| 1. |
- Björklund, Tomas, et al.
(author)
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Optimization of continuous in vivo DOPA production and studies on ectopic DA synthesis using rAAV5 vectors in Parkinsonian rats
- 2009
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In: Journal of Neurochemistry. - : Wiley. - 1471-4159 .- 0022-3042. ; 111:2, s. 355-367
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Journal article (peer-reviewed)abstract
- Viral vector-mediated gene transfer is emerging as a novel therapeutic approach with clinical utility in treatment of Parkinson's disease. Recombinant adeno-associated viral (rAAV) vector in particular has been utilized for continuous l-3,4 dihydroxyphenylalanine (DOPA) delivery by expressing the tyrosine hydroxylase (TH) and GTP cyclohydrolase 1 (GCH1) genes which are necessary and sufficient for efficient synthesis of DOPA from dietary tyrosine. The present study was designed to determine the optimal stoichiometric relationship between TH and GCH1 genes for ectopic DOPA production and the cellular machinery involved in its synthesis, storage, and metabolism. For this purpose, we injected a fixed amount of rAAV5-TH vector and increasing amounts of rAAV5-GCH1 into the striatum of rats with complete unilateral dopamine lesion. After 7 weeks the animals were killed for either biochemical or histological analysis. We show that increasing the availability of 5,6,7,8-tetrahydro-l-biopterin (BH4) in the same cellular compartment as the TH enzyme resulted in better efficiency in DOPA synthesis, most likely by hindering inactivation of the enzyme and increasing its stability. Importantly, the BH4 synthesis from ectopic GCH1 expression was saturable, yielding optimal TH enzyme functionality between GCH1 : TH ratios of 1 : 3 and 1 : 7.
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| 2. |
- Duffy, Darragh, et al.
(author)
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The ABCs of viral hepatitis that define biomarker signatures of acute viral hepatitis
- 2014
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In: Hepatology. - : Ovid Technologies (Wolters Kluwer Health). - 1527-3350 .- 0270-9139. ; 59:4, s. 1273-1282
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Journal article (peer-reviewed)abstract
- Viral hepatitis is the leading cause of liver disease worldwide and can be caused by several agents, including hepatitis A (HAV), B (HBV), and C (HCV) virus. We employed multiplexed protein immune assays to identify biomarker signatures of viral hepatitis in order to define unique and common responses for three different acute viral infections of the liver. We performed multianalyte profiling, measuring the concentrations of 182 serum proteins obtained from acute HAV- (18), HBV- (18), and HCV-infected (28) individuals, recruited as part of a hospital-based surveillance program in Cairo, Egypt. Virus-specific biomarker signatures were identified and validation was performed using a unique patient population. A core signature of 46 plasma proteins was commonly modulated in all three infections, as compared to healthy controls. Principle component analysis (PCA) revealed a host response based upon 34 proteins, which could distinguish HCV patients from HAV- and HBV-infected individuals or healthy controls. When HAV and HBV groups were compared directly, 34 differentially expressed serum proteins allowed the separation of these two patient groups. A validation study was performed on an additional 111 patients, confirming the relevance of our initial findings, and defining the 17 analytes that reproducibly segregated the patient populations. Conclusions: This combined discovery and biomarker validation approach revealed a previously unrecognized virus-specific induction of host proteins. The identification of hepatitis virus specific signatures provides a foundation for functional studies and the identification of potential correlates of viral clearance. (Hepatology 2014;59:1273-1282)
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| 3. |
- Fontes, Magnus, et al.
(author)
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The projection score - an evaluation criterion for variable subset selection in PCA visualization
- 2011
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In: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 12
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Journal article (peer-reviewed)abstract
- Background In many scientific domains, it is becoming increasingly common to collect high-dimensional data sets, often with an exploratory aim, to generate new and relevant hypotheses. The exploratory perspective often makes statistically guided visualization methods, such as Principal Component Analysis (PCA), the methods of choice. However, the clarity of the obtained visualizations, and thereby the potential to use them to formulate relevant hypotheses, may be confounded by the presence of the many non-informative variables. For microarray data, more easily interpretable visualizations are often obtained by filtering the variable set, for example by removing the variables with the smallest variances or by only including the variables most highly related to a specific response. The resulting visualization may depend heavily on the inclusion criterion, that is, effectively the number of retained variables. To our knowledge, there exists no objective method for determining the optimal inclusion criterion in the context of visualization. Results We present the projection score, which is a straightforward, intuitively appealing measure of the informativeness of a variable subset with respect to PCA visualization. This measure can be universally applied to find suitable inclusion criteria for any type of variable filtering. We apply the presented measure to find optimal variable subsets for different filtering methods in both microarray data sets and synthetic data sets. We note also that the projection score can be applied in general contexts, to compare the informativeness of any variable subsets with respect to visualization by PCA. Conclusions We conclude that the projection score provides an easily interpretable and universally applicable measure of the informativeness of a variable subset with respect to visualization by PCA, that can be used to systematically find the most interpretable PCA visualization in practical exploratory analysis.
