Quantitative proteomics in the characterization of T helper lymphocyte differentiation

The term proteome is used to define the complete set of proteins expressed in cells or tissues of an organism at a certain timepoint. Respectively, proteomics is used to describe the methods, which are used to study such proteomes. These methods include chromatographic and electrophoretic techniques for protein or peptide fractionation, mass spectrometry for their identification, and use of computational methods to assist the complicated data analysis. A primary aim in this Ph.D. thesis was to set-up, optimize, and develop proteomics methods for analysing proteins extracted from T-helper (Th) lymphocytes. First, high-throughput LC-MS/MS and ICAT labeling methods were set-up and optimized for analysing the microsomal fraction proteins extracted from Th lymphocytes. Later, iTRAQ method was optimized to study cytokine regulated protein expression in the nuclei of Th lymphocytes. High-throughput LC-MS/MS analyses, like ICAT and iTRAQ, produce large quantities of data and robust software and data analysis pipelines are needed. Therefore, different software programs used for analysing such data were evaluated. Moreover, a pre-filtering algorithm was developed to classify good-quality and bad-quality spectra prior to the database searches. Th-lymphocytes can differentiate into Th1 or Th2 cells based on surrounding antigens, co-stimulatory molecules, and cytokines. Both subsets have individual cytokine secretion profiles and specific functions. Th1 cells participate in the cellular immunity against intracellular pathogens, while Th2 cells have important role in the humoral immunity against extracellular parasites. An abnormal response of Th1 and Th2 cells and imbalance between the subsets are charasteristic of several diseases. Th1 specific reactions and cytokines have been detected in autoimmune diseases, while Th2 specific response and cytokine profile is common in allergy and asthma. In this Ph. D. thesis mass spectrometry-based proteomics was used to study the effects of Th1 and Th2 promoting cytokines IL-12 and IL-4 on the proteome of Th lymphocytes. Characterization of microsomal fraction proteome extracted from IL-12 treated lymphobasts and IL-4 stimulated cord blood CD4+ cells resulted in finding of cytokine regulated proteins. Galectin-1 and CD7 were down-regulated in IL-12 treated cells, while IL-4 stimulation decreased the expression of STAT1, MXA, GIMAP1, and GIMAP4. Interestingly, the transcription of both GIMAP genes was up-regulated in Th1 polarized cells and down-regulated in Th2 promoting conditions.

Tissue Proteomes: Quantitative Mass Spectrometry of Murine Liver and Ovarian Endometrioma

A human genome contains more than 20 000 protein-encoding genes. A human proteome, instead, has been estimated to be much more complex and dynamic. The most powerful tool to study proteins today is mass spectrometry (MS). MS based proteomics is based on the measurement of the masses of charged peptide ions in a gas-phase. The peptide amino acid sequence can be deduced, and matching proteins can be found, using software to correlate MS-data with sequence database information. Quantitative proteomics allow the estimation of the absolute or relative abundance of a certain protein in a sample. The label-free quantification methods use the intrinsic MS-peptide signals in the calculation of the quantitative values enabling the comparison of peptide signals from numerous patient samples. In this work, a quantitative MS methodology was established to study aromatase overexpressing (AROM+) male mouse liver and ovarian endometriosis tissue samples. The workflow of label-free quantitative proteomics was optimized in terms of sensitivity and robustness, allowing the quantification of 1500 proteins with a low coefficient of variance in both sample types. Additionally, five statistical methods were evaluated for the use with label-free quantitative proteomics data. The proteome data was integrated with other omics datasets, such as mRNA microarray and metabolite data sets. As a result, an altered lipid metabolism in liver was discovered in male AROM+ mice. The results suggest a reduced beta oxidation of long chain phospholipids in the liver and increased levels of pro-inflammatory fatty acids in the circulation in these mice. Conversely, in the endometriosis tissues, a set of proteins highly specific for ovarian endometrioma were discovered, many of which were under the regulation of the growth factor TGF-β1. This finding supports subsequent biomarker verification in a larger number of endometriosis patient samples.

