255 research outputs found
Biological Characterization of Gene Response to Insulin-Induced Hypoglycemia in Mouse Retina.
Glucose is the most important metabolic substrate of the retina and maintenance of normoglycemia is an essential challenge for diabetic patients. Chronic, exaggerated, glycemic excursions could lead to cardiovascular diseases, nephropathy, neuropathy and retinopathy. We recently showed that hypoglycemia induced retinal cell death in mouse via caspase 3 activation and glutathione (GSH) decrease. Ex vivo experiments in 661W photoreceptor cells confirmed the low-glucose induction of death via superoxide production and activation of caspase 3, which was concomitant with a decrease of GSH content. We evaluate herein retinal gene expression 4 h and 48 h after insulin-induced hypoglycemia. Microarray analysis demonstrated clusters of genes whose expression was modified by hypoglycemia and we discuss the potential implication of those genes in retinal cell death. In addition, we identify by gene set enrichment analysis, three important pathways, including lysosomal function, GSH metabolism and apoptotic pathways. Then we tested the effect of recurrent hypoglycemia (three successive 4h periods of hypoglycemia spaced by 48 h recovery) on retinal cell death. Interestingly, exposure to multiple hypoglycemic events prevented GSH decrease and retinal cell death, or adapted the retina to external stress by restoring GSH level comparable to control situation. We hypothesize that scavenger GSH is a key compound in this apoptotic process, and maintaining "normal" GSH level, as well as a strict glycemic control, represents a therapeutic challenge in order to avoid side effects of diabetes, especially diabetic retinopathy
Polymorphism in cyclohexanol
The crystal structures and phase behaviour of phase II and the metastable phases III0 and III of cyclohexanol, C6H11OH,
have been determined using high-resolution neutron powder,
synchrotron X-ray powder and single-crystal X-ray diffraction techniques. Cyclohexanol-II is formed by a transition from the plastic phase I cubic structure at 265 K and crystallizes in a tetragonal structure, space group P�4421c (Z0 = 1), in which the molecules are arranged in a hydrogen-bonded tetrameric ring motif. The structures of phases III0 and III are monoclinic, space groups P21/c (Z0 = 3) and Pc (Z0 = 2), respectively, and are characterized by the formation of hydrogen-bonded molecular chains with a threefold-helical and wave-like nature, respectively. Phase III crystallizes at 195 K from a sample of phase I that is supercooled to ca 100 K. Alternatively, phase III may be grown via phase III0, the latter transforming from supercooled phase I at ca 200 K. Phase III0 is particularly unstable and is metastable with respect to both I and II. Its growth is realised only under very restricted conditions, thus making its characterization especially
challenging. The cyclohexanol molecules adopt a chair
conformation in all three phases with the hydroxyl groups in
an equatorial orientation. No evidence was found indicating
hydroxyl groups adopting an axial orientation, contrary to the majority of spectroscopic literature on solid-state cyclohexanol; however, the H atom of the equatorial OH groups is found to adopt both in-plane and out-of-plane orientations
Large Anisotropic Thermal Expansion Anomaly near the Superconducting Transition Temperature in MgB2
An anisotropic lattice anomaly near the superconducting transition
temperature, Tc, was observed in MgB2 by high-resolution neutron powder
diffraction. The a-axis thermal expansion becomes negative near Tc, while the
c-axis thermal expansion is unaffected. This is qualitatively consistent with a
depletion of the boron-boron s-band as the superconducting gap opens, resulting
in weaker bonding. However, the observed anomaly is much larger than predicted
by the Ehrenfest relation, strongly suggesting that the phonon thermal
expansion also changes sign, as commonly observed in hexagonal layered
crystals. These two effects may be connected through subtle changes in the
phonon spectrum at Tc.Comment: 11 pages, 4 figure
Magnetic stress as a driving force of structural distortions: the case of CrN
We show that the observed transition from rocksalt to orthorhombic P
symmetry in CrN can be understood in terms of stress anisotropy. Using local
spin density functional theory, we find that the imbalance between stress
stored in spin-paired and spin-unpaired Cr nearest neighbors causes the
rocksalt structure to be unstable against distortions and justifies the
observed antiferromagnetic ordering. This stress has a purely magnetic origin,
and may be important in any system where the coupling between spin ordering and
structure is strong.Comment: 4 pages (two columns) 4 figure
A transcribed enhancer dictates mesendoderm specification in pluripotency.
