Detection of mRNA levels for the estrogen alpha, estrogen beta and androgen nuclear receptor genes in archival breast cancer tissue

Previous studies in our laboratory have shown association of nuclear receptor expression and histological breast cancer grade. To further investigate these findings, it was the objective of this study to determine if expression levels of the estrogen alpha, estrogen beta and androgen nuclear receptor genes varied in different breast cancer grades. RNA extracted from paraffin embedded archival breast tumour tissue was converted into cDNA and cDNA underwent PCR to enable quantitation of mRNA expression. Expression data was normalised against the 18S ribosomal gene multiplex and analysed using ANOVA. Analysis indicated a significant alteration of expression for the androgen receptor in different cancer grades (P=0.014), as well as in tissues that no longer possess estrogen receptor alpha proteins (P=0.025). However, expression of estrogen receptors alpha and beta did not vary significantly with cancer grade (P=0.057 and 0.622, respectively). Also, the expression of estrogen receptor alpha or beta did not change, regardless of the presence of estrogen receptor alpha protein in the tissue (P=0.794 and 0.716, respectively). Post-hoc tests indicate that the expression of the androgen receptor is increased in estrogen receptor negative tissue as well as in grade 2 and grade 3 tumours, compared to control tissue. This increased expression in late stage breast tumours may have implications to the treatment of breast tumours, particularly those lacking expression of other nuclear receptor genes.

A novel saliva-based microRNA biomarker panel to detect head and neck cancers

Background MicroRNAs (miRNAs) are known to play an important role in cancer development by post-transcriptionally affecting the expression of critical genes. The aims of this study were two-fold: (i) to develop a robust method to isolate miRNAs from small volumes of saliva and (ii) to develop a panel of saliva-based diagnostic biomarkers for the detection of head and neck squamous cell carcinoma (HNSCC). Methods Five differentially expressed miRNAs were selected from miScript™ miRNA microarray data generated using saliva from five HNSCC patients and five healthy controls. Their differential expression was subsequently confirmed by RT-qPCR using saliva samples from healthy controls (n = 56) and HNSCC patients (n = 56). These samples were divided into two different cohorts, i.e., a first confirmatory cohort (n = 21) and a second independent validation cohort (n = 35), to narrow down the miRNA diagnostic panel to three miRNAs: miR-9, miR-134 and miR-191. This diagnostic panel was independently validated using HNSCC miRNA expression data from The Cancer Genome Atlas (TCGA), encompassing 334 tumours and 39 adjacent normal tissues. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic capacity of the panel. Results On average 60 ng/μL miRNA was isolated from 200 μL of saliva. Overall a good correlation was observed between the microarray data and the RT-qPCR data. We found that miR-9 (P <0.0001), miR-134 (P <0.0001) and miR-191 (P <0.001) were differentially expressed between saliva from HNSCC patients and healthy controls, and that these miRNAs provided a good discriminative capacity with area under the curve (AUC) values of 0.85 (P <0.0001), 0.74 (P < 0.001) and 0.98 (P < 0.0001), respectively. In addition, we found that the salivary miRNA data showed a good correlation with the TCGA miRNA data, thereby providing an independent validation. Conclusions We show that we have developed a reliable method to isolate miRNAs from small volumes of saliva, and that the saliva-derived miRNAs miR-9, miR-134 and miR-191 may serve as novel biomarkers to reliably detect HNSCC. © 2014 International Society for Cellular Oncology.

