Proteomic Profiling of the Planarian Schmidtea mediterranea and its mucous reveals similarities with

human secretions and those predicted for parasitic flatworms

by

Donald Gerald Bocchinfuso

A thesis submitted in conformity with the requirements for the degree of Master of Science

Department of Molecular Genetics University of Toronto

© Copyright by Donald Gerald Bocchinfuso 2012

ii

Proteomic Profiling of the Planarian Schmidtea mediterranea and

its mucous reveals similarities with human secretions and those

predicted for parasitic flatworms

Donald Gerald Bocchinfuso

Master of Science

Department of Molecular Genetics University of Toronto

2012

Abstract

The freshwater planarian Schmidtea mediterranea has been used in research for over 100 years,

and is an emerging stem cell model. Exteriorly, planarians are covered in mucous secretions of

unknown composition. While the planarian genome has been sequenced, it remains mostly

unannotated. The goal my master’s research was to annotate the planarian proteome and mucous

sub-proteome. Using a proteogenomics approach, I elucidated the proteome and mucous sub-

proteome via mass spectrometry together with an in silico translated transcript database. I

identified 1604 proteins, which were annotated using the Swiss-Prot BLAST algorithm and Gene

Ontology analysis. The S. mediterranea proteome is highly similar to that predicted for the

trematode Schistosoma mansoni associated with schistosomiasis. Remarkably, orthologs of 119

planarian mucous proteins are present in human mucosal secretions and tear fluid. I suggest

planarians have potential to be a model system for parasitic worms and diseases underlined by

mucous aberrancies.

iii

Acknowledgments

Throughout my master’s tenure, I have had the privilege of working with and learning from

gifted and inspiring scientists, all of whom have contributed invaluably to my graduate training.

Primarily, I would like to thank my supervisor Dr. Michael Moran for providing me with the

opportunity to study in his laboratory. I am immensely grateful for his great patience and

leadership, under which I have significantly grown and progressed as a scientist.

Moreover, I would also like to thank all my fellow Moran lab members whose positive attitudes

made me look forward to each and every day in the lab. I am especially thankful to Dr. Jiefei

Tong and Mr. Paul Taylor, for without their help and expertise I would not have been able to

successfully complete my graduate project. As well, I would like to acknowledge Mr. Eric Ross

whose work greatly enhanced the breadth and relevance of my study, proving imperative in its

publication. To my supervisory committee members, Drs. Lori Frappier and Bret Pearson, your

guidance and insights were vital in directing my work and graduate training.

Finally, I am forever indebted to my family and friends for all of their love and support, and

without whom I could not have earned my master’s degree. I would like to thank all my

classmates at the University of Toronto who greatly enriched my experience as a graduate

student and who were a constant source of camaraderie and exuberance. I thank my close friends,

in particular my girlfriend Rachel who have unconditionally supported me throughout my

master’s tenure and in all my endeavors. Last but not least, to my beloved parents Paul and

Wendy, thank-you for everything; your continued motivation and encouragement have always

driven me to strive for the best.

Donald G. Bocchinfuso

June 2012, Toronto, ON.

iv

Table of Contents

Acknowledgments.......................................................................................................................... iii

Table of Contents ........................................................................................................................... iv

List of Tables ................................................................................................................................. vi

List of Figures ............................................................................................................................... vii

List of Appendices ....................................................................................................................... viii

List of Abbreviations ..................................................................................................................... ix

Chapter 1 Introduction .................................................................................................................... 1

1 Introduction ................................................................................................................................ 1

1.1 Proteomic Profiling of Model Organisms........................................................................... 1

1.2 Planarian Biology and Contemporary Planarian Research ................................................. 2

1.3 Experimental Challenges in Planarian Research – The Need for an Annotated Planarian Profile.................................................................................................................. 3

1.4 Overview of Protein Mass Spectrometry............................................................................ 5

1.5 Analyzing Mass Spectrometry Data ................................................................................... 7

1.6 Annotating Mass Spectrometry Data .................................................................................. 8

1.7 Transcriptomic Database Creation using Modern Sequencing Technologies .................... 9

1.8 Developing Planarians as a Model Organism................................................................... 10

1.9 Planarian Mucous and its Potential as a Mucous Model .................................................. 11

1.10 Proteomic Mucous Profiling using Mass Spectrometry ................................................... 12

1.11 Outline and Rationale for Thesis Research....................................................................... 13

Chapter 2 Materials and Methods ................................................................................................. 16

2 Materials and Methods............................................................................................................. 16

2.1 Preparation of Worm Lysates ........................................................................................... 16

2.2 Liquid Chromatography and Mass Spectrometry Analysis .............................................. 17

2.3 Database Creation ............................................................................................................. 18

v

2.4 Criteria for Peptide and Protein Identification and Protein Grouping .............................. 18

2.5 Gene Ontology Analysis ................................................................................................... 19

Chapter 3 Results .......................................................................................................................... 20

3 Results ...................................................................................................................................... 20

3.1 Mass Spectrometry Analysis............................................................................................. 20

3.2 Annotating Identified Proteins.......................................................................................... 22

3.3 Comparing Planarian Proteins to Published Proteomes.................................................... 25

3.4 Gene Ontology Annotation ............................................................................................... 30

Chapter 4 Discussion and Conclusions......................................................................................... 33

4 Discussion and Conclusions..................................................................................................... 33

4.1 Analyzing Mass Spectrometry Data ................................................................................. 33

4.2 Interpreting Mass Spectrometry Analyses ........................................................................ 34

4.3 Examining Protein Annotations........................................................................................ 35

4.4 Planarian Mucous as a Disease Model.............................................................................. 36

4.5 Planarians as a Model to Study Parasitic Worms ............................................................. 37

4.6 Conclusions and Future Directions ................................................................................... 39

Bibliography ................................................................................................................................. 41

vi

List of Tables

Table 1: Mucous Protein Overlap……………………………………………………………25

vii

List of Figures

Figure 1: Experiment Overview…….……………………………………………………………13

Figure 2: Representative MS/MS Acquisition...…………………………………………………19

Figure 3: Protein Overlap Among Analyzed Fractions.…………………………………………22

Figure 4: GO Analysis Results……………......…………………………………………………29

viii

List of Appendices

Electronic Appendix A: Peptide and Protein Reports…………………………………...CD-ROM

Electronic Appendix B: Protein Annotations……………………………………………CD-ROM

Electronic Appendix C: S. mansoni Protein Overlap……….…………………………...CD-ROM

ix

List of Abbreviations

4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid HEPES

Base pair bp

Basic local alignment search tool BLAST

Complementary DNA cDNA

Expectation value E-value

False-discovery rate FDR

Gene ontology GO

Glutathione S-transferase GST

Kilodalton kDa

Laeonereis acuta L. acuta

Linear trap quadruple LTQ

Liquid chromatography LC

Mass spectrometry MS

Multidimensional protein identification technology MudPIT

N-acetylcysteine NAC

Phosphate-buffered saline PBS

Schistosoma haematobium S. haematobium

Schistosoma japonicum S. japonicum

Schistosoma mansoni S. mansoni

x

Schmidtea mediterranea S. mediterranea

Signal peptide SP

Strong cation exchange SCX

Tandem mass spectrometry MS/MS

1

Chapter 1 Introduction

1 Introduction

1.1 Proteomic Profiling of Model Organisms

One of the cornerstones of modern molecular biology, proteomics involves the wide-scale

characterization of the expression, structure, and function of all proteins in a cell, tissue, or

organism (1, 2). This complete set of proteins, known as the proteome (3), is both larger and

more complex than the genome; an entity’s set of genetic information in its entirety (4).

Proteomics based approaches aimed at elucidating the proteome of cells, tissues, or organisms

are becoming ever more prevalent in the post-genomic era. Perhaps some of the most noteworthy

proteomics projects involve the characterization of the proteomes of model organisms used in

clinical and research settings. These non-human species play a fundamental role in the

investigations of the biological activities of numerous species, forming much of the core of

modern biological knowledge (5). Accordingly, the common descent and conservation of genetic

information amongst all living organisms over the course of evolution allows data generated

from the study of model organisms to be extrapolated and applied to other species (6).

Model organisms are especially useful in the study of human disease, as human experimentation

is often unfeasible or unethical (7). As such, many protein databases for model species used in

the exploration of human biological phenomena have already been compiled, including

noteworthy species such as mouse, yeast, Caenorhabditis elegans, and Drosophila melanogaster

(8). That being said, many species lack significant proteomic characterization, with an

incomplete or non-existent protein database. Since proteins participate in virtually every cellular

process (9), such a lack of knowledge greatly impedes both genomic and proteomic analyses,

establishing a need for the proteomic profiling of poorly studied species.

As the field of molecular biology continues to grow, researchers have begun to look at new

model species in solving their broad spectrum of biological questions. Species such as the

flowering plant Arabidopsis thaliana, the zebrafish Danio rerio, and the frog Xenopus laevis

have proven beneficial and are now commonplace in the research setting. One such novel model

species is the freshwater planarian Schmidtea mediterranea (herein referred to as planarians)

2

which has recently found great accolade in the area of stem cell biology. In truth, planarians are

not new to biological research, having made their debut nearly 200 years ago in the laboratories

of J.G. Dalyell (1814), J.R. Johnson (1822) (10), and later Harriet Randolph (1892), and the

famous drosophila biologist T.H. Morgan (1898) (11).

