High-Throughput Screening for Novel Prostate Cancer Drug Targets –Getting Personal

Prostate cancers form a heterogeneous group of diseases and there is a need for novel biomarkers, and for more efficient and targeted methods of treatment. In this thesis, the potential of microarray data, RNA interference (RNAi) and compound screens were utilized in order to identify novel biomarkers, drug targets and drugs for future personalized prostate cancer therapeutics. First, a bioinformatic mRNA expression analysis covering 9873 human tissue and cell samples, including 349 prostate cancer and 147 normal prostate samples, was used to distinguish in silico prevalidated putative prostate cancer biomarkers and drug targets. Second, RNAi based high-throughput (HT) functional profiling of 295 prostate and prostate cancer tissue specific genes was performed in cultured prostate cancer cells. Third, a HT compound screen approach using a library of 4910 drugs and drug-like molecules was exploited to identify potential drugs inhibiting prostate cancer cell growth. Nine candidate drug targets, with biomarker potential, and one cancer selective compound were validated in vitro and in vivo. In addition to androgen receptor (AR) signaling, endoplasmic reticulum (ER) function, arachidonic acid (AA) pathway, redox homeostasis and mitosis were identified as vital processes in prostate cancer cells. ERG oncogene positive cancer cells exhibited sensitivity to induction of oxidative and ER stress, whereas advanced and castrate-resistant prostate cancer (CRPC) could be potentially targeted through AR signaling and mitosis. In conclusion, this thesis illustrates the power of systems biological data analysis in the discovery of potential vulnerabilities present in prostate cancer cells, as well as novel options for personalized cancer management.

Building Next Generation Consortium Services – part 3: The National Metadata Repository, Discovery Service Finna, and the New Library System

Kristiina Hormia-Poutasen esitys CBUC-konferenssissa Barcelonassa 12.4.2013.

Repository content - lost in Discovery?

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Envia – a repository for environmental information access and discovery

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Self-deposit, discovery, and delivery of scientific GIS datasets using GeoHydra

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

A systematic mapping study of web services discovery mechanisms

Context: Web services have been gaining popularity due to the success of service oriented architecture and cloud computing. Web services offer tremendous opportunity for service developers to publish their services and applications over the boundaries of the organization or company. However, to fully exploit these opportunities it is necessary to find efficient discovery mechanism thus, Web services discovering mechanism has attracted a considerable attention in Semantic Web research, however, there have been no literature surveys that systematically map the present research result thus overall impact of these research efforts and level of maturity of their results are still unclear. This thesis aims at providing an overview of the current state of research into Web services discovering mechanism using systematic mapping. The work is based on the papers published 2004 to 2013, and attempts to elaborate various aspects of the analyzed literature including classifying them in terms of the architecture, frameworks and methods used for web services discovery mechanism. Objective: The objective if this work is to summarize the current knowledge that is available as regards to Web service discovery mechanisms as well as to systematically identify and analyze the current published research works in order to identify different approaches presented. Method: A systematic mapping study has been employed to assess the various Web Services discovery approaches presented in the literature. Systematic mapping studies are useful for categorizing and summarizing the level of maturity research area. Results: The result indicates that there are numerous approaches that are consistently being researched and published in this field. In terms of where these researches are published, conferences are major contributing publishing arena as 48% of the selected papers were conference published papers illustrating the level of maturity of the research topic. Additionally selected 52 papers are categorized into two broad segments namely functional and non-functional based approaches taking into consideration architectural aspects and information retrieval approaches, semantic matching, syntactic matching, behavior based matching as well as QOS and other constraints.

What about Th1/Th2 in cutaneous leishmaniasis vaccine discovery?

The T helper cell type 1 (Th1) response is essential to resist leishmaniasis, whereas the Th2 response favors the disease. However, many leishmanial antigens, which stimulate a Th1 immune response during the disease or even after the disease is cured, have been shown to have no protective action. Paradoxically, antigens associated with an early Th2 response have been found to be highly protective if the Th1 response to them is generated before infection. Therefore, finding disease-associated Th2 antigens and inducing a Th1 immune response to them using defined vaccination protocols is an interesting unorthodox alternative approach to the discovery of a leishmania vaccine.

Liber fostering Open Science and Knowledge Discovery

Presentation of Kristiina Hormia-Poutanen at the 25th Anniversary Conference of The National Repository Library of Finland, Kuopio 22th of May 2015.

