Topics on statistical design and analysis of cDNA microarray experiment

A microarray is a powerful tool for surveying the expression levels of many thousands of genes simultaneously. It belongs to the new genomics technologies which have important applications in the biological, agricultural and pharmaceutical sciences. In this thesis, we focus on the dual channel cDNA microarray which is one of the most popular microarray technologies and discuss three different topics: optimal experimental design; estimating the true proportion of true nulls, local false discovery rate (lFDR) and positive false discovery rate (pFDR) and dye effect normalization. The first topic consists of four subtopics each of which is about an independent and practical problem of cDNA microarray experimental design. In the first subtopic, we propose an optimization strategy which is based on the simulated annealing method to find optimal or near-optimal designs with both biological and technical replicates. In the second subtopic, we discuss how to apply Q-criterion for the factorial design of microarray experiments. In the third subtopic, we suggest an optimal way of pooling samples, which is actually a replication scheme to minimize the variance of the experiment under the constraint of fixing the total cost at a certain level. In the fourth subtopic, we indicate that the criterion for distant pair design is not proper and propose an alternative criterion instead. The second topic of this thesis is dye effect normalization. For cDNA microarray technology, each array compares two samples which are usually labelled with different dyes Cy3 and Cy5. It assumes that: for a given gene (spot) on the array, if Cy3-labelled sample has k times as much of a transcript as the Cy5-labelled sample, then the Cy3 signal should be k times as high as the Cy5 signal, and vice versa. This important assumption requires that the dyes should have the same properties. However, the reality is that the Cy3 and Cy5 dyes have slightly different properties and the relative efficiency of the dyes vary across the intensity range in a "banana-shape" way. In order to remove the dye effect, we propose a novel dye effect normalization method which is based on modeling dye response functions and dye effect curve. Real and simulated microarray data sets are used to evaluate the method. It shows that the performance of the proposed method is satisfactory. The focus of the third topic is the estimation of the proportion of true null hypotheses, lFDR and pFDR. In a typical microarray experiment, a large number of gene expression data could be measured. In order to find differential expressed genes, these variables are usually screened by a statistical test simultaneously. Since it is a case of multiple hypothesis testing, some kind of adjustment should be made to the p-values resulted from the statistical test. Lots of multiple testing error rates, such as FDR, lFDR and pFDR have been proposed to address this issue. A key related problem is the estimation of the proportion of true null hypotheses (i.e. non-expressed genes). To model the distribution of the p-values, we propose three kinds of finite mixture of unknown number of components (the first component corresponds to differentially expressed genes and the rest components correspond to non-differentially expressed ones). We apply a new MCMC method called allocation sampler to estimate the proportion of true null (i.e. the mixture weight of the first component). The method also provides a framework for estimating lFDR and pFDR. Two real microarray data studies plus a small simulation study are used to assess our method. We show that the performance of the proposed method is satisfactory

Reverse Engineering of Gene Regulatory Networks for Discovery of Novel Interactions in Pathways Using Gene Expression Data

A variety of chemicals in the environment have the potential to adversely affect the biological systems. We examined the responses of Rat (Rattus norvegicus) to the RDX exposure and female fathead minnows (FHM, Pimephales promelas) to a model aromatase inhibitor, fadrozole, using a transcriptional network inference approach. Rats were exposed to RDX and fish were exposed to 0 or 30mg/L fadrozole for 8 days. We analyzed gene expression changes using 8000 probes microarrays for rat experiment and 15,000 probe microarrays for fish. We used these changes to infer a transcriptional network. The central nervous system is remarkably plastic in its ability to recover from trauma. We examined recovery from chemicals in rats and fish through changes in transcriptional networks. Transcriptional networks from time series experiments provide a good basis for organizing and studying the dynamic behavior of biological processes. The goal of this work was to identify networks affected by chemical exposure and track changes in these networks as animals recover. The top 1254 significantly changed genes based upon 1.5-fold change and P\u3c 0.05 across all the time points from the fish data and 937 significantly changed genes from rat data were chosen for network modeling using either a Mutual Information network (MIN) or a Graphical Gaussian Model (GGM) or a Dynamic Bayesian Network (DBN) approach. The top interacting genes were queried to find sub-networks, possible biological networks, biochemical pathways, and network topologies impacted after exposure to fadrozole. The methods were able to reconstruct transcriptional networks with few hub structures, some of which were found to be involved in major biological process and molecular function. The resulting network from rat experiment exhibited a clear hub (central in terms of connections and direction) connectivity structure. Genes such as Ania-7, Hnrpdl, Alad, Gapdh, etc. (all CNS related), GAT-2, Gabra6, Gabbrl, Gabbr2 (GABA, neurotransmitter transporters and receptors), SLC2A1 (glucose transporter), NCX3 (Na-Ca exchanger), Gnal (Olfactory related), skn-la were showed up in our network as the \u27hub\u27 genes while some of the known transcription factors Msx3, Cacngl, Brs3, NGF1 etc. were also matched with our network model. Aromatase in the fish experiment was a highly connected gene in a sub-network along with other genes involved in steroidogenesis. Many of the sub-networks were involved in fatty acid metabolism, gamma-hexachlorocyclohexane degradation, and phospholipase activating pathways. Aromatase was a highly connected gene in a sub-network along with the genes LDLR, StAR, KRT18, HER1, CEBPB, ESR2A, and ACVRL1. Many of the subnetworks were involved in fatty acid metabolism, gamma-hexachlorocyclohexane degradation, and phospholipase activating pathways. A credible transcriptional network was recovered from both the time series data and the static data. The network included transcription factors and genes with roles in brain function, neurotransmission and sex hormone synthesis. Examination of the dynamic changes in expression within this network over time provided insight into recovery from traumas and chemical exposures

