Biology subjects

Brunak, S.

Publications and source records attributed to Brunak, S..

15 recordsLinked to original sources

bioRxiv2025

Population-scale analysis of inheritance patterns across 858,635 individuals reveals recent historical migration patterns across the North Sea from the Middle Ages to the Industrial Revolution

The North Seas historical migrations have impacted the genetic structure of its neighbouring populations. We analysed haplotype sharing among 858,635 modern individuals from Denmark and Britain to infer migration history from the Middle Ages to the modern day. We estimated the genetic relationship among 370,259 Danes using Danish healthcare registries and validated those with retrievable pedigree relationships from a national family registry. We also compared their haplotype sharing with 488,376 British individuals from the UK Biobank. Our analysis revealed the fine-grained population genetic history of Denmark and identified distinct coastal and island communities with a history of genetic isolation and bottlenecks. We observed a significant population decline in Jutland compared to Zealand from the late medieval period to the start of early modern period, accompanied by migration from Jutland to Zealand, corresponding to historical evidence. We identified two major haplotype sharing patterns between Denmark and Great Britain: (1) early coast-to-coast connections between South Jutland and eastern England, likely driven by migration, settlement, continuous trade and the movement of people across the North Sea from the Middle Ages through early modern times, and (2) later city-to-city connections such as those between London and Kobenhavn, likely shaped by the reorientation of economic and trading centres in Britain and Denmark, and the increased shipping from early modern times towards the Industrial Revolution. Further comparisons using other North Sea countries showed both shared and unique histories of genetic exchange with Denmark and Britain. Our study provides novel genetic evidence of migration across the North Sea from the Middle Ages to the Industrial Revolution. It highlights the power of nationwide biobanks in reconstructing fine-scale historical population movements among closely related populations.

genetics↗

bioRxiv2024

Lifestyle factors in the biomedical literature: comprehensive resources for named entity recognition

MotivationDespite lifestyle factors (LSFs) being increasingly acknowledged in shaping individual health trajectories, particularly in chronic diseases, they have still not been systematically described in the biomedical literature. This is in part because no named entity recognition (NER) system exists, which can comprehensively detect all types of LSFs in text. The task is challenging due to their inherent diversity, lack of a comprehensive LSF classification for dictionary-based NER, and lack of a corpus for deep learning-based NER. ResultsWe present a novel Lifestyle Factor Ontology (LSFO), which we used to develop a dictionarybased system for recognition and normalization of LSFs. Additionally, we introduce a manually annotated corpus for LSFs (LSF200) suitable for training and evaluation of NER systems, and use it to train a transformer-based system. Evaluating the performance of both NER systems on the corpus revealed an F-score of 64% for the dictionary-based system and 76% for the transformer-based system. Largescale application of these systems on PubMed abstracts and PMC Open Access articles identified over 300 million mentions of LSF in the biomedical literature. AvailabilityLSFO, the annotated LSF200 corpus, and the detected LSFs in PubMed and PMC-OA articles using both NER systems, are available under open licenses via the following GitHub repository: https://github.com/EsmaeilNourani/LSFO-expansion. This repository contains links to two associated GitHub repositories and a Zenodo project related to the study. LSFO is also available at BioPortal: https://bioportal.bioontology.org/ontologies/LSFO. Contactkaterina.nastou@cpr.ku.dk, soren.brunak@cpr.ku.dk, lars.juhl.jensen@cpr.ku.dk Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

The small and large intestine contain transcriptionally related mesenchymal stromal cell subsets that derive from embryonic Gli1+ mesothelial cells

