| 1. |
- Levefelt, Christer, et al.
(author)
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A fold-recognition approach to loop modeling
- 2006
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In: Journal of Molecular Modeling. - : Springer. - 1610-2940 .- 0948-5023. ; 12:2, s. 125-139
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Journal article (peer-reviewed)abstract
- A novel approach is proposed for modeling loop regions in proteins. In this approach, a prerequisite sequence-structure alignment is examined for regions where the target sequence is not covered by the structural template. These regions, extended with a number of residues from adjacent stem regions, are submitted to fold recognition. The alignments produced by fold recognition are integrated into the initial alignment to create an alignment between the target sequence and several structures, where gaps in the main structural template are covered by local structural templates. This one-to-many (1:N) alignment is used to create a protein model by existing protein-modeling techniques. Several alternative approaches were evaluated using a set of ten proteins. One approach was selected and evaluated using another set of 31 proteins. The most promising result was for gap regions not located at the C-terminus or N-terminus of a protein, where the method produced an average RMSD 12% lower than the loop modeling provided with the program MODELLER. This improvement is shown to be statistically significant.
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| 2. |
- Davies, G., et al.
(author)
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Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function
- 2018
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In: Nature Communications. - : Nature Publishing Group. - 2041-1723. ; 9:1
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Journal article (peer-reviewed)abstract
- General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16-102) and find 148 genome-wide significant independent loci (P < 5 × 10-8) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function.
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| 3. |
- Diaz Cruz, Maria Araceli
(author)
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Exploring vitamin D and steroid hormone receptors – from healthy elderly to prostate cancer cells
- 2022
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Doctoral thesis (other academic/artistic)abstract
- The genetic background together with environmental factors and lifestyle are key contributors to the health of an individual. Genetic background is inherited and irreversible unless mutations occur. However, lifestyle habits (i.e., diet, stress, physical activity, smoking, and alcohol consumption) are modifiable factors that contribute to health or disease by affecting methylation of DNA, which regulates transcription of genes.One of the most relevant lifestyle habits for health is maintaining adequate vitamin D levels in the body as vitamin D promotes calcium and phosphate absorption, supports the nervous and immune system function, and protects bone and muscle structure. Extreme low levels of vitamin D, vitamin D deficiency, has become a global public health concern, especially in the elderly population as vitamin D deficiency can lead to several health problems such as bone fracture, decreased muscle strength, cardiovascular and autoimmune diseases, depression, and breast, pancreatic, and prostate cancer.Prostate cancer is an uncontrolled growth of cells within the prostate gland in the male reproductive system. Human prostate carcinomas are sensitive to androgens, and hormonal ablation therapy gives a temporary remission, followed by a relapse to an androgen-insensitive state. This indicates that steroid hormones, especially androgens, play a significant role in human prostatic carcinogenesis. The molecular effect of vitamin D as a steroid hormone and which steroid hormone receptor (SHR) mediates this effect are not fully understood.This research project aims to increase our knowledge about SHRs, primarily the vitamin D receptors, in both health and disease, focusing on genomic, epigenomic, and transcriptomic perspectives in healthy elderly individuals and prostate cancer cells.The results from the studies in this thesis could help us understand the importance of a healthy lifestyle, which includes vitamin D for health, where we found specific methylation markers involved in the down-regulation of cancer pathways that are associated with high physical activity and vitamin D supplementation. We have further confirmed that SHRs rarely work in isolation but rather as a crosstalk at the genomic level to regulate their transcription. Hopefully, this will help clarify the modulation of transcriptional responses in SHRs and explain the development of steroid hormone-dependent cancers such as prostate cancer. Last, but not least, we revealed that genetic and transcriptional markers are associated with the putative vitamin D receptor the protein disulfide isomerase family A member 3 (PDIA3). The genetic markers were detected in a healthy elderly population under vitamin D supplementation. The transcriptional markers, PDIA3, and a novel discovered isoform of PDIA3 (PDIA3N) were related to the androgen and cancer stage of prostate cancer cells and therefore are proposed as candidate markers for clinical diagnosis of prostate cancer.Altogether, these findings support the relevance of studying vitamin D and steroid hormone receptors, especially the PDIA3 receptor, to understand some of the factors related to healthy aging and the etiology and progression of prostate cancer.
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| 4. |
- Ulfenborg, Benjamin, et al.
(author)
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A data analysis framework for biomedical big data : Application on mesoderm differentiation of human pluripotent stem cells
- 2017
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In: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 12:6
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Journal article (peer-reviewed)abstract
- The development of high-throughput biomolecular technologies has resulted in generation of vast omics data at an unprecedented rate. This is transforming biomedical research into a big data discipline, where the main challenges relate to the analysis and interpretation of data into new biological knowledge. The aim of this study was to develop a framework for biomedical big data analytics, and apply it for analyzing transcriptomics time series data from early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. To this end, transcriptome profiling by microarray was performed on differentiating human pluripotent stem cells sampled at eleven consecutive days. The gene expression data was analyzed using the five-stage analysis framework proposed in this study, including data preparation, exploratory data analysis, confirmatory analysis, biological knowledge discovery, and visualization of the results. Clustering analysis revealed several distinct expression profiles during differentiation. Genes with an early transient response were strongly related to embryonic-and mesendoderm development, for example CER1 and NODAL. Pluripotency genes, such as NANOG and SOX2, exhibited substantial downregulation shortly after onset of differentiation. Rapid induction of genes related to metal ion response, cardiac tissue development, and muscle contraction were observed around day five and six. Several transcription factors were identified as potential regulators of these processes, e.g. POU1F1, TCF4 and TBP for muscle contraction genes. Pathway analysis revealed temporal activity of several signaling pathways, for example the inhibition of WNT signaling on day 2 and its reactivation on day 4. This study provides a comprehensive characterization of biological events and key regulators of the early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. The proposed analysis framework can be used to structure data analysis in future research, both in stem cell differentiation, and more generally, in biomedical big data analytics.
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| 5. |
- Ulfenborg, Benjamin, 1985-, et al.
(author)
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Multi-Assignment Clustering : Machine learning from a biological perspective
- 2021
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In: Journal of Biotechnology. - : Elsevier. - 0168-1656 .- 1873-4863. ; 326, s. 1-10
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Journal article (peer-reviewed)abstract
- A common approach for analyzing large-scale molecular data is to cluster objects sharing similar characteristics. This assumes that genes with highly similar expression profiles are likely participating in a common molecular process. Biological systems are extremely complex and challenging to understand, with proteins having multiple functions that sometimes need to be activated or expressed in a time-dependent manner. Thus, the strategies applied for clustering of these molecules into groups are of key importance for translation of data to biologically interpretable findings. Here we implemented a multi-assignment clustering (MAsC) approach that allows molecules to be assigned to multiple clusters, rather than single ones as in commonly used clustering techniques. When applied to high-throughput transcriptomics data, MAsC increased power of the downstream pathway analysis and allowed identification of pathways with high biological relevance to the experimental setting and the biological systems studied. Multi-assignment clustering also reduced noise in the clustering partition by excluding genes with a low correlation to all of the resulting clusters. Together, these findings suggest that our methodology facilitates translation of large-scale molecular data into biological knowledge. The method is made available as an R package on GitLab (https://gitlab.com/wolftower/masc).
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