Biology subjects

Karpe, F.

Publications and source records attributed to Karpe, F..

4 recordsLinked to original sources

bioRxiv2019

TCF7L2 plays a complex role in human adipose progenitor biology which may contribute to genetic susceptibility to type 2 diabetes.

Non-coding genetic variation at TCF7L2 is the strongest genetic determinant of type 2 diabetes (T2D) risk in humans. TCF7L2 encodes a transcription factor mediating the nuclear effects of WNT signalling in adipose tissue (AT). Here we mapped the expression of TCF7L2 in human AT and investigated its role in adipose progenitor (AP) biology. APs exhibited the highest TCF7L2 mRNA abundance compared to mature adipocytes and adipose-derived endothelial cells. Obesity was associated with reduced TCF7L2 transcript levels in subcutaneous abdominal AT but increased expression in APs. In functional studies, TCF7L2 knockdown (KD) in APs led to dose-dependent activation of WNT/{beta}-catenin signaling, impaired proliferation and dose-dependent effects on adipogenesis. Whilst partial KD enhanced adipocyte differentiation, complete KD impaired lipid accumulation and adipogenic gene expression. Overexpression of TCF7L2 accelerated adipogenesis. Transcriptome-wide profiling revealed that TCF7L2 can modulate multiple aspects of AP biology including extracellular matrix secretion, immune signalling and apoptosis. The T2D-risk allele at rs7903146 was associated with reduced AP TCF7L2 expression and enhanced AT insulin sensitivity. Our study highlights a complex role for TCF7L2 in AP biology and suggests that in addition to regulating pancreatic insulin secretion, genetic variation at TCF7L2 may also influence T2D risk by modulating AP function.

cell biology↗

bioRxiv2019

Associations between outdoor temperature and bright sunlight with metabolites in two population-based European cohorts

Context: Outdoor temperature and bright sunlight may directly and/or indirectly modulate systemic metabolism. Objective: We assessed the associations between outdoor temperature and bright sunlight duration with metabolomics. Design: meta-analysis of two cross-sectional studies. Setting: Two population-based European cohort studies. Patients or other participants: Non-diabetic individuals from the Oxford BioBank (OBB; N=6,368; mean age 47.0 years, males 44%) and the Netherlands Epidemiology of Obesity (NEO; N=5,916; mean age 55.6 years, males 43%) studies. Intervention(s): Data on mean outdoor bright sunlight and temperature collected from local weather stations in the week prior to blood sampling. Main Outcome Measure(s): Serum levels of 148 metabolites measured using NMR spectroscopy, including 14 lipoprotein subclasses. Statistical analyses: Multivariable linear regression analyses adjusted for age, sex, body mass index, season and either outdoor temperature or bright sunlight. Summary statistics from the OBB and NEO cohorts were combined using fixed-effect meta-analyses. Results: A higher mean outdoor temperature was associated with increased concentrations of lipoprotein (sub)particles and certain amino acids such as phenylalanine and leucine. In contrast, longer mean hours of bright sunlight were specifically associated with lower concentrations of very low density lipoprotein (sub)particles. The direction of effects was consistent between the OBB and NEO, although effect sizes were generally larger in the OBB. Conclusions: Increased bright sunlight duration is associated with an improved metabolic profile whilst higher outdoor temperature may adversely impact cardiometabolic health.

physiology↗

bioRxiv2019

Regional fat depot masses are influenced by protein-coding gene variants

With the identification of a large number of genetic loci associated with human fat distribution and its importance for metabolic health, the question arises as to what the genetic drivers for discrete fat depot expansion might be. To date most studies have focussed on conventional anthropometric measures such as waist-to-hip ratio (WHR) adjusted for body mass index. We searched for genetic loci determining discrete fat depots mass size using an exome-wide approach in 3 large cohorts. Here we report an exome-wide analysis of non-synonymous genetic variants in 17,212 participants in which regional fat masses were quantified using dual-energy X-ray absorptiometry. The missense variant CCDC92S70C, previously associated with WHR, is associated specifically with reduced visceral and increased leg fat masses. Allele-specific expression analysis shows that the deleterious minor allele carrying transcript also has a constitutively higher expression. In addition, we identify two variants associated with the transcriptionally distinct fat depot arm fat (SPATA20K422R and UQCC1R51Q). SPATA20K422R, a rare novel locus with a large effect size specific to arm, and UQCC1R51Q, a common variant exome-wide significant in arm but showing similar trends in other subcutaneous fat depots. In terms of the understanding of human fat distribution, these findings suggest distinct regulation of discrete fat depot expansion. Author summaryHuman fat storing tissues are heterogeneous and comprise functionally and structurally distinct regional fat depots, the relative size of which appear to have significant implications for health. Whilst it is known that inter-individual differences in fat distribution have genetic drivers, studies to date have focussed on crude anthropometric approximations of region fat masses rather than precise measures. Here we describe an exome-wide analysis of a large collection of men and women who have undergone body scanning using dual-energy X-ray absorptiometry (DXA) to better define regional fat masses and identify new genetic drivers for human fat distribution. With this approach we identify three gene regions associated with distinct fat depots which can help to explain the variation in fat distribution between people and may lead to a better understanding of the depot specific fat tissue expansion.

genomics↗

bioRxiv2017

Causal Analyses, Statistical Efficiency And Phenotypic Precision Through Recall-By-Genotype Study Design

Genome-wide association studies have been useful in identifying common genetic variants related to a variety of complex traits and diseases; however, they are often limited in their ability to inform about underlying biology. Whilst bioinformatics analyses, studies of cells, animal models and applied genetic epidemiology have provided some understanding of genetic associations or causal pathways, there is a need for new genetic studies that elucidate causal relationships and mechanisms in a cost-effective, precise and statistically efficient fashion. We discuss the motivation for and the characteristics of the Recall-by-Genotype (RbG) study design, an approach that enables genotype-directed deep-phenotyping and improvement in drawing causal inferences. Specifically, we present RbG designs using single and multiple variants and discuss the inferential properties, analytical approaches and applications of both. We consider the efficiency of the RbG approach, the likely value of RbG studies for the causal investigation of disease aetiology and the practicalities of incorporating genotypic data into population studies in the context of the RbG study design. Finally, we provide a catalogue of the UK-based resources for such studies, an online tool to aid the design of new RbG studies and discuss future developments of this approach.

genetics↗