1.2 Planarian Biology and Contemporary Planarian Research

Planarians are hallmarked by a remarkable ability to regenerate large portions of missing body

parts (12, 13), making them the ideal model to study stem cell biology. It was this striking

phenomenon which fuelled the curiosity of Morgan and his contemporaries, leading them to

begin elucidating the underlying mechanisms governing planarian regeneration. Experimentation

led Randolph in 1892 to attribute planarian regeneration to a “neoblast” population of cells,

believed to give rise to the mesoderm cell layer (14). While the term neoblast remained,

Randolph’s observations were somewhat mistaken in that while planarians’ regenerative ability

is derived from neoblasts, the cells are in fact pluripotent, capable of differentiating into all cell

types in planarian (15-18). Indeed, neoblasts are somatic stem cells which represent the only

proliferating cells in adult planarians (19), stimulated to proliferate to regenerate lost body parts

during times on injury or amputation to regenerate lost body parts (20).

While regeneration establishes planarians as a functional model organism, planarians harbour

many other traits which further legitimize their model organism status. Planarians are one of the

simplest metazoans, and are very easy to culture in a laboratory setting (11, 21). Modern research

innovations have made the planarian easy to manipulate using various experimental techniques

(22, 23), making them amenable to studying gene function (23, 24), developmental plasticity (25,

26), and various other phenomena (27, 28). More explicitly, planarian-specific protocols

involving RNA interference and in situ hybridization have been developed specifically for

planarian experimentation. Additionally, planarians are a bona fide model to study cilia and cilia-

driven motility, as planarian undersides consist of a monostratified ciliated epithelium,

fundamental for planarian motility (26).

3

Externally, planarians are covered in a mucous secretion of unknown composition, implicated in

numerous biological processes essential for planarian survival (29, 30). During locomotion, a

planarian secretes generous amounts of mucous to create a low-friction surface on which to

propel itself via ciliary gliding (26, 31). This is important not only for predator evasion, but

during exposure to light, as planarians are strongly photophobic (32). Moreover, these mucosal

secretions have been implicated in innate immunity and in the maintenance of an exterior

osmotic balance (30, 33). As planarian mucous represents a substantial barrier against external

large molecules, it obstructs hybridization approaches such as in situ hybridization and

immunohistochemistry, prompting removal prior to experimentation using mucolytic agents such

as N-acetylcysteine (NAC) (34, 35).

Despite the availability of numerous planarian species, S. mediterranea remains the preferred

species for biological study in molecular research (11). Most other planarian species such as

Girardia tigrina (36) and Dugesia japonica (37) possess mixoploidy or polyploidy genomes,

with cells having multiple sets of chromosomes. This greatly challenges both the sequencing and

assembly of contiguous DNA segments using traditional methods, making difficult the

generation of an accurately sequenced genome (38). Comparatively, S. mediterranea has a

recently sequenced (39) diploid genome (40), consisting of approximately 4.8x108 basepairs

(25). This allows for high-throughout experimentation on the genome-wide scale, lending an

additional utility to the use of planarians as a model organism.

1.3 Experimental Challenges in Planarian Research – The Need for an Annotated Planarian Profile

With new advances in sequencing technology, fully sequenced genomes are becoming available

at an increasing rate (41, 42), causing not genome sequencing, but annotation to bottleneck

modern genomic studies (43). Without sufficient characterization, the generation of such copious

amounts of data is rendered virtually useless due to the difficulty of making significant

interpretations on unannotated data (42, 44). Thus, annotating the genomes of model organisms

is especially important, given their widespread significance to the greater research community

(45). In addition to annotation, genes predicted by genome sequencing must be validated through

comparative analysis with protein expression data (46). This is especially important for

4

eukaryotic genomes, which contain added complexities derived from the presence of introns and

varying rates of alternative splicing (47, 48). Proteomic data, such as peptide identifications from

mass spectrometry (MS), can provide such information to directly verify genes predicted from

sequencing projects (49), ascertaining the quality of sequence databases (50).

In analyzing sequences on a genome-wide scale, a complete protein profile of the species at hand

is a common source of proteomic data used for gene validation. Such datasets include data on

hundreds of proteins, providing a representative look at overall protein expression. A protein

profile is also crucial for analyzing and interpreting experimental data made on protein

identifications and characterizations, allowing both qualitative and quantitative measurements to

be made on protein expression (51). Protein profiles are an especially useful resource for model

organisms, given their widespread use as human disease models. Through comparative analysis

of proteomic datasets, proteins found in model organisms which are homologous to medically

relevant human proteins can be identified. Homologous proteins are those which have evolved

from a common ancestor and share similar functions (52), making them ideal subjects in the

study of their human counterparts.

While the planarian genome is sequenced, it remains largely unannotated lacking extensive

information on gene identifications and functions. Indeed, planarian studies hitherto have largely

centered on genomic analyses examining neoblast biology and the functions of select genes. This

lack of annotation impedes planarian’s efficacy as a model system, not only for human biology,

but potentially to serve as a model for other worm species as well. Proteomic-based approaches

on the other hand have only been recently used to study planarians (50, 53), with a complete

proteome profile yet to be created. Consequently, planarian genomic sequences have not been

extensively validated, with minimal proteomics having been done thus far (50). Given their

model organism status, there exists a substantial need for a planarian protein profile to be

completed.

5

1.4 Overview of Protein Mass Spectrometry

Although completing a protein profile of a species with an unannotated genome is both

experimentally and bioinformatically challenging, modern approaches involving protein mass

spectrometry have substantially assuaged the process (41, 54). Mass spectrometry is a technique

used to characterize molecules through accurate measurement of the mass-to-charge ratio of

charged particles using electromagnetic fields. This information can in turn be used to decipher

the identity of the analyte constituents. In addition to determining the masses and identifies of

particles, mass spectrometry is used to elucidate the elemental composition and chemical

structure of molecules, and is particularly useful for analyzing protein-based samples (55).

Historically, identification of proteins was limited to de novo methods (56, 57), most notably

Edman degradation which individually sequences amino acids in a peptide using chemical

fractionation (58). However, de novo methods are limited both in their overall throughput and

adaptability when working with chemically modified peptides, pressuring the implementation of

novel strategies. While mass spectrometry was known to be ideally suited to generating data on

which protein identifications could be made, it was not until the advent of the laser desorption

method of ionization in 1985 that mass spectrometry could be used to analyze macromolecules

such as proteins (59, 60). As mass spectrometry instruments function under vacuum conditions

to minimize extraneous contaminats such as air molecules, analyte samples are required to be in

a gaseous state prior to analysis. Macromolecules are particularly sensitive to degradation during

gas-phase ionization, thus preventing their analysis by mass spectrometry prior to the

development of the laser desorption method.

Currently, the most common method for placing proteins into the gaseous stage is electrospray

ionization, a technique first described in 1989 which garnered a Nobel Prize in 2002 (61, 62).

Following dissolution in an organic solvent, the protein analyte is pumped through a narrow

capillary tube whose terminal end is maintained at a high potential difference. Once the analyte

is pumped across this area of high voltage, it disperses into a fine aerosol due to Coulombic

repulsion, evaporating the solvent and rendering the analyte in gaseous form (63). The analyte

becomes charged due to the protonation of amino acid residues containing basic side chains such

as arginine, histidine, and lysine, and enters the mass spectrometer for analysis (64).

6

Discovery-centered proteomics, aimed at identifying as many proteins as possible in a given

sample such as in the compilation of a protein profile, most often adapts a “bottom-up” or

“shotgun” approach (65, 66). In this method, proteins are first subject to proteolytic digestion

with an enzyme such as trypsin, creating a complex mixture of peptides. The peptide mixture is

then separated using offline approaches such as gel electrophoresis, or by online liquid

chromatography (LC) methods. Complex preparations used in protein profiling most often adopt

the latter approach (65), using capillary-based high-performance liquid chromatography

instruments. As discussed above, this setup is ideal for placing peptides into the gaseous phase

using electrospray ionization, largely accounting for the technique’s widespread use in modern

proteomics.

Optimal chromatographic separation of peptides is crucial, as increased separation allows the

mass spectrometer to analyze a greater number of peptide species, leading to an increased

number of protein identifications. For this reason, online two-dimensional chromatography

methods have recently been developed which separates peptides using two chromatographic

phases (67, 68). Known as multidimensional protein identification technology (MudPIT) (68),

this method first separates peptides by ionic separation based on positive charge (strong-cation

exchange), followed by hydrophobicity (reversed-phase). Peptides are bound first by negatively

charged cationic exchange resin, and are eluted into second phase using a salt solution. Similarly,

peptides are bound by reversed-phase resin, a hydrophobic chain of carbon atoms, and eluted

using an organic solvent (69, 70). The process is repeated in a gradient fashion, with increasing

salt and organic solution concentrations.