Identification of genetic variants contributing to the migraine phenotype in different Australian populations

This project aimed to identify novel genetic risk variants associated with migraine in the Norfolk Island population. Statistical analysis and bioinformatics approaches such as polygenic modeling and gene clustering methods were carried out to explore genotypic and expression data from high-throughput techniques. This project had a particular focus on hormonal genes and other genetic variants and identified a modest effect size on the migraine phenotype.

targetTB: A target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis

Background: Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation. Results: We report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed. Conclusion: The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

Rice molecular breeding laboratories in the genomics era: Current status and future considerations

Using DNA markers in plant breeding with marker-assisted selection (MAS) could greatly improve the precision and efficiency of selection, leading to the accelerated development of new crop varieties. The numerous examples of MAS in rice have prompted many breeding institutes to establish molecular breeding labs. The last decade has produced an enormous amount of genomics research in rice, including the identification of thousands of QTLs for agronomically important traits, the generation of large amounts of gene expression data, and cloning and characterization of new genes, including the detection of single nucleotide polymorphisms. The pinnacle of genomics research has been the completion and annotation of genome sequences for indica and japonica rice. This information-coupled with the development of new genotyping methodologies and platforms, and the development of bioinformatics databases and software tools-provides even more exciting opportunities for rice molecular breeding in the 21st century. However, the great challenge for molecular breeders is to apply genomics data in actual breeding programs. Here, we review the current status of MAS in rice, current genomics projects and promising new genotyping methodologies, and evaluate the probable impact of genomics research. We also identify critical research areas to "bridge the application gap" between QTL identification and applied breeding that need to be addressed to realize the full potential of MAS, and propose ideas and guidelines for establishing rice molecular breeding labs in the postgenome sequence era to integrate molecular breeding within the context of overall rice breeding and research programs.

Cholesterol Disturbances in Inherited Neurodegenerative Diseases : Studies on Npc1, Ppt1 and Ctsd deficient mice

The central nervous system (CNS) is the most cholesterol-rich organ in the body. Cholesterol is essential to CNS functions such as synaptogenesis and formation of myelin. Significant differences exist in cholesterol metabolism between the CNS and the peripheral organs. However, the regulation of cholesterol metabolism in the CNS is poorly understood compared to our knowledge of the regulation of cholesterol homeostasis in organs reached by cholesterol-carrying lipoprotein particles in the circulation. Defects in CNS cholesterol homeostasis have been linked to a variety of neurodegenerative diseases, including common diseases with complex pathogenetic mechanisms such as Alzheimer s disease. In spite of intense effort, the mechanisms which link disturbed cholesterol homeostasis to these diseases remain elusive. We used three inherited recessive neurodegenerative disorders as models in the studies included in this thesis: Niemann-Pick type C (NPC), infantile neuronal ceroid lipofuscinosis and cathepsin D deficiency. Of these three, NPC has previously been linked to disturbed intracellular cholesterol metabolism. Elucidating the mechanisms with which disturbances of cholesterol homeostasis link to neurodegeneration in recessive inherited disorders with known genetic lesions should shed light on how cholesterol is handled in the healthy CNS and help to understand how these and more complex diseases develop. In the first study we analyzed the synthesis of sterols and the assembly and secretion of lipoprotein particles in Npc1 deficient primary astrocytes. We found that both wild type and Npc1 deficient astrocytes retain significant amounts of desmosterol and other cholesterol precursor sterols as membrane constituents. No difference was observed in the synthesis of sterols and the secretion of newly synthesized sterols between Npc1 wild type, heterozygote or knockout astrocytes. We found that the incorporation of newly synthesized sterols into secreted lipoprotein particles was not inhibited by Npc1 mutation, and the lipoprotein particles were similar to those excreted by wild type astrocytes in shape and size. The bulk of cholesterol was found to be secreted independently of secreted NPC2. These observations demonstrate the ability of Npc1 deficient astrocytes to handle de novo sterols, and highlight the unique sterol composition in the developing brain. Infantile neuronal ceroid lipofuscinosis is caused by the deficiency of a functional Ppt1 enzyme in the cells. In the second study, global gene expression studies of approximately 14000 mouse genes showed significant changes in the expression of 135 genes in Ppt1 deficient neurons compared to wild type. Several genes encoding for enzymes of the mevalonate pathway of cholesterol biosynthesis showed increased expression. As predicted by the expression data, sterol biosynthesis was found to be upregulated in the knockout neurons. These data link Ppt1 deficiency to disturbed cholesterol metabolism in CNS neurons. In the third study we investigated the effect of cathepsin D deficiency on the structure of myelin and lipid homeostasis in the brain. Our proteomics data, immunohistochemistry and western blotting data showed altered levels of the myelin protein components myelin basic protein, proteolipid protein and 2 , 3 -cyclic nucleotide 3 phosphodiesterase in the brains of cathepsin D deficient mice. Electron microscopy revealed altered myelin structure in cathepsin D deficient brains. Additionally, plasmalogen-derived alkenyl chains and 20- and 24-carbon saturated and monounsaturated fatty acids typical for glycosphingolipids were found to be significantly reduced, but polyunsaturated species were significantly increased in the knockout brains, pointing to a decrease in white matter. The levels of ApoE and ABCA1 proteins linked to cholesterol efflux in the CNS were found to be altered in the brains of cathepsin D deficient mice, along with an accumulation of cholesteryl esters and a decrease in triglycerols. Together these data demonstrate altered myelin architecture in cathepsin D deficient mice and link cathepsin D deficiency to aberrant cholesterol metabolism and trafficking. Basic research into rare monogenic diseases sheds light on the underlying biological processes which are perturbed in these conditions and contributes to our understanding of the physiological function of healthy cells. Eventually, understanding gained from the study of disease models may contribute towards establishing treatment for these disorders and further our understanding of the pathogenesis of other, more complex and common diseases.