Novel CO2 Regulated Proteins in Synechocystis PCC 6803

The large biodiversity of cyanobacteria together with the increasing genomics and proteomics metadata provide novel information for finding new commercially valuable metabolites. With the advent of global warming, there is growing interest in the processes that results in efficient CO2 capture through the use of photosynthetic microorganisms such as cyanobacteria. This requires a detailed knowledge of how cyanobacteria respond to the ambient CO2. My study was aimed at understanding the changes in the protein profile of the model organism, Synechocystis PCC 6803 towards the varying CO₂ level. In order to achieve this goal I have employed modern proteomics tools such as iTRAQ and DIGE, recombinant DNA techniques to construct different mutants in cyanobacteria and biophysical methods to study the photosynthetic properties. The proteomics study revealed several novel proteins, apart from the well characterized proteins involved in carbon concentrating mechanisms (CCMs), that were upregulated upon shift of the cells from high CO₂ concentration (3%) to that in air level (0.039%). The unknown proteins, Slr0006 and flavodiiron proteins (FDPs) Sll0217-Flv4 and Sll0219-Flv2, were selected for further characterization. Although slr0006 was substantially upregulated under C_i limiting conditions, inactivation of the gene did not result in any visual phenotype under various environmental conditions indicating that this protein is not essential for cell survival. However, quantitative proteomics showed the induction of novel plasmid and chromosome encoded proteins in deltaslr0006 under air level CO₂ conditions. The expression of the slr0006 gene was found to be strictly dependent on active photosynthetic electron transfer. Slr0006 contains conserved dsRNA binding domain that belongs to the Sua5/YrdC/YciO protein family. Structural modelling of Slr0006 showed an alpha/beta twisted open-sheet structure and a positively charged cavity, indicating a possible binding site for RNA. The 3D model and the co-localization of Slr0006 with ribosomal subunits suggest that it might play a role in translation or ribosome biogenesis. On the other hand, deletions in the sll0217-sll218- sll0219 operon resulted in enhanced photodamage of PSII and distorted energy transfer from phycobilisome (PBS) to PSII, suggesting a dynamic photoprotection role of the operon. Constructed homology models also suggest efficient electron transfer in heterodimeric Flv2/Flv4, apparently involved in PSII photoprotection. Both Slr0006 and FDPs exhibited several common features, including negative regulation by NdhR and ambiguous cellular localization when subjected to different concentrations of divalent ions. This strong association with the membranes remained undisturbed even in the presence of detergent or high salt. My finding brings ample information on three novel proteins and their functions towards carbon limitation. Nevertheless, many pathways and related proteins remain unexplored. The comprehensive understanding of the acclimation processes in cyanobacteria towards varying environmental CO₂ levels will help to uncover adaptive mechanisms in other organisms, including higher plants.

Comparison of five popular normalization methods using a spike-in proteomics data set

Mass spectrometry (MS)-based proteomics has seen significant technical advances during the past two decades and mass spectrometry has become a central tool in many biosciences. Despite the popularity of MS-based methods, the handling of the systematic non-biological variation in the data remains a common problem. This biasing variation can result from several sources ranging from sample handling to differences caused by the instrumentation. Normalization is the procedure which aims to account for this biasing variation and make samples comparable. Many normalization methods commonly used in proteomics have been adapted from the DNA-microarray world. Studies comparing normalization methods with proteomics data sets using some variability measures exist. However, a more thorough comparison looking at the quantitative and qualitative differences of the performance of the different normalization methods and at their ability in preserving the true differential expression signal of proteins, is lacking. In this thesis, several popular and widely used normalization methods (the Linear regression normalization, Local regression normalization, Variance stabilizing normalization, Quantile-normalization, Median central tendency normalization and also variants of some of the forementioned methods), representing different strategies in normalization are being compared and evaluated with a benchmark spike-in proteomics data set. The normalization methods are evaluated in several ways. The performance of the normalization methods is evaluated qualitatively and quantitatively on a global scale and in pairwise comparisons of sample groups. In addition, it is investigated, whether performing the normalization globally on the whole data or pairwise for the comparison pairs examined, affects the performance of the normalization method in normalizing the data and preserving the true differential expression signal. In this thesis, both major and minor differences in the performance of the different normalization methods were found. Also, the way in which the normalization was performed (global normalization of the whole data or pairwise normalization of the comparison pair) affected the performance of some of the methods in pairwise comparisons. Differences among variants of the same methods were also observed.

Regulatory Mechanisms involved in Th2 Cell Differentiation. A Proteomics Approach.