Enhancers and long noncoding RNAs (lncRNAs) are key determinants of lineage specification during development. Here, we evaluate remodeling of the enhancer landscape and modulation of the lncRNA transcriptome during mesendoderm specification. We sort mesendodermal progenitors from differentiating embryonic stem cells (ESCs) according to Eomes expression, and find that enhancer usage is coordinated with mesendoderm-specific expression of key lineage-determining transcription factors. Many of these enhancers are associated with the expression of lncRNAs. Examination of ESC-specific enhancers interacting in three-dimensional space with mesendoderm-specifying transcription factor loci identifies MesEndoderm Transcriptional Enhancer Organizing Region (Meteor). Genetic and epigenetic manipulation of the Meteor enhancer reveal its indispensable role during mesendoderm specification and subsequent cardiogenic differentiation via transcription-independent and -dependent mechanisms. Interestingly, Meteor-deleted ESCs are epigenetically redirected towards neuroectodermal lineages. Loci, topologically associating a transcribed enhancer and its cognate protein coding gene, appear to represent therefore a class of genomic elements controlling developmental competence in pluripotency
Competition between Magnetic and Structural Transition in CrN
CrN is observed to undergo a paramagnetic to antiferromagnetic transition
accompanied by a shear distortion from cubic NaCl-type to orthorhombic
structure. Our first-principle plane wave and ultrasoft pseudopotential
calculations confirm that the distorted antiferromagnetic phase with spin
configuration arranged in double ferromagnetic sheets along [110] is the most
stable. Antiferromagnetic ordering leads to a large depletion of states around
Fermi level, but it does not open a gap. Simultaneous occurence of structural
distortion and antiferromagnetic order is analyzed.Comment: 10 pages, 10 figure
An omics-based machine learning approach to predict diabetes progression:a RHAPSODY study
Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA 1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value. Methods: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA 1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel’s C statistic. Results: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0–11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3–11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA 1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA 1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance. Conclusions/interpretation: Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification. Data availability: Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch. Graphical Abstract: (Figure presented.).</p
Analysis of the dynamic co-expression network of heart regeneration in the zebrafish.
The zebrafish has the capacity to regenerate its heart after severe injury. While the function of a few genes during this process has been studied, we are far from fully understanding how genes interact to coordinate heart regeneration. To enable systematic insights into this phenomenon, we generated and integrated a dynamic co-expression network of heart regeneration in the zebrafish and linked systems-level properties to the underlying molecular events. Across multiple post-injury time points, the network displays topological attributes of biological relevance. We show that regeneration steps are mediated by modules of transcriptionally coordinated genes, and by genes acting as network hubs. We also established direct associations between hubs and validated drivers of heart regeneration with murine and human orthologs. The resulting models and interactive analysis tools are available at http://infused.vital-it.ch. Using a worked example, we demonstrate the usefulness of this unique open resource for hypothesis generation and in silico screening for genes involved in heart regeneration
Targeted disruption of Slc2a8 (GLUT8) reduces motility and mitochondrial potential of spermatozoa
GLUT8 is a class 3 sugar transport facilitator which is predominantly expressed in testis and also detected in brain, heart, skeletal muscle, adipose tissue, adrenal gland, and liver. Since its physiological function in these tissues is unknown, we generated a Slc2a8 null mouse and characterized its phenotype. Slc2a8 knockout mice appeared healthy and exhibited normal growth, body weight development and glycemic control, indicating that GLUT8 does not play a significant role for maintenance of whole body glucose homeostasis. However, analysis of the offspring distribution of heterozygous mating indicated a lower number of Slc2a8 knockout offspring (30.5:47.3:22.1%, Slc2a8+/+, Slc2a8+/−, and Slc2a8−/− mice, respectively) resulting in a deviation (p = 0.0024) from the expected Mendelian distribution. This difference was associated with lower ATP levels, a reduced mitochondrial membrane potential and a significant reduction of sperm motility of the Slc2a8 knockout in comparison to wild-type spermatozoa. In contrast, number and survival rate of spermatozoa were not altered. These data indicate that GLUT8 plays an important role in the energy metabolism of sperm cells
Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes:an IMI-RHAPSODY Study
Introduction: Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised “bottom-up” approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods: Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results: From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions: Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.</p
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