Human and mouse oligonucleotide-based array CGH

Array-based comparative genomic hybridization is a high resolution method for measuring chromosomal copy number changes. Here we present a validated protocol using in-house spotted oligonucleotide libraries for array comparative genomic hybridization (CGH). This oligo array CGH platform yields reproducible results and is capable of detecting single copy gains, multi-copy amplifications as well as homozygous and heterozygous deletions as small as 100 kb with high resolution. A human oligonucleotide library was printed on amine binding slides. Arrays were hybridized using a hybstation and analysed using BlueFuse feature extraction software, with >95% of spots passing quality control. The protocol allows as little as 300 ng of input DNA and a 90% reduction of Cot-1 DNA without compromising quality. High quality results have also been obtained with DNA from archival tissue. Finally, in addition to human oligo arrays, we have applied the protocol successfully to mouse oligo arrays. We believe that this oligo-based platform using ‘off-the-shelf’ oligo libraries provides an easy accessible alternative to BAC arrays for CGH, which is cost-effective, available at high resolution and easily implemented for any sequenced organism without compromising the quality of the results

Global gene expression profiling of healthy human brain and its application in studying neurological disorders

The human brain is the most complex structure known to mankind and one of the greatest challenges in modern biology is to understand how it is built and organized. The power of the brain arises from its variety of cells and structures, and ultimately where and when different genes are switched on and off throughout the brain tissue. In other words, brain function depends on the precise regulation of gene expression in its sub-anatomical structures. But, our understanding of the complexity and dynamics of the transcriptome of the human brain is still incomplete. To fill in the need, we designed a gene expression model that accurately defines the consistent blueprint of the brain transcriptome; thereby, identifying the core brain specific transcriptional processes conserved across individuals. Functionally characterizing this model would provide profound insights into the transcriptional landscape, biological pathways and the expression distribution of neurotransmitter systems. Here, in this dissertation we developed an expression model by capturing the similarly expressed gene patterns across congruently annotated brain structures in six individual brains by using data from the Allen Brain Atlas (ABA). We found that 84% of genes are expressed in at least one of the 190 brain structures. By employing hierarchical clustering we were able to show that distinct structures of a bigger brain region can cluster together while still retaining their expression identity. Further, weighted correlation network analysis identified 19 robust modules of coexpressing genes in the brain that demonstrated a wide range of functional associations. Since signatures of local phenomena can be masked by larger signatures, we performed local analysis on each distinct brain structure. Pathway and gene ontology enrichment analysis on these structures showed, striking enrichment for brain region specific processes. Besides, we also mapped the structural distribution of the gene expression profiles of genes associated with major neurotransmission systems in the human. We also postulated the utility of healthy brain tissue gene expression to predict potential genes involved in a neurological disorder, in the absence of data from diseased tissues. To this end, we developed a supervised classification model, which achieved an accuracy of 84% and an AUC (Area Under the Curve) of 0.81 from ROC plots, for predicting autism-implicated genes using the healthy expression model as the baseline. This study represents the first use of healthy brain gene expression to predict the scope of genes in autism implication and this generic methodology can be applied to predict genes involved in other neurological disorders

Molecular Characterization of Ductal Carcinoma In Situ: Pilot Studies

Ductal carcinoma in situ (DCIS); is thought directly to precede invasive breast cancer (IBC). Screening mammography has driven the incidence of this key precursor lesion to \u3e65,000 cases per year. However, little is known about the factors controlling the natural history or risk for recurrence following treatment of a particular patients DCIS. Though the heterogeneity of the disease is well established, no histologic or demographic criteria have been able to stratify DCIS for treatment. We hypothesize that at initial diagnosis there exist biologically distinct subsets of DCIS with associated prognoses that may be recognized by molecular markers. Molecular approaches have been limited by technical design issues related to the types of tissue available for analysis, namely degraded formalin-fixed paraffin embedded (FFPE) specimens and small core biopsy samples. However, new technologies promise to overcome these issues. In the first phase of our investigation, we aimed a) to pilot feasibility studies on the use of FFPE DCIS for molecular analyses including gene expression microarray and b) to pilot feasibility study of selective, high throughput sequencing through the use of exon capture on small input material that simulated expected DCIS core biopsy amounts. The results of this work offer specific technical guidelines for the molecular study of DCIS. Moreover, they have enabled the initiation of the second phase of this study, which aims to assess molecular profiles of DCIS recurrence and progression