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| 4. |
- Johnsson, Kerstin, et al.
(author)
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Low Bias Local Intrinsic Dimension Estimation from Expected Simplex Skewness
- 2015
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In: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 1939-3539. ; 37:1, s. 196-202
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Journal article (peer-reviewed)abstract
- In exploratory high-dimensional data analysis, local intrinsic dimension estimation can sometimes be used in order to discriminate between data sets sampled from different low-dimensional structures. Global intrinsic dimension estimators can in many cases be adapted to local estimation, but this leads to problems with high negative bias or high variance. We introduce a method that exploits the curse/blessing of dimensionality and produces local intrinsic dimension estimators that have very low bias, even in cases where the intrinsic dimension is higher than the number of data points, in combination with relatively low variance. We show that our estimators have a very good ability to classify local data sets by their dimension compared to other local intrinsic dimension estimators; furthermore we provide examples showing the usefulness of local intrinsic dimension estimation in general and our method in particular for stratification of real data sets.
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| 5. |
- Kitazawa, Taro, et al.
(author)
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A unique bipartite Polycomb signature regulates stimulus-response transcription during development.
- 2021
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In: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 53:3, s. 379-391
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Journal article (peer-reviewed)abstract
- Rapid cellular responses to environmental stimuli are fundamental for development and maturation. Immediate early genes can be transcriptionally induced within minutes in response to a variety of signals. How their induction levels are regulated and their untimely activation by spurious signals prevented during development is poorly understood. We found that in developing sensory neurons, before perinatal sensory-activity-dependent induction, immediate early genes are embedded into a unique bipartite Polycomb chromatin signature, carrying active H3K27ac on promoters but repressive Ezh2-dependent H3K27me3 on gene bodies. This bipartite signature is widely present in developing cell types, including embryonic stem cells. Polycomb marking of gene bodies inhibits mRNA elongation, dampening productive transcription, while still allowing for fast stimulus-dependent mark removal and bipartite gene induction. We reveal a developmental epigenetic mechanism regulating the rapidity and amplitude of the transcriptional response to relevant stimuli, while preventing inappropriate activation of stimulus-response genes.
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| 6. |
- Lilljebjörn, Henrik, et al.
(author)
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The correlation pattern of acquired copy number changes in 164 ETV6/RUNX1-positive childhood acute lymphoblastic leukemias
- 2010
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In: Human Molecular Genetics. - : Oxford University Press. - 0964-6906 .- 1460-2083. ; 19:16, s. 3150-3158
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Journal article (peer-reviewed)abstract
- The ETV6/RUNX1 fusion gene, present in 25% of B-lineage childhood acute lymphoblastic leukemia (ALL), is thought to represent an initiating event, which requires additional genetic changes for leukemia development. To identify additional genetic alterations, 24 ETV6/RUNX1-positive ALLs were analyzed using 500K single nucleotide polymorphism arrays. The results were combined with previously published data sets, allowing us to ascertain genomic copy number aberrations (CNAs) in 164 cases. In total, 45 recurrent CNAs were identified with an average number of 3.5 recurrent changes per case (range 0-13). Twenty-six percent of cases displayed a set of recurrent CNAs identical to that of other cases in the data set. The majority (74%), however, displayed a unique pattern of recurrent CNAs, indicating a large heterogeneity within this ALL subtype. As previously demonstrated, alterations targeting genes involved in B-cell development were common (present in 28% of cases). However, the combined analysis also identified alterations affecting nuclear hormone response (24%) to be a characteristic feature of ETV6/RUNX1-positive ALL. Studying the correlation pattern of the CNAs allowed us to highlight significant positive and negative correlations between specific aberrations. Furthermore, oncogenetic tree models identified ETV6, CDKN2A/B, PAX5, del(6q) and +16 as possible early events in the leukemogenic process.
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| 7. |
- Soneson, Charlotte, et al.
(author)
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A framework for list representation, enabling list stabilization through incorporation of gene exchangeabilities.
