2055 proteins (697 protein groups) got validated. A subset protein fasta database (Database 1)

was created using these proteins for later on searching and validation. Since our goal of this

project is to identify more of the potential interesting gene products which have been under-

discovered before, so exploring the novel protein coding regions is also very important. As a

result, in the second step a whole genome open reading frames (ORFs) analysis was performed

using the Mascot search engine focused on detecting proteins that are not in the annotated

56

databases. In order to reduce the numbers of random sequence matches of this whole genome

search, the mascot results from the annotated protein database search were chosen as the non-

target search during ProteoIQ processing. In this way, if a MS/MS spectrum was matching to

both genome search and protein database search, it was kept for further validation on the premise

that the genome database searching Mascot score is higher than the protein database searching

score. Consequently, only unique peptides identified by spectra that failed to match proteins in

the annotated sequences were clustered to the ORFs, and the new proteins were annotated after

the search. Finally, each annotation and the MS/MS spectra matching to the new genes were

manually verified, yielding 639 new candidate proteins as our ORFs database. Besides the ORFs

peptide identification, we also performed a wildcard-database search strategy for the

identification of unanticipated modifications and potential mutations with ByOnic, which is a

hybrid tool of de novo sequencing and database search. The reason we are doing this is because

there are several different kinds of lab strains for T. cruzi, such as Y, CL Brenner, Brazil,

Tulahuen, etc. In this experiment, we were using the native Brazil strain, while the T. cruzi

genome was performed on the laboratory CL-Brenner strain. Consequently, many of the genes

could differ by multiple point mutations and amino acid substitutions. These mutations will

change both the peptide parent ion mass and most of the fragment ion peaks, usually with an

unanticipated position. ByOnic can well handle the mutation search with any one-letter amino

acid change, thus it recovered a lot of “correct MS/MS identification spectra” which have been

thrown away during the regular Mascot search. A new subset of mutation database was created

containing these identified mutated proteins. There were total of 362 mutated protein candidates

obtained from the spectra and kept for further validation. Finally, the additional subset databases

(ORFs+Mutation) were combined with the annotated sequences and another 100K totally

57

random sequences were added together to generate our final database. All the MS/MS spectra

were searched again using this database in the following order of 1) tryptic with fixed

modifications, 2) tryptic with variable modifications and 3) semitryptic with fixed modifications

to validate the newly identified ORFs, modified and mutated peptides. Finally, each annotation

and the MS/MS spectra matching to the new genes were manually verified, yielding 2061

annotated proteins, 105 new ORFs identification (containing 78 unannotated trans-sialidase) and

314 mutated proteins. There were only 10 out of 100,000 random sequences picked up in our list,

indicating the data selection was very stringent.

DISCUSSION

To date, most of the T. cruzi proteomic studies have been focused on the insect epimastigote

stage and blood-form trypomastigote stage. There are only three papers15,17,18

reported on the

amasitogte proteome using the whole cell analysis without subcellular enrichment; and those

amastigotes were obtained by inducing the trypomastigotes in acidic medium in-vitro. The real

intracellular amastigote stage has never been investigated for its protein expression, although

some important targets of immune responses might come from this life form.27

Amastigote

specific antigens, such as amastigote surface protein (ASP)-2, amastigote surface protein-3 and

amastigote cytoplasmic antigen were identified in our experiment. These genes are preferentially

expressed in the intracellular amastigotes stages and are the targets of T. cruzi specific T cell

responses. It has been shown that peptides from ASP proteins are involved in the class I MHC

presentation pathway and activate CD8+ T cell responses. In recent mouse model research, the

vaccination experiments with a plasmid encoding an ASP-2 generated specific CD4+ Th1 and

CD8+ Tc1 immune responses and increased the survival rate of the mice against a fatal T. cruzi

infection to 65%.28

Further studies have shown that similar protective immunity could not be

58

achieved by immunization with a plasmid encoding trypomastigote-specific trans-sialidase

antigens.29

This has indicated that compared to other life stages, antigens identified in the

intracellular amastigotes are more important and expected to be the targets for host immune

responses and potentially become better vaccine candidates.

Better understanding of the intracellular amastigote especially the organelle/membrane

subproteome will facilitate the development of vaccination protocols. In our analysis, the

enrichment of organelle and membranes were proven to be quite effective with the evidence of

many newly identified low abundant gene products. T. cruzi is unable to synthesize sialic acid

itself so it relies on trans-sialidase to transfer the sialic acid from host sialoglycoconjugates onto

terminal galactose residues on its surface mucin molecules. The sialiation of surface

glycoproteins will both prevent complement activation and increase the infectivity. Trans-

sialidases are the major plasma membrane proteins on T. cruzi cell surface. Although there are

more than 1300 trans-sialidase genes in the whole T. cruzi genome, only a few of them got

identified in previous studies. For amastigote stages, this number is claimed to be even lower

compared to trypomastigotes. Herein we identified 307 trans-sialidase genes in 58 protein

groups, which increased the identification numbers a lot. In previous amastigote studies, there

were only 78 trans-sialidase (15 protein groups) detected in Atwood’s whole cell analysis.17

There was no evidence of the trans-sialidase identification in Paba’s studies.15,18

Some other

proteins such as ATPase, GP63, surface protein TolT, etc also got enriched in the membrane

fractions. Lysosomal/endosomal membrane protein p67 (Tc00.1047053510825.30) is a

lysosomal membrane protein and enoyl-CoA hydratase (Tc00.1047053508153.130) is expressed

in the mitochondrion, both of them were not detected in amastigotes before and now have been

experimentally confirmed of their expression in our identification list, majorly found in the

59

organelle preparation fraction. There were another 22 proteins annotated as “pseudogenes” got

identified, such as Tc00.1047053511237.30 (proline oxidase, pseudogene),

Tc00.1047053506923.10 (trans-sialidase, pseudogene) etc. Our data has suggested those are not

pseudogenes and confirmed them as real proteins.

Besides the subcellular enrichment, our data processing approaches also contributed a lot to the

additional new gene products identification, specifically on the non-annotated genes.

Tc00.1047053507089.170_m153 is an example of the mutated protein identification. Through

ByOnic database searching, peptide YNWLLNEMVLTR was identified by tandem mass spectra

and there were no protein sequences in the original database matching this peptide. But

hypothetical protein Tc00.1047053507089.170 contains a peptide YNWLLNEMILTR which

only has one amino acid difference. So we proposed there was a mutation occurred on this

peptide and the corresponding mutated protein was annotated as

Tc00.1047053507089.170_m153 (367 I—V). We put 367 I--V in the sequence annotation

indicating the amino acid mutation from I to V at sequence site 367. The m153 is just the

ordering number in our mutated protein list.

Overall, among the total 2490 identified proteins (890 protein groups), 337 of them were never

detected before in any life stages, which accounts for 14%. As for the amastigote stage, there

were 481 proteins thought to be new identification and this is around 19% within the whole list.

During classification for those mutated proteins, if their corresponding non-mutated genes

already have the mass spec evidence, then we don’t count them as new identification. For

example, ATPase beta subunit (Tc00.1047053509233.180_m53) has a unique mutated peptide

AVLVYGQMNEPPGAR, which has never been detected before. But Tc00.1047053509233.180

was shown to be previously identified in other studies.7 So we consider this protein has been

60

discovered before and not new even with novel peptide identification due to our mutation search.

While, as for the phosphoinositide-binding protein (Tc00.1047053510657.30_m307), we noticed

that the original non-mutated one Tc00.1047053510657.30 has no proof of mass spec

identification. In this case, this mutated protein was assigned to the new identification list and of

course the reason causing it to be discovered is because we performed the ByOnic wild card

mutation search and successfully identified a mutated peptide LESELAGLEER. The mutation

search also increased the peptide coverage a lot, making some of the previously ambiguous

identifications to the confident ones. Without mutation search, retrotransposon hot spot (RHS)

protein (Tc00.1047053508589.30) will only have one matched peptide GGLTEWFSSHGK with

a mascot score of 48. In our 1% protein FDR calculation, the min score for one peptide match

was allowed as 57, thus it was excluded from our list. But adding the identification of mutated

peptide YSAASNIVDIVDGFSGR and will help us to keep this identification since the min

mascot score for two peptides was defined as 33. In this way, many other initially “discarded

proteins” were dragged back into our identification list, which largely improved the coverage.