Th2-solujen erilaistumista ohjaavat säätelyverkostot ja niiden tutkiminen proteomiikan avulla Astma ja allergiat ovat laajalle levinneitä ja vakavia sairauksia, joista kärsivät miljoonat ihmiset ympäri maailmaa. Koe-eläimillä tehdyt tutkimukset osoittavat, että interleukiini-4 (IL-4) on tärkeä allergisen astman ja allergioiden kehittymiselle ja kroonistumiselle. Se ohjaa T-auttajasolujen (Th-solujen) kehittymistä Th2-tyypin soluiksi, joilla on merkittävä rooli näiden tautien puhkeamisessa. Th2-solut tuottavat myös itse IL-4:ä, joka edesauttaa taudin seuraavien vaiheiden kehittymistä. Erityisesti STAT6-proteiini, joka aktivoituu IL-4-stimulaation seurauksena, on tarpeen Th2- vasteen syntymiselle ja kroonistumiselle antigeenin aiheuttamassa keuhkoputkien astmaattisessa tulehduksessa. Väitöskirjatyöni tarkoituksena oli käyttää kaksidimensionaaliseen elektroforeesiin (2- DE) perustuvaa proteomiikkaa ja massaspektrometriaa uusien Th2-solujen erilaistumista säätelevien proteiinien tunnistamiseksi. Erilaistumattomat Th-solut eristettiin vastasyntyneen napaverestä tai hiiren pernasta. Solut aktivoitiin Tsolureseptorin ja ns. ko-stimulatoristen reseptorien kautta ja erilaistettiin joko Th1- tai Th2-suuntaan vastaavasti erilaistavien IL-12- ja IL-4-sytokiinien avulla. Ensimmäisessä tutkimuksessa in vitro -erilaistettujen Th1- ja Th2-solujen proteomeja verrattiin keskenään proteiinien ilmenemisessä tai proteiinimodifikaatioissa olevien erojen tunnistamiseksi. Kaksi muuta päätutkimusta keskittyivät IL-4:n aiheuttamaan proteiinitason säätelyyn ensimmäisen vuorokauden aikana T-soluaktivaation jälkeen. Näistä ensimmäisessä IL-4:n aiheuttamia eroja tunnistettiin aktivoiduista ihmisen Thsoluista. IL-4:n todettiin säätelevän useita proteiineja kaspaasien välittämissä signalointiteissä sekä lisäävän T-solujen elävyyttä ja aktivoitumista. Toisessa tutkimuksessa STAT6-poistogeenisten hiirien lymfosyyttien proteomia verrattiin villityypin kontrollisoluihin T-soluaktivaation ja IL-4-stimulaation jälkeen. Näissä tutkimuksissa karakterisoitiin useita uusia IL-4:n ja STAT6:n kohdeproteiineja ja löydettiin uusia säätelyverkostoja. Tutkimustulokset ovat johtaneet uusiin Th2-erilaistumismekanismeja koskeviin hypoteeseihin.

Quantitative elemental analysis of dry-ashed bark and wood samples of birch, spruce and pine from south-western Finland using PIXE

A total of over 200 different samples of bark and wood of Silver birch, Norway spruce and Scots pine were analysed. Samples were taken from several areas in western Finland, some with known sources of atmospheric heavy metal emission (Harjavalta, Ykspihlaja). Also analytical data for pine needles from some sites are reported. The chemical analyses were performed by thick-target particle-induced X-ray emission (PIXE) spectrometry after preconcentration by dry ashing of samples at 550oC. The following elements were quantified in most of the samples: P, S, K, Ca, Mn, Fe, Ni, Cu, Zn, Rb, Sr, Cd, Ba and Pb. The ash percentage and the chemical composition of ashes of different wood materials were also obtained, as dry ashing was used in the analytical procedure. The variations in elemental concentrations in wood and bark of an individual tree, expressed as RSDs, were mostly in the range 10 – 20 %. For several trees of the same species sampled from small areas (< 1 ha), the variations in elemental concentrations were surprisingly high (RSDs 20 – 50 %). In the vicinity of metal plants, effects of strong atmospheric heavy metal pollution (pollution factor above 100) were observed in pine bark. The increase of heavy metal content in wood samples from the same sites was quite small. Elemental concentrations in ashes of bark and wood, from areas with no local source of atmospheric pollution, were relatively uniform. Based on this observation an alternative way of demonstrating atmospheric pollution of tree bark is discussed.