Global microRNA profiles and signaling pathways in the development of cardiac hypertrophy

Hypertrophy is a major predictor of progressive heart disease and has an adverse prognosis. MicroRNAs (miRNAs) that accumulate during the course of cardiac hypertrophy may participate in the process. However, the nature of any interaction between a hypertrophy-specific signaling pathway and aberrant expression of miRNAs remains unclear. In this study, Spague Dawley male rats were treated with transverse aortic constriction (TAC) surgery to mimic pathological hypertrophy. Hearts were isolated from TAC and sham operated rats (n=5 for each group at 5, 10, 15, and 20 days after surgery) for miRNA microarray assay. The miRNAs dysexpressed during hypertrophy were further analyzed using a combination of bioinformatics algorithms in order to predict possible targets. Increased expression of the target genes identified in diverse signaling pathways was also analyzed. Two sets of miRNAs were identified, showing different expression patterns during hypertrophy. Bioinformatics analysis suggested the miRNAs may regulate multiple hypertrophy-specific signaling pathways by targeting the member genes and the interaction of miRNA and mRNA might form a network that leads to cardiac hypertrophy. In addition, the multifold changes in several miRNAs suggested that upregulation of rno-miR-331*, rno-miR-3596b, rno-miR-3557-5p and downregulation of rno-miR-10a, miR-221, miR-190, miR-451 could be seen as biomarkers of prognosis in clinical therapy of heart failure. This study described, for the first time, a potential mechanism of cardiac hypertrophy involving multiple signaling pathways that control up- and downregulation of miRNAs. It represents a first step in the systematic discovery of miRNA function in cardiovascular hypertrophy.

Bioinformatics analysis of biomarkers and transcriptional factor motifs in Down syndrome

In this study, biomarkers and transcriptional factor motifs were identified in order to investigate the etiology and phenotypic severity of Down syndrome. GSE 1281, GSE 1611, and GSE 5390 were downloaded from the gene expression ominibus (GEO). A robust multiarray analysis (RMA) algorithm was applied to detect differentially expressed genes (DEGs). In order to screen for biological pathways and to interrogate the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, the database for annotation, visualization, and integrated discovery (DAVID) was used to carry out a gene ontology (GO) function enrichment for DEGs. Finally, a transcriptional regulatory network was constructed, and a hypergeometric distribution test was applied to select for significantly enriched transcriptional factor motifs. CBR1, DYRK1A, HMGN1, ITSN1, RCAN1, SON, TMEM50B, and TTC3 were each up-regulated two-fold in Down syndrome samples compared to normal samples; of these, SON and TTC3 were newly reported. CBR1, DYRK1A, HMGN1, ITSN1, RCAN1, SON, TMEM50B, and TTC3 were located on human chromosome 21 (mouse chromosome 16). The DEGs were significantly enriched in macromolecular complex subunit organization and focal adhesion pathways. Eleven significantly enriched transcription factor motifs (PAX5, EGR1, XBP1, SREBP1, OLF1, MZF1, NFY, NFKAPPAB, MYCMAX, NFE2, and RP58) were identified. The DEGs and transcription factor motifs identified in our study provide biomarkers for the understanding of Down syndrome pathogenesis and progression.

Customer Discovery and Customer Validation in Lean Software Startups

The traditional business models and the traditionally successful development methods that have been distinctive to the industrial era, do not satisfy the needs of modern IT companies. Due to the rapid nature of IT markets, the uncertainty of new innovations‟ success and the overwhelming competition with established companies, startups need to make quick decisions and eliminate wasted resources more effectively than ever before. There is a need for an empirical basis on which to build business models, as well as evaluate the presumptions regarding value and profit. Less than ten years ago, the Lean software development principles and practices became widely well-known in the academic circles. Those practices help startup entrepreneurs to validate their learning, test their assumptions and be more and more dynamical and flexible. What is special about today‟s software startups is that they are increasingly individual. There are quantitative research studies available regarding the details of Lean startups. Broad research with hundreds of companies presented in a few charts is informative, but a detailed study of fewer examples gives an insight to the way software entrepreneurs see Lean startup philosophy and how they describe it in their own words. This thesis focuses on Lean software startups‟ early phases, namely Customer Discovery (discovering a valuable solution to a real problem) and Customer Validation (being in a good market with a product which satisfies that market). The thesis first offers a sufficiently compact insight into the Lean software startup concept to a reader who is not previously familiar with the term. The Lean startup philosophy is then put into a real-life test, based on interviews with four Finnish Lean software startup entrepreneurs. The interviews reveal 1) whether the Lean startup philosophy is actually valuable for them, 2) how can the theory be practically implemented in real life and 3) does theoretical Lean startup knowledge compensate a lack of entrepreneurship experience. A reader gets familiar with the key elements and tools of Lean startups, as well as their mutual connections. The thesis explains why Lean startups waste less time and money than many other startups. The thesis, especially its research sections, aims at providing data and analysis simultaneously.