Functional Classification of the Identified Proteins

Most of the identified proteins were classified and assigned functions using literature searching

and Database for Annotation Visualization and Integrated Discovery (DAVID)30

software

according to the Gene Ontology hierarchy. As shown in Figure 4.3, a variety of functional

annotations have been assigned, indicating our identification dataset contains an in-depth

distribution of the functional proteins. One major category of the classified functions is the

nucleotide binding. There are a total of 111 protein groups involved in this function such as RNA

binding proteins and GTP binding nuclear protein etc. Another relatively abundant distribution

group is involved in translation biological process, which contains 50 proteins. Most of them are

61

ribosomal proteins and elongation factors. Besides that, there are some other proteins involved in

the metabolism pathways. It was thought the fatty acid catabolism is more abundant in

amastigotes and provides the nutrients for corresponding energy metabolism. We have found a

number of enzymes involved in the fatty acid metabolism pathways such as fatty acyl CoA

syntetase, acyl-CoA dehydrogenase, enoyl-CoA hydratase/isomerase, 3-ketoacyl-CoA thiolase,

etc. Additionally, we identified a lot of cell surface proteins participating host cell invasion and

their escape from our immune system. Trans-sialidase and GP63 protein families are the major

ones in this category. Besides the T cell antigens such as trans-sialidases, we also firstly

identified a major human B-cell immunodominant antigen Tc40 like protein

(Tc00.1047053506659.10). This antigen has been discovered by a lot of patients’ serum samples

with Chagas’ disease. The most important aspect of this antigen is that Tc40 does not contain

tandemly repeated amino acid sequences. This feature makes it standing out from many other T.

cruzi antigens having tandem repeating units because previous studies have suggested that the

immune response to parasite repeating antigens cannot protect the host since it hides more

important epitopes from the host’s immune response.31

There were another 210 proteins

identified as hypothetical proteins with unknown function. Most of them were conserved from

other organisms. However, there were a few of them not annotated as "conserved", which means

they are unique sequences to T. cruzi. Hypothetical proteins Tc00.1047053511725.80,

Tc00.1047053511003.60, etc are the examples of them. These hypothetical proteins in our

identification lists could be further studied for their functional and localization discovery in other

biological researches.

Subcellular Distribution of the Identified Proteins

62

The subcellular distribution of the identified proteins were also investigated using DAVID linked

with Gene Ontology hierarchy and literature reviews (Figure 4.4). Besides hypothetical and

unknown proteins, cytoplasm proteins are the most abundant populations among all groups,

accounting for 19% of the total identification. The plasma membrane proteins account for 9%,

including trans-sialidase, GP63, ATPases and several other important protein families.

Mitochondrial proteins were identified as another abundant group in our subcellular

identifications with a percentage of 7%. ADP, ATP carrier proteins, Malate dehydrogenase,

enoyl-CoA hydratase etc are all from this organelle. We also have 6% proteins in the ER and

Golgi subcellular organelle fractions. For example, UDP-Gal or UDP-GlcNAc-dependent

glycosyltransferase is one of the glycosyltransferase proteins localized in Golgi apparatus and

catalyzes the addition of the monosaccharide group from a UTP-sugar to a small receptor

molecule. Calreticulin is an ER resident protein and functionalized as Ca2+ binding chaperones.

In general, the subcellular localization distribution indicates the enrichment strategies are

efficient since in regular whole cell analysis, the total membrane proteins only account for

around 3%.

CONCLUSION

Subcellular organelle and membrane proteomic analyses were successfully used to identify the T.

cruzi intracellular amastigotes proteome. In order to recover identifications outside the annotated

genes, the whole genome search and ByOnic mutation search were also performed. These data

processing methods largely increased our identification data sets and recovered many "good

MS/MS spectrum" not selected in the "annotated database searching". Totally, there were 2490

proteins within 890 protein groups observed in our experiment. 14% of them were never detected

in any life stages of T. cruzi and 19% of the identified proteins were not shown in previous

63

amastigote proteome data. The new identification sets contained many important cell surface

membrane proteins such as trans-sialidase, GP63, etc. Some other identified proteins involved in

the metabolism pathways indicated the amastigotes living conditions and energy source. This is

the first proteomic analysis of T. cruzi intracellular amastigote stage and could be potentially

contributed to the understanding of this parasite system biology and future vaccine selections.

64

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Almeida, I. C.; Cunha, E. S. N. L. Proteomics 2009, 9, 1782.

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(8) Nakayasu, E. S.; Gaynor, M. R.; Sobreira, T. J.; Ross, J. A.; Almeida, I. C.




(10) Ayub, M. J.; Atwood, J.; Nuccio, A.; Tarleton, R.; Levin, M. J. Biochem Biophys

Res Commun 2009, 382, 30.

(11) Ferella, M.; Nilsson, D.; Darban, H.; Rodrigues, C.; Bontempi, E. J.; Docampo,

R.; Andersson, B. Proteomics 2008, 8, 2735.

(12) Souza, R. A.; Henriques, C.; Alves-Ferreira, M.; Mendonca-Lima, L.; Degrave,

W. M. Anal Biochem 2007, 365, 144.

(13) Parodi-Talice, A.; Monteiro-Goes, V.; Arrambide, N.; Avila, A. R.; Duran, R.;

Correa, A.; Dallagiovanna, B.; Cayota, A.; Krieger, M.; Goldenberg, S.; Robello, C. J Mass

Spectrom 2007, 42, 1422.

(14) Atwood, J. A., 3rd; Minning, T.; Ludolf, F.; Nuccio, A.; Weatherly, D. B.;

Alvarez-Manilla, G.; Tarleton, R.; Orlando, R. J Proteome Res 2006, 5, 3376.

(15) Paba, J.; Santana, J. M.; Teixeira, A. R.; Fontes, W.; Sousa, M. V.; Ricart, C. A.


(16) Magalhaes, A. D.; Charneau, S.; Paba, J.; Guercio, R. A.; Teixeira, A. R.;

Santana, J. M.; Sousa, M. V.; Ricart, C. A. Proteome Sci 2008, 6, 24.





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(19) Parodi-Talice, A.; Duran, R.; Arrambide, N.; Prieto, V.; Pineyro, M. D.; Pritsch,

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(22) Seyfried, N. T.; Huysentruyt, L. C.; Atwood, J. A., 3rd; Xia, Q.; Seyfried, T. N.;

Orlando, R. Cancer Lett 2008, 263, 243.

(23) Sanders, P. R.; Gilson, P. R.; Cantin, G. T.; Greenbaum, D. C.; Nebl, T.; Carucci,

D. J.; McConville, M. J.; Schofield, L.; Hodder, A. N.; Yates, J. R., 3rd; Crabb, B. S. J Biol

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(30) Dennis, G., Jr.; Sherman, B. T.; Hosack, D. A.; Yang, J.; Gao, W.; Lane, H. C.;

Lempicki, R. A. Genome Biol 2003, 4, P3.

(31) Lesenechal, M.; Becquart, L.; Lacoux, X.; Ladaviere, L.; Baida, R. C.; Paranhos-

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68

Figure 4.1. Coomassie blue stained 1-D SDS-PAGE analysis of the subcellular organelle and

membrane fractions. Molecular weight of standard protein markers are given on the left side of

the gel (lane 1). Lan2 is the membrane fraction from sucrose cushion and lane 3 is the

corresponding membrane wash fraction. Lane 4 is the membrane fraction from detergent

resistant preparation and lane 5 is the membrane wash from that method. Lane 6 is the organelle

fraction and the cytosol fraction is shown in lane 7. All the six sample lanes were later on cut

into 20-30 slices for LC-MS/MS analysis.

69

Figure 4.2. Protein identification distribution across all the six fractions (percentage is calculated

by comparing the spectra counting). The two membrane wash fractions are in very low abundant

indicating little recovery from these two fractions.

70

Figure 4.3. Functional classification of identified annotated proteins among all fractions. Most

of them were classified using Database for Annotation Visualization and Integrated Discovery

(DAVID) software. Values represent the percentage distribution of proteins.

71

Figure 4.4. The subcellular localization of identified proteins. From the distribution, the

depletion of abundant soluble proteins is evident with the increased percentage of membrane and

organelle proteins.

72

CHAPTER 5

RESOLVING PROTEIN ISOFORMS IN PROTOZOAN PARASITE TRYPANOSOMA CRUZI

USING GELC-MS/MS APPROACH1

______________________________________________________________________ 1 Xiang Zhu, James A. Atwood III, Brent Weatherly, T.A. Minning, R.L. Tarleton, Ron Orlando.