Microarrays in molecular profiling of cancer - focus on head and neck squamous cell carcinoma

Microarrays have a wide range of applications in the biomedical field. From the beginning, arrays have mostly been utilized in cancer research, including classification of tumors into different subgroups and identification of clinical associations. In the microarray format, a collection of small features, such as different oligonucleotides, is attached to a solid support. The advantage of microarray technology is the ability to simultaneously measure changes in the levels of multiple biomolecules. Because many diseases, including cancer, are complex, involving an interplay between various genes and environmental factors, the detection of only a single marker molecule is usually insufficient for determining disease status. Thus, a technique that simultaneously collects information on multiple molecules allows better insights into a complex disease. Since microarrays can be custom-manufactured or obtained from a number of commercial providers, understanding data quality and comparability between different platforms is important to enable the use of the technology to areas beyond basic research. When standardized, integrated array data could ultimately help to offer a complete profile of the disease, illuminating mechanisms and genes behind disorders as well as facilitating disease diagnostics. In the first part of this work, we aimed to elucidate the comparability of gene expression measurements from different oligonucleotide and cDNA microarray platforms. We compared three different gene expression microarrays; one was a commercial oligonucleotide microarray and the others commercial and custom-made cDNA microarrays. The filtered gene expression data from the commercial platforms correlated better across experiments (r=0.78-0.86) than the expression data between the custom-made and either of the two commercial platforms (r=0.62-0.76). Although the results from different platforms correlated reasonably well, combining and comparing the measurements were not straightforward. The clone errors on the custom-made array and annotation and technical differences between the platforms introduced variability in the data. In conclusion, the different gene expression microarray platforms provided results sufficiently concordant for the research setting, but the variability represents a challenge for developing diagnostic applications for the microarrays. In the second part of the work, we performed an integrated high-resolution microarray analysis of gene copy number and expression in 38 laryngeal and oral tongue squamous cell carcinoma cell lines and primary tumors. Our aim was to pinpoint genes for which expression was impacted by changes in copy number. The data revealed that especially amplifications had a clear impact on gene expression. Across the genome, 14-32% of genes in the highly amplified regions (copy number ratio >2.5) had associated overexpression. The impact of decreased copy number on gene underexpression was less clear. Using statistical analysis across the samples, we systematically identified hundreds of genes for which an increased copy number was associated with increased expression. For example, our data implied that FADD and PPFIA1 were frequently overexpressed at the 11q13 amplicon in HNSCC. The 11q13 amplicon, including known oncogenes such as CCND1 and CTTN, is well-characterized in different type of cancers, but the roles of FADD and PPFIA1 remain obscure. Taken together, the integrated microarray analysis revealed a number of known as well as novel target genes in altered regions in HNSCC. The identified genes provide a basis for functional validation and may eventually lead to the identification of novel candidates for targeted therapy in HNSCC.

