| 1. |
- Valdiosera, Cristina, et al.
(författare)
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Four millennia of Iberian biomolecular prehistory illustrate the impact of prehistoric migrations at the far end of Eurasia
- 2018
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 115:13, s. 3428-3433
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Tidskriftsartikel (refereegranskat)abstract
- Population genomic studies of ancient human remains have shown how modern-day European population structure has been shaped by a number of prehistoric migrations. The Neolithization of Europe has been associated with large-scale migrations from Anatolia, which was followed by migrations of herders from the Pontic steppe at the onset of the Bronze Age. Southwestern Europe was one of the last parts of the continent reached by these migrations, and modern-day populations from this region show intriguing similarities to the initial Neolithic migrants. Partly due to climatic conditions that are unfavorable for DNA preservation, regional studies on the Mediterranean remain challenging. Here, we present genome-wide sequence data from 13 individuals combined with stable isotope analysis from the north and south of Iberia covering a four-millennial temporal transect (7,500-3,500 BP). Early Iberian farmers and Early Central European farmers exhibit significant genetic differences, suggesting two independent fronts of the Neolithic expansion. The first Neolithic migrants that arrived in Iberia had low levels of genetic diversity, potentially reflecting a small number of individuals; this diversity gradually increased over time from mixing with local hunter-gatherers and potential population expansion. The impact of post-Neolithic migrations on Iberia was much smaller than for the rest of the continent, showing little external influence from the Neolithic to the Bronze Age. Paleodietary reconstruction shows that these populations have a remarkable degree of dietary homogeneity across space and time, suggesting a strong reliance on terrestrial food resources despite changing culture and genetic make-up.
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| 2. |
- Abenius, Tobias, 1979, et al.
(författare)
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System-scale network modeling of cancer using EPoC
- 2012
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Ingår i: Advances in Experimental Medicine and Biology. - New York, NY : Springer New York. - 0065-2598. - 9781441972095 ; 736:5, s. 617-643
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Tidskriftsartikel (refereegranskat)abstract
- One of the central problems of cancer systems biology is to understand the complex molecular changes of cancerous cells and tissues, and use this understanding to support the development of new targeted therapies. EPoC (Endogenous Perturbation analysis of Cancer) is a network modeling technique for tumor molecular profiles. EPoC models are constructed from combined copy number aberration (CNA) and mRNA data and aim to (1) identify genes whose copy number aberrations significantly affect target mRNA expression and (2) generate markers for long- and short-term survival of cancer patients. Models are constructed by a combination of regression and bootstrapping methods. Prognostic scores are obtained from a singular value decomposition of the networks. We have previously analyzed the performance of EPoC using glioblastoma data from The Cancer Genome Atlas (TCGA) consortium, and have shown that resulting network models contain both known and candidate disease-relevant genes as network hubs, as well as uncover predictors of patient survival. Here, we give a practical guide how to perform EPoC modeling in practice using R, and present a set of alternative modeling frameworks.
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| 3. |
- Bengtsson-Palme, Johan, 1985, et al.
(författare)
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Strategies to improve usability and preserve accuracy in biological sequence databases
- 2016
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Ingår i: Proteomics. - : Wiley. - 1615-9853 .- 1615-9861. ; 16:18, s. 2454-2460
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Tidskriftsartikel (refereegranskat)abstract
- Biology is increasingly dependent on large-scale analysis, such as proteomics, creating a requirement for efficient bioinformatics. Bioinformatic predictions of biological functions rely upon correctly annotated database sequences, and the presence of inaccurately annotated or otherwise poorly described sequences introduces noise and bias to biological analyses. Accurate annotations are, for example, pivotal for correct identifications of polypeptide fragments. However, standards for how sequence databases are organized and presented are currently insufficient. Here, we propose five strategies to address fundamental issues in the annotation of sequence databases: (i) to clearly separate experimentally verified and unverified sequence entries; (ii) to enable a system for tracing the origins of annotations; (iii) to separate entries with high-quality, informative annotation from less useful ones; (iv) to integrate automated quality-control software whenever such tools exist; and (v) to facilitate post-submission editing of annotations and metadata associated with sequences. We believe that implementation of these strategies, for example as requirements for publication of database papers, would enable biology to better take advantage of large-scale data.
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| 4. |
- Cheng, Tuck Seng, et al.