Intestinal fibroblasts (FB) play essential roles in intestinal homeostasis. Here we show that the small and large intestinal lamina propria (LP) contain similar FB subsets that locate in specific anatomical niches and express distinct arrays of epithelial support genes. However, there were tissue specific differences in the transcriptional profile of intestinal FB subsets in the two sites. All adult intestinal LP mesenchymal stromal cells (MSC), including FB, smooth muscle cells (SMC) and pericytes derive from Gli1-expressing embryonic precursors which we identify as mesothelial cells. Trajectory analysis suggested that adult SMC and FB derive from distinct embryonic intermediates, and that adult FB subsets develop in a linear trajectory from CD81+ FB. Finally, we show that colonic subepithelial PDGFRhi FB comprise several functionally and anatomically distinct populations that originate from an Fgfr2-expressing FB intermediate. Collectively our results provide novel insights into MSC diversity, location, function and ontogeny, with implications for our understanding of intestinal development, homeostasis and disease.

developmental biology↗

bioRxiv2021

Pancreatic cancer risk predicted from disease trajectories using deep learning

Pancreatic cancer is an aggressive disease that typically presents late with poor patient outcomes. There is a pronounced medical need for early detection of pancreatic cancer, which can be addressed by identifying high-risk populations. Here we apply artificial intelligence (AI) methods to a dataset of 6 million patient records with 24,000 pancreatic cancer cases in the Danish National Patient Registry (DNPR) and, for comparison, a dataset of three million records with 3,900 pancreatic cancer cases in the United States Department of Veterans Affairs (US-VA) healthcare system. In contrast to existing methods that do not use temporal information, we explicitly train machine learning models on the time sequence of diseases in patient clinical histories and test the ability to predict cancer occurrence in time intervals of 3 to 60 months after risk assessment. For cancer occurrence within 36 months, the performance of the best model (AUROC=0.88, DNPR), trained and tested on disease trajectories, exceeds that of a model without longitudinal information (AUROC=0.85, DNPR). Performance decreases when disease events within a 3 month window before cancer diagnosis are excluded from training (AUROC[3m]=0.83). Independent training and testing on the US-VA dataset reaches comparable performance (AUROC=0.78, AUROC[3m]=0.76). These results raise the state-of-the-art level of performance of cancer risk prediction on real-world data sets and provide support for the design of prediction-surveillance programs based on risk assessment in a large population followed by affordable surveillance of a relatively small number of patients at highest risk. Use of AI on real-world clinical records has the potential to shift focus from treatment of late-stage to early-stage cancer, benefiting patients by improving lifespan and quality of life.

bioinformatics↗

bioRxiv2021

Deep integrative models for large-scale human genomics

Polygenic risk scores (PRSs) are expected to play a critical role in achieving precision medicine. Currently, PRS predictors are generally based on linear models using summary statistics, and more recently individual-level data. However, these predictors mainly capture additive relationships and are limited in data modalities they can use. Here, we developed a deep learning framework (EIR) for PRS prediction which includes a model, genome-local-net (GLN), specifically designed for large scale genomics data. The framework supports multi-task (MT) learning, automatic integration of other clinical and biochemical data, and model explainability. When applied to individual level data in the UK Biobank, we found that GLN outperformed LASSO for a wide range of diseases and in particularly autoimmune diseases. Furthermore, we show that this was likely due to modelling epistasis, and we showcase this by identifying widespread epistasis for Type 1 Diabetes. Furthermore, we trained PRS by integrating genotype, blood, urine and anthropometrics and found that this improved performance for 93% of 290 diseases and disorders considered. Finally, we found that including genotype data provided better calibrated PRS models compared to using measurements alone. EIR is available at https://github.com/arnor-sigurdsson/EIR.

bioinformatics↗

bioRxiv2021

SignalP 6.0 achieves signal peptide prediction across all types using protein language models

Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms. As experimental characterization of SPs is costly, prediction algorithms are applied to predict them from sequence data. However, existing methods are unable to detect all known types of SPs. We introduce SignalP 6.0, the first model capable of detecting all five SP types. Additionally, the model accurately identifies the positions of regions within SPs, revealing the defining biochemical properties that underlie the function of SPs in vivo. Results show that SignalP 6.0 has improved prediction performance, and is the first model to be applicable to metagenomic data. SignalP 6.0 is available at https://services.healthtech.dtu.dk/service.php?SignalP-6.0

bioinformatics↗

bioRxiv2021

Single-cell characterisation of mononuclear phagocytes in the human intestinal mucosa

