LKB1 and AMPK differentially regulate pancreatic β-cell identity.

Fully differentiated pancreatic β cells are essential for normal glucose homeostasis in mammals. Dedifferentiation of these cells has been suggested to occur in type 2 diabetes, impairing insulin production. Since chronic fuel excess ("glucotoxicity") is implicated in this process, we sought here to identify the potential roles in β-cell identity of the tumor suppressor liver kinase B1 (LKB1/STK11) and the downstream fuel-sensitive kinase, AMP-activated protein kinase (AMPK). Highly β-cell-restricted deletion of each kinase in mice, using an Ins1-controlled Cre, was therefore followed by physiological, morphometric, and massive parallel sequencing analysis. Loss of LKB1 strikingly (2.0-12-fold, E<0.01) increased the expression of subsets of hepatic (Alb, Iyd, Elovl2) and neuronal (Nptx2, Dlgap2, Cartpt, Pdyn) genes, enhancing glutamate signaling. These changes were partially recapitulated by the loss of AMPK, which also up-regulated β-cell "disallowed" genes (Slc16a1, Ldha, Mgst1, Pdgfra) 1.8- to 3.4-fold (E<0.01). Correspondingly, targeted promoters were enriched for neuronal (Zfp206; P=1.3×10(-33)) and hypoxia-regulated (HIF1; P=2.5×10(-16)) transcription factors. In summary, LKB1 and AMPK, through only partly overlapping mechanisms, maintain β-cell identity by suppressing alternate pathways leading to neuronal, hepatic, and other characteristics. Selective targeting of these enzymes may provide a new approach to maintaining β-cell function in some forms of diabetes.-Kone, M., Pullen, T. J., Sun, G., Ibberson, M., Martinez-Sanchez, A., Sayers, S., Nguyen-Tu, M.-S., Kantor, C., Swisa, A., Dor, Y., Gorman, T., Ferrer, J., Thorens, B., Reimann, F., Gribble, F., McGinty, J. A., Chen, L., French, P. M., Birzele, F., Hildebrandt, T., Uphues, I., Rutter, G. A. LKB1 and AMPK differentially regulate pancreatic β-cell identity

Replication and cross-validation of type 2 diabetes subtypes based on clinical variables: an IMI-RHAPSODY study

Aims/hypothesis Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic.Methods In three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA(1c), random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort's cluster centres. Finally, we compared the time to insulin requirement for each cluster.Results Five distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6-90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol [MDH] cluster), and the other not having any extreme characteristic (mild diabetes [MD]). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requirement, while the MDH cluster showed the slowest progression.Conclusions/interpretation Clusters based on C-peptide instead of HOMA2 measures resemble those based on HOMA2 measures, especially for SIDD, SIRD and MOD. By adding HDL-cholesterol, the MARD cluster based upon HOMA2 measures resulted in the current clustering into two clusters, with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA(1c), HDL-cholesterol, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts. Adding HDL-cholesterol to the clustering resulted in the identification of a cluster with very slow glycaemic deterioration.Molecular Epidemiolog

Transition Metal Catalysis Controlled by Hydrogen Bonding in the Second Coordination Sphere

Transition metal catalysis is of utmost importance for the development of sustainable processes in academia and industry. The activity and selectivity of metal complexes are typically the result of the interplay between ligand and metal properties. As the ligand can be chemically altered, a large research focus has been on ligand development. More recently, it has been recognized that further control over activity and selectivity can be achieved by using the "second coordination sphere", which can be seen as the region beyond the direct coordination sphere of the metal center. Hydrogen bonds appear to be very useful interactions in this context as they typically have sufficient strength and directionality to exert control of the second coordination sphere, yet hydrogen bonds are typically very dynamic, allowing fast turnover. In this review we have highlighted several key features of hydrogen bonding interactions and have summarized the use of hydrogen bonding to program the second coordination sphere. Such control can be achieved by bridging two ligands that are coordinated to a metal center to effectively lead to supramolecular bidentate ligands. In addition, hydrogen bonding can be used to preorganize a substrate that is coordinated to the metal center. Both strategies lead to catalysts with superior properties in a variety of metal catalyzed transformations, including (asymmetric) hydrogenation, hydroformylation, C-H activation, oxidation, radical-type transformations, and photochemical reactions