Finally, peptides are ionized into the gaseous phase and injected into the mass spectrometer,

where their masses are first determined by the instrument’s mass analyzer. The peptides are then

further fragmented via collision with inert gases or high-energy resonant excitation to analyze

their amino acid sequences and potential modifications (71). This sequential analysis, known as

tandem mass spectrometry (MS/MS), is fundamental in modern proteomics and has been

implemented in a wide range of mass spectrometry instruments (60).

7

1.5 Analyzing Mass Spectrometry Data

While mass spectrometers are able to produce an abundance of information, identifying

thousands of peptides in a single experiment, reconstructing peptides back into their constituent

proteins requires offline computer interpretation. Several search algorithms capable of

correlating raw mass spectrometry data with protein sequence databases have been developed

(60). Most contemporary search algorithms are based on refinements to the principle of peptide

mass mapping, which holds that the mass of a peptide derived from an enzymatically cleaved

protein can be used to link that peptide back to the protein from which it originated (72-74). With

the development of MS/MS, modern search algorithms are capable of making more confident

peptide matches by using the m/z data of peptide fragment to support initial alignments made by

whole peptide data alone (75, 76). Thus, protein search algorithms compare identified peptides

with a protein sequence database constructed from the same species as the analyzed sample.

For species like planarian for which no protein database exists, nucleic acid sequences may be

substituted for algorithm-based peptide searches (60). Using a simple computer script, genomic

sequences can be in silico translated into all six protein reading frames to generate sequences of

amino acids. The efficacy of this technique was first demonstrated using known protein (54), and

has since been used with larger genomes (77-80), including full genome translations (41). Mass

spectrometry centered on genomic sequences provides information both on the proteomic and

genomic levels, aiding in protein identification and validation of existing genome annotations

(81-83).

Subsequent to the correlation of mass spectrometry data with a sequence database using a search

algorithm, an accepted list of protein identifications must be compiled within certain threshold

criteria. For small datasets, protein identifications can be subject to manual verification by the

researcher to individually verify each peptide assignment (84). Evidently, this practice cannot be

applied to large datasets, such as protein profiles where hundreds of proteins are identified.

Instead, statistical models applied through automated computer algorithms are used to assess and

critique peptide assignments made by search algorithms (84, 85). Protein identifications are

accepted only if they can be made above a given certainty, or within a specified false-discovery

rate (FDR), limiting total protein identifications to a final, high-confidence dataset.

8

1.6 Annotating Mass Spectrometry Data

Following the processing of mass spectrometry data by search and identification critiquing

algorithms, protein identifications can be subject to various bioinformatical analyses which

provide annotation to identified proteins. For established species, protein annotations are usually

coupled to their respective sequences in the databases used for peptide mass mapping. This

simplifies the annotation process, as proteins can be both identified and annotated in a single run

by a search algorithm. Conversely, species which have not been well characterized often lack

annotated sequence databases, as is the case with planarians. Working with data derived from

such species requires alternate annotation strategies, ranging from manual annotation to large-

scale automated methods.

Although manual protein annotation provides the most accurate view of a protein’s structural and

functional characteristics, it normally requires extensive experimentation and is unpractical for

large datasets. Currently, numerous automated approaches are available which are capable of

efficiently annotating large datasets. Many of these tools utilize existing protein annotations from

well characterized species to annotate query proteins on the basis of sequence homology (86-88).

Homologus sequences derived from protein homologs share a high degree of similarity (52, 89),

a feature easily exploitable by computer software.

One of the most widely used tools for comparing sequences based on homology is the basic local

alignment search tool (BLAST) (87). Unlike other alignment tools (86), BLAST prioritizes speed

over sensitivity, making it amenable to analyzing large datasets (90). With BLAST, query

sequences are compared to a database containing sequences from numerous species, and

database constituents resembling the query sequences are identified. These annotations

accompanying these identified sequences can be used as annotation for the query sequence,

creating a rapid way to annotate data from poorly characterized species.

In addition to the fundamental functional annotations provided by BLAST and similar alignment

tools, identified proteins can also be more generally characterized, providing additional levels of

annotation information. A commonly used resource to provide such alternate annotation is Gene

Ontology (GO), a bioinformatics endeavour which classifies genes and proteins using both

functional and physical annotations (91). The GO database contains information on many

9

commonly used model species, and is continuously updated making it widely used in modern

bioinformatical analyses (92).

1.7 Transcriptomic Database Creation using Modern Sequencing Technologies

As previously mentioned, fully-sequenced genomes are becoming available at a rapid rate thanks

to advances in nucleic acid sequencing technologies. In truth, it is not only the genomes of

organisms which are being sequenced, but their transcriptomes as well. Perhaps even more

useful than a fully sequenced genome, a transcriptome represents all of the RNA molecules

expressed in a given cell, tissue, or organism (93). A fully sequenced transcriptome provides an

additional level of information not realized in genomic sequences in that it shows the

transcriptional structure of genes, as it represents only transcribed DNA. However, like their

genomic cousins, transcriptomes also benefit from validation using a protein profile, identifying

erroneous transcripts resulting from imperfections in the sequencing process.

Genomic and transcriptomic sequences are generated using relatively the same method, differing

only in the initial isolation of each respective nucleic acid species. Mainly, in the generation of a

transcriptome, RNA is isolated and reversed transcribed to its complementary DNA, known as

cDNA (93). In general, two approaches may be used in the sequencing of nucleic acids. The first

approach involves hybridization techniques which make use of microarray or tiling array

technologies (94-97). Hybridization approaches while high-throughout and relatively

inexpensive, are subject to high background levels resulting from cross-hybridization amongst

sequences, causing sequencing errors (98, 99). On the other hand, sequence-based methods

directly deduce sequences using a variety of tactics. Traditionally, sequencing was accomplished

using the Sanger method, a chain-termination approach hallmarked by its relative ease and

reliability (100, 101). DNA-sequencing methods have been continuously improved upon, with

newer technologies having replaced the Sanger method as the contemporary sequencing

standard.

Collectively called “next-generation sequencing”, these newer methods such as Illumina SDS

(102), Applied Biosystems SOLiD (103), and the Roche 454 System (104) are high-throughput

10

approaches which have significantly lowered sequencing costs (105). Next-generation

sequencing is now being used in large-scale sequencing projects, having already been applied to

several species, including human (106-108). Offering high reproducibility while minimizing

background noise levels, next-generation sequencing approaches are especially useful in the

sequencing of large, complex transcriptomes (103, 106).

1.8 Developing Planarians as a Model Organism

Having already demonstrated their suitability as a model organism, the potential of planarians as

a model system should not be limited to the regeneration and cilia fields. Given their simplistic

biology, planarians have the capacity to be used as a model system to study not only human

phenomena, but other invertebrate species relevant to human health (22, 109, 110). Accordingly,

the planarian transcriptome was recently fully sequenced using the Illumina method, generating a

database containing over 25,000 transcripts. In line with other next-generation sequencing

technologies, the Illumina method is a high-throughput approach commonly used in large-scale

sequencing projects (111, 112). Starting with an initial amplification step, DNA fragments are

sequenced via a synthesis process using chemically blocked nucleotides such to isolate each

nucleotide incorporation. Each of the four nucleotides carries a unique fluorescent tag, allowing

incorporations to be unambiguously identified by the Illumina instrument. Following analysis of

a new incorporation, the chemical block is removed and synthesis proceeds with another round

of incorporations.

As transcriptomes more accurately represent genes and gene structure than genomic sequences,

the planarian transcriptome sequence will undoubtedly help to further planarians as a model

species. The planarian transcriptome will be helpful in performing comparative analyses with

other genomes and proteomes, making it possible to accurately identify planarian genes which

have been conserved across species. Researchers have already postulated planarians use a model

to study other invertebrate species such as the parasitic flatworm Schistosoma mansoni. Serving

as one of the leading causes of the Schistosomiasis, S. masoni is a human parasite which also

infects other animals (113). Worldwide, over 200 million people suffer from schistosomiasis, a

disease which causes chronic illness in both adults and children (114).

11

Infection of S. mansoni and related species occurs in aqueous environment, when larval forms of

the parasites penetrate the skin of the infected individual. Following initial infection, parasite

larvae are transported via hepatic portal circulation to the liver, where they mature and mate.

Adult worms then migrate to different areas of the body, including the bladder and intestines

where they deposit eggs (113, 115). Long-term schistosomiasis is extremely detrimental, having

the ability to cause liver fibrosis, calcification of the bladder, and impaired cognitive

development in children (116). Given their dependence on a free living host, laboratory

cultivation of schistosomes like S. mansoni harbours many challenges, necessitating the need for

a free-living model.