Mononuclear phagocytes (MNP), including macrophages and classical dendritic cells (cDC), are highly heterogeneous cells with distinct functions. Understanding MNP complexity in the intestinal lamina propria (LP), particularly in humans, has proved difficult due to the expression of overlapping phenotypic markers and the inability to isolate these cells without contamination from gut-associated lymphoid tissues (GALT). Here, we exploited our novel method for isolation of human GALT-free LP to carry out single-cell (sc)RNA-seq, CITE-seq and flow cytometry analysis of human ileal and colonic LP MNPs. As well as classical monocytes, non-classical monocytes, mature macrophage subsets, cDC1s, and cDC2s, we identified a CD1c+ cDC subset with features of both cDC2 and monocytes, which were transcriptionally similar to the recently described cDC3. While similar MNP subsets were present in both ileal and colonic LP, the proportions and transcriptional profiles of these populations differed between these sites and in diseased states, indicating local specialization and environmental imprinting. Using computational trajectory tools, we identified putative early committed pre-cDC subsets and developmental intermediates of mature cDC1, cDC2 and cDC3, as well as monocyte-to-macrophage trajectories. Collectively, our results provide novel insights into the heterogeneity and development of intestinal LP MNP and an important framework for studying the role of these populations in intestinal homeostasis and disease. One sentence summaryFenton and Wulff et al. use single-cell methods to explore the complexity of the mononuclear phagocyte compartment of the human intestinal lamina propria, identifying distinct dendritic cell and macrophage subsets, site-specific transcriptional signatures, and lineage-specific precursors.

immunology↗

bioRxiv2020

Type I interferons promote germinal centers through B cell intrinsic signaling and dendritic cell dependent Th1 and Tfh cell lineages

Type I interferons (IFNs) play an essential role in antiviral immunity, correlate with severity of systemic autoimmune disease, and are likely to represent a key component of mRNA vaccine-adjuvanticity. Relevant to all, type I IFNs can enhance germinal center (GC) B cell responses but underlying signaling pathways are incompletely understood due to pleiotropic effects in multiple cell types. Here, we demonstrate that a succinct type I IFN response promotes GC formation and associated IgG subclass distribution primarily through signaling in cDCs and B cells. Type I IFN signaling in cDCs, distinct from cDC1, stimulates development of separable Tfh and Th1 cell subsets. However, Th cell-derived IFN-{gamma} induces T-bet expression and IgG2c isotype switching prior to this bifurcation and has no evident effects once GCs and bona fide Tfh cells developed. This pathway acts in synergy with early B cell-intrinsic type I IFN signaling, which reinforces T-bet expression in B cells and leads to a selective amplification of the IgG2c+ GC B cell response. Despite the strong Th1 polarizing effect of type I IFNs, the Tfh cell subset develops into IL-4 producing cells that control the overall magnitude of the GCs and promote generation of IgG1 + GC B cells. Thus, type I IFNs act on B cells and cDCs to drive GC formation and to coordinate IgG subclass distribution through parallel Th1 and Tfh cell-dependent pathways.

immunology↗

bioRxiv2019

Systems genetics analysis identify calcium signalling defects as novel cause of congenital heart disease

BackgroundCongenital heart disease (CHD) occurs in almost 1% of newborn children and is considered a multifactorial disorder. CHD may segregate in families due to significant contribution of genetic factors in the disease aetiology. The aim of the study was to identify pathophysiological mechanisms in families segregating CHD. MethodsWe used whole exome sequencing to identify rare genetic variants in ninety consenting participants from 32 Danish families with recurrent CHD. We applied a systems biology approach to identify developmental mechanisms influenced by accumulation of rare variants. We used an independent cohort of 714 CHD cases and 4922 controls for replication and performed functional investigations using zebrafish as in vivo model. ResultsWe identified 1,785 genes, in which rare alleles were shared between affected individuals within a family. These genes were enriched for known cardiac developmental genes and 218 of the genes were mutated in more than one family. Our analysis revealed a functional cluster, enriched for proteins with a known participation in calcium signalling. Replication confirmed increased mutation burden of calcium-signalling genes in CHD patients. Functional investigation of zebrafish orthologues of ITPR1, PLCB2 and ADCY2 verified a role in cardiac development and suggests a combinatorial effect of inactivation of these genes. ConclusionsThe study identifies abnormal calcium signalling as a novel pathophysiological mechanism in human CHD and confirms the complex genetic architecture underlying CHD.