Intra- and Inter-Individual Variance of Gene Expression in Clinical Studies

BACKGROUND: Variance in microarray studies has been widely discussed as a critical topic on the identification of differentially expressed genes; however, few studies have addressed the influence of estimating variance. METHODOLOGY/PRINCIPAL FINDINGS: To break intra- and inter-individual variance in clinical studies down to three levels--technical, anatomic, and individual--we designed experiments and algorithms to investigate three forms of variances. As a case study, a group of "inter-individual variable genes" were identified to exemplify the influence of underestimated variance on the statistical and biological aspects in identification of differentially expressed genes. Our results showed that inadequate estimation of variance inevitably led to the inclusion of non-statistically significant genes into those listed as significant, thereby interfering with the correct prediction of biological functions. Applying a higher cutoff value of fold changes in the selection of significant genes reduces/eliminates the effects of underestimated variance. CONCLUSIONS/SIGNIFICANCE: Our data demonstrated that correct variance evaluation is critical in selecting significant genes. If the degree of variance is underestimated, "noisy" genes are falsely identified as differentially expressed genes. These genes are the noise associated with biological interpretation, reducing the biological significance of the gene set. Our results also indicate that applying a higher number of fold change as the selection criteria reduces/eliminates the differences between distinct estimations of variance

Transcriptomic analysis of insecticide resistance in the lymphatic filariasis vector Culex quinquefasciatus

Culex quinquefasciatus plays an important role in transmission of vector-borne diseases of public health importance, including lymphatic filariasis (LF), as well as many arboviral diseases. Currently, efforts to tackle C. quinquefasciatus vectored diseases are based on either mass drug administration (MDA) for LF, or insecticide-based interventions. Widespread and intensive insecticide usage has resulted in increased resistance in mosquito vectors, including C. quinquefasciatus. Herein, the transcriptome profile of Ugandan bendiocarb-resistant C. quinquefasciatus was explored to identify candidate genes associated with insecticide resistance. High levels of insecticide resistance were observed for five out of six insecticides tested, with the lowest mortality (0.97%) reported to permethrin, while for DDT, lambdacyhalothrin, bendiocarb and deltamethrin the mortality rate ranged from 1.63–3.29%. Resistance to bendiocarb in exposed mosquitoes was marked, with 2.04% mortality following 1 h exposure and 58.02% after 4 h. Genotyping of the G119S Ace-1 target site mutation detected a highly significant association (p 8-fold increase vs unexposed controls). These results provide evidence that bendiocarb resistance in Ugandan C. quinquefasciatus is mediated by both target-site mechanisms and over-expression of detoxification enzymes

Expression of pyrethroid metabolizing P450 enzymes characterizes highly resistant Anopheles vector species targeted by successful deployment of PBO-treated bednets in Tanzania

Long lasting insecticidal nets (LLINs) are a proven tool to reduce malaria transmission, but in Africa efficacy is being reduced by pyrethroid resistance in the major vectors. A previous study that was conducted in Muleba district, Tanzania indicated possible involvement of cytochrome P450 monooxygenases in a pyrethroid resistance in An. gambiae population where pre-exposure to piperonyl butoxide (PBO) followed by permethrin exposure in CDC bottle bioassays led to partial restoration of susceptibility. PBO is a synergist that can block pyrethroid-metabolizing enzymes in a mosquito. Insecticide resistance profiles and underlying mechanisms were investigated in Anopheles gambiae and An. funestus from Muleba during a cluster randomized trial. Diagnostic dose bioassays using permethrin, together with intensity assays, suggest pyrethroid resistance that is both strong and very common, but not extreme. Transcriptomic analysis found multiple P450 genes over expressed including CYP6M2, CYP6Z3, CYP6P3, CYP6P4, CYP6AA1 and CYP9K1 in An. gambiae and CYP6N1, CYP6M7, CYP6M1 and CYP6Z1 in An. funestus. Indeed, very similar suites of P450 enzymes commonly associated with resistant populations elsewhere in Africa were detected as over expressed suggesting a convergence of mechanisms across Sub-Saharan African malaria vectors. The findings give insight into factors that may correlate with pyrethroid PBO LLIN success, broadly supporting model predictions, but revision to guidelines previously issued by the World Health Organization is warranted