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.Biolääketieteessä mikrosiruilla tutkitaan samanaikaisesti tuhansia molekyylejä solu- tai kudosnäytteestä. Mikrosirut koostuvat kiinteällä alustalla, kuten mikroskooppilasilla, olevista tuhansista pienistä pisteistä. Jokainen piste voi sisältää esimerkiksi 25-60 emäksen pituisia oligonukleotidejä, jotka vastaavat tiettyä geeniä. Näin mikrosirujen avulla voidaan tutkia vaikkapa useiden geenien ilmentymistä näytteestä. Mikrosiruilla on paljon sovelluksia biolääketieteen alalla. Erityisesti siruja on käytetty syöpätutkimuksessa. Mikrosiruja geenien ilmentymisen määrittämiseen valmistetaan paikallisesti tutkimuslaboratoriossa tai ostetaan kaupallisilta valmistajilta. Kaupallisia valmistajia on useita. Monimuotoisuus asettaa haasteita tiedon keräämiselle eri sirutyypeiltä ja kerätyn tiedon vertaamiselle. Tässä väitöskirjatyössä verrattiin geenien ilmentymisen tuloksia kolmelta erityyppiseltä mikrosirulta. Joukossa oli kaksi kaupallista sekä yksi itsetehty siru. Vertailu osoitti, että kaupalliset sirut antoivat samankaltaisempia tuloksia, korrelaatio sirujen välillä 0.78-0.86, kuin itsetehty siru. Vaikka tulokset osoittivat, että eri siruilta voidaan saada vertailukelpoista tietoa, ei vertailu tulosten välillä ole suoraviivaista. Haasteena ovat mikrosirujen tekniset eroavaisuudet sekä tiedon saattaminen vertailukelpoiseen muotoon. Mikrosirujen kehittyessä tutkimustyökalusta kliinisiin sovelluksiin standardoinnilla - ja sitä kautta tulosten paremmalla vertautuvuudella - on tärkeä tehtävä. Työssä tutkittiin myös biologisen tiedon yhdistämistä eri mikrosirumenetelmistä pään ja kaulan alueen syövässä. Mittaamalla mikrosiruilla geenin kopiomäärän muutoksia DNA-tasolla ja yhdistämällä tämä tieto geenin ilmentymistason muutoksiin saatiin tietoa syövälle merkityksellisistä geeneistä. Erityisesti korkeasteiset kopioluvun muutokset vaikuttivat geenien ilmentymiseen, sillä 14-32% näillä alueilla sijaisevista geeneistä oli myös kohonnut ilmentymistaso. Tilastollisin menetelmin osoitettiin satoja geenejä, joilla kopioluvun ja ilmentymän muutos näyttävät olevan yhteydessä toisiinsa. Tässä joukossa oli uusia kohdegeenejä ennestään tunnetuille kromosomialueille, kuten FADD ja PPFIA1 geenit kromosomissa 11. Työssä tunnistetut geenit antavat hyvän pohjan jatkotutkimuksille, jotka voivat vuosien kuluessa johtaa edistysaskeliin pään ja kaulan alueen syövän hoidossa

Network-Guided Analysis of Genes with Altered Somatic Copy Number and Gene Expression Reveals Pathways Commonly Perturbed in Metastatic Melanoma

