Aaltodoc Repository :: Browsing by Author "Kaski, Kimmo, Prof."

Browsing by Author "Kaski, Kimmo, Prof."

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Bioinformatics approaches for the analysis of lipidomics data
(VTT, 2010) Yetukuri, Laxmana Rao; Oresic, Matej, Research Prof., VTT; Department of Biomedical Engineering and Computational Science; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Aalto-yliopiston teknillinen korkeakoulu; Informaatio- ja luonnontieteiden tiedekunta; Kaski, Kimmo, Prof.
The potential impact of lipid research has been increasingly realised both in disease treatment and prevention. Recent advances in soft ionization mass spectrometry (MS) such as electrospray ionization (ESI) have permitted parallel monitoring of several hundreds of lipids in a single experiment and thus facilitated lipidomics level studies. These advances, however, pose a greater challenge for bioinformaticians to handle massive amounts of information-rich MS data from modern analytical instruments in order to understand complex functions of lipids. The main aims of this thesis were to 1) develop bioinformatics approaches for lipid identification based on ultra performance liquid chromatography coupled to mass spectrometry (UPLC/MS) data, 2) predict the functional annotations for unidentified lipids, 3) understand the omics data in the context of pathways and 4) apply existing chemometric methods for exploratory data analysis as well as biomarker discovery. A bioinformatics strategy for the construction of lipid database for major classes of lipids is presented using simplified molecular input line entry system (SMILES) approach. The database was annotated with relevant information such as lipid names including short names, SMILES information, scores, molecular weight, monoisotopic mass, and isotope distribution. The database was tailored for UPLC/MS experiments by incorporating the information such as retention time range, adduct information and main fragments to screen for the potential lipids. This database information facilitated building experimental tandem mass spectrometry libraries for different biological tissues. Non-targeted metabolomics screening is often get plagued by the presence of unknown peaks and thus present an additional challenge for data interpretation. Multiple supervised classification methods were employed and compared for the functional prediction of class labels for unidentified lipids to facilitate exploratory analysis further as well as ease the identification process. As lipidomics goes beyond complete characterization of lipids, new strategies were developed to understand lipids in the context of pathways and thereby providing insights for the phenotype characterization. Chemometric methods such as principal component analysis (PCA) and partial least squares and discriminant analysis (PLS/DA) were utilised for exploratory analysis as well as biomarker discovery in the context of different disease phenotypes.
Computational analysis of the metabolic phenotypes in type 1 diabetes and their associations with mortality and diabetic complications
(Aalto-yliopiston teknillinen korkeakoulu, 2010) Mäkinen, Ville-Petteri; Groop, Per-Henrik, Prof.; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Aalto-yliopiston teknillinen korkeakoulu; Kaski, Kimmo, Prof.
Type 1 diabetes is an autoimmune disease that destroys the secretion of insulin (in the pancreas); insulin is a vital hormone for maintaining normal glucose metabolism. Insulin replacement therapy can prevent the acute symptoms, but is not able to fully match the natural regulation, which puts a metabolic stress on tissues. For some patients, the stress manifests as gradual damage to blood vessels and the nervous system over the next few decades after diabetes diagnosis. The aim of the thesis was to describe the metabolic profiles and to investigate their connections with the spectrum of clinical symptoms. Simultaneously, new techniques were applied to measure the profiles (1H NMR spectroscopy) and to visualize the multivariate statistical associations (the self-organizing map). A total of 4,197 patients with type 1 diabetes were recruited for the thesis by the Finnish Diabetic Nephropathy Study. A quarter of the patients exhibited an obesity-related phenotype (high triglycerides, cholesterol, apolipoprotein B-100, low high-density lipoprotein cholesterol, high C-reactive protein). A third of the individuals had a diabetic kidney disease phenotype (high urinary albumin and serum creatinine). The combination of the two was associated with a 10-fold population-adjusted mortality. Nevertheless, there was no discernible metabolic threshold between the phenotype models, nor were there any single variable that could predict the outcomes accurately. These results suggest a need for multifactorial and multidisciplinary paradigms for the research, treatment and prevention of diabetic complications.
Computational models and methods for lipoprotein research
(Aalto University, 2011) Kumpula, Linda; Ala-Korpela, Mika, Prof., Universtity of Oulu; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Perustieteiden korkeakoulu; Kaski, Kimmo, Prof.
