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Identification of Serum Protein Biomarkers for Autistic Spectrum Disorder. Melissa Butkiewicz


Academic year: 2021

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Identification of Serum Protein Biomarkers for Autistic Spectrum




Clarkson University

Identification of Serum Protein Biomarkers for Autistic Spectrum Disorder

A Thesis Proposal by

Melissa Butkiewicz

Department of Chemistry & Biomolecular Science

Mentor: Dr. Costel Darie


Abstract: Autism spectrum disorder (ASD) is a developmental disorder with no known etiology. In the United States one in 150 children are diagnosed with ASD, making ASD as prevalent as Alzheimer’s disease. Identification of ASD in a child is usually performed by a child psychologist. However, so far, there are no biochemical means to identify ASD in the early stages of children. Therefore, using serum biomarkers for early detection of ASD in children will help diagnosis of ASD in its early stages, when behavioral therapy may help treat ASD. Identification of ASD serum biomarkers could be done with common proteomic analyses. In our study, we will use gel electrophoresis and mass spectrometry in a proteomics approach to identify serum biomarkers in ASD that will hopefully lead to increased early diagnosis of ASD through biochemical approaches and point to additional causes to ASD.


Proteomics is a field that combines the biochemical separation of protein samples and their identification by mass spectrometry. Classical proteomics involves separation of the protein samples by SDS-PAGE and identification of those proteins or fractions of proteins by mass spectrometry. Mass spectrometry (MS) is becoming one the leading tools in protein identification and characterization. Every mass spectrometer contains an ion source, a mass analyzer and a detector. Based on the ion source, there are two types of mass spectrometer: Matrix-Assisted Laser Desorption Ionization mass spectrometer (MALDI-MS) and electrospray ionization mass spectrometer (ESI-MS). When a liquid chromatography (LC or an HPLC) system is coupled online with a MS, the combination is LC-MS or LC-MS/MS. Although different in principle, MALDI-MS and LC-MS complement each other.

The steps used to identify proteins via mass spectrometry are: 1) sample fractionation and protein digestion, 2) separation of the peptide mixture by LC-MS/MS and 3) identification of proteins upon data analysis. As previously mentioned, in the simplest case, sample fractionation involves separation of a protein mixture by SDS-PAGE; however, usually combination of more that one biochemical fractionation approaches are used in a proteomics experiment. For example, the classical 2D-PAGE involves separation of the protein sample by isoelectric focusing in the first dimension and SDS-PAGE in the second dimension.

Blue Native PAGE (BN-PAGE) separates protein complexes based on external charge induced by Coomassie dye and according to their molecular mass. This method has long been used to analyze protein-protein interactions and protein complexes from organelles [6]. Separation of protein complexes from the BN-PAGE lane in a second dimension SDS-PAGE may reveal partners of a particular protein. BN-PAGE experiments may provide information about the size, number, subunit composition, stoichiometry and relative abundance of these protein complexes. Functional proteomics involves specialized proteomics or sub-proteomics such as separation and identification of the phosphorylated proteins (phospho-proteomics), glycosylated proteins


(glycol-proteomics) or protein-protein interactions (protein interactomics). When proteomes from two different conditions are compared (e.g. serum samples from patients with a disease or disorder and from normal subjects), the proteins that are detected in the sera of patients with a disease/disorder but that are not in the sera of normal subsets (and vice-versa) are named serum biomarkers. Successful biomarkers for diagnosis and prognosis have been identified for a wide variety of diseases, from malignant tumors – such as breast prostate, ovarian, colon cancer – to benign tumors. Identification of biomarkers involves use of the standard proteomics approaches mentioned above. Unfortunately, these approaches can be inconsistent, time-consuming, and expensive. Therefore, the development of alternative methods for the identification could efficiently reduce the number of people with diseases by providing early diagnosis. The need for early diagnostic tests and biomarkers for ASD is in high demand and could reduce the number of children with this disorder [5].

Our specific aim is to test a new approach for biomarker discovery. We plan to separate serum samples from normal patients and patients with ASD by BN-PAGE, cut the bands that are stained differently in each condition, digest the samples with trypsin, extract the peptides and analyze them by LC-MS/MS. Compared to previous methods used to study protein-protein interactions, there are several advantages of BN-PAGE for studying protein-protein interactions: 1) separation of protein complexes takes place under native conditions so even the transient interactions between proteins may be identified, 2) the method may simultaneously analyze association into- or dissociation form protein complexes of particular proteins as a result of disease progression, 3) by combining BN-PAGE with LC-MS/MS, both structural and functional information may be obtained (source). Combination of BN-PAGE and mass spectrometry to study protein complexes from the sera of patients with ASD also has the potential to be applied to other proteomics-based analyses of protein-protein interactions such as serum or plasma from normal patients and patients suffering with different diseases, for both diagnostic and prognostic purposes.


