Application of Volcano Plots in Analyses of mRNA Differential Expressions with Microarrays

Volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log10(p-value) from the t test). We review the basic and an interactive use of the volcano plot, and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide an unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility to apply volcano plots to other fields beyond microarray.Comment: 8 figure

Differential analysis of biological networks

In cancer research, the comparison of gene expression or DNA methylation networks inferred from healthy controls and patients can lead to the discovery of biological pathways associated to the disease. As a cancer progresses, its signalling and control networks are subject to some degree of localised re-wiring. Being able to detect disrupted interaction patterns induced by the presence or progression of the disease can lead to the discovery of novel molecular diagnostic and prognostic signatures. Currently there is a lack of scalable statistical procedures for two-network comparisons aimed at detecting localised topological differences. We propose the dGHD algorithm, a methodology for detecting differential interaction patterns in two-network comparisons. The algorithm relies on a statistic, the Generalised Hamming Distance (GHD), for assessing the degree of topological difference between networks and evaluating its statistical significance. dGHD builds on a non-parametric permutation testing framework but achieves computationally efficiency through an asymptotic normal approximation. We show that the GHD is able to detect more subtle topological differences compared to a standard Hamming distance between networks. This results in the dGHD algorithm achieving high performance in simulation studies as measured by sensitivity and specificity. An application to the problem of detecting differential DNA co-methylation subnetworks associated to ovarian cancer demonstrates the potential benefits of the proposed methodology for discovering network-derived biomarkers associated with a trait of interest

Children's biobehavioral reactivity to challenge predicts DNA methylation in adolescence and emerging adulthood.

A growing body of research has documented associations between adverse childhood environments and DNA methylation, highlighting epigenetic processes as potential mechanisms through which early external contexts influence health across the life course. The present study tested a complementary hypothesis: indicators of children's early internal, biological, and behavioral responses to stressful challenges may also be linked to stable patterns of DNA methylation later in life. Children's autonomic nervous system reactivity, temperament, and mental health symptoms were prospectively assessed from infancy through early childhood, and principal components analysis (PCA) was applied to derive composites of biological and behavioral reactivity. Buccal epithelial cells were collected from participants at 15 and 18 years of age. Findings revealed an association between early life biobehavioral inhibition/disinhibition and DNA methylation across many genes. Notably, reactive, inhibited children were found to have decreased DNA methylation of the DLX5 and IGF2 genes at both time points, as compared to non-reactive, disinhibited children. Results of the present study are provisional but suggest that the gene's profile of DNA methylation may constitute a biomarker of normative or potentially pathological differences in reactivity. Overall, findings provide a foundation for future research to explore relations among epigenetic processes and differences in both individual-level biobehavioral risk and qualities of the early, external childhood environment

INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

Gene Expression : From Microarrays to Functional Genomics

The time of the large sequencing projects has enabled unprecedented possibilities of investigating more complex aspects of living organisms. Among the high-throughput technologies based on the genomic sequences, the DNA microarrays are widely used for many purposes, including the measurement of the relative quantity of the messenger RNAs. However, the reliability of microarrays has been strongly doubted as robust analysis of the complex microarray output data has been developed only after the technology had already been spread in the community. An objective of this study consisted of increasing the performance of microarrays, and was measured by the successful validation of the results by independent techniques. To this end, emphasis has been given to the possibility of selecting candidate genes with remarkable biological significance within specific experimental design. Along with literature evidence, the re-annotation of the probes and model-based normalization algorithms were found to be beneficial when analyzing Affymetrix GeneChip data. Typically, the analysis of microarrays aims at selecting genes whose expression is significantly different in different conditions followed by grouping them in functional categories, enabling a biological interpretation of the results. Another approach investigates the global differences in the expression of functionally related groups of genes. Here, this technique has been effective in discovering patterns related to temporal changes during infection of human cells. Another aspect explored in this thesis is related to the possibility of combining independent gene expression data for creating a catalog of genes that are selectively expressed in healthy human tissues. Not all the genes present in human cells are active; some involved in basic activities (named housekeeping genes) are expressed ubiquitously. Other genes (named tissue-selective genes) provide more specific functions and they are expressed preferably in certain cell types or tissues. Defining the tissue-selective genes is also important as these genes can cause disease with phenotype in the tissues where they are expressed. The hypothesis that gene expression could be used as a measure of the relatedness of the tissues has been also proved. Microarray experiments provide long lists of candidate genes that are often difficult to interpret and prioritize. Extending the power of microarray results is possible by inferring the relationships of genes under certain conditions. Gene transcription is constantly regulated by the coordinated binding of proteins, named transcription factors, to specific portions of the its promoter sequence. In this study, the analysis of promoters from groups of candidate genes has been utilized for predicting gene networks and highlighting modules of transcription factors playing a central role in the regulation of their transcription. Specific modules have been found regulating the expression of genes selectively expressed in the hippocampus, an area of the brain having a central role in the Major Depression Disorder. Similarly, gene networks derived from microarray results have elucidated aspects of the development of the mesencephalon, another region of the brain involved in Parkinson Disease.The time of the large sequencing projects has enabled unprecedented possibilities of investigating more complex aspects of living organisms. Among the high-throughput technologies based on the genomic sequences, the DNA microarrays are widely used for many purposes, including the measurement of the relative quantity of the messenger RNAs. However, the reliability of microarrays has been strongly doubted as robust analysis of the complex microarray output data has been developed only after the technology had already been spread in the community. An objective of this study consisted of increasing the performance of microarrays, and was measured by the successful validation of the results by independent techniques. To this end, emphasis has been given to the possibility of selecting candidate genes with remarkable biological significance within specific experimental design. Along with literature evidence, the re-annotation of the probes and model-based normalization algorithms were found to be beneficial when analyzing Affymetrix GeneChip data. Typically, the analysis of microarrays aims at selecting genes whose expression is significantly different in different conditions followed by grouping them in functional categories, enabling a biological interpretation of the results. Another approach investigates the global differences in the expression of functionally related groups of genes. Here, this technique has been effective in discovering patterns related to temporal changes during infection of human cells. Another aspect explored in this thesis is related to the possibility of combining independent gene expression data for creating a catalog of genes that are selectively expressed in healthy human tissues. Not all the genes present in human cells are active; some involved in basic activities (named housekeeping genes) are expressed ubiquitously. Other genes (named tissue-selective genes) provide more specific functions and they are expressed preferably in certain cell types or tissues. Defining the tissue-selective genes is also important as these genes can cause disease with phenotype in the tissues where they are expressed. The hypothesis that gene expression could be used as a measure of the relatedness of the tissues has been also proved. Microarray experiments provide long lists of candidate genes that are often difficult to interpret and prioritize. Extending the power of microarray results is possible by inferring the relationships of genes under certain conditions. Gene transcription is constantly regulated by the coordinated binding of proteins, named transcription factors, to specific portions of the its promoter sequence. In this study, the analysis of promoters from groups of candidate genes has been utilized for predicting gene networks and highlighting modules of transcription factors playing a central role in the regulation of their transcription. Specific modules have been found regulating the expression of genes selectively expressed in the hippocampus, an area of the brain having a central role in the Major Depression Disorder. Similarly, gene networks derived from microarray results have elucidated aspects of the development of the mesencephalon, another region of the brain involved in Parkinson Disease