(författare)
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Circulating free insulin-like growth factor-I and prostate cancer : a case-control study nested in the European prospective investigation into cancer and nutrition
- 2024
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Ingår i: BMC Cancer. - : BioMed Central (BMC). - 1471-2407. ; 24:1
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Circulating total insulin-like growth factor-I (IGF-I) is an established risk factor for prostate cancer. However, only a small proportion of circulating IGF-I is free or readily dissociable from IGF-binding proteins (its bioavailable form), and few studies have investigated the association of circulating free IGF-I with prostate cancer risk.METHODS: We analyzed data from 767 prostate cancer cases and 767 matched controls nested within the European Prospective Investigation into Cancer and Nutrition cohort, with an average of 14-years (interquartile range = 2.9) follow-up. Matching variables were study center, length of follow-up, age, and time of day and fasting duration at blood collection. Circulating free IGF-I concentration was measured in serum samples collected at recruitment visit (mean age 55 years old; standard deviation = 7.1) using an enzyme-linked immunosorbent assay (ELISA). Conditional logistic regressions were performed to examine the associations of free IGF-I with risk of prostate cancer overall and subdivided by time to diagnosis (≤ 14 and > 14 years), and tumor characteristics.RESULTS: Circulating free IGF-I concentrations (in fourths and as a continuous variable) were not associated with prostate cancer risk overall (odds ratio [OR] = 1.00 per 0.1 nmol/L increment, 95% CI: 0.99, 1.02) or by time to diagnosis, or with prostate cancer subtypes, including tumor stage and histological grade.CONCLUSIONS: Estimated circulating free IGF-I was not associated with prostate cancer risk. Further research may consider other assay methods that estimate bioavailable IGF-I to provide more insight into the well-substantiated association between circulating total IGF-I and subsequent prostate cancer risk.
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| 5. |
- Clark, Andrew G., et al.
(författare)
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Evolution of genes and genomes on the Drosophila phylogeny
- 2007
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 450:7167, s. 203-218
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Tidskriftsartikel (refereegranskat)abstract
- Comparative analysis of multiple genomes in a phylogenetic framework dramatically improves the precision and sensitivity of evolutionary inference, producing more robust results than single-genome analyses can provide. The genomes of 12 Drosophila species, ten of which are presented here for the first time (sechellia, simulans, yakuba, erecta, ananassae, persimilis, willistoni, mojavensis, virilis and grimshawi), illustrate how rates and patterns of sequence divergence across taxa can illuminate evolutionary processes on a genomic scale. These genome sequences augment the formidable genetic tools that have made Drosophila melanogaster a pre-eminent model for animal genetics, and will further catalyse fundamental research on mechanisms of development, cell biology, genetics, disease, neurobiology, behaviour, physiology and evolution. Despite remarkable similarities among these Drosophila species, we identified many putatively non-neutral changes in protein-coding genes, non-coding RNA genes, and cis-regulatory regions. These may prove to underlie differences in the ecology and behaviour of these diverse species.
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| 6. |
- Deiana, Marco, et al.
(författare)
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A new G-quadruplex-specific photosensitizer inducing genome instability in cancer cells by triggering oxidative DNA damage and impeding replication fork progression
- 2023
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Ingår i: Nucleic Acids Research. - : Oxford University Press. - 0305-1048 .- 1362-4962. ; 51:12, s. 6264-6285
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Tidskriftsartikel (refereegranskat)abstract
- Photodynamic therapy (PDT) ideally relies on the administration, selective accumulation and photoactivation of a photosensitizer (PS) into diseased tissues. In this context, we report a new heavy-atom-free fluorescent G-quadruplex (G4) DNA-binding PS, named DBI. We reveal by fluorescence microscopy that DBI preferentially localizes in intraluminal vesicles (ILVs), precursors of exosomes, which are key components of cancer cell proliferation. Moreover, purified exosomal DNA was recognized by a G4-specific antibody, thus highlighting the presence of such G4-forming sequences in the vesicles. Despite the absence of fluorescence signal from DBI in nuclei, light-irradiated DBI-treated cells generated reactive oxygen species (ROS), triggering a 3-fold increase of nuclear G4 foci, slowing fork progression and elevated levels of both DNA base damage, 8-oxoguanine, and double-stranded DNA breaks. Consequently, DBI was found to exert significant phototoxic effects (at nanomolar scale) toward cancer cell lines and tumor organoids. Furthermore, in vivo testing reveals that photoactivation of DBI induces not only G4 formation and DNA damage but also apoptosis in zebrafish, specifically in the area where DBI had accumulated. Collectively, this approach shows significant promise for image-guided PDT.
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| 7. |
- Jansen, Willemijn J, et al.