As we enter the 21st century is it clear that biomedical research is one of the fastest growing fields in the world. Research has lead to treatments for diseases, tolerance of terminal viruses such as AIDS, and cancer treatments. Disorders such as maniac depression/bipolar disorder, obsessive compulsive disorder and the early stages of Alzheimer’s disease have benefited greatly from biomedical research. Although research has answered many medical demands, one solution remains unknown. Autism Spectrum Disorder is one of the most common disorders among humans, yet so little is known about the disorder besides behavioral evidence. Approximately 1 in 110 children are diagnosed with autism [2], making the disorder as prevalent as Alzheimer’s disease among the human population [1].

Autism spectrum disorder (ASD) is a disorder of brain function impairing the ability of a child to interact socially, communicate, learning languages, and imaginative play. As defined by the


DSM-IV classification system, ASD is diagnosed by lack of social or emotional reciprocity, inflexible adherence to specific, nonfunctional routines or rituals, delay or lack of the development of spoken language, marked impairment in the ability to initiate or sustain a conversation with others, and many other specific behavioral traits. The majority of children suffering from ASD are diagnosed between birth and 36 months of age [2].

The diagnosis of ASD proves to be a problem in our society. It is both an economical and social hardship for families that were not expecting or aware of the possibility of having an autistic child. With an estimated 1.5 million Americans suffering from ASD, it is evident that more research is needed to identify the neurological source of ASD [5]. Before a solution can be established, it is imperative that scientists can identify the disorder in forms other than observed behavior or psychological testing. Advances in biochemical research have led to identification of diseases and disorders via biomarkers. Biomarkers are indicators used in the human body to detect or measure the progress of a disease or disorder. Biomarkers have been used to detect cancer, and can help reach a better understanding of a disease or disorder.

Based on previous ASD research, it has been shown that there are differences in the protein patterns in the serum samples from patients with ASD, compared with the control samples. In addition, these proteins correlate between samples from ASD patients [8]. Identifying these proteins as biomarkers can aid in the early detection of ASD in children. Therefore, combination of two dimensional gel electrophoresis (BN-PAGE and SDS-PAGE) and mass spectrometry in a proteomics approach can lead to identification of proteins that are possibly serum biomarkers for ASD.

Preliminary Research:

During our literature search, we identified a published paper whose authors looked for serum biomarkers in patients with ASD, using MALDI-MS. We then asked the authors if it is possible to re-analyze the same samples using our approaches. Our wish was granted and we just received the samples. In published work [8], it is evident that in the MALDI-MS spectra, there is a difference between patients with ASD, when compared with the controls (Figure 1).

Figure 1: Representative example of a 1,000 – 12,00 Da patient MALDI-MS spectra of two ASD/ADHD(+), two ASD/ADHD(-) patients and two controls. The ASD/ADHD patients show a clear peak with a m/z ratio of 10.38 kDa. Additional peaks were identified with at 5.15 and 4.40 kDa (data not shown).


The most significant difference in the MALDI-MS spectra was observed at 10.38 kDa, providing evidence that there is a difference in the protein pattern in the sera of patients with ASD patients, compared with the normal control subjects. The peak was observed only in the serum samples from ASD patients, but not in the normal, control subjects, suggesting that this peak contain a protein or several proteins that could be a serum biomarker or serum biomarkers for early detection of ASD. However, the peak at 10.38 kDa identified by MALDI-MS was not sequenced and the identity of the protein(s) that correspond to this peak is unknown. Therefore, identification of the protein(s) that is (are) responsible for this peak will help elucidating the biochemical differences between the sera from patients with ASD and their normal matched controls. The discovery of a biomarker for ASD can lead to quicker and more efficient clinical diagnoses of ASD.

In our experiments, we plan to use SDS-PAGE and LC-MS/MS to specifically look for proteins with the molecular weigh between 8-12 kDa, hoping to identify the differences between the serum samples from ASD patients and normal subjects, observed as a peak at 10.38 kDa. We also plan to combine BN-PAGE (1D) and SDS-PAGE (2D) with LC-MS/MS to search for differences between the serum samples from ASD patients and normal subjects at the protein complex level. We do have the expertise to perform the proposed experiments. We do have expertise in both analysis of protein complexes and protein-protein interactions BN-PAGE (1D) and SDS-PAGE (2D) (or only BN-PAGE 1D only), and protein identification and quantitation using LC-MS/MS. Example of the work performed in our lab show that we will be able to succeed in our proposed work.

We will describe some of our results that reflect our expertise in mass spectrometry and proteomics, which will allow us to be successful in our proposal. We will also describe some results using new approaches for qualitative identification and label-free quantitation of proteins at the protein complex level. Finally, we will describe preliminary data where serum samples from patients with various forms of cancer and from normal subjects were separated by BN-PAGE. Our preliminary data using mass spectrometry and proteomics demonstrate that our lab will be able to succeed with the specific aims that we propose.