1.9 Planarian Mucous and its Potential as a Mucous Model

Planarian mucous also holds the ability to diversify planarians as a model species, given the

array of mucosal and secretion based diseases. In Schistosomiasis, schistosomes and their eggs

secrete numerous molecules into the host environment (117), some of which are immunogenic

and promote illness (115). Immune reactions typified by granuloma formation in areas of egg

deposition are caused by egg secretory products (118, 119), which have been correlated with an

increased risk of bladder cancer in chronic schistosomiasis (120). Moreover, mature

schistosomes secrete numerous proteins necessary for worm survival, propagating infection

whilst damaging host tissues. Targeted therapeutic strategies aimed at exploiting schistosome

secretory products have demonstrated middling efficacy (121), with a mucosal model being of

prospective benefit for future studies.

Diseases associated with mucous pathology are prevalent in humans and other animals (122),

and may benefit from research based on mucous models. One of the most common diseases

driven by a mucosal aberrancy is cystic fibrosis, an autosomal recessive disorder which affects

the lungs, pancreas, liver, and intestine (123). A deletion mutation which causes misfolding of

the CFTR protein causes abnormal transport of chloride and sodium across epithelial cells,

producing viscous mucous which causes breathing difficulties (124, 125). In addition, patients

suffer from pancreatic cyst formation, impaired growth, and are prone to frequent bacterial

infections, significantly shortening overall life span (125, 126).

12

Likewise, mucous hypersecretion is a disease in which an over abundance of mucous is

generated and secreted from airways into their respective lumens (127). Patients suffer from

acute asthma attacks, and the disease is correlated with an increased mortality in lung disease.

Although numerous therapies for treatment of mucous hypersecretion are available, many of

these remain unproven in a clinical setting, establishing the need for relevant research models

(127). With further characterization, planarian mucous has the potential to be used in research

aimed at examining the phenomena associated with mucosal diseases, as well as in the

development of therapeutics.

1.10 Proteomic Mucous Profiling using Mass Spectrometry

A plethora of mucosal substances from humans and other organisms have been characterized

using protein mass spectrometry. These datasets are fundamental in understanding mucous

biology and distinguishing mucousopathies, not only for humans but for other species as well.

The high protein content of mucous establishes mass spectrometry as the premier technology for

analyzing mucosal fractions, as evidenced by an increasing number of such focused publications.

Protein profiles for many different types of human mucosa have been completed, ranging from

nasal and epithelial secretions (128-130) to cervical mucous (131) and even human tear fluid.

Likewise, mucosal secretions from non-human species have also been profiled, such as those

from various species of fish (132, 133).

As mucous functions ubiquitously throughout the body and across species as a protective and

immunological barrier, it can be reasonably postulated that the protein content of mucous is quite

similar among different fractions. Protein overlap across mucous proteomes have already been

examined for various human secretions (134), with abundant protein overlap among fractions

having been observed. Presently, mucous protein overlap between human and non-human

fractions has not been examined, leaving unknown whether any protein overlap exists across

diverse species. Performing such analyses may prove beneficial, as non-human species may have

the potential to serve as models for mucous-based diseases should a significant overlap exist

among mucosal fractions.

13

While some non-human mucous proteomes have been profiled, there exists a general deficit of

high-content datasets, especially for model organism species. Numerous studies were completed

prior to the advent of mass spectrometry approaches which are capable of identifying hundreds

of proteins in a single sample. These studies have focused on a few select proteins, and have

provided only a glimpse into how proteins govern mucous function. Conclusively, additional

mass spectrometry centered profiling exercises will greatly benefit future studies aimed at

comparing human and non-human mucosal fractions.

1.11 Outline and Rationale for Thesis Research

Planarians’ standing as a significant model organism in contemporary search, in tandem with

their potential and anticipated employment as a novel model for established systems necessitates

their need for extensive proteomic investigation. The absence of a complete planarian profile

encumbers both genomic and proteomic analyses, limiting their efficacy as a model system. That

being said, the goal of my master’s research was to further develop planarians as a model

organism by creation of an annotated planarian protein profile and characterization of the

planarian mucous proteome using high resolution mass spectrometry.

Prior to experimentation, a searchable protein database was constructed from sequenced

planarian transcripts assembled from next-generation sequencing reads. Using an in silico

algorithm, transcripts were translated into all six possible reading frames to generate a protein

sequence database. In total, 1604 proteins were identified, with 452 proteins being identified in

three different mucosal fractions. The Swiss-Prot BLAST was used to annotate planarian

proteins based on their similarity with known proteins in other organisms, allowing comparative

analyses to be performed.

Following BLAST annotation, planarian proteins were further annotated by GO analysis, which

identified an enrichment of extracellularly defined proteins in each of the mucosal fractions.

Planarian mucous proteins were compared to proteins from human secretions, revealing striking

similarities between the two species. Moreover, identified planarian proteins were systematically

compared to the parasite S. mansoni (135), demonstrating a high overlap between the planarian

and S. mansoni proteomes (Fig. 1). These observations further establish planarians as a model

14

organism, possibly opening new avenues for the study of parasitic infections and

mucousopathies such as asthma, various lung diseases, and cystic fibrosis.

15

Figure 1. Experiment Overview. A schematic flowchart indicating the generation of a transcriptome database that was used to identify proteins following LC-MS/MS analysis of proteins isolated from whole worm or mucous preparations.

16

Chapter 2 Materials and Methods

2 Materials and Methods

2.1 Preparation of Worm Lysates

Lysates were generated from whole organisms of the CIW4 clonal strain of asexual Schmidtea

mediterranea, sized matched to 2 – 4 mm, using a tissue homogenizer and lysis buffer containing

20 mM HEPES buffer (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) (Cell Signaling

Technology, Boston, MA, USA) 8 M urea (EMD Chemicals, Darmstadt, Germany), 1 mM

sodium orthovanadate (BioShop Canada Inc., Burlington, ON, Canada), 2.5 mM sodium

pyrophosphate (Cell Signaling Technology), and 1 mM β-glycerophosphate (Cell Signaling

Technology). Whole organisms were also treated with 5% NAC (Sigma-Aldrich, St. Louis, MO,

USA) in 1X phosphate-buffered saline (PBS) (BioShop) for 8 min to remove their mucous

coating (35), and subsequently lysed as described above. Lysates were subsequently sonicated on

continuous mode using three 30 s pulses with a sonic dismembrator (model 100; Fisher

Scientific, Pittsburgh, PA, USA). Mucosal extracts were generated by incubating whole

organisms with 5% NAC in 3.3 mg/mL sodium bicarbonate (EMD) (136) as described above,

and the NAC/mucous solution was extracted by pipetting. The extracted solution was

concentrated using a 3 kilodalton (kDa) molecular weight cut-off centrifugal filter (Millipore

Ireland Ltd., Carrigtwohill, Ireland), and incubated with 20% acetone (Sigma-Aldrich) at -20°C

overnight to precipitate proteins. Precipitated proteins were pelleted by centrifugation at 15,000 x

g for 10 min, and the resultant pellet was resuspended in 100 mM ammonium biocarbonate

(BioShop) containing 8 M urea. Alternatively, a mucous fraction was generated by placing

planarians and water (40 mL) into 15 cm polystyrene Petri dishes (Sarstedt Inc., Newton, NC,

USA). Planarians were exposed to visible light for 3 h to induce motility, after which planarians

were removed from the dishes. All water was collected, and the surface of each dish was washed

vigorously with a solution of 8 M urea in 100 mM ammonium bicarbonate. The water and urea

fractions were combined, and the resultant mixture was concentrated. All samples were reduced

by using 45 mM dithiothreitol (Cell Signaling Technology) for 20 min at 60°C and subsequently

alkylated by using 100 mM iodoacetamide (BioShop) for 15 min at 23°C in the dark. Lysates

were digested overnight at 23°C with trypsin (Thermo Scientific, Waltham, MA, USA) in

17

HEPES buffer containing tosyl phenylalanyl chloromethyl ketone protease inhibitor, and

proteolysis was quenched with 1% trifluoroacetic acid (EMD).

2.2 Liquid Chromatography and Mass Spectrometry Analysis

An integrated nano-LC system (Easy-nLC; Thermo Fisher Scientific, Odense, Denmark) was

used to perform a fully automated 9-cycle MudPIT analysis (69) on peptide samples from whole

and NAC treated worms using inline strong cation exchange (SCX) and reversed-phase

chromatography (70). Peptides from mucous extracts and the water/urea mixture were analyzed

using only reversed-phase chromatography. LC was performed as described in Taylor 2009 (70).

Briefly, samples analyzed by MudPIT were loaded onto a 100 µm fused silica microcapillary

column packed with 5 µm Magic C18 100 Å reversed-phase material (Michrom Bioresources

Inc., Auburn, CA, USA) and Luna 5 µm SCX 100 Å strong cation exchange resin (Phenomenex,

Torrance, CA, USA). A high-performance liquid chromatography gradient was established

consisting of 0%, 10%, 20%, 25%, 30%, 35%, 40%, 60%, and 100% ammonium acetate salt

bumps, followed by a water/acetonitrile gradient. Samples analyzed using only reversed-phase

chromatography were loaded onto an identical column packed only with reversed-phase resin,

and subject to a water/acetonitrile gradient. Eluted peptides from both column setups were

electrosprayed directly into a linear ion trap-Orbitrap Fourier transform mass spectrometer

(LTQ-Orbitrap Classic; Thermo Fisher Scientific, Bremen, Germany) using a nanoelectrospray

ion source (Proxeon Biosystems A/S, Odense, Denmark). MS spectra were obtained using a

method which consisted of one MS full scan (400-1500 m/z) in the Orbitrap mass analyzer, an

automatic gain control target of 500,000 with a maximum ion injection of 500 ms, one

microscan, and a resolution of 60,000 (full-width half-maximum). MS/MS spectra were obtained

in the linear ion trap analyzer using the six most intense ions at 35% normalized collision energy.

