BackgroundDiet is a major contributor to metabolic disease risk, but there is controversy as to whether increased incidences of diseases such as non-alcoholic fatty liver disease arise from consumption of saturated fats or free sugars. Here, we investigate whether a sub-set of triacylglycerols (TAGs) were associated with hepatic steatosis and whether they arise from de novo lipogenesis (DNL) from the consumption of carbohydrates.ResultsWe conduct direct infusion mass spectrometry of lipids in plasma to study the association between specific TAGs and hepatic steatosis assessed by ultrasound and fatty liver index in volunteers from the UK-based Fenland Study and evaluate clustering of TAGs in the National Survey of Health and Development UK cohort. We find that TAGs containing saturated and monounsaturated fatty acids with 16–18 carbons are specifically associated with hepatic steatosis. These TAGs are additionally associated with higher consumption of carbohydrate and saturated fat, hepatic steatosis, and variations in the gene for protein phosphatase 1, regulatory subunit 3b (PPP1R3B), which in part regulates glycogen synthesis. DNL is measured in hyperphagic ob/ob mice, mice on a western diet (high in fat and free sugar) and in healthy humans using stable isotope techniques following high carbohydrate meals, demonstrating the rate of DNL correlates with increased synthesis of this cluster of TAGs. Furthermore, these TAGs are increased in plasma from patients with biopsy-confirmed steatosis.ConclusionA subset of TAGs is associated with hepatic steatosis, even when correcting for common confounding factors. We suggest that hepatic steatosis risk in western populations is in part driven by increased DNL following carbohydrate rich meals in addition to the consumption of saturated fat.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1439-8) contains supplementary material, which is available to authorized users.

Both normal tissue development and cancer growth are driven by a branching process of cell division and mutation accumulation that leads to intra-tissue genetic heterogeneity. However, quantifying somatic evolution in humans remains challenging. Here, we show that multisample genomic data from a single time point of normal and cancer tissues contains information on single-cell divisions. We present a new theoretical framework that, applied to whole-genome sequencing data of healthy tissue and cancer, allows inferring the mutation rate and the cell survival/death rate per division. On average, we found that cells accumulate 1.14 mutations per cell division in healthy haematopoiesis and 1.37 mutations per division in brain development. In both tissues, cell survival was maximal during early development. Analysis of 131 biopsies from 16 tumours showed 4 to 100 times increased mutation rates compared to healthy development and substantial inter-patient variation of cell survival/ death rates.

Cancer is driven by complex evolutionary dynamics involving billions of cells.Increasing effort has been dedicated to sequence single tumour cells, but obtaining robust measurements remains challenging. Here we show that multi-region sequencing of bulk tumour samples contains quantitative information on single--cell divisions that is accessible if combined with evolutionary theory.Using high--throughput data from 16 human cancers, we measured the in vivo per--cell point mutation rate (mean: 1.69×10 !! bp per cell division) and per--cell survival rate (mean: 0.57) in individual patient tumours from colon, lung and renal cancers. Per--cell mutation rates varied 50--fold between individuals, and per--cell survival rates were between nearly--homeostatic and almost perfect cell doublings, equating to tumour ages between 1 and 19 years. Furthermore, reanalysing a recent dataset of 89 whole--genome sequenced healthy haematopoietic stem cells, we find 1.14 mutations per genome per cell division and near perfect cell doublings (per--cell survival rate: 0.96) during early haematopoietic development. Our analysis measures in vivo the most fundamental properties of human cancer and healthy somatic evolution at single--cell resolution within single individuals.