Statistical and Functional Analysis of Genomic and Proteomic Data

High-throughput technologies have led to an explosion in the availability of data at the genome scale. Such data provide important information about cellular processes and causes of human diseases, as well as for drug discovery. Deciphering the biologically relevant results from these data requires comprehensive analytical methods. In this dissertation, we present methods for gene and protein expression data analysis. Our major contributions include a method for differential in-gelelectrophoresis data analysis capable of removing protein-specific dye bias in the data, a method for finding unknown biological groups using expression data, and a method for identifying active and inactive signaling pathways in a gene expression signature based on the enrichment of downstream target genes of pathways

The state of the art in the analysis of two-dimensional gel electrophoresis images

Software-based image analysis is a crucial step in the biological interpretation of two-dimensional gel electrophoresis experiments. Recent significant advances in image processing methods combined with powerful computing hardware have enabled the routine analysis of large experiments. We cover the process starting with the imaging of 2-D gels, quantitation of spots, creation of expression profiles to statistical expression analysis followed by the presentation of results. Challenges for analysis software as well as good practices are highlighted. We emphasize image warping and related methods that are able to overcome the difficulties that are due to varying migration positions of spots between gels. Spot detection, quantitation, normalization, and the creation of expression profiles are described in detail. The recent development of consensus spot patterns and complete expression profiles enables one to take full advantage of statistical methods for expression analysis that are well established for the analysis of DNA microarray experiments. We close with an overview of visualization and presentation methods (proteome maps) and current challenges in the field

Integrative analysis of the heat shock response in Aspergillus fumigatus

<p>Abstract</p> <p>Background</p> <p><it>Aspergillus fumigatus </it>is a thermotolerant human-pathogenic mold and the most common cause of invasive aspergillosis (IA) in immunocompromised patients. Its predominance is based on several factors most of which are still unknown. The thermotolerance of <it>A. fumigatus </it>is one of the traits which have been assigned to pathogenicity. It allows the fungus to grow at temperatures up to and above that of a fevered human host. To elucidate the mechanisms of heat resistance, we analyzed the change of the <it>A. fumigatus </it>proteome during a temperature shift from 30°C to 48°C by 2D-fluorescence difference gel electrophoresis (DIGE). To improve 2D gel image analysis results, protein spot quantitation was optimized by missing value imputation and normalization. Differentially regulated proteins were compared to previously published transcriptome data of <it>A. fumigatus</it>. The study was augmented by bioinformatical analysis of transcription factor binding sites (TFBSs) in the promoter region of genes whose corresponding proteins were differentially regulated upon heat shock.</p> <p>Results</p> <p>91 differentially regulated protein spots, representing 64 different proteins, were identified by mass spectrometry (MS). They showed a continuous up-, down- or an oscillating regulation. Many of the identified proteins were involved in protein folding (chaperones), oxidative stress response, signal transduction, transcription, translation, carbohydrate and nitrogen metabolism. A correlation between alteration of transcript levels and corresponding proteins was detected for half of the differentially regulated proteins. Interestingly, some previously undescribed putative targets for the heat shock regulator Hsf1 were identified. This provides evidence for Hsf1-dependent regulation of mannitol biosynthesis, translation, cytoskeletal dynamics and cell division in <it>A. fumigatus</it>. Furthermore, computational analysis of promoters revealed putative binding sites for an AP-2alpha-like transcription factor upstream of some heat shock induced genes. Until now, this factor has only been found in vertebrates.</p> <p>Conclusions</p> <p>Our newly established DIGE data analysis workflow yields improved data quality and is widely applicable for other DIGE datasets. Our findings suggest that the heat shock response in <it>A. fumigatus </it>differs from already well-studied yeasts and other filamentous fungi.</p

The identification of biomarkers of chemotherapy resistance in breast cancer using comparative proteomics