Quantitative market and sales forecast accuracy analysis

The main objective of this master’s thesis was to quantitatively study the reliability of market and sales forecasts of a certain company by measuring bias, precision and accuracy of these forecasts by comparing forecasts against actual values. Secondly, the differences of bias, precision and accuracy between markets were explained by various macroeconomic variables and market characteristics. Accuracy and precision of the forecasts seems to vary significantly depending on the market that is being forecasted, the variable that is being forecasted, the estimation period, the length of the estimated period, the forecast horizon and the granularity of the data. High inflation, low income level and high year-on-year market volatility seems to be related with higher annual market forecast uncertainty and high year-on-year sales volatility with higher sales forecast uncertainty. When quarterly market size is forecasted, correlation between macroeconomic variables and forecast errors reduces. Uncertainty of the sales forecasts cannot be explained with macroeconomic variables. Longer forecasts are more uncertain, shorter estimated period leads to higher uncertainty, and usually more recent market forecasts are less uncertain. Sales forecasts seem to be more uncertain than market forecasts, because they incorporate both market size and market share risks. When lead time is more than one year, forecast risk seems to grow as a function of root forecast horizon. When lead time is less than year, sequential error terms are typically correlated, and therefore forecast errors are trending or mean-reverting. The bias of forecasts seems to change in cycles, and therefore the future forecasts cannot be systematically adjusted with it. The MASE cannot be used to measure whether the forecast can anticipate year-on-year volatility. Instead, we constructed a new relative accuracy measure to cope with this particular situation.

Delivery Reliability: Timely and Quantitative Accuracy in a Forest Industry Company

The primary objective of this thesis was to research delivery reliability of mill business unit of a forest industry company, especially timely and quantitative accuracy of sales orders. Delivery reliability is an important factor of customer satisfaction, which has a great influence for success of a company. The secondary objective was to find out reasons for possible problems of delivery reliability and give propositions for improvable performances. The empirical part of the thesis based on reporting database of the forest industry company’s ERP-software and detailed information of the mill system. The delivery reliability results of the mill business unit were compared to delivery reliability of similar mill business unit inside the forest industry company. The research results expressed problems in the supply chain. The delivery reliability reporting should be also developed further. This would advance delivery reliability monitoring. The improvement propositions of the thesis were logistic operation mode estimation, particular benchmarking of the compared mill business unit and more detailed survey on production delivery reliability.

Quantitative Sustainability Disclosure –An international comparison and its impact on investor valuation

This research focuses on the link between quantitative sustainability disclosure and information asymmetry. It builds upon previous research which links information asymmetry with voluntary disclosure. Stakeholders from the financial services sector claim that sustainability disclosure needs to be more numerical and comparable between companies. This research covers 111 firms from Denmark, Finland, the Netherlands and Sweden from non-service industries and studies how quantitative their sustainability disclosure is, and whether or not there is a negative relation with information asymmetry. The results support the hypotheses, where two out of three information asymmetry proxies have a significant negative relation with quantitative disclosure. Size is supported as a moderating factor. Quantitativity also proves to have a significant link with third party sustainability ratings. The direct link between quantitativity and cost of capital is not however supported.

A quantitative view on fuzzy numbers

Since its introduction, fuzzy set theory has become a useful tool in the mathematical modelling of problems in Operations Research and many other fields. The number of applications is growing continuously. In this thesis we investigate a special type of fuzzy set, namely fuzzy numbers. Fuzzy numbers (which will be considered in the thesis as possibility distributions) have been widely used in quantitative analysis in recent decades. In this work two measures of interactivity are defined for fuzzy numbers, the possibilistic correlation and correlation ratio. We focus on both the theoretical and practical applications of these new indices. The approach is based on the level-sets of the fuzzy numbers and on the concept of the joint distribution of marginal possibility distributions. The measures possess similar properties to the corresponding probabilistic correlation and correlation ratio. The connections to real life decision making problems are emphasized focusing on the financial applications. We extend the definitions of possibilistic mean value, variance, covariance and correlation to quasi fuzzy numbers and prove necessary and sufficient conditions for the finiteness of possibilistic mean value and variance. The connection between the concepts of probabilistic and possibilistic correlation is investigated using an exponential distribution. The use of fuzzy numbers in practical applications is demonstrated by the Fuzzy Pay-Off method. This model for real option valuation is based on findings from earlier real option valuation models. We illustrate the use of number of different types of fuzzy numbers and mean value concepts with the method and provide a real life application.