Novel DNA microarray in sepsis diagnostics

Sepsis is defined as a documented infection with systemic inflammatory response syndrome (SIRS). When pathogens have been detected by blood culturing method, the condition is classified as a bloodstream infection (BSI). The frequency of severe sepsis is approximately 90.4 cases per 100 000 population in Europe and circa 751 000 cases annually in United States. Sepsis is associated with high mortality rates ranging up to 50 % in most severe cases. The presence of immunocompromising conditions, chronic diseases, prosthetic devices such as intravenous lines or urinary catheters and higher age are factors which typically increase the infection risk. Currently, common causative bacteria such as Staphylococcus aureus, other staphylococci, Escherichia coli and Klebsiella pneumoniae are detected using blood culturing method. It is time-consuming, especially in case of fastidious and slow growing bacteria and thus initial empirical therapy typically contains broad-spectrum antimicrobial(s). Rapid methods for sepsis/BSI diagnostics are needed to improve patient outcomes, decrease length of stay in hospital and related costs. When causative pathogens are identified earlier, also appropriate antimicrobials can be administered earlier. The aim of this study was to develop a polymerase chain reaction (PCR) and microarray-based assay for the detection of main causative pathogens and methicillin resistance marker from patients with suspected sepsis/BSI. The assay, which utilized the Prove-it TubeArray platform, was first developed for detection of 12 bacterial species, coagulase negative Staphylococcus group and methicillin resistance marker. The performance of this assay was evaluated with blood culture samples. The bacterial panel was further improved for the detection of over 50 causative pathogens in sepsis/BSI. This optimized assay was clinically validated with over 3300 blood culture samples collected from HUSLAB, Finland and UCLH, United Kingdom. The developed assay, named Prove-it Sepsis, demonstrated 94.7 % sensitivity and 98.8 % specificity. Based on this validation study, the assay was CE-marked for in vitro diagnostics in Europe. This diagnostics assay with the improved target panel was also successfully transferred and optimized to the Prove-it StripArray platform, whose capacity of 1-96 simultaneous analyses responds to the need of hospital laboratories dealing with larger sample amounts. Another aim of this study was to evaluate the PCR and microarray assay s suitability for identification of pathogens directly from whole blood samples without a culturing step. The assay was combined with a selective bacterial deoxyribonucleic acid (DNA) isolation method and the performance of this combination was evaluated with spiked blood samples. Detection limit of 11-600 colony forming units per mL was obtained depending on the target organism. In addition, analytical sensitivity of 1-21 genome equivalents for the PCR and microarray assay was demonstrated. These results showed proof-of-concept for the combination assay and feasibility of the PCR and microarray assay to be used for more sensitive applications after an extensive optimization phase. Molecular assays have opened a new era in microbiological laboratories and brought a broadened perspective parallel to the conventional culturing and phenotype-based method. Also in this study, genotype-based characterization was utilized to offer more accurate identification than conventional culturing. In future, understanding the clinical relevance of DNAemia/circulating DNA may open new strategies to the management of septic patients using nucleic acids-based assays.Sepsis tarkoittaa vakavaa yleisinfektiota ja tulehdusreaktio-oireyhtymää, johon liitetään usein veriviljelypositiivisuus. Yleisyys Euroopassa on 90.4 tapausta 100 000 ihmistä kohden ja Yhdysvalloissa noin 751 000 tapausta vuosittain. Sepsikseen