Decoding heterogeneous big data in an integrative way

Biotechnologies in post-genomic era, especially those that generate data in high-throughput, bring opportunities and challenges that are never faced before. And one of them is how to decode big heterogeneous data for clues that are useful for biological questions. With the exponential growth of a variety of data, comes with more and more applications of systematic approaches that investigate biological questions in an integrative way. Systematic approaches inherently require integration of heterogeneous information, which is urgently calling for a lot more efforts. In this thesis, the effort is mainly devoted to the development of methods and tools that help to integrate big heterogeneous information. In Chapter 2, we employed a heuristic strategy to summarize/integrate genes that are essential for the determination of mouse retinal cells in the format of network. These networks with experimental evidence could be rediscovered in the analysis of high-throughput data set and thus would be useful in the leverage of high-throughput data. In Chapter 3, we described EnRICH, a tool that we developed to help qualitatively integrate heterogeneous intro-organism information. We also introduced how EnRICH could be applied to the construction of a composite network from different sources, and demonstrated how we used EnRICH to successfully prioritize retinal disease genes. Following the work of Chapter 3 (intro-organism information integration), in Chapter 4 we stepped to the development of method and tool that can help deal with inter-organism information integration. The method we proposed is able to match genes in a one-to-one fashion between any two genomes. In summary, this thesis contributes to integrative analysis of big heterogeneous data by its work on the integration of intro- and inter-organism information

Efficiency and Robustness Issues in Complex Statistical Designs for Two-Color Microarray Experiments

Identifikation unterschiedlich exprimierter Gene ist eines der wichtigsten Ziele eines Microarray-Experimentes. Die Verwendung eines effizienten Designs in einem Microarray-Experiment kann die Power des statistischen Verfahrens erhöhen. Neben der Effizienz ist auch die Robustheit eine wichtige Frage bei der Auswahl guter Microarray-Designs vor allem aufgrund der vielen fehlenden Werte, die bei Microarray-Exprimenten auftreten. In dieser Dissertation wird das $E$-Optimalitaetskriterium als Effizienz-Ma\ss genutzt und drei weitere Kriterien werden vorgestellt, anhand derer die Robustheit eines Microarray-Designs quantifiziert werden soll.Fuer eine gegebene Anzahl von vorhandenen Arrays und Behandlungsmodalitaeten koennen verschiedene Microarray-Designs betrachtet werden. Die Zahl moeglicher Designs kann sehr gross sein. Deshalb ist eine vollstaendige Analyse der Effizienz und Robustheit rechentechnisch undurchführbar. Aus diesem Grund wird eine Methode vorgeschlagen, die auf einem genetischen Algorithmus basiert. Damit werden gute Microarray-Designs fuer eine gegebene Anzahl von Fragen ausgewaehlt. Diese Methode kann zur Auswahl guter Designs sowohl für das ein- als auch zwei-faktorielle Experiment verwendet werden. Zudem wird die Anwendung beider Kriterien, des Effizienz- und des Robustheitskriteriums, bei der Design-Auswahl demonstriert. Effiziente und robuste Designs werden fuer ein faktorielles Experiment mit verschiedenen Array Anzahlen beispielhaft durchgerechnet

Characterisation of insecticide resistance in Anopheles gambiae from Burkina Faso and its impact on current malaria control strategies