Cancer genomes frequently contain somatic copy number alterations (SCNA) that can significantly perturb the expression level of affected genes and thus disrupt pathways controlling normal growth. In melanoma, many studies have focussed on the copy number and gene expression levels of the BRAF, PTEN and MITF genes, but little has been done to identify new genes using these parameters at the genome-wide scale. Using karyotyping, SNP and CGH arrays, and RNA-seq, we have identified SCNA affecting gene expression (‘SCNA-genes’) in seven human metastatic melanoma cell lines. We showed that the combination of these techniques is useful to identify candidate genes potentially involved in tumorigenesis. Since few of these alterations were recurrent across our samples, we used a protein network-guided approach to determine whether any pathways were enriched in SCNA-genes in one or more samples. From this unbiased genome-wide analysis, we identified 28 significantly enriched pathway modules. Comparison with two large, independent melanoma SCNA datasets showed less than 10% overlap at the individual gene level, but network-guided analysis revealed 66% shared pathways, including all but three of the pathways identified in our data. Frequently altered pathways included WNT, cadherin signalling, angiogenesis and melanogenesis. Additionally, our results emphasize the potential of the EPHA3 and FRS2 gene products, involved in angiogenesis and migration, as possible therapeutic targets in melanoma. Our study demonstrates the utility of network-guided approaches, for both large and small datasets, to identify pathways recurrently perturbed in cancer

<i>Staphylococcus aureus</i> virulence gene studies : a comparative microarray based approach

The development and application of a partial composite S. aureus virulence-associated gene micro array is described. Epidemic, pandemic and sporadic lineages of healthcare associated(HA-) and community-associated (CA-) S. aureus were compared. The clonal population structure was supported but further evidence for large-scale recombination events was obtained. Phage structural genes linked with the CA phenotype were identified and in silico analysis revealed these to be correlated with phage serogroup. CA strains generally carried a PVL-associated phage either of the A or Fb serogroup, whilst the HA strains predominantly carried sero group B phage. It is proposed that carriage of PVL associated phage rather than the specific pvl genes is correlated with the CA phenotype.These findings further support the role of the accessory genome in shaping the epidemiology of S. aureus.The microarray was used to study gene expression in isogenic strains differing by a deletion in the agr locus. Microarray analysis revealed significant differences between the levels of expression of several genes of the normal and mutant strains. However, RNAIII levels in the non-mutant strain were found to be cell density independent, indicating that the expected quorum sensing mechanism was not functional.Expression profiles of cells grown under biofilm simulating conditions were compared to their planktonic counterparts. Biofilm cells displayed a typical expression profile that was different from both the actively growing planktonic exponential cells and the planktonic stationary cells. The strongest feature of the biofilm state was high level expression of the haemolysin genes. This model therefore is amenable to exploitation in studies designed to improve our understanding of the mechanisms underlining biofilm survival and regulation after long periods of growth

A framework for the informed normalization of printed microarrays

Microarray technology has become an essential part of contemporary molecular biological research. An aspect central to any microarray experiment is that of normalization, a form of data processing directed at removing technical noise while preserving biological meaning, thereby allowing for more accurate interpretations of data. The statistics underlying many normalization methods can appear overwhelming to microarray newcomers, a situation which is further compounded by a lack of accessible, non-statistical descriptions of common approaches to normalization. Normalization strategies significantly affect the analytical outcome of a microarray experiment, and consequently it is important that the statistical assumptions underlying normalization algorithms are understood and met before researchers embark upon the processing of raw microarray data. Many of these assumptions pertain only to whole-genome arrays, and are not valid for custom or directed microarrays. A thorough diagnostic evaluation of the nature and extent to which technical noise affects individual arrays is paramount to the success of any chosen normalization strategy. Here we suggest an approach to normalization based on extensive stepwise exploration and diagnostic assessment of data prior to, and after, normalization. Common data visualization and diagnostic approaches are highlighted, followed by descriptions of popular normalization methods, and the underlying assumptions they are based on, within the context of removing general technical artefacts associated with microarray data

Between-species differences in gene copy number are enriched among functions critical for adaptive evolution in Arabidopsis halleri

Complete dataset expanded from Additional file 3. (XLS 16282 kb