To be submitted to Journal of Proteome Research.

73

ABSTRACT

The protozoan parasite Trypanosoma cruzi is the etiologic agent of Chagas’ disease. Recent

completion of the genome sequencing has indicated over 30% of its genome is comprised of

multiple gene families especially some surface membrane genes. The protein isoforms from

these families usually have similar sequences and redundant functions. This result increases the

difficulty for the high-throughput proteomic studies. In regular bottom-up proteomics

experiments, identified peptides must be mapped to protein sequences for reporting of protein

identifications. However, differentiating between protein isoforms is complicated by the fact that

peptides are analyzed rather than intact proteins. Thus if a peptide is shared between two

proteins, without additional information, it is impossible to distinguish which protein is actually

expressed or if both proteins are expressed. Herein we report the application of GeLC-MS/MS

approach to analyze the Trypanosoma cruzi membrane proteome. Overall, we identified 1029

protein groups from the membrane enriched fractions. The GeLC approach also helps us

effectively resolve some protein isoforms’ identification including trans-sialidases, GP63, etc

which are potential vaccine candidates for Chagas’ disease.

74

INTRODUCTION

Approximate 18 million people in Latin American countries are infected with Trypanosoma

cruzi (T. cruzi) which is the causative agent of Chagas disease.1 The infection usually has the

consequence of heart rhythm abnormalities causing sudden death. Each year more than 50,000

people are died from Chagas disease.1,2

The T. cruzi life cycle stages are developed between

reduviid insect vectors and mammalian hosts. Epimastigotes reside in the vector midgut and they

can replicate and differentiate into metacyclic trypomastigotes, which are the infective forms

transmitted to mammalian hosts. The metacyclic trypomastigotes enter various host cells and

differentiate into amastigote forms which replicate through binary fission. These intracellular

amastigotes transform to trypomastigotes that are circulated in the blood stream and invade other

cells in the body. The cycle is continued when some of these trypomastigotes are ingested by the

insect vectors during their blood meal. The trypomastigotes finally undergo differentiation into

epimastigotes in the insect vector’s midgut. Currently there are no effective vaccines available

for this disease and the treatments have been restricted to highly toxic chemotherapeutic agents,

which have been proven unsatisfactory for the chronic stage of the disease and exhibit dangerous

side effects.3 Recent studies have shown that some functional proteins involved in the parasite

invasion and survival mechanism within the mammalian hosts could become potential vaccine

candidates and drug targets.3-6

Therefore comprehensive system biology studies become essential

for discovery of these targets. The T. cruzi genome sequencing has been completed recently

using a hybrid CL Brenner strain.7 However like other trypanosomatid parasites (T. brucei and

L.major), T. cruzi regulates their gene expression mostly post-transcriptionally, which results in

the poor correlation between mRNA and protein levels. Consequently directly exploring the

75

organism proteome becomes very important for discovering various gene products through

differential life stages.8-10

Shotgun proteomics especially MudPIT is one of the most popular approaches used for

comprehensive proteome discovery.11,12

It usually uses SCX and RPLC as a combination of the

peptides separation and detects the separated fractions by tandem mass spectrometry. The

advantage of the shotgun proteomics approach is the better digestion efficiency and protein

coverage for global proteome.13

In 2005, Atwood used this multidimensional LC-MS/MS

approach to identify 2784 proteins from all the four developmental stages; this is by far the most

comprehensive T. cruzi proteome identification datasets.8 While one of the problems in

interpreting the results of shotgun proteomics experiments is the difficult distinguishment of

protein isoforms from the identified peptides. This case becomes particularly important for T.

cruzi because at least 30% of this parasite’s genome is composed of multi-copy gene families.7,8

The largest gene families include some cell surface proteins such as trans-sialidase, mucins,

mucin-associated surface proteins (MASPs), and the surface glycoprotein gp63 protease. These

gene products especially the trans-sialidase members are major targets of host cell immune

responses, thus could become potential vaccine candidates.4,5,14-16

At the same time the largely

expressed variable trans-sialidase isoforms could play important roles for their immune

evasion.15

Finding out which trans-sialidases are probably expressed on cell surface is important

for the vaccine development. So selecting proper ways to resolve these protein families’

identification is important but also challenging because these protein isoforms usually contain

very similar sequences with some shared peptides. Without additional information, we can only

assign these shared peptides to certain protein groups and it is impossible to decide which

specific protein or several proteins in this group are truly identified.

76

With the aim of resolving complex protein isoforms’ identification in T. cruzi, we recently

performed a membrane proteomic analysis on T. cruzi CL-Brenner lab strain of trypomastigote

life stage using the GeLC-MS/MS approach.17

In this organism, many important protein families

are cell surface proteins, thus enrichment of the plasma membrane fractions is necessary.7,8

The

reason we choose trypomastigotes instead of other developmental stages is because this stage has

been verified to express relatively the largest number of surface membrane protein families such

as trans-sialidases, etc and it is the infective form present in the host blood stream and interacts

with the host’s immune system.8,18

The desirable GeLC-MS/MS approach is favored over

MudPIT shotgun proteomics by improved membrane proteins solubility, less complex mixtures,

and the availability to link identified peptides with corresponding proteins.17,19

These advantages

could help us identify and differentiate some previously unresolved protein isoforms through

combining the protein molecular weight information, unique peptides, and ways of protein

grouping.


Parasite Preparation

The CL-Brenner lab strain of trypomastigotes were grown in monolayers of Vero cells (ATCC

no. CCL-81) in RPMI supplemented with 5% horse serum as previously described.20

Emergent

trypomastigotes were harvested daily and examined by light microscopy to determine the

percentages of trypomastigotes. The parasite cells (5 x 108) were harvested by centrifugation at

3,000 x g for 15 min at room temperature, washed three times with ice-cold PBS buffer, and

subjected to fractionation.

Membrane Enrichment

77

Membrane proteins were enriched using the sucrose cushion method as previously described

with minor modifications.21

Briefly cells were suspended in 3 mL of ice-cold lysis buffer (10

mM HEPES, 1 mM EDTA, pH 7.2) containing protease inhibitors and then homogenized by 25

strokes of a 7 mL Dounce homogenizer. An equal amount of sucrose buffer (10 mM HEPES,

1mM EDTA, 500 mM sucrose, pH 7.2) was added with additional 25 strokes of homogenizer.

The samples were centrifuged at 6,000 x g for 10 min at 4 C to pellet cellular debris. The

supernatant was collected and centrifuged at 150,000 x g for 1 hour at 4 C. Supernatant was

removed and the crude pellet membrane was incubated in 100 mM sodium carbonate solution

(pH 11.3) for 15 min at 4 C. After incubation, the membrane pellet was collected by centrifuging

at 150,000 x g for 1 hour at 4 C. Additional wash was performed by incubating the membrane

pellet in same ice-cold lysis buffer containing 1% Triton X-100.


Crude membrane pellet was resuspended in 20 l Laemmli buffer (Sigma-Aldrich) and boiled at

80 C for 15 min. Solublized proteins were separated by 1-D SDS-PAGE using NuPAGE 4-12%

Bis-Tris (Invitrogen) gradient gels at 150 V for 2 hours. Gel lanes were washed twice in ddH2O

for 15 min and then cut into 30 slices. Proteins were reduced by incubating the gel bands in 10

mM DTT/100 mM Ambic (ammonium bicarbonate) solution at 56 C for 1 h. Then the proteins

were carboxyamidomethylated with 55 mM iodoacetamide/100 mM Ambic for 1 h at room






78

LC-MS/MS Analysis




acetonitrile-0.1% formic acid, respectively. The peptide samples were loaded for 50 min using

positive N2 pressure on a PicoFrit 8-cm by 50-μm column (New Objective, Woburn, MA)

packed with 5-μm-diameter C18 beads. Peptides were eluted from the column into the mass

spectrometer during a 90 min linear gradient from 5 to 60% of total solution composed of mobile

phase B at a flow rate of 200 nl min−1

. The instrument was set to acquire MS/MS spectra on the

nine most abundant precursor ions from each MS scan with a repeat count of 1 and repeat

duration of 5 s. Dynamic exclusion was enabled for 200 s. Raw tandem mass spectra were

converted into the mzXML format and then into peak lists using ReAdW software followed by

mzMXL2Other software.22

The peak lists were then searched using Mascot 2.2 (Matrix Science,

Boston, MA) and X!Tandem (version 2.2) softwares.