Background:Chemotherapy resistance is a major obstacle in effective neoadjuvant treatment for locally advanced breast cancer. The ability to predict tumour response would allow chemotherapy administration to be directed towards only those patients who would benefit, thus maximising treatment efficiency. This project aimed to identify predictive protein biomarkers associated with chemotherapy resistance, using proteomic analysis of fresh breast cancer tissue samples.Materials and Methods:Chemotherapy-sensitive (CS) and chemotherapy-resistant (CR) tumour samples were collected from breast cancer patients who received neoadjuvant therapy consisting of epirubicin with cyclophosphamide followed by docetaxel. Comparative proteomic analysis was performed, to identify differentially expressed proteins (DEPs) between CS and CR invasive ductal carcinoma samples, using 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) with MALDI-TOF/TOF mass spectrometry and antibody microarray analysis. DEPs were submitted to Ingenuity Pathway Analysis (IPA) to identify any canonical pathway links, confirmed using western blotting and clinically validated in a pilot series of archival breast cancer samples, from patients treated with neoadjuvant chemotherapy.Results:Five datasets were generated by antibody microarray analysis, revealing 38 targets. Of these, 7 DEPs were identified in at least 2 datasets and these included 14-3-3 theta/tau, BID and Bcl-xL. Three datasets were generated using 2D-PAGE with MALDI-TOF/TOF MS, containing 132 unique DEPs. These included several isoforms of 14-3-3 proteins. The differential expression of 14-3-3, BID and Bcl-xL was confirmed by immunoblotting in samples used for the discovery phase. Clinical validation using immunohistochemical analysis of archival breast cancers revealed 14-3-3 theta/tau and tBID to be significantly associated with chemotherapy resistance.Discussion:The use of comparative proteomic techniques using fresh clinical tumour samples, for the search for putative biomarkers of chemotherapy resistance has been successful. Two DEPs; 14-3-3 theta/tau and tBID have passed through all stages of the biomarker discovery pipeline, and present themselves as putative predictive biomarkers of neoadjuvant chemotherapy resistance in breast cancer

Identification of the Feline Humoral Immune Response to Bartonella henselae Infection by Protein Microarray

Background: Bartonella henselae is the zoonotic agent of cat scratch disease and causes potentially fatal infections in immunocompromised patients. Understanding the complex interactions between the host’s immune system and bacterial pathogens is central to the field of infectious diseases and to the development of effective diagnostics and vaccines. Methodology: We report the development of a microarray comprised of proteins expressed from 96 % (1433/1493) of the predicted ORFs encoded by the genome of the zoonotic pathogen Bartonella henselae. The array was probed with a collection of 62 uninfected, 62 infected, and 8 ‘‘specific-pathogen free’ ’ naïve cat sera, to profile the antibody repertoire elicited during natural Bartonella henselae infection. Conclusions: We found that 7.3 % of the B. henselae proteins on the microarray were seroreactive and that seroreactivity was not evenly distributed between predicted protein function or subcellular localization. Membrane proteins were significantly most likely to be seroreactive, although only 23 % of the membrane proteins were reactive. Conversely, we found that proteins involved in amino acid transport and metabolism were significantly underrepresented and did not contain any seroreactive antigens. Of all seroreactive antigens, 52 were differentially reactive with sera from infected cats, and 53 were equally reactive with sera from infected and uninfected cats. Thirteen of the seroreactive antigens were found to be differentially seroreactive between B. henselae type I and type II. Based on these results, we developed a classifier algorith

Identification and characterization of the activated defence response in the commercially important Agarophyte, Gracilaria Gracilis, following exposure to disease elicitors

To our knowledge, this study represents the first analysis of gene expression using cDNA microarrays in the red macroalga G. gracilis. Western hybridization analysis was used to establish whether the observed changes in gene expression following exposure to disease elicitors positively correlated to changes at the protein level

Multivariate meta-analysis of proteomics data from human prostate and colon tumours