liitetään korkea kuolleisuus, jopa 50 %. Heikentynyt immuunipuolustus, krooniset sairaudet sekä korkea ikä saattavat lisätä sairastumisriskiä. Yleisimpiä aiheuttajabakteereita ovat muun muassa Staphylococcus aureus ja muut stafylokokit, Escherichia coli ja Klebsiella pneumoniae. Resistentit ja multi-resistentit bakteerikannat ovat yleensä hoidollisesti vaikeimpia, koska tehokkaan mikrobilääkehoidon kohdistaminen saattaa olla vaikeaa. Tällä hetkellä sepsis osoitetaan veriviljelydiagnostiikan avulla, jolloin mikrobi pyritään tunnistamaan potilaan verestä. Viljely on hidas menetelmä vaativissa kasvuolosuhteissa kasvavien mikrobien kohdalla, siksi potilaan empiirinen ensihoito koostuu yleensä laajakirjoisesta mikrobilääkkeestä tai lääkeyhdistelmistä. Nopeutetun diagnostiikan avulla mikrobi(t) pystyttäisiin tunnistamaan nopeammin ja näin ollen kohdistettu lääkehoito aloittamaan aikaisemmin. Tämän työn tavoitteena oli kehittää PCR-monistus- ja mikrosirutekniikkaan perustuva testi sepsiksen aiheuttajamikrobien tunnistamiseen. Ensin kehitettiin tunnistus 12 bakteerilajille, koagulaasinegatiiviselle stafylokki-ryhmälle sekä metisilliiniresistenssi-geenimarkkerille positiivisesta veriviljelynäytteestä. Testialustaksi optimoitiin Prove-it TubeArray -mikrosiru, jolla pystyi analysoimaan 1-24 näytettä kerrallaan. Testin toimivuus arvioitiin kerätyillä veriviljelynäytteillä. Seuraavassa vaiheessa mikrobipaneeli laajennettiin kattamaan yli 50 sepsiksen aiheuttajamikrobia. Tämän parannetun testiversion toimivuus arvioitiin yli 3300 veriviljelynäytteen avulla, jotka oli kerätty HUSLAB:ssa Suomessa ja UCHL:ssä Isossa-Britaniassa. PCR- ja mikrosirutesti nimettiin Prove-it Sepsis -testiksi, jolle määritettiin 94.7 %:n herkkyys ja 98.8 %:n tarkkuus, kun testitulosta verrattiin veriviljelyn mikrobilöydöksiin. Tämän arvioinnin perusteella testi CE-merkittiin in vitro diagnostiikkaan Euroopassa. Kehitystä jatkettiin Prove-it TubeArray -testialustan lisäksi myös Prove-it StripArray -testialustalle, jolla saattoi analysoida 1-96 näytettä samanaikaisesti. Useamman näytteen yhtäaikainen analysointi vastaa paremmin tarvetta isoissa laboratorioissa, joissa näytekapasiteetti on suurempi. Lisäksi tutkittiin PCR- ja mikrosirutestin soveltuvuutta mikrobitunnistukseen suoraan potilaan verinäytteestä ilman rikastusvaihetta. Spesifinen bakteeri-DNA:n eristysmenetelmä potilasverinäytteestä yhdistettiin PCR- ja mikrosirutestin kanssa. Tätä yhdistelmää arvioitiin verinäytteillä, joihin oli lisätty tietty pitoisuus bakteereita. Analysoinnin tuloksena tämän yhdistelmätestin herkkyydeksi määritettiin bakteerilajista riippuen 11-600 pesäkettä muodostavaa yksikköä per mL. Lisäksi PCR- ja mikrosirutestin analyyttiseksi herkkyydeksi määritettiin 1-21 genomiekvivalenttia. Tulokset osoittivat, että PCR- ja mikrosirutesti saattaisi olla kehitettävissä myös herkempiin sovelluksiin kuin rikastettuun näytemateriaaliin, esimerkiksi muokkaamalla testiä yhdessä kuvatun DNA-eristysmenetelmän kanssa. Molekyylipohjaiset testit ovat jo avanneet uuden aikakauden mikrobiologisissa laboratorioissa. Mikrobien geenipohjainen luokittelu ja karakterisointi tarjoavat sellaisia mahdollisuuksia, joita fenotyyppipohjaisella luokittelulla ei pystytä välttämättä saavuttamaan. Näitä havaintoja tehtiin myös tässä tutkimuksessa, kun PCR- ja mikrosirutesti tunnisti bakteereja potilasnäytteistä, joissa viljely epäonnistui tai ei antanut oikeaa tulosta. Sepsispotilaan verenkierrosta löytyvän bakteeri-DNA:n kliininen merkittävyys infektioissa ei ole vielä täysin selvää. Sen ymmärtämisen myötä voidaan kehittää nopeampia nukleiinihappopohjaisia strategioita sepsispotilaan diagnosointiin

Serum-dependent transcriptional networks identify distinct functional roles for H-Ras and N-Ras during initial stages of the cell cycle

Transcriptional and functional analysis reveals that the H-Ras and N-Ras isoforms have different roles in the initial phases of the mouse cell cycl