(författare)
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Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis.
- 2015
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Ingår i: JAMA. - : American Medical Association (AMA). - 1538-3598 .- 0098-7484. ; 313:19, s. 1924-38
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Tidskriftsartikel (refereegranskat)abstract
- Cerebral amyloid-β aggregation is an early pathological event in Alzheimer disease (AD), starting decades before dementia onset. Estimates of the prevalence of amyloid pathology in persons without dementia are needed to understand the development of AD and to design prevention studies.
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| 8. |
- Kling, Teresia, 1985, et al.
(författare)
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Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content
- 2015
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Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 43:15
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Tidskriftsartikel (refereegranskat)abstract
- Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool (cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets. © 2015 The Author(s).
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| 9. |
- Sánchez, José, 1979
(författare)
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Comparative network analysis of human cancer: sparse graphical models with modular constraints and sample size correction
- 2013
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In the study of transcriptional data for different groups (e.g. cancer types) it's reasonable to assume that some dependencies between genes on a transcriptional or genetic variants level are common across groups. Also, that this property is preserved locally, thus defining a modular structure in the model networks. For ease of interpretation, sparsity in the resulting model is also desirable. In this thesis we assume genomic data to have a multivariate normal distribution and estimate the networks by optimization of a penalized log-likelihood function for the corresponding inverse covariance matrices. We apply the fused elastic net penalty for sparsity and commonality. To achieve modular topology we propose a novel adaptive penalty. This adaptive penalty is computed from an initial zero-consistent solution. We also propose a generalization of the method which allows for fusion penalties defined by a graph. This method can be used to correct estimates when the groups have different sample sizes. It can also be use to correctly penalize in the presence of ordered variables such as survival. We optimize the penalized log-likelihood using the alternating directions method of multiplier (ADMM). Simulation studies show that our method more accurately identifies differential connectivity (network edges that differ between cancer classes) compared with standard methods. We also apply our method to the investigation of tumor data in glioblastoma, breast and ovarian cancer, integrating two types of data, mRNA (messenger RNA expression) and CNA (copy number aberration), by defining a prior distribution of the plausible links in the corresponding networks.
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| 10. |
- Sánchez, José, 1979
(författare)
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Network models with applications to genomic data: generalization, validation and uncertainty assessment
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The aim of this thesis is to provide a framework for the estimation and analysis of transcription networks in human cancer. The methods we develop are applied to data collected by The Cancer Genome Atlas (TCGA) and supporting simulations are based on derived models in order to reflect real data structure. Nevertheless, our proposed models apply to network construction for any data type. The thesis includes four papers, all of them adressing different aspects of network estimation. Statistical analysis of high-dimensional data requires regularization. Network model validation amounts to selection of regularization parameters which control sparsity and, possibly, some common structure across different data classes (here, types of cancer). In paper I we present a bootstrap-based method to perform sparsity selection and robust network construction. We show, by simulation studies, that our proposed methods select sparsity to control false positive rate, rather than match the size of the true underlying network. In paper II we address the problem of uncertainty in network estimation. Since network estimation is very unstable, uncertainty is an important issue to focus on, in order to avoid overintepretation of results. Using ideas from information theory, we introduce a method that assesses uncertainty by presenting a set of network candidate estimates, rather than a single network model. The method enables us to show that different network topologies have different estimation properties, and that each network estimation method's performance depends on this topology. It is often of interest to identify and study the commonalities and differences in network estimates across several classes (here, types of cancer) and data types. Statistical network models, like the graphical lasso, provide a framework in which several classes and data types can be integrated. Paper III makes use of such framework and presents a method that allows for large scale sparse inverse covariance estimation of several classes. Through application of priors, we account for plausible connections across different data types. The proposed method also encourages the expected modular structure of biological networks and corrects for unbalanced sample sizes across classes. The estimated networks are part of a publicly accessible resource termed Cancer Landscapes (\url{cancerlandscapes.org}), which provides a setting for interactive analysis in relation of pathway and pharmacological databases, diagnoses, survival associations and drug targets. Traditionally, the analysis of genomic data has aimed for the study of differential expression. In paper IV we propose a way to integrate differential expression analysis with network estimation. To that end we extend upon existing methods in order to jointly estimate sparse mean vectors and precision matrices across several classes, thus gaining over analyses that focus on one or the other. Additionally, by assuming a block diagonal structure in the precision matrices, the problem can be recast into an ensemble classifier where each block becomes part of either a linear or a quadratic discriminant function.
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