<p>Abstract</p> <p>Background</p> <p>There is a vast need to find clinically applicable protein biomarkers as support in cancer diagnosis and tumour classification. In proteomics research, a number of methods can be used to obtain systemic information on protein and pathway level on cells and tissues. One fundamental tool in analysing protein expression has been two-dimensional gel electrophoresis (2DE). Several cancer 2DE studies have reported partially redundant lists of differently expressed proteins. To be able to further extract valuable information from existing 2DE data, the power of a multivariate meta-analysis will be evaluated in this work.</p> <p>Results</p> <p>We here demonstrate a multivariate meta-analysis of 2DE proteomics data from human prostate and colon tumours. We developed a bioinformatic workflow for identifying common patterns over two tumour types. This included dealing with pre-processing of data and handling of missing values followed by the development of a multivariate Partial Least Squares (PLS) model for prediction and variable selection. The variable selection was based on the variables performance in the PLS model in combination with stability in the validation. The PLS model development and variable selection was rigorously evaluated using a double cross-validation scheme. The most stable variables from a bootstrap validation gave a mean prediction success of 93% when predicting left out test sets on models discriminating between normal and tumour tissue, common for the two tumour types. The analysis conducted in this study identified 14 proteins with a common trend between the tumour types prostate and colon, i.e. the same expression profile between normal and tumour samples.</p> <p>Conclusions</p> <p>The workflow for meta-analysis developed in this study enabled the finding of a common protein profile for two malign tumour types, which was not possible to identify when analysing the data sets separately.</p

Gene and Protein Profiling of the Preeclamptic Placenta

Aims State-of-the-art methodology was used to screen and profile the placenta, gene and protein expression, for changes related to preeclampsia (PE) and cases with increased resistance in the uterine arteries. Women with increased resistance in the uterine arteries have increased risk of developing PE. Since not all of them develop PE, this group, identified by Doppler ultrasound, was included to search for genes and/or proteins that may protect them from developing PE. Results The PE placenta showed increased gene expression of fetal hemoglobin (Hb). Protein expression analysis confirmed the accumulation of free Hb, particularly the gamma chain was detected in the vascular lumen. Patients with increased resistance in the uterine arteries, expressed as a notch in blood velocity tracings recorded with Doppler ultrasound. Notching without PE, showed increased expression of genes related to apoptosis and antigen presentation in their placentas. In the notch placentas that later developed PE, an increased expression of genes related to inflammatory cell movement was seen. Antibody microarray screening of maternal plasma showed that late and early onset PE as well as PE with notching and IUGR showed different inflammatory responses. Conclusions The changes in gene expression suggested that PE may be a three-stage disease with notch as a reversible middle stage. Accumulation of inflammatory cells in the notch placenta may cause inflammation that drives the pathophysiology into PE. Increased expression of antigen presenting genes may protect the notch placenta from pro-inflammatory damage thereby preventing progression into PE. Free fetal Hb was identified as a possible placental factor that further induces inflammation and tissue damage. Increased maternal plasma levels of free fetal Hb may be used as a prognostic and diagnostic marker for PE. The maternal immune reaction and inflammatory response may be important factors that further determine the severity and the clinical manifestations of PE

Discovery and application of colorectal cancer protein markers for disease stratification

Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise methods of CRC stratification are needed. The intracellular protein expression from 28 CRC primary tumours and corresponding normal intestinal mucosa was analysed using saturation-DIGE/MS and Explorer antibody microarrays. Changes in protein abundance were identified at each stage of CRC. Proteins associated with proliferation, glycolysis, reduced adhesion, endoplasmic reticulum stress, angiogenesis, and response to hypoxia represent changes to CRC and its microenvironment during development. Molecular changes in CRC cells and their microenvironment can be incorporated into clinic-pathological data to help sub-classify tumours and personalise treatment. DotScan antibody microarray analysis was used to profile the surface proteome of cells derived from 50 CRC samples and corresponding normal intestinal mucosa. Fluorescence multiplexing enabled the analysis of two different sub-populations of cells from each sample: EpCAM+ cells (CRC cells or normal epithelial cells in normal mucosa) and CD3+ T-cells (tumour-infiltrating lymphocytes). Unsupervised hierarchical clustering of the CRC and T-cell surface profiles defined four clinically relevant clusters, which showed some correlation with histopathological and clinical characteristics such as cancer cell differentiation, peri-tumoural inflammation and stimulation of infiltrating T-cells. The observed relationship between the surface antigen expression profiles of patients’ CRC cells and their corresponding tumour infiltrating T-cells suggests that CRC surface proteins may play a direct role in influencing the activity (and hence surface protein expression) of neighbouring T-cells and/or vice versa. We conclude that the application of surface profiling may provide improved patient stratification, allowing more reliable prediction of disease progression and patient outcome