- 2012
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In: Biostatistics. - : Oxford University Press (OUP). - 1468-4357 .- 1465-4644. ; 13, s. 129-141
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Journal article (peer-reviewed)abstract
- Analysis of multivariate data sets from, for example, microarray studies frequently results in lists of genes which are associated with some response of interest. The biological interpretation is often complicated by the statistical instability of the obtained gene lists, which may partly be due to the functional redundancy among genes, implying that multiple genes can play exchangeable roles in the cell. In this paper, we use the concept of exchangeability of random variables to model this functional redundancy and thereby account for the instability. We present a flexible framework to incorporate the exchangeability into the representation of lists. The proposed framework supports straightforward comparison between any 2 lists. It can also be used to generate new more stable gene rankings incorporating more information from the experimental data. Using 2 microarray data sets, we show that the proposed method provides more robust gene rankings than existing methods with respect to sampling variations, without compromising the biological significance of the rankings.
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| 8. |
- Soneson, Charlotte, et al.
(author)
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A method for visual identification of small sample subgroups and potential biomarkers
- 2011
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In: Annals of Applied Statistics. - 1932-6157. ; 5:3, s. 2131-2149
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Journal article (peer-reviewed)abstract
- In order to find previously unknown subgroups in biomedical data and generate testable hypotheses, visually guided exploratory analysis can be of tremendous importance. In this paper we propose a new dissimilarity measure that can be used within the Multidimensional Scaling framework to obtain a joint low-dimensional representation of both the samples and variables of a multivariate data set, thereby providing an alternative to conventional biplots. In comparison with biplots, the representations obtained by our approach are particularly useful for exploratory analysis of data sets where there are small groups of variables sharing unusually high or low values for a small group of samples.
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| 9. |
- Soneson, Charlotte, et al.
(author)
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Early changes in the hypothalamic region in prodromal Huntington disease revealed by MRI analysis.
- 2010
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In: Neurobiology of Disease. - : Elsevier BV. - 0969-9961. ; 40, s. 531-543
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Journal article (peer-reviewed)abstract
- Huntington disease (HD) is a fatal neurodegenerative disorder caused by an expanded CAG repeat. Its length can be used to estimate the time of clinical diagnosis, which is defined by overt motor symptoms. Non-motor symptoms begin before motor onset, and involve changes in hypothalamus-regulated functions such as sleep, emotion and metabolism. Therefore we hypothesized that hypothalamic changes occur already prior to the clinical diagnosis. We performed voxel-based morphometry and logistic regression analyses of cross-sectional MR images from 220 HD gene carriers and 75 controls in the Predict-HD study. We show that changes in the hypothalamic region are detectable before clinical diagnosis and that its grey matter contents alone are sufficient to distinguish HD gene carriers from control cases. In conclusion, our study shows, for the first time, that alterations in grey matter contents in the hypothalamic region occur at least a decade before clinical diagnosis in HD using MRI.
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| 10. |
- Soneson, Charlotte, et al.
(author)
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Incorporation of gene exchangeabilities improves the reproducibility of gene set rankings
- 2014
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In: Computational Statistics & Data Analysis. - : Elsevier BV. - 0167-9473. ; 71, s. 588-598
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Journal article (peer-reviewed)abstract
- Gene set-based analysis methods have recently gained increasing popularity for analysis of microarray data. Several studies have indicated that the results from such methods are more reproducible and more easily interpretable than the results from single gene-based methods. A new method for ranking gene sets with respect to their association with a given predictor or response, using a new framework for robust gene list representation, is proposed. Employing the concept of exchangeability of random variables, this method attempts to account for the functional redundancy among the genes. Compared to other evaluated methods for gene set ranking, the proposed method yields rankings that are more robust with respect to sampling variations in the underlying data, which allows more reliable biological conclusions. (C) 2012 Elsevier B.V. All rights reserved.
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| 11. |
- Soneson, Charlotte, et al.
(author)
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Integrative analysis of gene expression and copy number alterations using canonical correlation analysis
- 2010
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In: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 11:191, s. 1-20
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Journal article (peer-reviewed)abstract
- Background: With the rapid development of new genetic measurement methods, several types of genetic alterations can be quantified in a high-throughput manner. While the initial focus has been on investigating each data set separately, there is an increasing interest in studying the correlation structure between two or more data sets. Multivariate methods based on Canonical Correlation Analysis (CCA) have been proposed for integrating paired genetic data sets. The high dimensionality of microarray data imposes computational difficulties, which have been addressed for instance by studying the covariance structure of the data, or by reducing the number of variables prior to applying the CCA. In this work, we propose a new method for analyzing high-dimensional paired genetic data sets, which mainly emphasizes the correlation structure and still permits efficient application to very large data sets. The method is implemented by translating a regularized CCA to its dual form, where the computational complexity depends mainly on the number of samples instead of the number of variables. The optimal regularization parameters are chosen by cross-validation. We apply the regularized dual CCA, as well as a classical CCA preceded by a dimension-reducing Principal Components Analysis (PCA), to a paired data set of gene expression changes and copy number alterations in leukemia. Results: Using the correlation-maximizing methods, regularized dual CCA and PCA+CCA, we show that without pre-selection of known disease-relevant genes, and without using information about clinical class membership, an exploratory analysis singles out two patient groups, corresponding to well-known leukemia subtypes. Furthermore, the variables showing the highest relevance to the extracted features agree with previous biological knowledge concerning copy number alterations and gene expression changes in these subtypes. Finally, the correlation-maximizing methods are shown to yield results which are more biologically interpretable than those resulting from a covariance-maximizing method, and provide different insight compared to when each variable set is studied separately using PCA. Conclusions: We conclude that regularized dual CCA as well as PCA+CCA are useful methods for exploratory analysis of paired genetic data sets, and can be efficiently implemented also when the number of variables is very large.