Identification of potential hub beta cells using single-cell RNA-Seq

Nephrolog

Biological Sensing of Nitric Oxide in Macrophages and Atherosclerosis Using a Ruthenium-Based Sensor

Macrophage-derived nitric oxide (NO) plays a critical role in atherosclerosis and presents as a potential biomarker. We assessed the uptake, distribution, and NO detection capacity of an irreversible, ruthenium-based, fluorescent NO sensor (Ru-NO) in macrophages, plasma, and atherosclerotic plaques. In vitro, incubation of Ru-NO with human THP1 monocytes and THP1-PMA macrophages caused robust uptake, detected by Ru-NO fluorescence using mass-cytometry, confocal microscopy, and flow cytometry. THP1-PMA macrophages had higher Ru-NO uptake (+13%, p < 0.05) than THP1 monocytes with increased Ru-NO fluorescence following lipopolysaccharide stimulation (+14%, p < 0.05). In mice, intraperitoneal infusion of Ru-NO found Ru-NO uptake was greater in peritoneal CD11b+F4/80+ macrophages (+61%, p < 0.01) than CD11b+F4/80− monocytes. Infusion of Ru-NO into Apoe−/− mice fed high-cholesterol diet (HCD) revealed Ru-NO fluorescence co-localised with atherosclerotic plaque macrophages. When Ru-NO was added ex vivo to aortic cell suspensions from Apoe−/− mice, macrophage-specific uptake of Ru-NO was demonstrated. Ru-NO was added ex vivo to tail-vein blood samples collected monthly from Apoe−/− mice on HCD or chow. The plasma Ru-NO fluorescence signal was higher in HCD than chow-fed mice after 12 weeks (37.9%, p < 0.05). Finally, Ru-NO was added to plasma from patients (N = 50) following clinically-indicated angiograms. There was lower Ru-NO fluorescence from plasma from patients with myocardial infarction (−30.7%, p < 0.01) than those with stable coronary atherosclerosis. In conclusion, Ru-NO is internalised by macrophages in vitro, ex vivo, and in vivo, can be detected in atherosclerotic plaques, and generates measurable changes in fluorescence in murine and human plasma. Ru-NO displays promising utility as a sensor of atherosclerosis.Achini K. Vidanapathirana, Jarrad M. Goyne, Anna E. Williamson, Benjamin J. Pullen, Pich Chhay, Lauren Sandeman, Julien Bensalem, Timothy J. Sargeant, Randall Grose, Mark J. Crabtree, Run Zhang, Stephen J. Nicholls, Peter J. Psaltis, and Christina A. Bursil

Replication and cross-validation of type 2 diabetes subtypes based on clinical variables: an IMI-RHAPSODY study.

Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic. In three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA &lt;sub&gt;1c&lt;/sub&gt; , random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort's cluster centres. Finally, we compared the time to insulin requirement for each cluster. Five distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6-90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol [MDH] cluster), and the other not having any extreme characteristic (mild diabetes [MD]). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requirement, while the MDH cluster showed the slowest progression. Clusters based on C-peptide instead of HOMA2 measures resemble those based on HOMA2 measures, especially for SIDD, SIRD and MOD. By adding HDL-cholesterol, the MARD cluster based upon HOMA2 measures resulted in the current clustering into two clusters, with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA &lt;sub&gt;1c&lt;/sub&gt; , HDL-cholesterol, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts. Adding HDL-cholesterol to the clustering resulted in the identification of a cluster with very slow glycaemic deterioration

Mind-Body Interventions to Reduce Risk for Health Disparities Related to Stress and Strength Among African American Women: The Potential of Mindfulness-Based Stress Reduction, Loving-Kindness, and the NTU Therapeutic Framework

Abstract available at publisher's website