Probing MST1 Kinase Sequence and Structure for Rational Drug Discovery (Targeting diabetes by blocking protein-protein interactions)

Apoptotic beta cell death is an underlying cause majorly for type I and to a lesser extent for type II diabetes. Recently, MST1 kinase was identified as a key apoptotic agent in diabetic condition. In this study, I have examined MST1 and closely related kinases namely, MST2, MST3 and MST4, aiming to tackle diabetes by exploring ways to selectively block MST1 kinase activity. The first investigation was directed towards evaluating possibilities of selectively blocking the ATP binding site of MST1 kinase that is essential for the activity of the enzymes. Structure and sequence analyses of this site however revealed a near absolute conservation between the MSTs and very few changes with other kinases. The observed residue variations also displayed similar physicochemical properties making it hard for selective inhibition of the enzyme. Second, possibilities for allosteric inhibition of the enzyme were evaluated. Analysis of the recognized allosteric site also posed the same problem as the MSTs shared almost all of the same residues. The third analysis was made on the SARAH domain, which is required for the dimerization and activation of MST1 and MST2 kinases. MST3 and MST4 lack this domain, hence selectivity against these two kinases can be achieved. Other proteins with SARAH domains such as the RASSF proteins were also examined. Their interaction with the MST1 SARAH domain were evaluated to mimic their binding pattern and design a peptide inhibitor that interferes with MST1 SARAH dimerization. In molecular simulations the RASSF5 SARAH domain was shown to strongly interact with the MST1 SARAH domain and possibly preventing MST1 SARAH dimerization. Based on this, the peptidic inhibitor was suggested to be based on the sequence of RASSF5 SARAH domain. Since the MST2 kinase also interacts with RASSF5 SARAH domain, absolute selectivity might not be achieved.

Customer Discovery and Customer Validation in Lean Software Startups

The traditional business models and the traditionally successful development methods that have been distinctive to the industrial era, do not satisfy the needs of modern IT companies. Due to the rapid nature of IT markets, the uncertainty of new innovations‟ success and the overwhelming competition with established companies, startups need to make quick decisions and eliminate wasted resources more effectively than ever before. There is a need for an empirical basis on which to build business models, as well as evaluate the presumptions regarding value and profit. Less than ten years ago, the Lean software development principles and practices became widely well-known in the academic circles. Those practices help startup entrepreneurs to validate their learning, test their assumptions and be more and more dynamical and flexible. What is special about today‟s software startups is that they are increasingly individual. There are quantitative research studies available regarding the details of Lean startups. Broad research with hundreds of companies presented in a few charts is informative, but a detailed study of fewer examples gives an insight to the way software entrepreneurs see Lean startup philosophy and how they describe it in their own words. This thesis focuses on Lean software startups‟ early phases, namely Customer Discovery (discovering a valuable solution to a real problem) and Customer Validation (being in a good market with a product which satisfies that market). The thesis first offers a sufficiently compact insight into the Lean software startup concept to a reader who is not previously familiar with the term. The Lean startup philosophy is then put into a real-life test, based on interviews with four Finnish Lean software startup entrepreneurs. The interviews reveal 1) whether the Lean startup philosophy is actually valuable for them, 2) how can the theory be practically implemented in real life and 3) does theoretical Lean startup knowledge compensate a lack of entrepreneurship experience. A reader gets familiar with the key elements and tools of Lean startups, as well as their mutual connections. The thesis explains why Lean startups waste less time and money than many other startups. The thesis, especially its research sections, aims at providing data and analysis simultaneously.

A Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discovery

Understanding the machinery of gene regulation to control gene expression has been one of the main focuses of bioinformaticians for years. We use a multi-objective genetic algorithm to evolve a specialized version of side effect machines for degenerate motif discovery. We compare some suggested objectives for the motifs they find, test different multi-objective scoring schemes and probabilistic models for the background sequence models and report our results on a synthetic dataset and some biological benchmarking suites. We conclude with a comparison of our algorithm with some widely used motif discovery algorithms in the literature and suggest future directions for research in this area.