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Brunak, S.

Publications and source records attributed to Brunak, S..

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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

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

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

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

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