Automatic gain control targets were 10,000 with a maximum ion injection time of 100 ms. A

minimum ion intensity of 1000 was required to trigger an MS/MS spectrum. Dynamic exclusion

was applied using a maximum exclusion list of 500 with one repeat count, with an exclusion

duration of 40 s.

18

2.3 Database Creation

Transcripts were assembled from 206 million 100 base pair (bp) pairs reads from an

IlluminaHiseq, 85 million 100 bp pairs of reads from an IlluminaGAxII and ~233 million 40 bp

single end reads for an IlluminaGAxII. Each set of reads was assembled independently using the

Trinity Assembly (137) pipeline with default parameters. The resulting transcriptome assemblies

were then trimmed and assembled together using the Velvet implementation available in

Geneious (138) with default parameters. The resultant transcripts were filtered for contaminates

(i.e. did not match genomic sequence) and transcripts which did not encode an open-reading

frame of >100 amino acids (300 bp) were discarded. Finally, we compared our transcripts to de

novo transcriptome assemblies from published datasets (45, 50, 139). Transcripts present in these

assemblies which were missing from our assembly were added to our final transcriptome dataset.

2.4 Criteria for Peptide and Protein Identification and Protein Grouping

The complete method used for identification of peptides and proteins is described in Gortzak-

Uzan et al. (85). Briefly MS/MS data were analyzed by using the search engine X!Tandem

(CYCLONE 2010.12.01.2) (www.thegpm.org). Search analyses were performed assuming

trypsin digestion allowing one missed cleavage, with a fragment ion mass tolerance error of 0.4

Da and precursor ion mass tolerance of 20 ppm. The iodoacetamide derivative of cysteine was

specified as a fixed modification, while the oxidized form of methionine and N-terminal

glutamate to pyroglutamate acid conversion were specified as variable modifications. Using a

Python (version 2.6.2)-based tool a false-positive rate was calculated on the peptide level by

using a scrambled version of the same database as used for initial searching. Peptides were

binned into three charge states (+2, +3, +4), and X!Tandem expectation values were calculated to

minimize peptides matching to decoy sequences for each charge state. The total value of reverse

spectra to total forward spectra was set at 0.2%, resulting in a low number of decoy sequences in

the final protein list (<0.5%). In generating a final list of proteins, only proteins identified with

≥2 unique peptides and ≥7 amino acids were accepted. Using a database grouping algorithm

designed to minimize protein interference, proteins were grouped favouring parsimonious

clustering (140, 141). Identified proteins were annotated with the entire UniProtKB/Swiss-Prot

19

(519,348 entries) and UniProtKB/TrEMBL (11,636,205 entries) databases (release 2010_09),

using the Swiss-Prot BLAST algorithm. BLAST homology was determined by using the best

BLAST match, regardless of species, with an expectation value (E-value) inclusion threshold of

0.001. Complete information on all peptide and protein identifications, including identification

probabilities and sequence coverage can be found in Electronic Appendix A.

2.5 Gene Ontology Analysis

Gene Ontology analysis was performed with Swiss-Prot accession numbers by using the

ProteinCenter software suite (version 3.8.2014, Thermo Fisher Scientific, Odense, Denmark) on

March 16, 2012. Proteins were searched using the entire Ensembl human protein database as

background at a FDR of 5%.

20

Chapter 3 Results

3 Results

3.1 Mass Spectrometry Analysis

First, I homogenized and lysed whole planarians and subjected their proteins to an in-solution

trypsin digestion as described in Materials and Methods. The resultant peptide solutions I

analyzed by a MudPIT method combining SCX and reversed-phase LC. Eluted peptides were

ionized by electrospray and injected into an LTQ-Orbitrap instrument. I performed an identical

analysis on whole worms following treatment with NAC to remove the external mucous coating.

The neutral pH NAC mucous extract which I generated, far less complex than the whole worm

preparations, was analyzed by one-dimensional reversed-phase chromatography prior to MS/MS.

Another solitary mucous fraction, the mucous trail, I produced by allowing planarians to migrate

freely across the surface of a 15 cm Petri dish. After removing planarians from the dish, I

harvested surface-adhered mucous for MS/MS analysis (see Materials and Methods). Shown in

Figure 2 is a representative example of an MS/MS acquisition and identification for ion

817.39(m/z) corresponding to the mucous peptide HGGIDLGFNMPSFGGK.

It should be noted that I also produced a mucous extract by using an acidic NAC solution (pH 2),

which is commonly used during the preparation of worms for staining and microscopic imaging.

This approach was abandoned since I suspected that it resulted in the release of membrane and/or

internal proteins as an artifact of the harsh nature of the method, which rendered worms

immobile and flat as a consequence of the immediate lethality of the treatment. By contrast, the

bicarbonate-buffered NAC protocol allowed me to collect mucous while worms remained viable

as evidenced by their continued motility including slow contraction of the dorsal muscles during

NAC treatment, causing them to assume a bent, crescent shape.

21

Figure 2. Orbitrap-FT data for the peptide HGGIDLGFNMPSFGGK at m/z 817.39. A, The MS spectrum for the doubly charged peptide at 817.39. B, The fragmentation spectrum (MS/MS) with the fragment ions annotated.

22

3.2 Annotating Identified Proteins

Our collaborators (Bret J. Pearson – University of Toronto, Eric Ross - Stowers Institute for

Medical Research) created a planarian transcriptome database containing greater than 25,000

planarian transcripts, I created a protein sequence database by using an algorithm designed to

translate the transcriptome into all six possible reading frames. A six-frame translation of the

database was necessary because our collaborators assembled it by using hundreds of millions of

short sequencing reads without direction, making unknown which direction/reading frame

transcripts were in. This approach is commonly used in searching mass spectrometry data with

sequences in which the correct reading frame for translation is unknown (54, 142).

Subsequently, I used the translated database to analyze my MS data with the search engine

X!Tandem, as described in Materials and Methods. Our collaborators from the Kislinger lab

(University of Toronto) then assembled a final list of protein identifications at a false-discovery

rate of <0.5%. A total of 1604 planarian proteins, each identified by at least two unique peptides

were identified (Electronic Appendix A). The complete list of identified proteins with annotation

is available in Electronic Appendix B. This dataset contains the transcript accession number, the

Swiss-Prot protein description, accession number, E-value, and the number of identified peptides

for each protein.

Initially, I differentially assessed the mucous proteome by comparing whole worms to worms

that had been treated with NAC to remove their mucous. From this analysis, I observed 236

NAC-sensitive proteins that were unique to whole, untreated planarians, and 7 unique to the

NAC-treated worms (Fig. 3A, Electronic Appendix B). The MS analysis of the buffered NAC

extract which I performed revealed 249 proteins (Electronic Appendix B). The majority of NAC

extract proteins (247) were detected in whole worms, and a fewer number (227) were found in

the NAC-treated worms (Fig. 3A). Collectively, 452 non redundant proteins were implicated as

mucous proteins by their presence in the NAC extract, mucous trail fraction, or NAC-sensitive

worm association.

Proteins which I identified by MS were annotated by our collaborator (Eric Ross) by comparing

identified protein sequences to characterized proteins from all available species using the Swiss-

Prot BLAST algorithm (Electronic Appendix B). The top BLAST entry corresponding to each

identified protein was used to annotate protein hits, with an E-value inclusion threshold of 0.001.

23

In total, 1252 identified proteins were matched to a BLAST entry, whereas 352 had no BLAST

match. Of the 249 proteins identified in the buffered NAC extract, 189 had a corresponding

BLAST match, as did 22 of the 35 mucous trail fraction proteins. Twenty-two mucous trail

proteins were also found in the buffered NAC extract, while 34 mucous trail proteins were also

identified in untreated planarians. In total, out of all 452 candidate mucous proteins I identified,

299 had a corresponding BLAST match.

24

Figure 3. Venn diagrams depicting overlap in non-redundant proteins among analyzed

fractions. A, Overlap between whole worm, NAC-treated worms, and NAC extract samples. B, Overlap between NAC extract and mucous trail samples.

25

3.3 Comparing Planarian Proteins to Published Proteomes

I compared the planarian mucous proteins which I Identified to member proteins of published

secretomes, from human mucous (129-131), and human tear fluid (143). Strikingly, 119

planarian mucous proteins, which group into 70 related protein families, appeared to be

orthologs or very similar to proteins identified in these characterized secretomes. Table 1 shows

the overlap between identified NAC extract, mucous trail, and NAC-sensitive proteins with

proteins from published secretomes described above. Comparatively, nasal mucous shared 8

proteins with the NAC extract, 2 NAC-sensitive proteins, and 2 proteins with mucous trail.