A target database was created using the 42288 annotated sequences obtained from the National

Center for Biotechnology Information (www.ncbi.nih.gov). A decoy database (decoy) was then

constructed by reversing the sequences in the normal database. Searches were performed against

the normal and decoy databases using the following parameters: fully tryptic enzymatic cleavage

with two possible missed cleavages, peptide tolerance of 800 ppm, fragment ion tolerance of 0.6

Da. Fixed modification was set as carbamidomethyl due to carboxyamidomethylation of cysteine

residues (+57 Da) and variable modifications were chosen as oxidation of methionine residues

(+16 Da), deamidation of asparagine residues (+1 Da), Gln to Pyro-Glu (-17 Da) and Glu to

79

Pyro-Glu (-18 Da). Statistically significant proteins from both searches were determined at a 1%

protein false discovery rate (FDR) using the ProValT algorithm, as implemented in ProteoIQ

(BioInquire, LLC, Athens, GA). 23

In ProteoIQ, database search results were grouped according

to gel bands. This allowed protein within groups to be resolved based on comparing

experimental and theoretical molecular weights in our GeLCMS approach.


Membrane Protein Preparation

T. cruzi cell surface membrane proteins account for the largest portion of protein families within

the whole genome. In order to resolve the problem of protein families’ identification, we have to

effectively perform a membrane proteomics of T. cruzi; in the meanwhile, the membrane

proteins coated on the parasite surfaces usually play very important roles in host cell entry and

immune evasion. Proteomic studies on these protein families could help us understand the nature

of parasites invasion and survival mechanisms and explore the way for vaccine and drug

development. Although with the significant importance, there have been limited proteomic

studies specifically targeting these membrane protein expressions in T. cruzi24,25

, especially on

the mammalian trypomastigote and amastigote stages.26

Most previous proteomic studies on T.

cruzi were more focused on easily prepared insect stage epimastigote24,25,27

and used whole cell

analysis without any enrichment.8,10,18,28-30

Those global proteomic analyses inevitably missed a

large number of membrane proteins since the cytoplasm soluble proteins dominated the

identifications because of their relatively high abundance. Our lab’s previous results also indicate

the epimastigote proteome expresses less surface membrane proteins than trypomastigote stage.8

Compared to the soluble proteins, membrane proteins are usually of low abundance, high

hydrophobicity and basic isoelectric points, thus making the isolation and identification to be a

80

challenging task. In our strategy for enriching the membrane fractions, we utilized the commonly

used sucrose cushion method to reduce the amount of highly abundant cytosol proteins and

cytoskeletal proteins, such as alpha tubulin, beta tubulin, etc. Meanwhile, identification of trans-

sialidase and several other surface membrane proteins would attract more of our interest since

they are the largest protein families presented on the parasite surface and proposed to be

potential targets for vaccine development. Unlike normal embedded integral membrane proteins,

they are linked to the plasma membrane via a C-terminal glycosylphosphatidylinositol (GPI)

anchor. Recent studies have shown that those GPI anchored proteins usually reside on some

specific membrane domains, which are called “lipid rafts”. The rafts are mainly composed of

sphingolipid and cholesterol. Sphingolipid contains long, largely saturated acyl chains allowing

them to pack tightly together and form a liquid-ordered state.31-34

This rigid tight structure has

been claimed to be resistant to some non-ionic detergent such as Triton X-100 at low

temperatures. Upon treatment with Triton X-100 at 4 C, Membranes other than the “lipid raft”

regions will be disrupted and release the embedded proteins. Based on this information, we

introduced Triton X-100 in our preparation at 4 C trying to enrich and observe more GPI

anchored proteins like trans-sialidase and mucins, etc. Enriched membrane protein fractions were

analyzed using GeLC-MS/MS approach for isoforms’ identification. Briefly the membrane

pellets were first dissolved using Laemmli buffer that contain 4% SDS and then separated by 1-

D SDS-PAGE gel electrophoresis (Figure 5.1). After separation, the gel lanes were sliced into 30

fractions and then those fractions were subjected to in-gel trypsin digestion. All the individual

fractions were analyzed through on-line LC-MS/MS using LTQ ion trap. Resulting spectra were

searched against both Mascot and X!Tandem followed by validation using ProteoIQ at maximum

1% FDR.

81

Protein Identification

There were total 1029 protein groups containing 4996 total proteins identified at a maximum 1%

protein false discovery rate. The combination search engine of Mascot and X!Tandem seems to

be very useful since each one of them can have some uniquely identified high confident peptides.

Together with other jointly identified peptides, this combination searching approach has

effectively increased the protein coverage, which is very helpful for our protein isoforms’

differentiation. For example the trans-sialidase protein AAP80764.1 was identified by 28

peptides using Mascot and 27 peptides using X!Tandem, among which 25 peptides are shared.

Our cell surface membrane enrichment strategy looks efficient with the identification of 57 trans-

sialidase protein groups and several other surface membrane proteins such as GP63, TolT and

MASP etc, which shows great enrichment compared to all previous global analysis. Viewing

from the top 50 protein groups, although some regular high abundant proteins like beta tubulin,

alpha tubulin and heat shock protein were still present, but there were around 8 membrane

proteins including 5 trans-sialidases identified. In the list, the top one trans-sialidase is the tenth

most abundant protein, and there were other two trans-sialidases detected in the top 20 abundant

proteins. While in Atwood’s whole cell trypomastigote proteome study, the most abundant trans-

sialidase was only ranked as No 284, and there were only 8 trans-sialidase proteins among the

top 400 groups.8 These comparisons apparently indicate after membrane extraction, the

membrane proteins especially the GPI anchored cell surface proteins were largely enriched and

some of the very low abundant membrane proteins previously ignored could now be detected.

This enrichment is also supported by the fact that several high abundant cytosolic soluble

proteins in the whole cell trypomastigote proteome were highly depleted in our preparation

method and could be barely identified in our experiment. Those proteins include the 9th most

82

abundant protein NADH:flavin oxidoreductase/NADH oxidase, the 22th most abundant protein

thiol-dependent reductase, and several other proteins in top 50 identifications in Atwood’s

trypomastigote proteome.8

Resolving the Important Protein Families

T. cruzi trypomastigote is the life stage that circulates in the host blood stream and performs the

cell invasion function. During this process the host immune system will respond to them

immediately and rely on some antigen-specific T cells and antibodies to kill the pathogens. One

of the major strategies for T. cruzi to escape the host immune response is that they can express

several large members of surface antigen proteins. Trans-sialidase is one of the most important

surface protein families for T. cruzi. This large protein family is encoded by more than 1300

genes. T. cruzi is unable to synthesize sialic acid itself so it relies on trans-sialidase to transfer

the sialic acid from host sialoglycoconjugates onto terminal galactose residues on its surface

mucin molecules. The sialiation of surface glycoproteins will both prevent complement

activation and increase the infectivity. Thus the trans-sialidase proteins are critical for parasite

survival and potentially to be the vaccine target. However, among those hundreds of trans-

sialidases, only a small number of them have enzymatic activity. Expressing together with those

active trans-sialidase enzymes, the large number of non-enzymatic family members could deflect

the immune response from the real targets and mislead the T cell responses by offering their

altered peptides. Several other immunodominant genes also express large number of isoforms on

the cell surface. The second largest family is MASP with close to 1300 genes, Mucins have 817

gene products and GP63 gets around 403 genes. Besides these surface membrane proteins, genes

like retrotransposon hot spot (RHS) protein, heat shock proteins, ribosomal proteins, etc also

have large number of members in their gene families (Table 5.1). Within the significant