The Gerbera cDNA Microarray: A Tool for Large-Scale Identification of Genes Involved in Flower Development

During the past ten years, large-scale transcript analysis using microarrays has become a powerful tool to identify and predict functions for new genes. It allows simultaneous monitoring of the expression of thousands of genes and has become a routinely used tool in laboratories worldwide. Microarray analysis will, together with other functional genomics tools, take us closer to understanding the functions of all genes in genomes of living organisms. Flower development is a genetically regulated process which has mostly been studied in the traditional model species Arabidopsis thaliana, Antirrhinum majus and Petunia hybrida. The molecular mechanisms behind flower development in them are partly applicable in other plant systems. However, not all biological phenomena can be approached with just a few model systems. In order to understand and apply the knowledge to ecologically and economically important plants, other species also need to be studied. Sequencing of 17 000 ESTs from nine different cDNA libraries of the ornamental plant Gerbera hybrida made it possible to construct a cDNA microarray with 9000 probes. The probes of the microarray represent all different ESTs in the database. From the gerbera ESTs 20% were unique to gerbera while 373 were specific to the Asteraceae family of flowering plants. Gerbera has composite inflorescences with three different types of flowers that vary from each other morphologically. The marginal ray flowers are large, often pigmented and female, while the central disc flowers are smaller and more radially symmetrical perfect flowers. Intermediate trans flowers are similar to ray flowers but smaller in size. This feature together with the molecular tools applied to gerbera, make gerbera a unique system in comparison to the common model plants with only a single kind of flowers in their inflorescence. In the first part of this thesis, conditions for gerbera microarray analysis were optimised including experimental design, sample preparation and hybridization, as well as data analysis and verification. Moreover, in the first study, the flower and flower organ-specific genes were identified. After the reliability and reproducibility of the method were confirmed, the microarrays were utilized to investigate transcriptional differences between ray and disc flowers. This study revealed novel information about the morphological development as well as the transcriptional regulation of early stages of development in various flower types of gerbera. The most interesting finding was differential expression of MADS-box genes, suggesting the existence of flower type-specific regulatory complexes in the specification of different types of flowers. The gerbera microarray was further used to profile changes in expression during petal development. Gerbera ray flower petals are large, which makes them an ideal model to study organogenesis. Six different stages were compared and specifically analysed. Expression profiles of genes related to cell structure and growth implied that during stage two, cells divide, a process which is marked by expression of histones, cyclins and tubulins. Stage 4 was found to be a transition stage between cell division and expansion and by stage 6 cells had stopped division and instead underwent expansion. Interestingly, at the last analysed stage, stage 9, when cells did not grow any more, the highest number of upregulated genes was detected. The gerbera microarray is a fully-functioning tool for large-scale studies of flower development and correlation with real-time RT-PCR results show that it is also highly sensitive and reliable. Gene expression data presented here will be a source for gene expression mining or marker gene discovery in the future studies that will be performed in the Gerbera Laboratory. The publicly available data will also serve the plant research community world-wide.

