Proteomics

Melanoma starts on the surface of the skin where it is easily seen. It is curable
when detected early, but can be fatal if allowed to progress and spread. Melanoma can
spread downwards through the skin, ultimately reaching the blood and lymphatic vessels,
and metastasize. Thus, one goal is to detect melanoma early before it metastasizes. A high
throughput proteomics approach has been applied to better understand the processes that
underlie tumor formation and progression. Three studies were pursued: I) proteome
comparison of the matched primary WM-115 and metastatic WM-266-4 melanoma cell
lines; II) proteome comparison between the matched melanoma Hs 895.T and fibroblast
Hs 895Sk cell lines; and III) comprehensive proteome cataloging of two metastatic
melanoma cell lines Hs 895.T and SK-MEL-2. From these studies we identified proteins
that are involved in cellular functions such as metabolism, signal transduction, and DNA
binding, as well as structural and heat shock proteins. We hypothesized about a possible
oxidative stress pathway involved in melanoma progression, initiated the creation of a
melanoma proteome database, and also identified some proteins not previously studied in melanoma (such as cyclophilin A, ADP-ribosylation factor-1, 14-3-3 zeta ATP syntase, Rho
GTPase, Plastin T, galectin 1 and 3, annex in II, enolase 1, cofilin, RhoGDI, Rap 1,
G6PG, GAPDH, TKT, HK, and nuclear chloride channel protein). These results mark a
step forward in the development of a metstatic melanoma protein database, the
understanding of the chemical pathways that are involved in metastatic melanoma
development, and identification of possible new targets for inhibitor development.

After the sequencing of many complete genomes, we are in a post-genomic era in which the most important task has changed from gathering genetic information to organizing the mass of data as well as under standing how components interact with each other. The former is usually undertaking using bioinformatics methods, while the latter task is generally termed proteomics. Success in both parts demands correct statistical significance assignments for results found. In my dissertation. I study two concrete examples: global sequence alignment statistics and peptide sequencing/identification using mass spectrometry. High-performance liquid chromatography coupled to a mass spectrometer (HPLC/MS/MS), enabling peptide identifications and thus protein identifications, has become the tool of choice in large-scale proteomics experiments. Peptide identification is usually done by database searches methods. The lack of robust statistical significance assignment among current methods motivated the development of a novel de novo algorithm, RAId, whose score statistics then provide statistical significance for high scoring peptides found in our custom, enzyme-digested peptide library. The ease of incorporating post-translation modifications is another important feature of RAId. To organize the massive protein/DNA data accumulated, biologists often cluster proteins according to their similarity via tools such as sequence alignment. Homologous proteins share similar domains. To assess the similarity of two domains usually requires alignment from head to toe, ie. a global alignment. A good alignment score statistics with an appropriate null model enable us to distinguish the biologically meaningful similarity from chance similarity. There has been much progress in local alignment statistics, which characterize score statistics when alignments tend to appear as a short segment of the whole sequence. For global alignment, which is useful in domain alignment, there is still much room for exploration/improvement. Here we present a variant of the direct polymer problem in random media (DPRM) to study the score distribution of global alignment. We have demonstrate that upon proper transformation the score statistics can be characterized by Tracy-Widom distributions, which correspond to the distributions for the largest eigenvalue of various ensembles of random matrices.

This research is concerned with analyzing a set of viral genomes to elucidate the underlying characteristics and determine the information-theoretic aspects of the genomic signatures. The goal of this study thereof, is tailored to address the following: (i) Reviewing various methods available to deduce the features and characteristics of genomic sequences of organisms in general, and particularly focusing on the genomes pertinent to viruses; (ii) applying the concepts of information-theoretics (entropy principles) to analyze genomic sequences; (iii) envisaging various aspects of biothermodynamic energetics so as to determine the framework and architecture that decide the stability and patterns of the subsequences in a genome; (iv) evaluating the genomic details using spectral-domain techniques; (v) studying fuzzy considerations to ascertain the overlapping details in genomic sequences; (vi) determining the common subsequences among various strains of a virus by logistically regressing the data obtained via entropic, energetics and spectral-domain exercises; (vii) differentiating informational profiles of coding and non-coding regions in a DNA sequence to locate aberrant (cryptic) attributes evolved as a result of mutational changes and (viii) finding the signatures of CDS of genomes of viral strains toward rationally conceiving plausible designs of vaccines. Commensurate with the topics indicated above, necessary simulations are proposed and computational exercises are performed (with MatLabTM R2009b and other software as needed). Extensive data gathered from open-literature are used thereof and, simulation results are verified. Lastly, results are discussed, inferences are made and open-questions are identified for future research.

Acupuncture has been used for thousands of years to treat a wide range of diseases, but the mechanisms involved in the process have remained a mystery. The present study measures EEG responses to stimulation of a specific acupuncture point, GB37 (Guang Ming), with two different types of manual needle stimulation. Previous studies stimulated for a maximum of 2 minutes. The present study reflects the normal acupuncture treatment time of 20 minutes, with EEG recordings during and for 10 minutes prior to and after stimulation. Our results show no changes in the global spatial and temporal properties of EEG during and shortly after acupuncture treatment of acupoint GB37. The second part of this study examines the global protein expression of glutamic acid decarboxylase (GAD) knockout mice. GAD is the rate-limiting enzyme in the synthesis of GABA, the major inhibitory neurotransmitter in the brain. The protein content of wild type, hetero-, and homozygous GAD knockout mice brains were determined using a LC-MS-based gel-free shotgun profiling of complex protein mixtures. The data was analyzed using the Raculovic algorithm to determine the proteins differences. A short list of 32 proteins was determined with four that have been shown to be significant proteins that influence cell survival and excitotoxicity in the brain and have potential relationships with GABA. These proteins include VATPase, Glutamine synthetase, Beta-synuclein, and Micortuble associated protein (MAP). The proteomics results provide a preliminary best guess list of proteins influencing GAD and GABA production.