83

importance, while the identification of these protein families is always difficult and challenging

task because typically proteins from the same family have very similar structure, function and

peptide sequence. For example in our case, many identified trans-sialidases shared some high

frequently detected peptides like FAGVGGGALWPVSQQGQNQR,

HQWQPIYGSTPVTPTGSWETGK and LLGLSYDEK, etc. Without additional information, we

can only assign the above shared peptides to the same protein group and it is hard to decide

which specific protein or several proteins are correctly identified. This could explain although we

had 57 identified trans-sialidase groups, within these groups there were total 612 trans-sialidase

genes. The first straightforward way we used to differentiate these protein isoforms among

different groups was to find out some specific unique peptides. Taking trans-sialidase as

example, in our identification we identified 57 trans-sialidase and among them there were 28

defined as unique ones because they have the unique peptides only expressed in one protein

group and not in all other 56 trans-sialidase groups. Trans-sialidase EAN94054.1 has shared

peptides GMSADGCSDPSVVEWK and VKEVLATWK with several other trans-sialidases such

as EAN88146.1, EAN89851.1, and AAG32026.1 etc, but the peptide DTTGDETVSSLR is not

belonged to any of those trans-sialidase sequences, thus it can be uniquely assigned to trans-

sialidase EAN94054.1 group. Using this way, we can differentiate some protein isoforms even

they share some peptides. In our proteomic identification, several trans-sialidase proteins could

even be recognized with 5or 6 unique peptides. Although some of them only got one unique

peptide, while that peptide makes it become the unique one in the whole 1300 trans-sialidase

genes from the database so those proteins were believed to be identified with high confidence

even having only one peptide evidence. In addition to trans-sialidase protein families, we also

detected several other important membrane protein groups such as surface protein TolT, MASP,

84

and gp63 proteins with their respective unique peptides. Unlike the trans-sialidase the

identification numbers of these cell surface proteins were relatively lower especially for MASP

we only found one. This result suggested the true expression level for these proteins might not be

as high abundant as trans-sialidases although their gene families are also large. The other

possibility is because the high dense glycosylation makes them undesirable to be detected by

regular LC-MS/MS approach.

GeLCMS Approach to Resolve Protein Isoforms’ Identification

Proteins having unique peptides can be categorized into different protein groups, while within

the same protein group; some of these peptides will become shared. The protein that accounted

for all the peptides within a protein group was thought as “TOP Protein”, if more than one

proteins had all peptides in the group, the one with higher sequence coverage was considered as

“TOP”. Protein assignments listed as “OTHER Protein” contained a subset of peptides that were

observed in the “TOP” identification but could not be distinguished as unique proteins because

of shared peptide representation. This was particularly common for large gene families. The

largest trans-sialidase protein group in our list even contains 89 members, the “TOP Protein” has

4 peptides and considered as the most significant one in this group. However, it does not mean

all the others in the group were not identified, it just simply means that the peptides identified for

them don’t allow them to be distinguished from others in the group and they have less sequence

coverage than the “TOP Protein”. It is difficult to know whether several or all of the members in

a protein group are expressed or not unless we find some other useful information to differentiate

them. Herein we utilized the GeLC-MS/MS approach trying to resolve the protein isoforms’

identification problem by combining protein molecular weight information and protein grouping

in ProteoIQ’s data clustering validation. In our experiments, the gel lane was manually cut into

85

30 bands from top to bottom and named from band01 (top band) to band30 (bottom band), each

band was performed in-gel tryptic digestion and analyzed using LC-MS/MS. Those generated

band fraction spectrum files were searched individually against both Mascot and X!Tandem. In

ProteoIQ validation and clustering process, the database search results were grouped according

to gel band; in such way we could not only find out which proteins those identified peptides

belong to, but also get the idea of the real molecular weight range for the proteins digested to

those identified peptides. Compared to those unresolved protein’s theoretical molecular weight,

we can then be aware of which protein is more likely to be expressed. The 25% trimmed average

mass of all identified proteins in one gel band was chosen to show the protein mass distribution

for the 30 gel slices. As shown in Figure 5.2 the general trend of the protein molecular weight on

the gel was desirable. In general the average protein mass got smaller for lower gel bands, which

was in agreement with the actual gel electrophoresis experiment. Some of the unexpected points

were due to protein aggregation, complex formation, undetected PTMs and degradation during

the preparation. Especially, some small proteins such as histones, tubulins etc were very easy to

form complex, which made them also be detected in most high molecular weight gel bands.

Since protein groups having unique peptides can be clearly distinguished by each other, we first

validate the feasibility of our GeLC approach on those protein groups as a template. Heat shock

protein EAN99073.1 (MW 84K) and EAN86069.1 (MW 38K) are both belong to heat shock

protein families that are involved in protein folding and intracellular trafficking functions. We

identified three peptides with the sequence of DTELSFCTPQVCER, EELAENLGTIAGSGSK

and QLLDIVACSLYTEK which are shared by these two proteins. At the same time we found

five unique peptides (FISGAYDSPMFR, LHYVVDAPLSIR, MVENVPEPTADK,

SDIDYPLVSLEEYR, YNFHFNPK) for EAN99073.1 and two unique ones

86

(ELQSAASGAQAAEK, GYLWESDGTGTFK) for EAN86069.1. The unique peptides of

EAN99073.1 were generated from gel band 12-13 which contained most of the proteins having

an approximate MW range of 70K-80K, which was in agreement with the MW of 84k for heat

shock protein EAN99073.1. As for the other heat shock protein EAN86069.1, its unique peptide

ELQSAASGAQAAEK was obtained from gel band 28 which falls into the protein MW range of

30K-40K. It gave us the evidence this peptide was coming from the 38K heat shock protein.

Additionally because it is unique to EAN86069.1, it excludes the possibility of protein

degradation from other proteins. This has verified the feasibility of our GeLC-MS/MS approach.

Although these two protein isoforms contain similar sequences, we can still differentiate them

with the molecular weight information (Figure 5.3). We also applied this processing method for

our concerned trans-sialidase proteins. Trans-sialidase protein EAN87032.1 (MW 50K) and

EAN96545.1 (MW 108K) share the identified peptide VYESVDMGK, while we also identified

several unique peptides for both of them. And most of these unique peptides were falling into the

proper MW range on the gel. In Gel band 8 with an approximate MW range of 90K-100K we

found peptide GTDIITATIGSK which is the unique sequence of EAN96545.1. Unique peptide

LLIVTSGSVIPQLLR was identified to EAN87032.1, and the source file of this peptide was

shown in band 17 which majorly contained 50K-60K proteins.

After we have established that the molecular weight information on the gel can be applied in the

protein isoform’s differentiation, we could then use this processing method to resolve some

protein isoforms without proper unique peptides. For example Peptides LLVRPLDGPLVVPR,

GRPVVGVINYNPR, GIEGGPPMLPPMRNPAAPGGR and CPLFSDVCLTMLK were

identified to a GP63 protein group. In this group, protein EAN84769.1 contains all these peptides

and has the best sequence coverage thus ranked as “TOP Protein”. Besides this one, there are

87

several “Other Proteins” in this group we also believe their expression according to our

GeLCMS information. For instance, two small GP63 proteins EAN84143.1 and EAN81541.1

have protein MW for 37K and 30K, most of the MS/MS spectra for identified peptides

LLVRPLDGPLVVPR and GRPVVGVINYNPR were obtained from gel band 21 and 22 with a

proper MW range of 40K-50K range. Furthermore, these two peptides are part of the sequence

from those two proteins. This has suggested these “Other proteins” within this single group in

our assignment are also likely to be expressed. Similar examples were also found for some other

protein groups. Trans-sialidase AAP80764.1 (MW 95K) was assigned as “TOP Protein” and it

contains 30 peptides, most of which were obtained from band 7 and 8. In this group

EAN86623.1 (MW 36K) got 2 peptides from band 26 and 1 peptide from band 23 which all

indicated this protein being most likely expressed as well. While for another small trans-sialidase

EAN82031.1 (MW 22K) in this group, all its identified peptides were coming from band 7-9,

which couldn’t make us believe this protein to be really detected in our experiment.

CONCLUSION

In conclusion, we demonstrated a GeLC-MS/MS approach to resolve protein isoforms based on

combining shotgun proteomic results with molecular weight information and protein grouping. A

membrane proteomic study of T. cruzi trypomastigotes provided a unique set of large protein

family members to assess the feasibility of this approach. The ability of resolving these

important surface membrane protein families provides us the useful information about the

identification for some potential vaccine targets. We anticipate that this approach will find

applicability in the proteomic analyses of other organisms and will assist in resolving protein

groups arising from redundant database entries.

88

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Rodrigues, M. M. Vaccine 1998, 16, 768.

(5) Wizel, B.; Garg, N.; Tarleton, R. L. Infect Immun 1998, 66, 5073.

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(9) Cuervo, P.; Domont, G. B.; De Jesus, J. B. J Proteomics, 73, 845.

(10) Paba, J.; Santana, J. M.; Teixeira, A. R.; Fontes, W.; Sousa, M. V.; Ricart, C. A.



Garvik, B. M.; Yates, J. R., 3rd Nat Biotechnol 1999, 17, 676.