Role of cell cycle regulators in development of the inner ear

The inner ear originates from an ectodermal thickening called the otic placode. The otic placode invaginates and closes to an otic vesicle, the otocyst. The otocyst epithelium undergoes morphogenetic changes and cell differentiation, leading to the formation of the labyrinth-like mature inner ear. Epithelial-mesenchymal interactions control inner ear morphogenesis, but the modes and molecules are largely unresolved. The expressions of negative cell cycle regulators in the epithelium of the early-developing inner ear have also not been elucidated. The mature inner ear comprises the hearing (cochlea) and balance (vestibular) organs that contain the nonsensory and sensory cells. In mammals, the inner ear sensory cells, called hair cells, exit the cell cycle during embryogenesis and are mitotically quiescent during late-embryonic differentiation stages and postnatally. The mechanisms that maintain this hair cell quiescense are largely unresolved. In this work I examined 1) the epithelial-mesenchymal interactions involved in inner ear morphogenesis, 2) expression of negative cell cycle regulators in the epithelium of the early developing inner ear and 3) the molecular mechanisms that maintain the postmitotic state of inner ear sensory cells. We observed that during otocyst stages, epithelial fibroblast growth factor 9 (Fgf9) communicates with the surrounding mesenchyme, where its receptors are expressed. Fgf9 inactivation leads to reduced proliferation of the surrounding vestibular mesenchyme and to the absence of semicircular canals. Semicircular canal development is blocked, since fusion plates do not form. These results show that the mesenchyme directs fusion plate formation and give direct evidence for the existence of reciprocal epithelial-mesenchymal interactions in the developing inner ear. Cyclin-dependent kinase inhibitors (CKIs) are negative regulators of proliferation. We show that the members of the Cip/Kip family of CKIs (p21Cip1, p27Kip1 and p57Kip2) are expressed in the early-developing inner ear. Our expression data suggest that CKIs divide the otic epithelium into proliferative and nonproliferative compartments that may underlie shaping of the otocyst. At later stages, CKIs regulate proliferation of the vestibular appendages, and this may regulate their continual growth. In addition to restricting proliferation, CKIs may play a role in regional differentiation of various epithelial cells. Differentiating and adult inner ear hair cells are postmitotic and do not proliferate in response to serum or mitogenic growth factors. In our study, we show that this is the result of the activity of negative cell cycle regulators. Based on expression profiles, we first focused on the retinoblastoma (Rb) gene, which functions downstream of the CKIs. Analysis of the inner ear phenotype of Rb mutant mice show, that the retinoblastoma protein regulates the postmitotic state of hair cells. Rb inactivation leads to hyperplasia of vestibular and cochlear sensory epithelia that is a result of abnormal cell cycle entry of differentiated hair cells and of delayed cell cycle exit of the hair cell precursor cells. In addition, we show that p21Cip1 and p19Ink4d cooperate in maintaining the postmitotic state of postnatal auditory hair cells. Whereas inactivation of p19Ink4d alone leads to low-level S-phase entry (Chen et al., 2003) and p21Cip1 null mutant mice have a normal inner ear phenotype, codeletion of p19Ink4d and p21Cip1 triggers high-level S-phase entry of auditory hair cells during early postnatal life, which leads to supernumerary hair cells. The ectopic hair cells undergo apoptosis in all of the mutant mice studied, DNA damage being the immediate cause of this death. These findings demonstrate that the maintenance of the postmitotic state of hair cells is regulated by Rb and several CKIs, and that these cell cycle regulators are critical for the lifelong survival of hair cells. These data have implications for the future design of therapies to induce hair cell regrowth.

Identification of genes associated with tumorigenesis of meibomian cell carcinoma by microarray analysis

Meibomian cell carcinoma (MCC) is a malignant tumor of the meibomian glands located in the eyelids. No information exists on the cytogenctic and genetic aspects of MCC. There is no report on the gene expression profile of MCC. Thus there is a need, for both scientific and clinical reasons, to identify genes and pathways that are involved in the development and progression of MCC. We analyzed the gene expression profile of MCC by the microarray technique. Forty-four genes were upregulated and 149 genes were downregulated in MCC. Differential expression data were confirmed for 5 genes by semiquantitative RT-PCR in MCC tumors: GTF2H4, RBM12, UBE2D3, DDX17, and LZTS1. We found dysregulation of two major pathways in MCC: MAPK and JAK/STAT. Clusters of genes on chromosomes 1, 12, and 19 were dysregUlated in MCC. The data presented here will facilitate the identification of specific markers and therapeutic targets for the treatment of MCC patients. (c) 2007 Elsevier Inc. All rights reserved.

