Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^

Development of Gene Expression Markers of Acute Heat-Light Stress in Reef-Building Corals of the Genus Porites

Coral reefs are declining worldwide due to increased incidence of climate-induced coral bleaching, which will have widespread biodiversity and economic impacts. A simple method to measure the sub-bleaching level of heat-light stress experienced by corals would greatly inform reef management practices by making it possible to assess the distribution of bleaching risks among individual reef sites. Gene expression analysis based on quantitative PCR (qPCR) can be used as a diagnostic tool to determine coral condition in situ. We evaluated the expression of 13 candidate genes during heat-light stress in a common Caribbean coral Porites astreoides, and observed strong and consistent changes in gene expression in two independent experiments. Furthermore, we found that the apparent return to baseline expression levels during a recovery phase was rapid, despite visible signs of colony bleaching. We show that the response to acute heat-light stress in P. astreoides can be monitored by measuring the difference in expression of only two genes: Hsp16 and actin. We demonstrate that this assay discriminates between corals sampled from two field sites experiencing different temperatures. We also show that the assay is applicable to an Indo-Pacific congener, P. lobata, and therefore could potentially be used to diagnose acute heat-light stress on coral reefs worldwide.

Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Molecular Determinants Critical to the Development of Glioblastoma

In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma. Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I–IV), and ‘key genes’ within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. All of these genes were up-regulated, except MPP2 (down regulated). These 10 genes were able to predict tumor status with 96–100% confidence when using logistic regression, cross validation, and the support vector machine analysis. Markov genes interact with NFkβ, ERK, MAPK, VEGF, growth hormone and collagen to produce a network whose top biological functions are cancer, neurological disease, and cellular movement. Three of the 10 genes - EGFR, COL4A1, and CDK4, in particular, seemed to be potential ‘hubs of activity’. Modified expression of these 10 Markov Blanket genes increases lifetime risk of developing glioblastoma compared to the normal population. The glioblastoma risk estimates were dramatically increased with joint effects of 4 or more than 4 Markov Blanket genes. Joint interaction effects of 4, 5, 6, 7, 8, 9 or 10 Markov Blanket genes produced 9, 13, 20.9, 26.7, 52.8, 53.2, 78.1 or 85.9%, respectively, increase in lifetime risk of developing glioblastoma compared to normal population. In summary, it appears that modified expression of several ‘key genes’ may be required for the development of glioblastoma. Further studies are needed to validate these ‘key genes’ as useful tools for early detection and novel therapeutic options for these tumors.

Elemental analysis of biological matrices by laser ablation high resolution inductively coupled plasma mass spectrometry (LA-HR-ICP-MS) and high resolution inductively coupled plasma mass spectrometry (HR -ICP -MS)

The need for elemental analysis of biological matrices such as bone, teeth, and plant matter for sourcing purposes has emerged within the forensic and geochemical laboratories. Trace elemental analyses for the comparison of materials such as glass by inductively coupled plasma mass spectrometry (ICP-MS) and laser ablation ICP-MS has been shown to offer a high degree of discrimination between different manufacturing sources. Unit resolution ICP-MS instruments may suffer from some polyatomic interferences including 40Ar16O+, 40Ar 16O1H+, and 40Ca 16O+ that affect iron measurement at trace levels. Iron is an important element in the analysis of glass and also of interest for the analysis of several biological matrices. A comparison of the analytical performance of two different ICP-MS systems for iron analysis in glass for determining the method detection limits (MDLs), accuracy, and precision of the measurement is presented. Acid digestion and laser ablation methods are also compared. Iron polyatomic interferences were reduced or resolved by using dynamic reaction cell and high resolution ICP-MS. MDLs as low as 0.03 μg g-1 and 0.14 μg g-1 for laser ablation and solution based analyses respectively were achieved. The use of helium as a carrier gas demonstrated improvement in the detection limits of both iron isotopes (56Fe and 57Fe) in medium resolution for the HR-ICP-MS and with a dynamic reaction cell (DRC) coupled to a quadrupole ICP-MS system. ^ The development and application of robust analytical methods for the quantification of trace elements in biological matrices has lead to a better understanding of the potential utility of these measurements in forensic chemical analyses. Standard reference materials (SRMs) were used in the development of an analytical method using HR-ICP-MS and LA-HR-ICP-MS that was subsequently applied on the analysis of real samples. Bone, teeth and ashed marijuana samples were analyzed with the developed method. ^ Elemental analysis of bone samples from 12 different individuals provided discrimination between individuals, when femur and humerus bones were considered separately. Discrimination of 14 teeth samples based on elemental composition was achieved with the exception of one case where samples from the same individual were not associated with each other. The discrimination of 49 different ashed plant (cannabis) samples was achieved using the developed method. ^