genetics↗

bioRxiv2018

Bow-tie motifs enable protein multifunctionality by connecting cellular processes in the interactome

Abstract (172/175 words)Cell type-specific protein interactomes are promising resources for the understanding of cell functionality and genetic perturbations in cellular states. However, while many low-and top-level aspects of interactome topology have been mapped, we still lack an understanding of the topological motifs at the intermediate level, where connectivity between cellular processes takes place. We combine conventional dendritic cell lineage 1 (cDC1)-specific expression data with a high-quality protein interactome, subsequently creating an information-rich cDC1-specifc interactome matrix that displays topological information on an unprecedented 36.7 million protein pairs at once. By dissecting the interactome-matrix, we reveal that the bow-tie motif is a major connector of cellular processes and an extremely widespread topology in the protein interactome. In aggregation, the bow-tie motifs form a scaffold within the interactome that hierarchically link cellular processes together. We further demonstrate that the bow-tie motifs design can enable protein multifunction. Our generic approach allows a topological characterization and visualization of any large network, facilitating a more effective usage of community-produced interaction data, and is applicable to any cell types interactome.

systems biology↗

bioRxiv2018

Pathway and network analysis of more than 2,500 whole cancer genomes

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes, we performed multi-faceted pathway and network analyses of non-coding mutations across 2,583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project. While few non-coding genomic elements were recurrently mutated in this cohort, we identified 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We found that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing was primarily targeted by non-coding mutations in this cohort, with samples containing non-coding mutations exhibiting similar gene expression signatures as coding mutations in well-known RNA splicing factors. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

cancer biology↗

bioRxiv2018

Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: an overview of the data from the epidemiological studies within the IMI DIRECT Consortium

Abstract/SummaryO_ST_ABSBackground and aimsC_ST_ABSUnderstanding the aetiology, clinical presentation and prognosis of type 2 diabetes (T2D) and optimizing its treatment might be facilitated by biomarkers that help predict a persons susceptibility to the risk factors that cause diabetes or its complications, or response to treatment. The IMI DIRECT (Diabetes Research on Patient Stratification) Study is a European Union (EU) Innovative Medicines Initiative (IMI) project that seeks to test these hypotheses in two recently established epidemiological cohorts. Here, we describe the characteristics of these cohorts at baseline and at the first main follow-up examination (18-months).\n\nMaterials and methodsFrom a sampling-frame of 24,682 European-ancestry adults in whom detailed health information was available, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm and enrolled into a prospective cohort study (n=2127) undertaken at four study centres across Europe (Cohort 1: prediabetes). We also recruited people from clinical registries with recently diagnosed T2D (n=789) into a second cohort study (Cohort 2: diabetes). The two cohorts were studied in parallel with matched protocols. Endogenous insulin secretion and insulin sensitivity were modelled from frequently sampled 75g oral glucose tolerance (OGTT) in Cohort 1 and with mixed-meal tolerance tests (MMTT) in Cohort 2. Additional metabolic biochemistry was determined using blood samples taken when fasted and during the tolerance tests. Body composition was assessed using MRI and lifestyle measures through self-report and objective methods.\n\nResultsUsing ADA-2011 glycaemic categories, 33% (n=693) of Cohort 1 (prediabetes) had normal glucose regulation (NGR), and 67% (n=1419) had impaired glucose regulation (IGR). 76% of the cohort was male, age=62(6.2) years; BMI=27.9(4.0) kg/m2; fasting glucose=5.7(0.6) mmol/l; 2-hr glucose=5.9(1.6) mmol/l [mean(SD)]. At follow-up, 18.6(1.4) months after baseline, fasting glucose=5.8(0.6) mmol/l; 2-hr OGTT glucose=6.1(1.7) mmol/l [mean(SD)]. In Cohort 2 (diabetes): 65% (n=508) were lifestyle treated (LS) and 35% (n=271) were lifestyle + metformin treated (LS+MET). 58% of the cohort was male, age=62(8.1) years; BMI=30.5(5.0) kg/m2; fasting glucose=7.2(1.4)mmol/l; 2-hr glucose=8.6(2.8) mmol/l [mean(SD)]. At follow-up, 18.2(0.6) months after baseline, fasting glucose=7.8(1.8) mmol/l; 2-hr MMTT glucose=9.5(3.3) mmol/l [mean(SD)].\n\nConclusionThe epidemiological IMI DIRECT cohorts are the most intensely characterised prospective studies of glycaemic deterioration to date. Data from these cohorts help illustrate the heterogeneous characteristics of people at risk of or with T2D, highlighting the rationale for biomarker stratification of the disease - the primary objective of the IMI DIRECT consortium.\n\nAbbreviations