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| 12. |
- Soneson, Charlotte
(author)
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Statistically Guided Visualization and Exploratory Analysis of Omics Data
- 2011
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Doctoral thesis (other academic/artistic)abstract
- This thesis deals with methods for extracting robust and relevant information from high-dimensional data sets, and statistically guided visualization methods for representing the data in an informative and easily accessible way. High-dimensional data sets are becoming increasingly prevalent in many different scientific disciplines. In this thesis, we focus particularly on so called "omics" data. The "omics" suffix is often used to represent biological research fields where the aim is to study relations and interactions within entire systems of biological entities, such as genes or proteins. The thesis is based on five papers. In the first two papers, we develop a method for stabilizing rankings of variables or variable sets obtained from an experiment. The stabilization effect is achieved by incorporating information concerning the exchangeability of variable pairs into the ranking. We propose a general framework for representation of variable lists, into which the variable pair exchangeabilities can be easily incorporated and which allows straightforward comparison of any two lists. In the third paper, we consider relevant dimension reduction of high-dimensional data sets and propose a new dissimilarity measure which can be used within the Multidimensional Scaling framework to obtain a low-dimensional representation of a data set. The proposed dissimilarity measure treats the variables and experimental units of the data jointly and symmetrically and yields a low-dimensional representation where patterns encoded by small groups of variables or units are more readily visible than with conventional methods such as Principal Component Analysis. The fourth paper provides a straightforward and intuitively appealing criterion for variable subset evaluation in the context of visualization. Finally, in the fifth paper we apply multivariate, correlation-based algorithms to integrate different types of high-dimensional genomic data. We show that by shifting the focus from maximizing the covariance toward maximizing the correlation between the extracted patterns we can extract more biologically relevant knowledge. The focus shift is made possible by considering the dual formulation of the applied methods which in this case is more computationally efficient.
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| 13. |
- Sun, Yingyu, et al.
(author)
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A glioma classification scheme based on coexpression modules of EGFR and PDGFRA
- 2014
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In: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424. ; 111:9, s. 3538-3543
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Journal article (peer-reviewed)abstract
- We hypothesized that key signaling pathways of glioma genesis might enable the molecular classification of gliomas. Gene coexpression modules around epidermal growth factor receptor (EGFR) (EM, 29 genes) or platelet derived growth factor receptor A (PDGFRA) (PM, 40 genes) in gliomas were identified. Based on EM and PM expression signatures, nonnegative matrix factorization reproducibly clustered 1,369 adult diffuse gliomas WHO grades II-IV from four independent databases generated in three continents, into the subtypes (EM, PM and EMlowPMlow gliomas) in a morphology-independent manner. Besides their distinct patterns of genomic alterations, EM gliomas were associated with higher age at diagnosis, poorer prognosis, and stronger expression of neural stem cell and astrogenesis genes. Both PM and EMlowPMlow gliomas were associated with younger age at diagnosis and better prognosis. PM gliomas were enriched in the expression of oligodendrogenesis genes, whereas EMlowPMlow gliomas were enriched in the signatures of mature neurons and oligodendrocytes. The EM/PM-based molecular classification scheme is applicable to adult low-grade and high-grade diffuse gliomas, and outperforms existing classification schemes in assigning diffuse gliomas to subtypes with distinct transcriptomic and genomic profiles. The majority of the EM/PM classifiers, including regulators of glial fate decisions, have not been extensively studied in glioma biology. Subsets of these classifiers were coexpressed in mouse glial precursor cells, and frequently amplified or lost in an EM/PM glioma subtypespecific manner, resulting in somatic copy number alteration-dependent gene expression that contributes to EM/PM signatures in glioma samples. EM/PM-based molecular classification provides a molecular diagnostic framework to expedite the search for new glioma therapeutic targets.
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