Genetic Mechanisms Underlying Autism Spectrum Disorders

Autism is a childhood-onset developmental disorder characterized by deficits in reciprocal social interaction, verbal and non-verbal communication, and dependence on routines and rituals. It belongs to a spectrum of disorders (autism spectrum disorders, ASDs) which share core symptoms but show considerable variation in severity. The whole spectrum affects 0.6-0.7% of children worldwide, inducing a substantial public health burden and causing suffering to the affected families. Despite having a very high heritability, ASDs have shown exceptional genetic heterogeneity, which has complicated the identification of risk variants and left the etiology largely unknown. However, recent studies suggest that rare, family-specific factors contribute significantly to the genetic basis of ASDs. In this study, we investigated the role of DISC1 (Disrupted-in-schizophrenia-1) in ASDs, and identified association with markers and haplotypes previously associated with psychiatric phenotypes. We identified four polymorphic micro-RNA target sites in the 3 UTR of DISC1, and showed that hsa-miR-559 regulates DISC1 expression in vitro in an allele-specific manner. We also analyzed an extended autism pedigree with genealogical roots in Central Finland reaching back to the 17th century. To take advantage of the beneficial characteristics of population isolates to gene mapping and reduced genetic heterogeneity observed in distantly related individuals, we performed a microsatellite-based genome-wide screen for linkage and linkage disequilibrium in this pedigree. We identified a putative autism susceptibility locus on chromosome 19p13.3 and obtained further support for previously reported loci at 1q23 and 15q11-q13. To follow-up these findings, we extended our study sample from the same sub-isolate and initiated a genome-wide analysis of homozygosity and allelic sharing using high-density SNP markers. We identified a small number of haplotypes shared by different subsets of the genealogically connected cases, along with convergent biological pathways from SNP and gene expression data, which highlighted axon guidance molecules in the pathogenesis of ASDs. In conclusion, the results obtained in this thesis show that multiple distinct genetic variants are responsible for the ASD phenotype even within single pedigrees from an isolated population. We suggest that targeted resequencing of the shared haplotypes, linkage regions, and other susceptibility loci is essential to identify the causal variants. We also report a possible micro-RNA mediated regulatory mechanism, which might partially explain the wide-range neurobiological effects of the DISC1 gene.

Studies of the Human Transcriptome

Gene expression is one of the most critical factors influencing the phenotype of a cell. As a result of several technological advances, measuring gene expression levels has become one of the most common molecular biological measurements to study the behaviour of cells. The scientific community has produced enormous and constantly increasing collection of gene expression data from various human cells both from healthy and pathological conditions. However, while each of these studies is informative and enlighting in its own context and research setup, diverging methods and terminologies make it very challenging to integrate existing gene expression data to a more comprehensive view of human transcriptome function. On the other hand, bioinformatic science advances only through data integration and synthesis. The aim of this study was to develop biological and mathematical methods to overcome these challenges and to construct an integrated database of human transcriptome as well as to demonstrate its usage. Methods developed in this study can be divided in two distinct parts. First, the biological and medical annotation of the existing gene expression measurements needed to be encoded by systematic vocabularies. There was no single existing biomedical ontology or vocabulary suitable for this purpose. Thus, new annotation terminology was developed as a part of this work. Second part was to develop mathematical methods correcting the noise and systematic differences/errors in the data caused by various array generations. Additionally, there was a need to develop suitable computational methods for sample collection and archiving, unique sample identification, database structures, data retrieval and visualization. Bioinformatic methods were developed to analyze gene expression levels and putative functional associations of human genes by using the integrated gene expression data. Also a method to interpret individual gene expression profiles across all the healthy and pathological tissues of the reference database was developed. As a result of this work 9783 human gene expression samples measured by Affymetrix microarrays were integrated to form a unique human transcriptome resource GeneSapiens. This makes it possible to analyse expression levels of 17330 genes across 175 types of healthy and pathological human tissues. Application of this resource to interpret individual gene expression measurements allowed identification of tissue of origin with 92.0% accuracy among 44 healthy tissue types. Systematic analysis of transcriptional activity levels of 459 kinase genes was performed across 44 healthy and 55 pathological tissue types and a genome wide analysis of kinase gene co-expression networks was done. This analysis revealed biologically and medically interesting data on putative kinase gene functions in health and disease. Finally, we developed a method for alignment of gene expression profiles (AGEP) to perform analysis for individual patient samples to pinpoint gene- and pathway-specific changes in the test sample in relation to the reference transcriptome database. We also showed how large-scale gene expression data resources can be used to quantitatively characterize changes in the transcriptomic program of differentiating stem cells. Taken together, these studies indicate the power of systematic bioinformatic analyses to infer biological and medical insights from existing published datasets as well as to facilitate the interpretation of new molecular profiling data from individual patients.