Lipoproteins are self-assembled nanoparticles for water-insoluble lipid transportation in the circulation. Lipoprotein particles form a key metabolic system in a variety of normal physiological processes but also play an essential role in many pathological conditions. In particular, certain lipoprotein abnormalities are associated with the development of atherosclerosis, a disease state of arteries, common in cardiovascular disease. Computational modelling is a potential but so far rarely used method to study lipoprotein particles. This thesis contributes to lipoprotein research by various computational approaches where experimentally isolated and biochemically characterised lipoprotein particles serve as a starting point. This thesis deals with estimating the number of lipid molecules within lipoprotein particles, i.e., composition information, and approximating the molecular structure of lipoprotein particles in each subclass. It also proceed the ultracentrifugal particle isolation by a kind of in silico sub-classification resulting from utilisation of the self-organising map (SOM) method. This, when applied to experimental data, with lipoprotein lipid concentration and composition information combined, shows that there is variability in the compositional/metabolic relations between individuals, i.e., distinct lipoprotein phenotypes. Furthermore, this thesis introduces a method to estimate lipoprotein particle concentrations in each subclass, which also provides a reference particle library for NMR-based lipoprotein particle concentration estimation. Applications of the models to experimental data show that triglyceride and cholesterol ester molecules, which are conventionally held as core lipids, may also locate in significant amounts in the surface. The lipoprotein phenotype analysis shows that per particle compositions, which appear as a fundamental issue in metabolic and clinical corollaries, can not be deduced solely from the regularly measured plasma lipid concentrations nor from the particle concentration estimates.
Data integration, pathway analysis and mining for systems biology
(VTT, 2010) Peddinti, Venkata Gopalacharyulu; Oresic, Matej, Research Prof., VTT; Department of Biomedical Engineering and Computational Science; Neurotieteen ja lääketieteellisen tekniikan laitos; Informaatio- ja luonnontieteiden tiedekunta; Kaski, Kimmo, Prof.
Post-genomic molecular biology embodies high-throughput experimental techniques and hence is a data-rich field. The goal of this thesis is to develop bioinformatics methods to utilise publicly available data in order to produce knowledge and to aid mining of newly generated data. As an example of knowledge or hypothesis generation, consider function prediction of biological molecules. Assignment of protein function is a non-trivial task owing to the fact that the same protein may be involved in different biological processes, depending on the state of the biological system and protein localisation. The function of a gene or a gene product may be provided as a textual description in a gene or protein annotation database. Such textual descriptions lack in providing the contextual meaning of the gene function. Therefore, we need ways to represent the meaning in a formal way. Here we apply data integration approach to provide rich representation that enables context-sensitive mining of biological data in terms of integrated networks and conceptual spaces. Context-sensitive gene function annotation follows naturally from this framework, as a particular application. Next, knowledge that is already publicly available can be used to aid mining of new experimental data. We developed an integrative bioinformatics method that utilises publicly available knowledge of protein-protein interactions, metabolic networks and transcriptional regulatory networks to analyse transcriptomics data and predict altered biological processes. We applied this method to a study of dynamic response of Saccharomyces cerevisiae to oxidative stress. The application of our method revealed dynamically altered biological functions in response to oxidative stress, which were validated by comprehensive in vivo metabolomics experiments. The results provided in this thesis indicate that integration of heterogeneous biological data facilitates advanced mining of the data. The methods can be applied for gaining insight into functions of genes, gene products and other molecules, as well as for offering functional interpretation to transcriptomics and metabolomics experiments.
Dynamics of single biopolymer translocation and sedimentation
(Aalto-yliopiston teknillinen korkeakoulu, 2010) Lehtola, Ville; Linna, Riku, Dr.; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Aalto-yliopiston teknillinen korkeakoulu; Kaski, Kimmo, Prof.
In this Thesis the dynamics of translocation and sedimentation of a single biopolymer (typically DNA, RNA or a protein) is studied. A coarse-graining paradigm is invoked to justify the various computational models by use of which the results are obtained. The transport of biopolymers through a nano-scale pore in a membrane is a ubiquitous process in biology. Experimental interest in translocation process focuses on its potential applicability in ultra-fast sequencing of DNA and RNA molecules. Polymer translocation has been under intense study for over a decade. Inspite of the vast theoretical research, the experimental results on the driven case have not been explained. We claim that the reason for this is that the translocation process must be treated as (at least) two dynamically distinct cases, where the dynamics takes place either close to or out of equilibrium. Here, we find that the latter case corresponds to the experiments. We make a comprehensive investigation on how the process can be discriminated based on its dynamics, and define and use some indicators to this end. In addition, we study the role of hydrodynamics, and find it to govern the dynamics when the process takes place out of equilibrium. Sedimentation is a natural process induced by gravity that can be applied experimentally in a quickened form by the use of ultracentrifuges, and which is similar to electrophoresis. Our study on behavior of single polymers under non-equilibrium conditions falls within the rapidly developing field of nano- and microfluidics that has important applications in "lab-on-a-chip" based technologies. In polymer sedimentation, we study the settling of the polymer in a steady-state in the limit of zero Péclet number, i.e. where no thermal fluctuations exist. The hydrodynamic coupling of the polymer beads leads to chaotic time-dependent behavior of the chain conformations that in turn are coupled with the velocity fluctuations of the polymer's center of mass.