(12) Wolters, D. A.; Washburn, M. P.; Yates, J. R., 3rd Anal Chem 2001, 73, 5683.

(13) Yates, J. R., 3rd J Mass Spectrom 1998, 33, 1.

(14) Fralish, B. H.; Tarleton, R. L. Vaccine 2003, 21, 3070.

(15) Frasch, A. C. Parasitol Today 2000, 16, 282.

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Sullivan, S.; Heiges, M.; Craven, S. H.; Rosenberg, C. S.; Collins, M. H.; Sette, A.; Postan, M.;

Tarleton, R. L. PLoS Pathog 2006, 2, e77.

(17) Schirle, M.; Heurtier, M. A.; Kuster, B. Mol Cell Proteomics 2003, 2, 1297.



(19) Shevchenko, A.; Wilm, M.; Vorm, O.; Mann, M. Anal Chem 1996, 68, 850.



Orlando, R. Cancer Lett 2008, 263, 243.



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(25) Ferella, M.; Nilsson, D.; Darban, H.; Rodrigues, C.; Bontempi, E. J.; Docampo,

R.; Andersson, B. Proteomics 2008.

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Alvarez-Manilla, G.; Tarleton, R.; Orlando, R. J Proteome Res 2006, 5, 3376.

(27) Sant'Anna, C.; Nakayasu, E. S.; Pereira, M. G.; Lourenco, D.; de Souza, W.;

Almeida, I. C.; Cunha, E. S. N. L. Proteomics 2009, 9, 1782.

(28) Parodi-Talice, A.; Duran, R.; Arrambide, N.; Prieto, V.; Pineyro, M. D.; Pritsch,

O.; Cayota, A.; Cervenansky, C.; Robello, C. Int J Parasitol 2004, 34, 881.

(29) Sodre, C. L.; Chapeaurouge, A. D.; Kalume, D. E.; de Mendonca Lima, L.;

Perales, J.; Fernandes, O. Arch Microbiol 2009, 191, 177.

(30) Parodi-Talice, A.; Monteiro-Goes, V.; Arrambide, N.; Avila, A. R.; Duran, R.;

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92

Table 5.1. Identified large gene families in the membrane preparation

93

Figure 5.1. Silver-stained 1-D SDS-PAGE analysis of the membrane fraction generated from

trypomastigote of T. cruzi. Molecular weight of protein markers are given on the left side of the

gel. Crude membrane pellets were dissolved using Laemmli buffer that contain 4% SDS. The

right sample lane was later on cut into 30 slices for MS analysis.

94

Figure 5.2. Illustration of the mass distribution across all the 30 gel bands. Due to the occurrence

of protein aggregation, unknown PTMs and degradation, 25% trimmed (from both sides) average

mass of all identified proteins in each gel band was selected to reflect the protein molecular

weight change on the gels.

95

Figure 5.3. Graph showing the two identified heat shock protein isoforms EAN99073.1 (84K)

and EAN860696.1 (38K) with their corresponding peptides distribution on the gel bands.

Although these two proteins have shared peptide sequences (shown inside black dotted text box),

they could be differentiated by their unique peptides (shown in bold colors). From the GeLC

view, the 84K protein has most of the unique peptides from band 12-13 (major MW range 70K-

80K). Additionally, the unique peptide ELQSAASGAQAAEK was found in band 28 (major

MW range 30K-40K), which indicate the existence of the 38K protein.

96

CHAPTER 6

GELC-MS/MS ANALYSIS ON EMBRYONIC STEM CELL PROTEIN DEGRADATION1

_____________________________________________________________________ 1 Xiang Zhu, Matt Bechard, Stephen Dalton, Ron Orlando. To be submitted to Journal of

Biomolecular Techniques.

97

ABSTRACT

1D In-gel tryptic digestion followed by LC-MS/MS analysis known as GeLC-MS/MS is a

technically simple but powerful approach for proteomic analysis. Here we report the application

of GeLC-MS/MS technique to analyze the mouse embryonic stem cell proteome and focus on

looking for some potential protein degradation products. Our identification data has shown that

this approach is efficient and helpful for discovering the protein degradation process, which

plays essential roles in biological cellular functions and activities.

98

INTRODUCTION

GeLC-MS/MS approach has been proven to be a powerful and efficient technique to analyze

complex protein mixtures.1,2

It is a combination of 1D gel electrophoresis protein separation and

on-line LC-MS/MS analysis of in-gel digested peptides for protein identification. Compared to

the gel-free shotgun proteomic methods such as MudPIT3,4

, this technique provides several

important advantages. First, slicing the gel lane into 20-30 small bands separates the protein

mixtures into narrow molecular weight range, which significantly increase the dynamic depth of

the analysis. Because the generated in-gel digested peptides from each gel band are analyzed

separately, some of the low abundant proteins could also be identified as long as their molecular

weight is not close to the high abundant proteins in the complex protein mixture. While for gel-

free digestion, tryptic peptides from the high abundant proteins could be detected across most of

the fractions, making some low abundant ones ignored. Second, for most mass spectrometry

experiments especially ESI, detergents and buffer salts always make negative effects and it's not

very easy to remove them during gel-free preparation. The situation could be even worse with

the membrane proteins since they have to get dissolved in certain concentration of the detergent

before digestion. On the other hand, higher concentration of the detergent will deactivate the

trypsin, thus making the analysis results unsatisfied. The gel based approach will easily wash out

the detergents and salts before digestion, making the analysis especially the membrane proteome

identification more high-throughput.5

Another important feature of the GeLC-MS/MS technique is that it can not only identify the

peptides from the MS/MS spectra, but also track the original gel bands for these peptides on the

gel lane. Combining the results of both spectra identification and corresponding molecular

weight range, we can explore some more detailed information about our identified proteins and

99

better understand the system biological process. For example, protein isoforms usually contain

very similar sequences with some shared peptides. If there are no unique peptides, MudPIT

shotgun proteomics can only assign these shared peptides to certain protein groups and it is

impossible to decide which specific protein or several proteins in this group might be truly

identified. However, GeLC-MS/MS technique can tell us the real molecular weight range for the

proteins digested to those identified peptides. Compared to the protein’s theoretical molecular

weight, we can then be aware of which protein in the family is more likely to be expressed. The

other potential application for GeLC-MS/MS proteomic approach is that we can utilize this

method to look for some possible protein degradation products. Protein degradation especially

the proteasomal degradation pathway has attracted many interests these years.6-9

One of the most

important degradation pathway identified in recent years is the discovery of the ubiquitin

proteasome system (UPS) which regulates the degradation of intracellular proteins in

eukaryotes.6,8,10-12

The UPS mediated protein degradation is associated with a number of

biological processes such as intracellular signaling, cell division, gene transcription etc. More

importantly, the aberrations in the degradation pathways are often associated with many human

diseases such as cystic fibrosis, emphysema, Alzheimer disease, and Parkinson disease etc.13-21

Effective targeting and control of the degradation pathways could bring the potential new

treatment method to these diseases. Recently, the ubiquitin mediated protein degradation process

has also been studied in the embryonic stem cell system.22,23

Embryonic stem (ES) cells are the

pluripotent stem cells derived from the early embryos.24-26

They are capable of self-renewal and

differentiating into any types of adult cells.27-29

Because of the pluripotency and self-renewal

capability, ES cells have been proposed to be the ideal system for regenerative medicine and

tissue replacement. In order to apply the ES cells into treatment of the diseases and medical

100

tissue transplantation, studies on factors regulating the cell differentiation is critically important.

Octamer-binding transcription factor 4 (OCT4) is an essential transcription factor for regulating

stem cell differentiation process.30-34

Researchers have found that certain E3 ubiquitin-protein

ligase can interact with OCT4 and regulate degradation of OCT4 through the 26S

proteasome.22,23

These findings indicate using OCT4 ubiquitin ligase-targeting drugs may be

applicable to direct the stem cell differentiation. System biology using genomic and proteomic

approaches could largely contribute to the understanding of the protein degradation process. In

this paper, the GeLC-MS/MS technique was utilized as a simple and efficient method to evaluate

some protein degradation process in an embryonic stem cell system. The identified peptides from

all gel bands were first matched to their derived proteins. Then the theoretical molecular weight

of these proteins was compared with the actual molecular weight range on the 1D gel. When the

protein is expressed on a gel band with a much lower molecular weight than the theoretical one,

it could be a potential evidence for the protein degradation. Our method revealed a number of

protein degradation products which are not desirable to be identified in gel-free shotgun

proteomic approach.