A 16-Gene Signature Distinguishes Anaplastic Astrocytoma from Glioblastoma

Anaplastic astrocytoma (AA; Grade III) and glioblastoma (GBM; Grade IV) are diffusely infiltrating tumors and are called malignant astrocytomas. The treatment regimen and prognosis are distinctly different between anaplastic astrocytoma and glioblastoma patients. Although histopathology based current grading system is well accepted and largely reproducible, intratumoral histologic variations often lead to difficulties in classification of malignant astrocytoma samples. In order to obtain a more robust molecular classifier, we analysed RT-qPCR expression data of 175 differentially regulated genes across astrocytoma using Prediction Analysis of Microarrays (PAM) and found the most discriminatory 16-gene expression signature for the classification of anaplastic astrocytoma and glioblastoma. The 16-gene signature obtained in the training set was validated in the test set with diagnostic accuracy of 89%. Additionally, validation of the 16-gene signature in multiple independent cohorts revealed that the signature predicted anaplastic astrocytoma and glioblastoma samples with accuracy rates of 99%, 88%, and 92% in TCGA, GSE1993 and GSE4422 datasets, respectively. The protein-protein interaction network and pathway analysis suggested that the 16-genes of the signature identified epithelial-mesenchymal transition (EMT) pathway as the most differentially regulated pathway in glioblastoma compared to anaplastic astrocytoma. In addition to identifying 16 gene classification signature, we also demonstrated that genes involved in epithelial-mesenchymal transition may play an important role in distinguishing glioblastoma from anaplastic astrocytoma.

EARLY FLOWERING4 recruitment of EARLY FLOWERING3 in the nucleus sustains the Arabidopsis circadian clock

The plant circadian clock is proposed to be a network of several interconnected feedback loops, and loss of any component leads to changes in oscillator speed. We previously reported that Arabidopsis thaliana EARLY FLOWERING4 (ELF4) is required to sustain this oscillator and that the elf4 mutant is arrhythmic. This phenotype is shared with both elf3 and lux. Here, we show that overexpression of either ELF3 or LUX ARRHYTHMO (LUX) complements the elf4 mutant phenotype. Furthermore, ELF4 causes ELF3 to form foci in the nucleus. We used expression data to direct a mathematical position of ELF3 in the clock network. This revealed direct effects on the morning clock gene PRR9, and we determined association of ELF3 to a conserved region of the PRR9 promoter. A cis-element in this region was suggestive of ELF3 recruitment by the transcription factor LUX, consistent with both ELF3 and LUX acting genetically downstream of ELF4. Taken together, using integrated approaches, we identified ELF4/ELF3 together with LUX to be pivotal for sustenance of plant circadian rhythms. © 2012 American Society of Plant Biologists.

A dependent partition-valued process for multitask clustering and time evolving network modelling

The fundamental aim of clustering algorithms is to partition data points. We consider tasks where the discovered partition is allowed to vary with some covariate such as space or time. One approach would be to use fragmentation-coagulation processes, but these, being Markov processes, are restricted to linear or tree structured covariate spaces. We define a partition-valued process on an arbitrary covariate space using Gaussian processes. We use the process to construct a multitask clustering model which partitions datapoints in a similar way across multiple data sources, and a time series model of network data which allows cluster assignments to vary over time. We describe sampling algorithms for inference and apply our method to defining cancer subtypes based on different types of cellular characteristics, finding regulatory modules from gene expression data from multiple human populations, and discovering time varying community structure in a social network.