| 1. |
- Marques, Sueli, et al.
(author)
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Transcriptional Convergence of Oligodendrocyte Lineage Progenitors during Development
- 2018
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In: Developmental Cell. - : Elsevier BV. - 1878-1551 .- 1534-5807. ; 46:4, s. 504-517
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Journal article (peer-reviewed)abstract
- Pdgfra+ oligodendrocyte precursor cells (OPCs) arise in distinct specification waves during embryogenesis in the central nervous system (CNS). It is unclear whether there is a correlation between these waves and different oligodendrocyte (OL) states at adult stages. Here, we present bulk and single-cell transcriptomics resources providing insights on how transitions between these states occur. We found that post-natal OPCs from brain and spinal cord present similar transcriptional signatures. Moreover, post-natal OPC progeny of E13.5 Pdgfra+ cells present electrophysiological and transcriptional profiles similar to OPCs derived from subsequent specification waves, indicating that Pdgfra+ pre-OPCs rewire their transcriptional network during development. Single-cell RNA-seq and lineage tracing indicates that a subset of E13.5 Pdgfra+ cells originates cells of the pericyte lineage. Thus, our results indicate that embryonic Pdgfra+ cells in the CNS give rise to distinct post-natal cell lineages, including OPCs with convergent transcriptional profiles in different CNS regions.
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| 2. |
- Svahn, Sara L, et al.
(author)
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Six Tissue Transcriptomics Reveals Specific Immune Suppression in Spleen by Dietary Polyunsaturated Fatty Acids
- 2016
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In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 11:5
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Journal article (peer-reviewed)abstract
- Dietary polyunsaturated fatty acids (PUFA) are suggested to modulate immune function, but the effects of dietary fatty acids composition on gene expression patterns in immune organs have not been fully characterized. In the current study we investigated how dietary fatty acids composition affects the total transcriptome profile, and especially, immune related genes in two immune organs, spleen (SPL) and bone marrow cells (BMC). Four tissues with metabolic function, skeletal muscle (SKM), white adipose tissue (WAT), brown adipose tissue (BAT), and liver (LIV), were investigated as a comparison. Following 8 weeks on low fat diet (LFD), high fat diet (HFD) rich in saturated fatty acids (HFD-S), or HFD rich in PUFA (HFD-P), tissue transcriptomics were analyzed by microarray and metabolic health assessed by fasting blood glucose level, HOMA-IR index, oral glucose tolerance test as well as quantification of crown-like structures in WAT. HFD-P corrected the metabolic phenotype induced by HFD-S. Interestingly, SKM and BMC were relatively inert to the diets, whereas the two adipose tissues (WAT and BAT) were mainly affected by HFD per se (both HFD-S and HFD-P). In particular, WAT gene expression was driven closer to that of the immune organs SPL and BMC by HFDs. The LIV exhibited different responses to both of the HFDs. Surprisingly, the spleen showed a major response to HFD-P (82 genes differed from LFD, mostly immune genes), while it was not affected at all by HFD-S (0 genes differed from LFD). In conclusion, the quantity and composition of dietary fatty acids affected the transcriptome in distinct manners in different organs. Remarkably, dietary PUFA, but not saturated fat, prompted a specific regulation of immune related genes in the spleen, opening the possibility that PUFA can regulate immune function by influencing gene expression in this organ.
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| 3. |
- Väremo, Leif, et al.
(author)
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Proteome- and Transcriptome-Driven Reconstruction of the Human Myocyte Metabolic Network and Its Use for Identification of Markers for Diabetes
- 2015
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In: Cell Reports. - : Elsevier BV. - 2211-1247. ; 11:6, s. 921-933
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Journal article (peer-reviewed)abstract
- Skeletal myocytes are metabolically active and susceptible to insulin resistance and are thus implicated in type 2 diabetes (T2D). This complex disease involves systemic metabolic changes, and their elucidation at the systems level requires genome-wide data and biological networks. Genome-scale metabolic models (GEMs) provide a network context for the integration of high-throughput data. We generated myocyte-specific RNA-sequencing data and investigated their correlation with proteome data. These data were then used to reconstruct a comprehensive myocyte GEM. Next, we performed a meta-analysis of six studies comparing muscle transcription in T2D versus healthy subjects. Transcriptional changes were mapped on the myocyte GEM, revealing extensive transcriptional regulation in T2D, particularly around pyruvate oxidation, branched-chain amino acid catabolism, and tetrahydrofolate metabolism, connected through the downregulated dihydrolipoamide dehydrogenase. Strikingly, the gene signature underlying this metabolic regulation successfully classifies the disease state of individual samples, suggesting that regulation of these pathways is a ubiquitous feature of myocytes in response to T2D.