Olfactory cleft mucous shared 7 proteins with mucous trail, 31 proteins with the NAC extract,

and 7 similar to NAC-sensitive proteins. Cervical mucous shared 2 proteins with mucous trail, 35

proteins with the NAC extract, and 13 NAC-sensitive proteins. Tear fluid shared 8 proteins with

mucous trail, 77 proteins with the NAC extract, and had 34 NAC-sensitive proteins. Collectively,

this represents a 7%, 40%, 47%, and 20% overlap for nasal mucous, olfactory cleft mucous,

cervical mucous, and tear fluid respectively.

To assess whether planarians could be used as a model system to study parasitic worm species,

our collaborator (Eric Ross) systematically compared the proteins I identified to an S. mansoni

gene database, annotated with proteins similar to S. mansoni queries using the Swiss-Prot

BLAST algorithm (135). Of the 1604 S. mediterranea, proteins I identified, 1369 were also

found in the S. mansoni proteome, representing an overlap of 85%. Interestingly, the mucous

proteins I identified in the three mucous fractions (NAC extract; trail mucous; NAC-sensitive)

were also similar to proteins in the S. mansoni parasite, with overlap exceeding 75% (82%, 77%,

78%, respectively) (Electronic Appendix C).

26

Table 1. Overlap between identified mucous proteins with proteins from published secretomes.

Mucous Protein or Protein Family Related Protein Identified in Human Secretomes

Swiss-Prot Accession Number of

Putative Human Orthologs

Protein Name NAC-

Sensitive

NAC

Extract Trail Tear Fluid

Olfactory Cleft

Mucous

Cervical

Mucous

Nasal

Mucous

14-3-3 protein epsilon, zeta

P92177, Q5ZKC9

14-3-3 epsilon, zeta/delta, beta/alpha

14-3-3 epsilon, sigma, zeta/delta

40S ribosomal protein S8, S9, S11, S12, S14, S15, S21, S23, S27, 60S P1, L6, L7a, L8, L17, L27, L32, L34, L35a

P62844, Q6RF66, P55833, P08570, Q09JW2, Q7ZV82, Q9NB34, P32046

Q8WQI5, P55935, Q54PX9, P62263, Q9GRJ3, P21533, P32429, Q962T1, P04646

60S acidic RP P0, 40S RP S3, S27a, Similar to 40S RP SA

Actin, Actin-2 Q964E0, P53471

Q964E0, P53471

Actin-like protein 2, Actin, cytoplasmic 2, Actin-like protein 3

Actin 1, 2 Beta-actin Actin, alpha-2

Actophorin P37167 Cofilin Adenosyl- homocysteinase

O93477 Adenosylhomocysteinase

Adenylyl cyclase associated protein 1

Q3SYV4 Adenylyl cyclase-associated protein 1

Alpha-1, 2 macroglobulin

Q63041, Q7SIH1

Alpha-2-macroglobulin precursor

α2, β2-macroglobulin

Annexin A7 P20072 Annexin A1, A2, A3, A4, V, Isoform 1 of Annexin A7

Annexin A1, A2, A3, A7

Annexin A1, A2, A3, A5

Annexin A2

Basement membrane proteoglycan

Q06561 BM heparan sulfate proteoglycan precursor

Calcium-binding protein 16 kDa, 20 kDa

Q07167, P15845

Calcium-binding protein A4, 45 kDa precursor

Calreticulin P14211 Calreticulin precursor Calreticulin (precursor)

Calumenin P27730 Splice Isoform 1 of Calumenin precursor

Catalase P00432 Catalase Catalase

Chitinase 4 O04138 Chitinase 3-like protein 2 precursor

Collagen alpha-1I, 1II, 1V, -2 I, V

Q9JI03, P02466, P05997

P02454, Q6P4Z2

Collagen alpha 1(VI) chain precursor

Coronin-1C Q9ULV4 Coronin-1A Cystatin-A P56567 Cystatin C precursor Cystatin SN Cystatin A, B

27

Dihydropyrimidine dehydrogenase

Q12882 Dihydropyrimidine dehydrogenase precursor

Dipeptidyl peptidase 1, 3

P53634,

A7RZW4 Dipeptidyl peptidase 4

DJ-1 Q5XJ36 DJ-1

Dynein light chain 1, 8

Q7SXN5 Q7SXN5, Q78P75

Dynein heavy chain

Elongation factor 1α, 2

Q90835, P29691

Elongation factor 1-alpha, delta, gamma, 2

Elongation factor Tu (precursor)

Elongation factor 1-alpha 1

Enolase Q27655 Alpha-enolase α-Enolase Eukaryotic translation initiation factor 3D

Q6TH15 EIF 3, 4A-I

Fatty-acid binding protein

P07483 Fatty-acid binding protein

Filamin-1 Q8BTM8 Filamin A, SI 1of Filamin B

Fructose-1,6-bisphosphatase 1

P00637 Fructose-1,6-bisphosphatase

Fructose-bisphosphate aldolase

Q9GP32 Fructose-bisphosphate aldolase A

Gelsolin-like protein 1, 2

Q7JQD3, Q8MPM1

Gelsolin precursor

Glucose-6-phosphate isomerase

P06744 Glucose-6-phosphate isomerase

78 kDa glucose-regulated protein

Q16956 Glucose-regulated protein precursor

Glucose-regulated protein 78 kDa

Glutathione S-transferase, mu 1, mu 28

P09792,

Q9N0V4, P46428

Glutathione S-transferase

Glutathione S-transferase A1, P

Glutathione-S-transferase

Glyceraldehyde-3-phosphate dehydrogenase

P20287 Glyceraldehyde 3-phosphate dehydrogenase

Glyceraldehyde-3-phosphate dehydrogenase

Glyoxylate/ Hydroxypyruvate reductase

Q9UBQ7 Glyoxylate/hydroxypyruvate reductase

Golgi apparatus protein 1

Q02391 Golgi apparatus protein 1

Guanine nucleotide- binding protein beta-1

P17343 Guanine nucleotide-binding protein beta-2

Heat shock protein 10 kDa, 60 kDa, 40-3, cognate 70, -3, 71

O89114, P29844

Q5DC69, P18687, P29844,

Q5NVM9

HSP β, β170 kDa 1B, 4, 90 α2, HS cognate 71 kDa

HSP 27, 60, 70, HSC 70

HSP 70 1, 1L, 5, 6, 8, HSP beta-1, HSP 90-alpha, beta

28

Heterogenous ribonucleoprotein K, U1 small nuclear ribonucleoprotein A

O19049, P43332

SI RNP D0, RNP F Hetero nuclear RNP K

Histone H1-gamma, H2B, H3

P07796, P07794

P07796, P02299

Histone H2A.e Histone H4 Histone H2B, H4

Inorganic pyrophosphatase

Q6FRB7 Inorganic pyrophosphatase

Isocitrate Dehydrogenase α (probable)

Q9VWH4 Isocitrate dehydrogenase

Isocitrate dehydrogenase

Isocitrate dehydrogenase α

Major Vault Protein Q5EAJ7 Major vault protein

Malate dehydrogenase

P40926 Malate dehydrogenase

α-Mannosidase Q29451 α-Mannosidase II

Matrix metalloproteinase-19

Q9JHI0 Matrix metalloproteinase-9 precursor

Myosin heavy chain, light chain 2

P24733, P54357

Myosin heavy chain

Peptidase inhibitor 16, Kunitz-type serine protease inhibitor 6

Q9ET66 Q9ET66, P83606

Protease C1 inhibitor precursor

Peptidyl-prolyl cis-trans isomerase B, FKBP2

Q32PA9 Q26551 Peptidyl-prolyl cis-trans isomerase A, C

Peptidyl-prolyl cis–trans isomerase A

Peroxiredoxin-6 O35244 Peroxiredoxin 1, 4, 5, 6

Peroxiredoxin 1, 2, 5, 6

Peroxiredoxin-1, 5

Thiol-specific antioxidant protein

Phosphoglycerate kinase

P41759 Phosphoglycerate kinase 1

Phosphoglycerate kinase 1

Plastin-1 Q14651 Plastin 3 variant, L-plastin

Plastin-1d, 2e

Profilin-4 Q9D6I3 Profilin-1

Prominin-1 O43490 Prominin-1 precursor

Protein disulfide- isomerase 2, A3, A4

P08003 Q17770, P38657

Protein dissulfide-isomerase A6

Protein disulfide-isomerase A3

Protein disulfide isomerase precursor

Puromycin-sensitive aminopeptidase

P55786 Puromycin-sensitive aminopeptidase

Rap-2A P10114 Rab-1A

Rab GDP dissociation inhibitor beta

P50397 Rab GDP dissociation inhibitor beta

29

Rho GTPase activating protein 1

Q17R89 Rho-GTPase-activating protein 1

Septin 4, 7 O43236, Q16181

Septin 2, 7

Serine/threonine-protein phosphatase PGAM5

Q502L2 Serine-threonine phosphatase 2A, PP-1

Spectrin α, β chain Q00963 P13395 Splice Isoform 1 of Spectrin α chain

Stress-induced-phosphoprotein 1

O54981 Stress-induced-phosphoprotein 1

Superoxide Dismutase [Cu-Zn]