epidemiology↗

bioRxiv2018

Glycolytic repression and reduced GLP-1 secretion by active Farnesoid X Receptor in enteroendocrine L cells achieved via PKLR

Rise in intestinal glucose increases GLP-1 secretion by enteroendocrine L cells. GLP-1, in turn, stimulates insulin secretion. Farnesoid X Receptor (FXR) represses this pathway by manipulating the L cell glycolysis, thus reducing insulin- and GLP-1 secretion. The mechanism by which FXR manipulates the L cell glycolysis is unclear. In this study, we construct an L cell specific protein-protein interactome and identify all significantly active protein complexes, inferred by co-expression scores, in FXR-activated and control L cells. Contrary to previous reports, we find extensive glycolytic enzyme activity in FXR-activated L cells. We present how FXRs repression of the glycolytic enzyme, pyruvate kinase (PKLR), is causing the reduction in glycolytic activity. This mechanistic insight may aid development of drugs targeting the GLP-1 pathway.

systems biology↗

bioRxiv2017

Protective role of the vulture facial and gut microbiomes aid adaptation to scavenging

BackgroundVultures have adapted the remarkable ability to feed on carcasses that may contain microorganisms that would be pathogenic to most other animals. The holobiont concept suggests that the genetic basis of such adaptation may not only lie within their genomes, but additionally in their associated microbes. To explore this, we generated shotgun DNA sequencing datasets of the facial and gut microbiomes from the black and turkey vultures. We characterized i) the functional potential and taxonomic diversity of their microbiomes, ii) the potential pathogenic challenges they face, and iii) elements in the microbiome that could play a protective role to the vultures face and gut.\n\nResultsWe found elements involved in diseases, such as periodontitis and pneumonia (more abundant in the face), and gas gangrene and food poisoning (more abundant in the gut). Interestingly, we found taxa and functions with potential for playing health beneficial roles, such as antilisterial bacteria in the gut, and genes for the production of antiparasites and antiinsectisides in the face. Based on the identified phages, we suggest that phages aid in the control, and possibly elimination as in phage therapy, of microbes reported as pathogenic to a variety of species. Interestingly, we also identified Adineta vaga in the gut, an invertebrate that feeds on dead bacteria and protozoans, suggesting a defensive predatory mechanism. Finally, we suggest a colonization resistance role though biofilm formation played by Fusobacteria and Clostridia in the gut.\n\nConclusionsOur results highlight the importance of complementing genomic analyses with metagenomics in order to obtain a clearer understanding of the host-microbial alliance and show the importance of microbiome-mediated health protection for adaptation to extreme diets, such as scavenging.

bioinformatics↗

bioRxiv2017

Text mining of 15 million full-text scientific articles

Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823-2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein-protein, disease-gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only.

bioinformatics↗