Malaria is the primary cause of death in Burkina Faso and affects mostly pregnant women and children under five years old. The control of Anopheles mosquitoes responsible for the transmission of the disease by universal coverage with long lasting insecticide treated bednets (LLINS) is one of the keys strategies adopted by many malaria endemic countries. Pyrethroids remain the only insecticide class approved to be used on nets. The proportion of mosquitoes resistant to this insecticide class has steadily increased since its first report in the rice fields of Vallée du Kou in 1999 and it is now rare to find any mosquitoes from this region of Burkina Faso surviving standard WHO susceptibility diagnostic dose assays. However, without data on the magnitude of resistance it is difficult to predict the impact that this may have on malaria vector control. This study assessed the strength of the pyrethroid resistance and the underlying mechanisms, before determining whether LLINs adequately kill wild resistant mosquitoes using both newly acquired LLINs and nets previously used by householders. Mosquitoes collected in the rice field of Vallée du Kou from 2011 to 2013 were tested against the four insecticides classes approved in public health. The time taken to obtain 50 % mortality (LT50) using 0.05% WHO deltamethrin papers was determined in 2011 and 2012 while CDC bottle bioassays were used to establish the concentration of deltamethrin needed to achieve 50 % morality (LC50) in 2013. The data confirmed the high prevalence of resistance to DDT and pyrethroid in Anopheles gambiae s.l from Vallée du Kou. The LT50 increased from 98 mins to 1315 mins in just one year, representing an increase > 10 fold while the LC50 recorded in 2013 exceed 1000 fold compared to laboratory susceptible Kisumu strain Anopheles gambiae s.l populations from the bioassays were screened for target site mutations in the sodium channel. The dramatic increase in the strength of resistance was not accompanied by an increase in genes frequency, as the frequencies of the 1014F and 1575Y mutations remained stable at approximately 0.8 and 0.3 respectively in the three years. Microarray was used to identify alternative candidate genes associated with the increasing level of resistance to pyrethroids. Quantitative PCR was used to demonstrate that a subset of these candidate genes, including P450s, GSTs, CCEs, aquaporin and chymotrypsin increased significantly between 2011 and 2013 and may be contributing to the increase in resistance observed recently in this study site The bio-efficacy of used and new LLINs of six different brands, distributed as part of the National Control Malaria Program (NMCP) of Burkina Faso was assessed using cone bioassays. Whilst some of the nets gave acceptable levels of mortality against the laboratory susceptible Kisumu strain, none were able to kill more than 45 % of the resistant wild mosquitoes from Vallée du Kou. These results demonstrate that the high levels of resistance in An. gambiae s.l in Burkina Faso is having a real impact on the ability of LLINs to kill mosquitoes which will have serious implications for malaria control in this region

Evolutionary genetics of insecticide resistance in Culex quinquefasciatus

Culex quinquefasciatus mosquitoes play an important role in the transmission of vector-borne diseases of public health importance including lymphatic filariasis (LF) as well as many arboviruses. Insecticide-based approaches are one of the most important interventions to mitigate disease burden; nevertheless increased resistance of vectors to insecticides imposes a challenge for the sustainability and effectiveness of both current and future vector control interventions. Hence, understanding the dynamics and likely mechanisms underlying the evolution of resistance will be critical to effective decision-making in insecticide resistance management strategies. The present study was set out to investigate the genetic basis of insecticide resistance in C. quinquefasciatus from Uganda. Two objectives were developed, 1) to investigate patterns of insecticide resistance across the south of the country and how this might reflect local selection and genetic structure and 2) to investigate the basis of the molecular mechanisms underlying resistance to all four classes of insecticides recommended for vector control. The population genetic study compared and contrasted microsatellite markers and two resistance-associated loci (Vgsc-1014F and Ace1-119S). While no significant difference in genetic diversity across populations were detected by microsatellites, higher frequency of Vgsc-1014F compared to the Ace1-119S mutations was observed in all populations suggesting that the Ugandan Eastern – Southwest populations are under a heterogeneous selection pressure, which created a pattern of local adaptation in these populations. Additionally, the copy number (CN) assay developed in this study indicated the presence of CN variation in the voltage gated sodium channel (Vgsc) gene in about 10% of the individuals assayed from these populations. Genotypic/phenotypic association tests conducted on bendiocarb resistant-individuals suggested that this resistant phenotype was not underlying solely by the 119S target-site mutation in the Ace-1 gene. Indeed, synergist bioassays show an increase of mortality of around 25% in mosquitoes pre-exposed to either TTP or PBO, indicating a possible resistance mediated by detoxification enzymes. Using a novel whole-transcriptome microarray we profiled the bendiocarb-resistant phenotype and implicated two P450s (Cyp-Cx1 and Cyp6n23) with the highest up-regulation expression compared to a susceptible strain. Remarkably, the predicted Cyp-Cx1 is closely related to two P450s from the family Cyp6, which were already validated in vitro as insecticide metabolizers in A. gambiae and A. aegypti, which corroborates a likely association of metabolic resistance in the investigated bendiocarb-resistant phenotype. Taken together the results yielded by genomic and transcriptomic experiments provide evidence that Ugandan C. quinquefasciatus mosquitoes are under heterogeneous selection pressure imposed by insecticides from distinct classes, and that the evolution of insecticide resistance is mediated by at least two main genetic mechanisms; target-site mutations (Vgsc-1014F and Ace1-119S) as well as over-expression of detoxification enzymes