Results that reflect our expertise in mass spectrometry and proteomics:

1. Peptide mass fingerprinting and protein identification using MALDI-MS and LC-MS/MS. Figure 2 shows examples of MALDI-MS and LC-MS/MS analyses of peptide mixtures that led to identification of proteins. The proteins were separated on SDS-PAGE and the gel pieces were excised and digested with trypsin, followed by extraction and concentration of the peptide mixture. The peptide mixture was then analyzed by MALDI-MS or LC-MS/MS and the results were submitted to the Mascot database for protein identification.


We were able to rapidly identify proteins using both MALDI-MS and LC-MS/MS systems, demonstrating that our expertise in mass spectrometry in general and proteomics in particular is more than adequate for performing the proposed work.

2. Analysis of stable and transient protein interactions using LC-MS/MS. Although we have extensive experience using MALDI-MS and LC-MS/MS, our expertise is not limited only to protein identification and analysis of their post-translational modifications (PTMs), but it also extends to analysis of both stable and transient protein-protein interactions. Examples of analysis of subunit composition (and molecular mass) of a homo-complex and a heterocomplex are shown in Figure 4. For this, we separated a mammalian cell lysate by Blue Native-PAGE (BN-PAGE; separates protein complexes under native conditions and according to their molecular weight), cut the bands according to their molecular mass in the initial gel, digested the proteins and analyzed the peptide mixture by LC-MS/MS, followed by data analysis. Once a subunit of a particular protein complex with a specified mass was identified, we looked for additional subunits in the same gel band and further compared these data with the current literature in terms of subunit composition, mass and protein interactions. In these experiments, we identified both homo- and heterocomplexes. Among putative homocomplexes, we identified Valosin-containing

Figure 2: A: MALDI-MS analysis of a peptide mixture. The peaks marked with letters alpha, beta or gamma correspond to peptides from three different proteins. The greater the identification of peaks that correspond to peptides from same protein, the higher the protein coverage of that identified protein and the higher the chances for

correct identification. B:

LC-MS/MS analysis of a peptide

mixture. The LC-MS/MS

experiment involves fractionation of the peptide mixture prior to MS analysis by liquid chromatography

(LC), and the recorded

chromatogram is named total ion current (TIC). The TIC contains many singly and multiply charged peaks that correspond to peptides, and are visible as MS mass spectra. In the MS spectra, some of the high intensity peaks are selected for fragmentation and each produces a MS/MS spectrum that contains

sequence information about a


protein (89 kDa protein), a homohexamer of 540 kDa [7]. In our experiments, we identified this protein in band B4 (Figure 4; Mascot scores 1790), at a molecular mass of 550-650 kDa. Among the heterocomplexes identified by BN-PAGE and LC-MS/MS was proteasome (prosome). This is a hetero-28-mer (25) alpha-beta protein complex with a molecular mass of 700 kDa. In our experiments, we identified five different alpha subunits: alpha 1, (Mascot score 80), alpha 3 (Mascot score 75), alpha 4 (Mascot score 56), alpha 6 (Mascot score 183) and alpha 7 (Mascot score 63) and four different beta subunits: beta 1 (Mascot score 41), beta 4 (Mascot score 123), beta 5 (Mascot score 118) and beta 6 (Mascot score 92) in a single experiment. Our experimentally determined mass of the proteasome was 650-750 kDa (detected in bands B1 and B2), in agreement with its calculated theoretical mass of 700 kDa. Examples of MSMS spectra of identified peptides that were part of either a homocomplex (Valosin-containing protein, peak 1093.49 (2+), peptide EDEEESLNEVGYDDIGGCR) or a heterocomplex (Proteasome subunit alpha 6, peak 643.33 (2+), peptide AINQGGLTSVAVR and Proteasome subunit beta 6, peak 586.79 (2+), peptide LAAIQESGVER) are shown in Figure 3.

These data suggest that the combination of BN-PAGE and LC-MS/MS is a powerful tool for determining the mass of a particular protein complex and the identity of its subunit composition. These data also demonstrate that our expertise in mass spectrometry is not limited only to the analysis of proteins and identification of their identity and their PTMs, but it extends to the analysis of stable (Figure 3) or transient (data not shown) protein-protein interactions.

Figure 3: Analysis of the cell lysates from unstimulated and stimulated cells by BN-PAGE and LC-MS/MS. A: The cell lysates were separated by BN-PAGE and the gel bands B1-B8 were excised, digested by trypsin, analyzed by LC-MS/MS and submitted to the Mascot search engine for protein identification. B: MS/MS spectra of peptides that were part of Proteasome alpha 6 (a) and Proteasome beta 6 (b) and Valosin-containing protein (c).


