Age-related mitochondrial DNA depletion and the impact on pancreatic beta cell function

Type 2 diabetes is characterised by an age-related decline in insulin secretion. We previously identified a 50% age-related decline in mitochondrial DNA (mtDNA) copy number in isolated human islets. The purpose of this study was to mimic this degree of mtDNA depletion in MIN6 cells to determine whether there is a direct impact on insulin secretion. Transcriptional silencing of mitochondrial transcription factor A, TFAM, decreased mtDNA levels by 40% in MIN6 cells. This level of mtDNA depletion significantly decreased mtDNA gene transcription and translation, resulting in reduced mitochondrial respiratory capacity and ATP production. Glucose-stimulated insulin secretion was impaired following partial mtDNA depletion, but was normalised following treatment with glibenclamide. This confirms that the deficit in the insulin secretory pathway precedes K+ channel closure, indicating that the impact of mtDNA depletion is at the level of mitochondrial respiration. In conclusion, partial mtDNA depletion to a degree comparable to that seen in aged human islets impaired mitochondrial function and directly decreased insulin secretion. Using our model of partial mtDNA depletion following targeted gene silencing of TFAM, we have managed to mimic the degree of mtDNA depletion observed in aged human islets, and have shown how this correlates with impaired insulin secretion. We therefore predict that the age-related mtDNA depletion in human islets is not simply a biomarker of the aging process, but will contribute to the age-related risk of type 2 diabetes

Quantifying the Universality of the Stellar Initial Mass Function in Old Star Clusters

We present a new technique to quantify cluster-to-cluster variations in the observed present-day stellar mass functions of a large sample of star clusters. Our method quantifies these differences as a function of both the stellar mass and the total cluster mass, and offers the advantage that it is insensitive to the precise functional form of the mass function. We applied our technique to data taken from the ACS Survey for Globular Clusters, from which we obtained completeness-corrected stellar mass functions in the mass range 0.25-0.75 M$_{\odot}$ for a sample of 27 clusters. The results of our observational analysis were then compared to Monte Carlo simulations for globular cluster evolution spanning a range of initial mass functions, total numbers of stars, concentrations, and virial radii. We show that the present-day mass functions of the clusters in our sample can be reproduced by assuming an universal initial mass function for all clusters, and that the cluster-to-cluster differences are consistent with what is expected from two-body relaxation. A more complete exploration of the initial cluster conditions will be needed in future studies to better constrain the precise functional form of the initial mass function. This study is a first step toward using our technique to constrain the dynamical histories of a large sample of old Galactic star clusters and, by extension, star formation in the early Universe.Comment: 11 pages, 4 figures, 4 tables, accepted for publication in MNRAS, proof corrections made in updated versio

The Distribution of Collisionally Induced Blue Stragglers in the Colour-Magnitude Diagram

A primary production mechanism for blue stragglers in globular clusters is thought to be collisionally-induced mergers, perhaps mediated by dynamical encounters involving binary stars. We model the formation and evolution of such blue stragglers, and produce theoretical distributions of them in the colour-magnitude diagram. We use a crude representation of cluster dynamics and detailed binary-single star encounter simulations to produce cross sections and rates for a variety of collisions. The results of the collisions are determined based on SPH simulations of realistic star models. The evolution of the collision products are then followed in detail. We use our results to explore the effects of a variety of input assumptions on the number and kind of blue stragglers created by collisions. In particular, we describe the changes in the blue straggler distribution that result from using realistic collision products rather than the ``fully-mixed'' assumption, and from changes in assumptions about the number and orbital period distribution of the primordial binary population. We then apply our models to existing data from the core of M3, where the large blue straggler population is thought to be dominated by collision products. We find that we have difficulty successfully modeling the observed blue stragglers under a single consistent set of assumptions. However, if 3 particularly bright blue stragglers are considered to be part of a different observed population, the remainder can be successfully modeled using realistic encounter products and assuming a 20% binary fraction with plausible period distribution.Comment: 36 pages including 8 figures, submitted to Astrophysical Journa

Dissecting the Colour-Magnitude Diagram: A Homogeneous Catalogue of Stellar Populations in Globular Clusters

We present a homogeneous catalogue for blue straggler, red giant branch, horizontal branch and main-sequence turn-off stars in a sample of 35 clusters taken from the ACS Survey for Globular Clusters. As a result of the superior photometry and relatively large field of view offered by the ACS data, this new catalogue is a significant improvement upon the one presented in Leigh, Sills & knigge (2007). Using our catalogue, we study and compare the radial distributions of the different stellar populations. We have confirmed our previous result (Knigge, Leigh & Sills 2009) that there is a clear, but sub-linear, correlation between the number of blue stragglers found in the cluster core and the total stellar mass contained within it. By considering a larger spatial extent than just the core, our results suggest that mass segregation is not the dominant effect contributing to the observed sub-linearity. We also investigate the radial distributions of the different stellar populations in our sample of clusters. Our results are consistent with a linear relationship between the number of stars in these populations and the total mass enclosed within the same radius. Therefore, we conclude that the cluster dynamics does not significantly affect the relative distributions of these populations in our sample.Comment: 14 pages, 5 figures, accepted for publication in MNRA

The Effect of Pre-Main Sequence Stars on Star Cluster Dynamics

We investigate the effects of the addition of pre-main sequence evolution to star cluster simulations. We allowed stars to follow pre-main sequence tracks that begin at the deuterium burning birthline and end at the zero age main sequence. We compared our simulations to ones in which the stars began their lives at the zero age main sequence, and also investigated the effects of particular choices for initial binary orbital parameters. We find that the inclusion of the pre-main sequence phase results in a slightly higher core concentration, lower binary fraction, and fewer hard binary systems. In general, the global properties of star clusters remain almost unchanged, but the properties of the binary star population in the cluster can be dramatically modified by the correct treatment of the pre-main sequence stage.Comment: 40 pages ApJ preprint style Accepted by Ap

Post-load glucose subgroups and associated metabolic traits in individuals with type 2 diabetes:An IMI-DIRECT study

AIM: Subclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D. METHODS: The study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders. RESULTS: At baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1-3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18-1.92) for subgroup 2 and 1.88 (-0.08-3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose. CONCLUSIONS: Different glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk

Whole blood co-expression modules associate with metabolic traits and type 2 diabetes : an IMI-DIRECT study

Background The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D. Methods Clusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts. Results We identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling. Conclusions Our results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.Peer reviewe

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities. [Abstract copyright: © 2023. The Author(s).