Effects of Endothelin 3 on the Tumor-Angiogenic Response in a Novel Melanoma Mouse Model

Skin cancer is the most common form of cancer in the United States. Melanoma is a particular type of skin cancer, which arises from the malignant transformation of melanocytes and generally exhibits a high propensity to metastasize. Melanoma progression is dependent on angiogenesis to deliver the oxygen and nutrients required to maintain the altered metabolism of rapidly proliferating tumorigenic cells. Recent studies have implicated the growth factor Endothelin 3 (Edn3) in melanoma progression and metastasis. The aim of this study was to examine the role that Edn3 plays in the angiogenesis of melanocytic lesions. For this purpose, Dct-Grm1 transgenic mice, which spontaneously acquire melanocytic lesions through the aberrant expression of the metabotropic glutamate receptor 1 (mGluR1), were crossed with K5-Edn3 transgenic mice that overexpress Edn3. Tumors in the Dct-Grm1/K5-Edn3 experimental population were examined and compared to the control Dct-Grm1 population using immuno-fluorescent staining targeted against the vascular endothelial cell marker CD31. Proteomic arrays were also used and identified changes in the expression of specific angiogenic factors. CD31 antibody staining results revealed an increased vascular density in Dct-Grm1/K5-Edn3 tumors compared with tumors from the Dct-Grm1 controls. Analysis of the relative expression of angiogenic proteins showed an upregulation of various vascular factors in tumors from the Dct-Grm1/K5-Edn3 population, including VEGF-B, MMP-8, MMP-9, and Angiogenin. These results suggest that endothelin signaling promotes angiogenesis in melanocytic lesions. Targeting the factors upregulated by Edn3 signaling may prove effective in hindering melanoma progression.

Analysis of organomercurials in environmental and biological samples by capillary column gas chromatography with atomic fluorescence detection

The general method for determining organomercurials in environmental and biological samples is gas chromatography with electron capture detection (GC-ECD). However, tedious sample work up protocols and poor chromatographic response show the need for the development of new methods. Here, Atomic Fluorescence-based methods are described, free from these deficiencies. The organomercurials in soil, sediment and tissue samples are first released from the matrices with acidic KBr and cupric ions and extracted into dichloromethane. The initial extracts are subjected to thiosulfate clean up and the organomercury species are isolated as their chloride derivatives by cupric chloride and subsequent extraction into a small volume of dichloromethane. In water samples the organomercurials are pre-concentrated using a sulfhydryl cotton fiber adsorbent, followed by elution with acidic KBr and CuSO 4 and extraction into dichloromethane. Analysis of the organomercurials is accomplished by capillary column chromatography with atomic fluorescence detection.

A Case-Only Genome-wide Association Study of Gender- and Age-specific Risk Markers for Childhood Leukemia

Males and age group 1 to 5 years show a much higher risk for childhood acute lymphoblastic leukemia (ALL). We performed a case-only genome-wide association study (GWAS), using the Illumina Infinium HumanCoreExome Chip, to unmask gender- and age-specific risk variants in 240 non-Hispanic white children with ALL recruited at Texas Children’s Cancer Center, Houston, Texas. Besides statistically most significant results, we also considered results that yielded the highest effect sizes. Existing experimental data and bioinformatic predictions were used to complement results, and to examine the biological significance of statistical results. Our study identified novel risk variants for childhood ALL. The SNP, rs4813720 (RASSF2), showed the statistically most significant gender-specific associations (P < 2 x 10-6). Likewise, rs10505918 (SOX5) yielded the lowest P value (P < 1 x 10-5) for age-specific associations, and also showed the statistically most significant association with age-at-onset (P < 1 x 10-4). Two SNPs, rs12722042 and 12722039, from the HLA-DQA1 region yielded the highest effect sizes (odds ratio (OR) = 15.7; P = 0.002) for gender-specific results, and the SNP, rs17109582 (OR = 12.5; P = 0.006), showed the highest effect size for age-specific results. Sex chromosome variants did not appear to be involved in gender-specific associations. The HLA-DQA1 SNPs belong to DQA1*01:07and confirmed previously reported male-specific association with DQA1*01:07. Twenty one of the SNPs identified as risk markers for gender- or age-specific associations were located in the transcription factor binding sites and 56 SNPs were non-synonymous variants, likely to alter protein function. Although bioinformatic analysis did not implicate a particular mechanism for gender- and age-specific associations, RASSF2 has an estrogen receptor-alpha binding site in its promoter. The unknown mechanisms may be due to lack of interest in gender- and age-specificity in associations. These results provide a foundation for further studies to examine the gender- and age-differential in childhood ALL risk. Following replication and mechanistic studies, risk factors for one gender or age group may have a potential to be used as biomarkers for targeted intervention for prevention and maybe also for treatment.