We also analyzed the serum samples from patients with various types of cancer and from normal subjects by BN-PAGE. Example of such separation is shown in figure 4. Here, we separated serum samples from normal male (Figure 4A, #1-9) and female (Figure 4B, #1-9) subjects, and from male (Figure 4A, #10-19) and female (Figure 4B, #10-19) patients with cancer.

As demonstrated by our preliminary data, overall, we have the means and expertise to perform the proposed experiments and believe that we will be successful in completing our proposed work.

Research Methodology:

The goal of our proposed experiments is to identify proteins that specifically associate into- or dissociate from- protein complexes in sera of the patients with ASD. We will also obtain quantitative information about the relative abundances of these proteins and protein complexes. The proposed studies have been designed based on the hypothesis that protein complexes specific to ASD are formed and that deciphering the composition of these complexes will reveal significant insights about mechanisms of ASD.

The main strategy for these experiments is to separate the serum samples from patients with ASD and from their matched controls by BN-PAGE or by BN-PAGE and SDS-PAGE, excise the gel bands/spots that are differently stained, digest the samples, extract the peptide mixtures, analyze them by LC-MS/MS, perform the database search, followed by data analysis. The workflow of our proposed research is summarized in Figure 5.

The first step that we will perform is to optimize the running conditions for BN-PAGE (1D) and SDS-PAGE (2D) of the serum samples. Initially, we will separate serum samples from normal subjects from our tissue repository. Specifically, we will optimize the running conditions that will produce the best protein load/gel lane in both BN-PAGE (1D) and SDS-PAGE (2D). Once

Figure 4: BN-PAGE of serum samples from normal male (A, #1-9) and female (B, #1-9) subjects, and from male (A, #10-19) and female (B, #10-19) patients with cancer. The gels were stained with Coomassie Brilliant Blue.


our running conditions will be optimized, we will perform the same experiments, except that the serum samples will be depleted from immunoglobulins G (IgGs), the second most abundant protein after serum albumin. Once we will have the running conditions optimized, we will repeat these steps with serum samples from patients with ASD and from their normal matched controls.


Below is the proposed outline for the proposed project. Upon completion of the research, there will be a 3 month span of time where I will be finalizing the thesis paper and preparing the oral presentation. These three months will be enough time to complete the paper and include any last minute results from the spring 2011 semester.

Month Stage Goals of Each Stage 1 Initial


Complete Mass Spectrometry Training

Perfect Sample Preparation Techniques

Begin 2D BN-PAGE (1D) and SDS-PAGE (2D) Analysis 2

3 Protein Purification

Complete the optimization of the sample preparation, complete the 2D PAGE

Figure 5: The workflow of our proposed experiments. The serum samples witll be separated by BN-PAGE (1D) and then SDS-PAGE (2D, not shown) and then the proteins will be digested, analized by LC-MS/MS for protein identification and quantitation, followed by data analysis. Optimization of running conditions will be performed using serum samples from normal subjects. The experiments will then be repeated, with IgG-depleted samples. Once optimized, the experiments will be repeaded in both 1D and 2D, using both undepleted and depleted serum samples from patients with ASD and their normal matched controls.


analysis, start analysis using undepleted serum samples. Complete analysis of ASD serum samples

Complete the MS analysis of all samples

Complete data analysis Complete Rough Draft of Thesis 4 5 6 7 8 9 10 Final

Results Correct Discrepancies between Data Sets

Finish Thesis Paper 11


13 Thesis Completion

Turn In Thesis Paper and Present Research



1. Autism Spectrum Disorders (ASDs). [online article] 2008 October 19 [cited 2010

February 12]; Available from: http://www.nichd.nih.gov/health/topics/asd.cfm.

2. What is autism? [online article] 2010 [cited 2010 January 10]; Available from:


3. Darie, C., Proteomic Approach to Biomarkers. 2009, Clarkson University.

4. Dass, C., Fundamentals of contemporary mass spectrometry. Wiley-Interscience series on mass spectrometry, ed. D.M. Desiderio and N.M. Nibbering. 2007, Hoboken: John Wiley & Sons. 585.

5. Gerlai, J. and R. Gerlai, Autism: a large unmet medical need and a complex research

problem. Physiol Behav, 2003. 79(3): p. 461-70.

6. Schagger, H. and G. von Jagow, Blue native electrophoresis for isolation of membrane

protein complexes in enzymatically active form. Anal Biochem, 1991. 199(2): p. 223-31.

7. Schmidt, W.E., et al., Valosin: isolation and characterization of a novel peptide from

porcine intestine. FEBS Lett, 1985. 191(2): p. 264-8.

8. Taurines, R., et al., Serum protein profiling and proteomics in autistic spectrum disorder

using magnetic bead-assisted mass spectrometry. Eur Arch Psychiatry Clin Neurosci,


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