O73872 O73872 Superoxide dismutase [Cu-Zn]

Superoxide dismutase [Mn]

Syntenin-1 O00560 Syntenin-1

Thioredoxin Q98TX1 Thioredoxin Thioredoxin

Thymidine phosphorylase

P19971 Thymidine phosphorylase precursor

Triosephosphate isomerase

B0BM40 Triosephosphate isomerase 1 variant

Triosephosphate isomerase

Triosephosphate isomerase

Tropomyosin Q8WR63 Tropomyosin α3 Tropomyosin-1α, β

Tubulin alpha-1B, 2/4, beta-2, -2C

Q6P9V9, P41383, Q9NFZ6, P68371

Q6P9V9, P41383, P68371

Tubulin alpha-1, alpha-3, beta-2

Tubulin alpha-1, 6, 8, beta-2

Ubiquitin-1 Q8SWD4 Q8SWD4 Ubiquitin

30

3.4 Gene Ontology Annotation

In order to perform GO analyses (91), our collaborator (Eric Ross) annotated identified planarian

proteins with homologous human matches (Eric Ross), using the Swiss-Prot BLAST algorithm. I

performed GO analysis on all identified planarian proteins with a human BLAST match,

including individual analyses for NAC-sensitive, the NAC extract, and the mucous trail

fractions. Using the ProteinCenter software suite (Thermo Fisher Scientific), I performed

statistical analyses on the GO classifications of cellular compartmentalization (Fig. 4A),

molecular function (Fig. 4B), and biological process (Fig. 4C).

Upon examination of the cellular compartmentalization classification analysis, I observed an

enrichment for extracellular proteins in each of the mucosal extracts, in comparison to the entire

planarian proteome. While 12% of proteins in the entire planarian proteome were classified as

having an extracellular localization, 18% (43/236) of the NAC-sensitive set of proteins, more

than a quarter (27%; 67/249) of the NAC extract proteins, and greater than 40% (15/35) of the

mucous trail proteins were annotated as extracellular, with an overrepresentation of extracellular

proteins over the background Ensembl human protein database.

Additionally, I also analyzed the signal peptide (SP) sequence content of the mucosal proteins

which I identified, reinforcing the conclusion that the mucous fractions were enriched for

secreted/extracellular proteins. While the whole planarian proteome is comprised of 13% SP-

containing proteins, the NAC extract had 18% SP proteins, and 20% of the set of NAC-sensitive

proteins contained an SP sequence. The mucous trail fraction showed the greatest enrichment for

SP-containing proteins at 30%.

31

32

Figure 4. GO analysis results for annotated whole worm, NAC-sensitive, NAC extract, and mucous trail proteins. A, cellular compartmentalization. B, molecular function. C, biological process.

33

Chapter 4 Discussion and Conclusions

4 Discussion and Conclusions

4.1 Analyzing Mass Spectrometry Data

The six-frame translation approach I adapted to generate a planarian protein sequence database

has been widely used to analyze mass spectrometry data. As described in the Introduction, the

efficacy of using nucleic acid sequences to search mass spectrometry data was first shown in a

proof-of-principle study using known proteins (54). More recently, this aptly named

“proteogenomics” method (144) has since been used with larger genomes (77-80), including full

genome translations (41, 145). Integrating transcriptomic sequences with proteomic data

provides an additional level of information not realized with genomic sequences in that genes

can be validated on the transcriptional level, as transcriptomic sequences represent only

transcribed DNA (93).

While generating a protein sequence database was relatively straightforward, assembling a final

list of protein identifications from database search results was inherently more challenging.

Commonly, peptide assignments made by database search algorithms such as X!Tandem are

critiqued statistically by using algorithms such as “Peptide Prophet” (84). Following this initial

critiquing, peptide assignments which can be confidently made to a given statistical threshold are

assembled into their corresponding proteins using grouping algorithms like “Protein Prophet”

(146). Protein grouping is especially important when analyzing multiple samples, as grouping

algorithms ultimately determine the protein content of each analyzed sample. Collectively, these

algorithms work to verify peptide and protein identifications by assessing individual assignment

probabilities on the peptide and protein levels.

While statistical verification methods are extensively used in modern proteomics (147-150),

alternative strategies are also used. Initially, I subjected my MS data to statistical critiquing by

the Peptide and Protein Prophet algorithms, which are part of the more comprehensive software

package “Scaffold 3”. Following analysis of the critiquing results, I determined that protein

grouping had not been optimally executed, as evidenced by a minimal protein overlap amongst

34

mucosal fractions. To resolve this, I decided to adopt an alternate peptide and protein critiquing

scheme originally described in Gortzak-Uzan 2007 (85).

This approach was initially developed by our collaborators in the Kislinger lab, and employs a

peptide level FDR to resolve protein identifications. The algorithm ascertains peptide

assignments by comparing identified peptides to a “decoy” version of the same database used for

initial searching, which in my case involved the use of a randomly scrambled version of my

translated planarian database. Prior to analysis, I combined data from all my respective mass

spectrometry experiments into a single entity, in order to reduce false-negative identifications.

Accepted proteins were grouped by using an algorithm favouring parsimonious clustering, which

accurately grouped proteins amongst samples, in particular between whole and NAC treated

worms. This clustering more accurately defined NAC-sensitive proteins, indicated by an

increased overlap between the protein content of NAC-sensitive and NAC extract fractions.

4.2 Interpreting Mass Spectrometry Analyses

From the differential analysis I performed which identified 236 NAC-sensitive proteins, it is

evident that NAC-treated worms had fewer proteins due to the effective removal of their mucous

fraction. Nonetheless, I identified 7 proteins as being unique to NAC-treated worms which

suggests that, due to the sampling nature of the MS/MS protocol, the recorded protein lists I have

constructed have not fully accounted for the entire worm proteome. The majority of the 247

NAC extract proteins which I identified were detected in whole worms, while 227 were found in

NAC treated worms. This suggests the NAC treatment was enriched for mucous proteins, but

also that many mucous proteins may not reside exclusively in the mucous compartment. In

support of this, the GO analysis I performed on the cellular compartmentalization classification

indicated that many mucosal proteins had multiple localization annotations. Indeed, many

proteins were annotated as being both cytoplasmically and extracellularly localized.

The mucous fraction recovered from Petri dishes previously inhabited by planarians was found

to contain only 35 proteins, significantly fewer than the NAC-sensitive and NAC extract

fractions, and may represent a distinct “trail” subset of the planarian mucous proteome.

Consistent with my observation is the literature finding that some mucous-producing species are

35

known to display bilateral secretion, secreting biochemically different mucosal fractions (151,

152). These fractions function independently of one another, consisting of a “trail” left behind

during locomotion, and a portion more closely associated to the animal’s exterior used in times

of inactivity. The trail portion in many species is used not only to permit the locomotion of the

trail-producing individual, but to allow others to travel on the same trail, communally reducing

energy expenditures (153, 154). In gastropods, this “inactive” portion contains up to 2.7 times

more protein by mass than the trail portion (151), varying in composition among gastropod

species (155).

4.3 Examining Protein Annotations

The GO analyses which I performed demonstrated a clear enrichment for extracellular proteins

in planarian mucous, as is expected for an extracellular fraction. While GO analysis often yields

broad and overlapping annotations, it remains an effective method to analyze previously

unexamined proteomes (143), as is the case with planarian. Not all identified planarian mucous

proteins hold GO annotations for the extracellular region, which is true for other published

secretomes. This raises questions about the mechanism by which these proteins enter the

extracellular compartment, and may also be an indication, that my fractionation methodology

was not perfected. GO analysis of the NAC extract revealed an underrepresentation of

membrane-associated proteins, as referenced against the Ensembl human protein database. The

low level of membrane-localized proteins supports the conclusion that the NAC treatment did

not significantly disrupt membrane-associated proteins, causing them to partition into the NAC

fraction as artifacts.

The SP enrichment analysis which I completed verified that each of the three mucous fractions I

generated was enriched for SP sequence-containing proteins. As SPs target proteins through the

secretory pathway (156), enrichment for SP-containing proteins serves to validate the identified

proteins as bona fide mucous proteome constituents. Although not all the identified mucous

proteins are predicted to contain an SP, this alone does not affect their legitimacy as extracellular

constituents, as not all extracellular proteins contain this feature (157, 158). Many proteins are

extracellularly secreted by non-traditional mechanisms which circumvent the standard

endoplasmic reticulum-Golgi apparatus pathway of secretion (159, 160). Indeed, protein

36

secretion has been shown to proceed by a variety of mechanisms ranging from ionophore-

stimulated mechanisms (160), to the exocytosis of intracellular membranes (159, 161).