Metabolomics meets genetics - from an NMR metabolomics platform to the genetic architecture of serum metabolites
(Aalto University, 2012) Tukiainen, Taru; Ala-Korpela, Mika, Prof., University of Oulu, Finland; Ripatti, Samuli, Adj. Prof., Institute for Molecular Medicine Finland, Finland; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Perustieteiden korkeakoulu; School of Science; Kaski, Kimmo, Prof.
Metabolomics is a recently emerged field of science studying metabolites and how their levels change with biological perturbations. A key requirement for metabolomics analyses is a technology that can capture a multitude of metabolite information in a single measurement. As many of the available platforms have lacked automation in the metabolomics experimentation, including the data analysis and handling, the measurements have been costly and time-consuming, and thus metabolomics data had not been widely applied in large-scale studies. Metabolomics profiling, however, has great potential to provide further biological knowledge by, for example, elucidating in detail the mechanisms and pathways underlying disease. The first two publications of this thesis present a high-throughput proton nuclear magnetic resonance (NMR) -based serum metabolomics platform designed to facilitate the use of metabolomics data in large biomedical studies. The platform allows the highly-automated metabolomics profiling of tens of thousands of samples per year in a cost-effective manner and with the implemented models more than a hundred metabolites, including lipoprotein subclasses, other lipids and small molecules, can be quantified from the serum NMR data. The metabolomics profiling provided by the NMR-based platform has gained wide interest; the platform has run non-stop since it was set up in late 2008 as many Finnish and international cohorts have had their samples measured and used the data in several publications. In the two other publications included in this thesis, the quantitative metabolite data obtained through the platform was combined with detailed data on genetic variants in more than 8000 Finnish individuals. This unique data set was used a) to comprehensively characterize, in terms of metabolite and genetic associations, the genomic regions known to associate with blood lipid levels, and b) to dissect genetic components associated with the changes in the metabolite levels. A wealth of biological information was uncovered in these studies including new metabolic associations for the known genetic regions and several new genetic regions associated with the metabolites. These findings can help to understand the links between the genes and clinical conditions. Together the results of this thesis show how detailed metabolomics data greatly complements the conventional laboratory measurements and support the use of this data in biomedical studies as means to provide valuable biological knowledge.
Network biology : applications in medicine and biotechnology
(VTT, 2011) Lindfors, Erno; Oresic, Matej, Research Prof.; Department of Biomedical Engineering and Computational Science; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Perustieteiden korkeakoulu; Kaski, Kimmo, Prof.
The concept of systems biology emerged over the last decade in order to address advances in experimental techniques. It aims to characterize biological systems comprehensively as a complex network of interactions between the system's components. Network biology has become a core research domain of systems biology. It uses a graph theoretic approach. Many advances in complex network theory have contributed to this approach, and it has led to practical applications spanning from disease elucidation to biotechnology during the last few years. Herein we applied a network approach in order to model heterogeneous biological interactions. We developed a system called megNet for visualizing heterogeneous biological data, and showed its utility by biological network visualization examples, particularly in a biomedical context. In addition, we developed a novel biological network analysis method called Enriched Molecular Path detection method (EMPath) that detects phenotypic specific molecular paths in an integrated molecular interaction network. We showed its utility in the context of insulitis and autoimmune diabetes in the non-obese diabetic (NOD) mouse model. Specifically, ether phosholipid biosynthesis was down-regulated in early insulitis. This result was consistent with a previous study (Orešič et al., 2008) in which serum metabolite samples were taken from children who later progressed to type 1 diabetes and from children who permanently remained healthy. As a result, ether lipids were diminished in the type 1 diabetes progressors. Also, in this thesis we performed topological calculations to investigate whether ubiquitous complex network properties are present in biological networks. Results were consistent with recent critiques of the ubiquitous complex network properties describing the biological networks, which gave motivation to tailor another method called Topological Enrichment Analysis for Functional Subnetworks (TEAFS). This method ranks topological activities of modules of an integrated biological network under a dynamic response to external stress. We showed its utility by exposing an integrated yeast network to oxidative stress. Results showed that oxidative stress leads to accumulation of toxic lipids.

Browsing by Author "Kaski, Kimmo, Prof."

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