Elemental Analysis of Biological Matrices by Laser Ablation High Resolution Inductively Coupled Plasma Mass Spectrometry (LA-HR-ICP-MS) and High Resolution Inductively Coupled Plasma Mass Spectrometry (HR-ICP-MS)

The need for elemental analysis of biological matrices such as bone, teeth, and plant matter for sourcing purposes has emerged within the forensic and geochemical laboratories. Trace elemental analyses for the comparison of aterials such as glass by inductively coupled plasma mass spectrometry (ICP-MS) and laser ablation ICP-MS has been shown to offer a high degree of discrimination between different manufacturing sources. Unit resolution ICP-MS instruments may suffer from some polyatomic interferences including 40Ar16O+, 40Ar16O1H+, and 40Ca16O+ that affect iron measurement at trace levels. Iron is an important element in the analysis of glass and also of interest for the analysis of several biological matrices. A comparison of the nalytical performance of two different ICP-MS systems for iron analysis in glass for determining the method detection limits (MDLs), accuracy, and precision of the measurement is presented. Acid digestion and laser ablation methods are also compared. Iron polyatomic interferences were reduced or resolved by using dynamic reaction cell and high resolution ICP-MS. MDLs as low as 0.03 ìg g-1 and 0.14 ìg g-1 for laser ablation and solution based analyses respectively were achieved. The use of helium as a carrier gas demonstrated improvement in the detection limits of both iron isotopes (56Fe and 57Fe) in medium resolution for the HR-ICP-MS and with a dynamic reaction cell (DRC) coupled to a quadrupole ICP-MS system. The development and application of robust analytical methods for the quantification of trace elements in biological matrices has lead to a better understanding of the potential utility of these measurements in forensic chemical analyses. Standard reference materials (SRMs) were used in the development of an analytical method using HR-ICP-MS and LA-HR-ICP-MS that was subsequently applied on the analysis of real samples. Bone, teeth and ashed marijuana samples were analyzed with the developed method. Elemental analysis of bone samples from 12 different individuals provided discrimination between individuals, when femur and humerus bones were considered separately. Discrimination of 14 teeth samples based on elemental composition was achieved with the exception of one case where samples from the same individual were not associated with each other. The discrimination of 49 different ashed plant (cannabis)samples was achieved using the developed method.

A case-only genome-wide association study of gender- and age-specific risk markers for childhood leukemia

Males and age group 1 to 5 years show a much higher risk for childhood acute lymphoblastic leukemia (ALL). We performed a case-only genome-wide association study (GWAS), using the Illumina Infinium HumanCoreExome Chip, to unmask gender- and age-specific risk variants in 240 non-Hispanic white children with ALL recruited at Texas Children’s Cancer Center, Houston, Texas. Besides statistically most significant results, we also considered results that yielded the highest effect sizes. Existing experimental data and bioinformatic predictions were used to complement results, and to examine the biological significance of statistical results. ^ Our study identified novel risk variants for childhood ALL. The SNP, rs4813720 (RASSF2), showed the statistically most significant gender-specific associations (P < 2 x 10-6). Likewise, rs10505918 (SOX5) yielded the lowest P value (P < 1 x 10-5 ) for age-specific associations, and also showed the statistically most significant association with age-at-onset (P < 1 x 10-4). Two SNPs, rs12722042 and 12722039, from the HLA-DQA1 region yielded the highest effect sizes (odds ratio (OR) = 15.7; P = 0.002) for gender-specific results, and the SNP, rs17109582 (OR = 12.5; P = 0.006), showed the highest effect size for age-specific results. Sex chromosome variants did not appear to be involved in gender-specific associations. ^ The HLA-DQA1 SNPs belong to DQA1*01:07and confirmed previously reported male-specific association with DQA1*01:07. Twenty one of the SNPs identified as risk markers for gender- or age-specific associations were located in the transcription factor binding sites and 56 SNPs were non-synonymous variants, likely to alter protein function. Although bioinformatic analysis did not implicate a particular mechanism for gender- and age-specific associations, RASSF2 has an estrogen receptor-alpha binding site in its promoter. The unknown mechanisms may be due to lack of interest in gender- and age-specificity in associations. These results provide a foundation for further studies to examine the gender- and age-differential in childhood ALL risk. Following replication and mechanistic studies, risk factors for one gender or age group may have a potential to be used as biomarkers for targeted intervention for prevention and maybe also for treatment.^