Cell Culture and Sample Preparation

R1 ES cells were cultured in the absence of feeders on tissue culture grade plastic-ware pre-

coated with 0.1% gelatin-phosphate buffered saline (PBS), as described previously.25,35

ES cell

culture medium consisted of Dulbecco's Modified Eagle Medium (DMEM, Gibco BRL)

supplemented with 10% foetal calf serum (FCS, Commonwealth Serum Laboratories), 1 mM L-

glutamine, 0.1 mM 2-mercaptoethanol, 100 U/ml penicillin, 100 U/ml streptomycin and 1000

U/ml recombinant human LIF (ESGRO) at 37°C under 10% CO2. Protein samples were prepared

101

as previously described with minor modifications.36

Briefly cells were suspended in 3 mL of ice-

cold lysis buffer (10 mM HEPES, 1 mM EDTA, pH 7.2) containing protease inhibitors and then

homogenized by 25 strokes of a 7 mL Dounce homogenizer. An equal amount of sucrose buffer

(10 mM HEPES, 1mM EDTA, 500 mM sucrose, pH 7.2) was added with additional 25 strokes of

homogenizer. The samples were centrifuged at 6,000 x g for 10 min at 4 C to pellet cellular

debris. The supernatant was collected and centrifuged at 150,000 x g for 1 hour at 4 C. Protein

pellets were collected and stored at -80°C for further preparation.


Protein pellet was resuspended in 20 l Laemmli buffer (Sigma-Aldrich) and boiled at 80 C for

15 min. Solublized proteins were separated by 1-D SDS-PAGE using NuPAGE 4-12% Bis-Tris

(Invitrogen) gradient gels at 150 V for 2 hours. Gel lanes were washed twice in ddH2O for 15

min and then cut into 25 slices. Proteins were reduced by incubating the gel bands in 10 mM

DTT/100 mM ammonium bicarbonate (Ambic) solution at 56 C for 1 h. Then the proteins were

carboxyamidomethylated with 55 mM iodoacetamide/100 mM Ambic for 1 h at room






LC-MS/MS Analysis




102

acetonitrile-0.1% formic acid, respectively. Peptides were eluted from the C18 column into the

mass spectrometer during a 60 min linear gradient from 5 to 60% mobile phase B at a flow rate

of 4 l/min. The instrument was set to acquire MS/MS spectra on the nine most abundant

precursor ions from each MS scan with a repeat count of 1 and repeat duration of 5 s. Dynamic

exclusion was enabled for 200 s. Generated raw tandem mass spectra were converted into the

mzXML format and then into peak lists using ReAdW software followed by mzMXL2Other

software.37

The peak lists were then searched using Mascot 2.2 (Matrix Science, Boston, MA).


A target database was created using the 56729 annotated sequences obtained from the mouse

protein database in International Protein Index (IPI, version 3.68, European Bioinformatics

Institute, www.ebi.ac.uk/IPI/). A decoy database (decoy) was then constructed by reversing the

sequences in the normal database. Searches were performed against the normal and decoy

databases using the following parameters: fully tryptic enzymatic cleavage with two possible

missed cleavages, peptide tolerance of 1000 ppm, fragment ion tolerance of 0.6 Da. Fixed

modification was set as carbamidomethyl due to carboxyamidomethylation of cysteine residues

(+57 Da) and variable modifications were chosen as oxidation of methionine residues (+16 Da)

and deamidation of asparagine residues (+1 Da). Statistically significant proteins from both

searches were determined at a ≤1% protein false discovery rate (FDR) using the ProValT

algorithm, as implemented in ProteoIQ (BioInquire, LLC, Athens, GA).38

In ProteoIQ, database

search results were grouped according to gel bands. This allowed protein identifiaction

expression pattern to be viewed easily on individual gel band in our GeLC-MS/MS approach.


Proteome Analysis based on GeLC-MS/MS Strategy

103

Around 1 x 106

embryonic stem cells (ES) from the R1 mouse stem cell line were analyzed as a

model for protein expression and potential protein degradation studies in evaluation of our

GeLC-MS/MS technique. A simple 1D SDS PAGE gel separation was performed prior to mass

spectrometry analysis (Figure 6.1). The gel lane was equally cut into 25 gel bands and those

fractions were subjected to in-gel trypsin digestion. Peptide mixtures in each individual fraction

were further separated through a RPLC chromatography and analyzed using MS/MS technique

by LTQ for spectrum identification. To validate the confidence in peptide spectrum

identification, tandem mass spectra were searched against a target and reversed mouse database

using the Mascot search algorithm. Results from the Mascot search were then processed using

ProteoIQ to cluster non-redundant peptides from all fractions to protein identification at a ≤1%

false discovery rate (FDR). Proteins were further grouped according to homology of identified

peptides. For each homology group, the protein that accounted for all the peptides within a

protein group was thought as “TOP Protein”, if more than one proteins had all peptides in the

group, the one with higher sequence coverage was considered as “TOP”. Protein assignments

listed as “OTHER Protein” contained a subset of peptides that were observed in the “TOP”

identification but could not be distinguished as unique proteins because of shared peptide

representation. Overall, our analysis resulted in a total identification of 781 proteins (202 protein

groups) from the embryonic stem cells. Some of the gene products were claimed to be ES-

specific genes according to a recent comparative study on transcriptional profiling of mouse

embryonic, hematopoietic and neural stem cells.39,40

For example, mago-nashi homolog

(IPI00132692.2) is a nucleus protein involved in mRNA splicing and participates in the

nonsense-mediated decay (NMD) pathway. This gene was detected only in embryonic stem cells

but not available in other types of the stem cells and was defined as ES-specific gene.39,40

Solute

104

carrier family 2 (IPI00134191.3) is another important ES-specific gene found in our experiment.

It's located mostly in cell membrane and has a basic function of glucose transportation. Defects

in this gene can cause the blood-brain barrier glucose transport defect disease.41

Some other ES-

specific genes detected in our experiment include Catenin alpha-1 (IPI00112963.1),

CCAAT/enhancer-binding protein (IPI00752710.1), and Nidogen-2 (IPI129903.1), etc. The

GeLC-MS/MS approach has shown here a diverse range of the identified gene products in terms

of the protein size. The smallest identified protein in our analysis is a mitochondrial membrane

protein ATP synthase subunit E (IPI00111770.7), which only has a molecular weight of 8K Da.

While ataxia telangiectasia and rad3 related protein (IPI00123119.4) which is also known as a

serine/threonine protein kinase has a molecular weight up to 300K Da. From the ProteoIQ

generated 2D virtual gel (Figure 6.2), we can also see that the theoretical isoelectric point was

distributed through 4.11 (Heat shock protein 90K Da alpha, IPI00830977.2) to 12.65 (6K Da

protein, IPI00831580.1) across all the identified proteins. The large dynamic range of the

characterized proteins was also proved by the identification of a number of membrane proteins.

Searching with TMHMM 2.0, our data has shown that 14% of the identified proteins had at least

one trans-membrane domain and 47 proteins were verified to contain more than one trans-

membrane domain. Protein cationic amino acid transporter 5 (IPI00346772.7) even got 13 such

domains in the whole sequence. Under gel-free conditions, these proteins are very hard to be

dissolved and efficiently digested, hence will become more difficult for identification.

GeLCMS Approach to Reveal Possible Protein Degradation Process

Many cellular processes are associated with protein degradation specifically through the

ubiquitin-proteasome pathway, which is controlled with some highly specific enzymes including

the ubiquitin-activating enzyme E1, ubiquitin-conjugating enzyme E2 and E3 ubiquitin-protein

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ligase. The E3 ubiquitin ligase plays a crucial role in the degradation process since it has the

function of targeting specific protein substrates for degradation by the 26S proteasome complex.

Recent study has shown that some E3 ubiquitin ligase may regulate the OCT4 protein expression

level in ES cells, which will further affect the stem cell differentiation.22

Utilizing the GeLC-

MS/MS approach, this type of ubiquitin ligase (IPI00118376.1) was successfully detected in our

experiment and it could be used as a reference to indicate the existence of protein degradation

process in the embryonic stem cells. Besides the diverse protein identification, the GeLC-

MS/MS technique also offered an easy way to reveal some protein degradation products.