4.4 Planarian Mucous as a Disease Model

Given its significant overlap with proteins found in human secretions, the planarian mucous

proteome may prove to be a useful model in human disease studies. As described in Results, I

showed that 119 planarian proteins appeared to be orthologs or very similar to proteins identified

in characterized human secretomes. This not only provided validation to my annotation of

planarian mucous proteins, but to the best of my knowledge for the first time revealed mucous

proteins conserved across diverse species.

Some overlapping proteins most likely play specific roles within the mucous environment. For

example, collagen which is present in different isoforms in both planaria mucous and tear fluid,

is hygroscopic in nature, consequently serving as an external emollient (162). Peroxiredoxins

which serve as antioxidants in mucous (163) are both found in planarian mucous and other

secretomes (Table 1). In other worm species, such as the annelid Laeonereis acuta, antioxidant

proteins play a substantial role in protecting the worm against environmental reactive oxygen

species (12). Specifically, L. acuta secrete large amounts of mucous which contains the

antioxidant species catalase, superoxide dismutase, and glutathione peroxidase. These enzymes

intercept or degrade environmental peroxyl and hydroxyl radicals originating from organic

matter in their aqueous environments (164). Likewise, the antioxidant activity of the mucosa

covering respiratory tract epithelial cells in humans has been shown to be crucial for protecting

against radical damage from environmental pollutants and bodily microorganisms (165).

Diseases associated with mucous pathology are prevalent in humans and other animals (122) and

may benefit from research based on mucous models, especially in the development and testing of

therapeutic agents. In the treatment of mucous hypersecretion, a condition correlated with asthma

and poor prognosis in lung disease (166), practitioners sometimes rely on the use of unproven

products which would benefit from testing in a model system (127). The use of mucous models

is also important in the development of drugs which pass through mucosal layers, but do not

necessarily target the mucous itself. Many drugs bind to and interact with mucous, affecting drug

37

uptake, release, and overall efficacy (167). This is perhaps especially important in cystic fibrosis,

a disease hallmarked by thick, dense mucous which impedes drug delivery and diffusion (168).

Mucous models hold therapeutic importance in oral health care, where the protective and

emollient properties of mucous are of particular interest. This is primarily evident in the

treatment of mouth dryness, a common condition for which contemporary therapies do not

sufficiently emulate natural saliva (169). The biochemical properties of mucous are also of

commercial interest, as mucosal substances are used in coating biomaterials for low friction

coefficient implants (170). Such commercial applications may not only benefit from planarians

as a mucous model, but from planarian mucous or synthetic derivatives.

The planarian mucous proteome shares many proteins with tear fluid, making planarian a

pertinent model for studying tear fluid in addition to mucous. Tear models hold practical for

studying both the physical and chemical properties of tears, something which has shown to be

important in the research and development of many commercial applications (171). Disease

studies can also benefit from a proteomics-defined tear fluid model, as many ocular diseases

result from irregularities in the tear fluid proteome. Specifically, conditions such as diabetic dry

eye disease have been linked to decreased reactive oxygen species protection (172), resulting

from changes to protective proteins such as peroxiredoxins, found in both human tear fluid (173)

and planarian mucous.

4.5 Planarians as a Model to Study Parasitic Worms

The high overlap between the S. mediterranea and S. mansoni proteomes further establishes

planarians as a model to study S. mansoni and other parasitic flatworms such as Schistosoma

japonicum, which themselves present numerous experimental challenges (22). Specifically,

parasitic species rely on free-living hosts for survival and propagation, requiring elaborate

culturing methods to maintain them in a laboratory setting (174, 175). Culturing of schistosome

species for example usually entails maintenance of a living colony of freshwater snails (174),

such as Biomphalaria genus members for S. mansoni (176), and Bulinus genus members for

Schistosoma haematobium (177). These snail populations are necessary for the large-scale

production of both the parasites and their eggs, as they serve as intermediate parasite hosts (178).

38

The extensive overlap between S. mansoni gene products and planarian mucous proteins is also

noteworthy given that the pathogenicity of some parasitic worm species is driven by secretory

products released into the host environment (179). Some of these proteins, such as serine and

metallo proteases, and nucleoside diphosphate kinase were also found in planarian mucous. In

humans, schistosomes release proteases which aid in skin penetration during initial infection by

disrupting epithelial basement membranes (180-182). Multiple proteases which I identified in my

planarian mucous fractions were found to overlap with predicted S. mansoni proteins, including

aminopeptidases and metalloproteinases. S. mansoni proteases have the potential to serve as

therapeutic targets in the treatment of schistosome infection, and having already been the subject

of targeted research aimed at interfering with their activity (183).

Following successful invasion into the host, immature worms circulate and mature into adults,

laying eggs in various tissues throughout the body (184, 185). Deposited eggs secrete proteins

which elicit the production of host anti-inflammatory cytokines (186, 187), allowing them to

evade host immune responses (188). These secreted egg antigens have been thoroughly studied

(189, 190), and have been the subject of vaccine-based therapeutics centered on their

exploitation (191). Interestingly, I identified a planarian protein which was identified by BLAST

annotation as being one of these egg antigen proteins. The protein, which was BLAST-matched

to the S. mansoni major egg antigen protein, was found in whole worms and may prove useful in

studies directed at furthering characterizing schistosoma egg antigens.

In addition to being used to drive initial infection, schistosomes secrete proteases to degrade host

erythrocytes to obtain hemoglobin which they use to acquire essential amino acids (192-194).

Multiple schistosome proteases have been implicated in erythrocyte degradation (195), including

members of the cathepsin family which I identified in planarians. As is the case with the other

various schistosome proteins I have discussed, the cathepsins and related proteases implicated in

erythrocyte degradation have been proposed to be druggable targets for anti-schistosome

therapies (196, 197). Once again, this lends further significance to the high overlap I observed

between the planarian proteins I identified and the S. mansoni proteome.

Another protein important to schistosome survival and fecundity are the glutathione s-transferase

(GST) family of enzymes (198). The GST family is comprised of numerous isoenzymes which

catalyze the conjugation of the tripeptide glutathione to a multitude of substrates, functioning in

39

the detoxification of foreign compounds (199, 200). Accordingly, GST members have been

investigated as potential therapeutic targets in schistosomes, particularly in S. mansoni. In my

planarian fractions I identified multiple GST family members, including a protein homologously

matched to a 28 kDa GST which has been implicated in targeted therapeutics. Mainly,

approaches which used monoclonal antibodies against the 28 kDa GST member were shown to

reduce both fecundity and egg viability during in vivo S. mansoni infections (201). Not only did I

demonstrate the presence of GST members in planarians, these proteins were shown to overlap

with S. mansoni GSTs in the comparative analysis which was performed. This creates the

possibility that planarians may be used as a model in the development of future strategies which

target GSTs in S. mansoni.

4.6 Conclusions and Future Directions

My master’s work has provided annotation for the planarian proteome and mucous sub-

proteome, broadening the potential of an already established model system. Annotation of the

mucous proteome creates abundant possibilities for examining both the physiological and

biochemical functions of mucosal proteins within the context of the mucous environment. Given

the wide range of functions of planarian mucous, from locomotion and substrate adhesion, to

predation and innate immunity, it is quite possible that mucosal proteins carry out these

responsibilities as a function of previously undocumented mechanisms and properties.

Furthermore, as I have discussed extensively throughout my thesis, I propose that planarians may

be used to identify and validate conserved schistosome proteins as targets against which new

drugs or therapeutic modalities may be developed.

As many identified planarian proteins which I identified by MS share no significant BLAST

match, there exists the need for further genome and proteome annotation and functional

characterization. The proteins unmatched to a homologous mate by BLAST analysis will require

individual assessments by manual annotation, and may prove especially interesting if they are

associated with tissue regeneration or other biological properties that distinguish planarians as a

model system. MS experimentation also has the ability to facilitate analysis of these proteins, as

de novo peptide sequencing strategies may be used to elucidate their amino acid sequences.

Furthermore, the three-dimensional structure of proteins can be studied by MS analysis,

40

supplementing more traditional approaches to analyzing molecular structures such as nuclear

magnetic resonance spectroscopy and X-ray crystallography (202).

In order to further the potential of planarians as a model to study schistosomes, the planarian

proteins which I have identified and annotated should be compared to other schistosome species.

In addition to S. mansoni, several schistosome species are responsible for causing

schistosomiasis in humans, including S. japonicum and S. haematobium (203). For species like S.

japonicum which have fully sequenced genomes (204), such comparative analyses are relatively

straightforward, and hold immense benefit.

As I have demonstrated, my experimental pipeline combining high-resolution mass spectrometry

and automated protein annotation is suitable for analyzing the proteomes of understudied model

organisms. Other model species which have not been proteomically defined or have just recently

emerged as novel model system can also benefit from this pipeline, making it amenable to many

fields of biology. MS instruments are continuously being improved, benefitting from ever-

increasing mass resolutions, allowing them to identify significantly more proteins than their

predecessors. Consequently, future protein profiling exercises promise to yield a much greater

wealth of information, perhaps being able to decipher an entire proteome in a single MS analysis.

41

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