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| 4. |
- Väremo, Leif, 1986, et al.
(author)
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Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods
- 2013
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In: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 41:8, s. 4378-4391
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Journal article (peer-reviewed)abstract
- Gene set analysis (GSA) is used to elucidate genome-wide data, in particular transcriptome data. A multitude of methods have been proposed for this step of the analysis, and many of them have been compared and evaluated. Unfortunately, there is no consolidated opinion regarding what methods should be preferred, and the variety of available GSA software and implementations pose a difficulty for the end-user who wants to try out different methods. To address this, we have developed the R package Piano that collects a range of GSA methods into the same system, for the benefit of the end-user. Further on we refine the GSA workflow by using modifications of the gene-level statistics. This enables us to divide the resulting gene set P-values into three classes, describing different aspects of gene expression directionality at gene set level. We use our fully implemented workflow to investigate the impact of the individual components of GSA by using microarray and RNA-seq data. The results show that the evaluated methods are globally similar and the major separation correlates well with our defined directionality classes. As a consequence of this, we suggest to use a consensus scoring approach, based on multiple GSA runs. In combination with the directionality classes, this constitutes a more thorough basis for an enriched biological interpretation.
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| 5. |
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| 6. |
- Väremo, Leif, 1986
(author)
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Systems Biology of Type 2 Diabetes in Skeletal Muscle
- 2016
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Doctoral thesis (other academic/artistic)abstract
- Type 2 diabetes (T2D) is a heterogeneous and complex disease that currently affects more than 350 million people worldwide. A wide range of risk factors influence the pathogenesis of T2D, including genetic and epigenetic components, as well as controllable factors such as diet, obesity, and sedentary lifestyle. T2D is characterized by abnormally high blood glucose levels as a consequence of the development of insulin resistance in multiple tissues (primarily skeletal muscle, liver, and adipose tissue) in combination with impaired insulin secretion in the pancreas. Skeletal muscle accounts for around 75-80% of the insulin-stimulated glucose uptake from the blood. Consequently, deficiency in glucose uptake mediated by insulin resistance in skeletal myocytes is an important factor for the disrupted glucose homeostasis associated with T2D. In fact, skeletal muscle insulin resistance can appear long before the onset of the disease itself, making it one of the primary defects preceding the development of T2D. The pathophysiology of T2D and the mechanisms underlying the development of insulin resistance in skeletal muscle are not yet fully understood. In light of the multifactorial complexity of T2D we have adopted a systems biology approach to study skeletal muscle in response to this disease, using network modeling of metabolism and analysis of genome-wide data from human subjects.We developed three tools for analyzing gene expression data and facilitating its interpretation. The R package piano enables functional characterization and interpretation of gene expression profiles (and other omics data), through so called gene-set analysis (GSA). The skeletal myocyte genome-scale metabolic model (GEM), that we reconstructed based on transcriptome and proteome data, constitutes a comprehensive map of the myocyte metabolic network that can be used for simulation and integration of genome-wide data. The Python tool Kiwi visualizes the output from GSA using metabolite gene-sets and the topology of a GEM so that significant metabolite subnetworks affected by gene expression changes can be identified.Leveraged by these tools, we performed two studies of T2D. In the first study, we carried out a meta-analysis of muscle tissue transcriptome data from 6 published datasets, providing a holistic insight into the metabolic state of T2D muscle. In particular, we identified a metabolic signature that has the power to predict T2D in individual subjects, highlighting its potential use for biomarkers or drug targets. In the second study, we analyzed transcriptome data from primary differentiated myocytes to explore inherent properties associated with T2D and obesity. We found a remarkable similarity between the transcriptional profiles in response to T2D and obesity, independent of each other, and identified a possible epigenetic mechanism behind these patterns. We performed a systematic characterization of the individual intrinsic effects of T2D and obesity, which are hardwired in the myocytes rather than attributable to a diabetic or obese extracellular environment. In summary, this thesis provides novel methods for analysis of genome-wide data and contributes to disentangling the complexity of T2D.
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