Initially, the gel lane was approximately equally cut into 25 bands from top to bottom and named

from band01 (top band) to band25 (bottom band). Protein mixtures in each band were digested

and resulting peptides were analyzed using LC-MS/MS. Those generated band fraction spectrum

files were searched individually against Mascot. In ProteoIQ validation and clustering process,

the database search results were grouped according to gel band; in such way we could not only

find out which proteins those identified peptides belong to, but also have the idea of the real

molecular weight range for the proteins digested to those identified peptides. From comparison,

we could then be aware of which protein might get involved in the degradation process if it

showed a significantly lower molecular weight from the gel than the theoretical molecular

weight. In order to reflect the actual protein mass distribution over the total 25 gel slices, we

calculated the 25% trimmed average mass of all identified proteins in each gel band and used

them to see the molecular weight pattern on the gel. The reason for using 25% trimmed average

was because it could give a more statistically satisfied estimate of central tendency since the

protein degradation, post translational modifications were being considered. Some experimental

contamination and mistakes were also inevitable, which made this statistical measurement more

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acceptable. As shown in Figure 6.3 the general trend of the protein molecular weight on the gel

was desirable. For the first 19 bands on the gel lane, the average protein mass was generally

decreasing from top to the bottom, which was in agreement with the actual gel electrophoresis

experiment. The last few bands' molecular weight were observed in a reverse trend and indicated

some proteins in these low molecular weight gel bands actually had a relatively higher molecular

weight thus made the average mass of the corresponding band increased. This could be explained

that some of the degradation products were expressed in these fractions. Serum albumin

(IPI00131695.3) is one of the abundant proteins identified in our experiment. The major function

of this protein is to regulate the colloidal osmotic pressure of blood. Degradation of the serum

albumin protein was observed in tumor-bearing mice so studies on this protein's degradation

could have essential medical significance.42

We have identified several peptides of this protein

from gel band 7 and 8, which contained most of the proteins having an approximate MW range

of 50K-70K. In general, this is in agreement with the theoretical MW of 69K Da of serum

albumin protein. At the same time, there were some other serum albumin peptides detected in

lower gel bands, such as band 21, 22 which felled into the protein MW range of 30K-40K Da.

Identification of this protein in two different MW range was most likely caused by the protein

degradation. Figure 6.4 showed the MS/MS spectra of the identified peptide

LGEYGFQNAILVR from gel band 22 as an example, this peptide is more close to the C-

terminal region which indicates a possible C-terminal protein degradation product of serum

albumin. The region specific degradation detection by GeLC-MS/MS could be used as a

reference for some further detailed degradation studies, such as using antibodies to target specific

region of the protein. Ataxia telangiectasia and rad3 related protein (IPI00123119.4) is an

enzyme that activates cell cycle checkpoints and responds to DNA damage. It is the largest

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protein identified in our list with a MW of 300K Da. Peptides matching to this protein were all

observed in band 19 and 20, which should be enriched with 30K-40K Da proteins. This

observation should also indicate that this identified protein was from the degraded products. The

explanation of not finding the non-degradation products is probably due to the relative low

expression of this gene in our preparation. Hypothetical protein LOC239673 (IPI00222228.5)

gave another example for the detection of protein degradation products. This hypothetical protein

has a MW of 58K Da and was identified by three different peptides in our GeLC-MS/MS

method. Peptide LALDIEIATYR and SLNLDSIIAEVK were both detected in band 5, 6 which

should contain this 58K Da protein in full sequence. They were also shown to be expressed in

low MW bands like 19, 22 etc which belong to the degraded products. While the other peptide

FLEQQNKVLETK was only found in small MW gel bands such as 18, 20 so this peptide could

come from the degradation part of this protein as well. Some of the MS/MS spectra examples of

these two proteins' degradation peptides were also shown in Figure 6.4.

CONCLUSION

In summary, we have demonstrated the utility of GeLC-MS/MS technique for the proteomic

analysis of embryonic stem cells and the application as a simple approach to identify the protein

degradation products. Compared to 2D gel and gel-free MudPIT method, this technique requires

less separation, facilitates the overall process and increases the dynamic range of the identified

protein mixtures. More importantly, combining the proteomic identification and MW

information of protein expression on the gel make this technique able to reveal some protein

degradation process, which is not feasible in gel-free shotgun proteomics. More comprehensive

studies on the ES cell protein degradation products and related pathways could make valuable

contribution to the development of stem cell differentiation researches.

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Figure 6.1. Coomassie blue stained 1-D SDS-PAGE analysis of the embryonic stem (ES) cell

protein mixtures. Molecular weight of standard protein markers are given on the left side of the

gel. Protein pellets were dissolved using Laemmli buffer that contains 2% SDS. The right sample

lane was later on cut into 25 slices for LC-MS/MS analysis.

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Figure 6.2. 2D virtual gel image generated using ProteoIQ software. A total of identified 781

proteins were distributed through 8K-300K (Da) in Mass and 4.11-12.65 in pI.

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Figure 6.3. Mass distribution across all the 25 gel bands. Due to the consideration of protein

aggregation, unknown PTMs, degradation and possible contaminants, 25% trimmed (from both

sides) average mass of all identified proteins in each individual gel band was selected to reflect

the protein molecular weight change on the gels.

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Figure 6.4. MS/MS spectra of peptides examples from degraded proteins. (A) Peptide

LGEYGFQNAILVR from Serum albumin (IPI00131695.3). Although this peptide was shown in

a gel band (band 22) having a much lower molecular weight (30K-40K Da) than the theoretical

one (69K Da), this peptide was believed to be identified with a series of extensive y ions. Similar

examples were found in (B) and (C). LMPMVTDNK is the peptide identified in ataxia

telangiectasia and rad3 related protein (IPI00123119.4) degradation products. SLNLDSIIAEVK

indicate the degradation process of hypothetical protein LOC239673 (IPI00222228.5).

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CHAPTER 7

CONCLUSIONS

The overall purpose of this work was to develop and apply methods for comprehensive

proteomic analysis with the goal to identify low abundant gene products and resolve protein

isoforms and degradation products.

Chapter 3: The membrane subproteome of T. cruzi was investigated using two different

methods. There were a total of 551 protein groups identified, 38% of which are membrane

proteins. Among them, some important cell surface genes were verified for their expression, such

as trans-sialidase, MASP, Mucins, GP63, etc. These GPI anchored surface proteins are involved

in parasite survival and cell invasions and are studied as potential vaccine targets. Both

membrane preparation methods were proven to be efficient. The sucrose cushion method

depleted more soluble proteins, while the detergent resistant method seemed to enrich more GPI

anchored proteins. A combination of these two methods was applied for further membrane

enrichment (project in Chapter 5).

Chapter 4: The membrane and organelle enrichment method was applied to analyze the T.

cruzi intracellular amastigote proteome. In order to recover identifications other than the

annotated genes, the whole genome ORFs search and ByOnic mutation search were also

performed. There were total of 2490 proteins within 890 protein groups identified in this

experiment. 14% of them were never detected in all four life stages of T. cruzi and 19% of the

identified proteins were not shown in previous amastigote proteome data. The data processing

method of incorporating ORFs and mutation search largely increased the identification coverage.

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This is the first proteomic analysis of T. cruzi intracellular amastigote stage and novel protein

identifications could be potentially contributed to the knowledge of this parasite system biology

and future vaccine selections.

Chapter 5: We report that GeLC-MS/MS technique can be effectively applied to

differentiate protein isoforms, which is particularly important for T. cruzi. We identified 1029

protein groups from the plasma membrane enriched fractions. The identification includes some

important gene products participating the parasite invasion and survival process. While most of

those genes are expressed as protein families, which are difficult to be differentiated. The GeLC-

MS/MS approach not only provides a dynamic range of identification, but also contributes to

differentiate some previously unresolved protein isoforms through combining the molecular

weight information, unique peptides, and methods of protein grouping.

Chapter 6: The GeLC-MS/MS approach was also utilized to evaluate some protein

degradation process in an embryonic stem cell system. The identified peptides from all gel slices

were first clustered to their derived proteins. Then the theoretical molecular weight of these

proteins was compared with the actual molecular weight range calculated from the 1D gel. When

the protein is discovered on a gel band with a much lower molecular weight than the theoretical

one, it could be thought as a potential evidence for the protein degradation. Further studies on the

ES cell protein degradation products and pathways could make valuable contribution to the stem

cell differentiation researches.

XIANG ZHU - University of Georgia

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