Human pancreatic islet miRNA-mRNA networks of altered miRNAs due to glycemic status.

MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression via mRNA targeting, playing important roles in the pancreatic islets. We aimed to identify molecular pathways and genomic regulatory regions associated with altered miRNA expression due to glycemic status, which could contribute to the development of type 2 diabetes (T2D). To this end, miRNAs were identified by a combination of differential miRNA expression and correlation analysis in human islet samples from donors with normal and elevated blood glucose levels. Analysis and clustering of highly correlated, experimentally validated gene targets of these miRNAs revealed two islet-specific clusters, which were associated with key aspects of islet functions and included a high number of T2D-related genes. Finally, cis-eQTLs and public GWAS data integration uncovered suggestive genomic signals of association with insulin secretion and T2D. The miRNA-driven network-based approach presented in this study contributes to a better understanding of impaired insulin secretion in T2D pathogenesis.

Heterogeneous alpha-cell population modeling of glucose-induced inhibition of electrical activity.

Glucagon release from the pancreatic alpha-cells is regulated by glucose, but the underlying mechanisms are far from understood. It is known that the alpha-cell population is very heterogeneous, but - compared to the insulin-secreting beta-cells - the consequences of this cell-to-cell variation are much less studied. Since the alpha-cells are not electrically coupled, large differences in the single cell responses are to be expected, and this variation may contribute to the confusion regarding the mechanisms of glucose-induced suppression of glucagon release. Using mathematical modeling of alpha-cells with realistic cell-to-cell parameter variation based on recent experimental results, we show that the simulated alpha-cells exhibit great diversity in their electrophysiological behavior. To robustly reproduce experimental recordings from alpha-cell exposed to a rise in glucose levels, we must assume that both intrinsic mechanisms and paracrine signals contribute to glucose-induced changes in electrical activity. Our simulations suggest that the sum of different electrophysiological responses due to alpha-cell heterogeneity is involved in glucose-suppressed glucagon secretion, and that more than one mechanism contribute to control the alpha-cell populations' behavior. Finally, we apply regression analysis to our synthetic alpha-cell population to infer which membrane currents influence electrical activity in alpha-cells at different glucose levels. The results from such statistical modeling suggest possible disturbances underlying defect regulation of alpha-cell electrical behavior in diabetics. Thus, although alpha-cells appear to be inherently complex and heterogeneous as reflected in published data, realistic modeling of the alpha-cells at the population level provides insight into the mechanisms of glucagon release.

From Local to Global Modeling for Characterizing Calcium Dynamics and Their Effects on Electrical Activity and Exocytosis in Excitable Cells.

Electrical activity in neurons and other excitable cells is a result of complex interactions between the system of ion channels, involving both global coupling (e.g., via voltage or bulk cytosolic Ca2+ concentration) of the channels, and local coupling in ion channel complexes (e.g., via local Ca2+ concentration surrounding Ca2+ channels (CaVs), the so-called Ca2+ nanodomains). We recently devised a model of large-conductance BKCa potassium currents, and hence BKCa-CaV complexes controlled locally by CaVs via Ca2+ nanodomains. We showed how different CaV types and BKCa-CaV stoichiometries affect whole-cell electrical behavior. Ca2+ nanodomains are also important for triggering exocytosis of hormone-containing granules, and in this regard, we implemented a strategy to characterize the local interactions between granules and CaVs. In this study, we coupled electrical and exocytosis models respecting the local effects via Ca2+ nanodomains. By simulating scenarios with BKCa-CaV complexes with different stoichiometries in pituitary cells, we achieved two main electrophysiological responses (continuous spiking or bursting) and investigated their effects on the downstream exocytosis process. By varying the number and distance of CaVs coupled with the granules, we found that bursting promotes exocytosis with faster rates than spiking. However, by normalizing to Ca2+ influx, we found that bursting is only slightly more efficient than spiking when CaVs are far away from granules, whereas no difference in efficiency between bursting and spiking is observed with close granule-CaV coupling.

Genomic Prediction in Tetraploid Ryegrass Using Allele Frequencies Based on Genotyping by Sequencing.

Perennial ryegrass is an outbreeding forage species and is one of the most widely used forage grasses in temperate regions. The aim of this study was to investigate the possibility of implementing genomic prediction in tetraploid perennial ryegrass, to study the effects of different sequencing depth when using genotyping-by-sequencing (GBS), and to determine optimal number of single-nucleotide polymorphism (SNP) markers and sequencing depth for GBS data when applied in tetraploids. A total of 1,515 F2 tetraploid ryegrass families were included in the study and phenotypes and genotypes were scored on family-pools. The traits considered were dry matter yield (DM), rust resistance (RUST), and heading date (HD). The genomic information was obtained in the form of allele frequencies of pooled family samples using GBS. Different SNP filtering strategies were designed. The strategies included filtering out SNPs having low average depth (FILTLOW), having high average depth (FILTHIGH), and having both low average and high average depth (FILTBOTH). In addition, SNPs were kept randomly with different data sizes (RAN). The accuracy of genomic prediction was evaluated by using a "leave single F2 family out" cross validation scheme, and the predictive ability and bias were assessed by correlating phenotypes corrected for fixed effects with predicted additive breeding values. Among all the filtering scenarios, the highest estimates for genomic heritability of family means were 0.45, 0.74, and 0.73 for DM, HD and RUST, respectively. The predictive ability generally increased as the number of SNPs included in the analysis increased. The highest predictive ability for DM was 0.34 (137,191 SNPs having average depth higher than 10), for HD was 0.77 (185,297 SNPs having average depth lower than 60), and for RUST was 0.55 (188,832 SNPs having average depth higher than 1). Genomic prediction can help to optimize the breeding of tetraploid ryegrass. GBS data including about 80-100 K SNPs are needed for accurate prediction of additive breeding values in tetraploid ryegrass. Using only SNPs with sequencing depth between 10 and 20 gave highest predictive ability, and showed the potential to obtain accurate prediction from medium-low coverage GBS in tetraploids.

Optimized Use of Low-Depth Genotyping-by-Sequencing for Genomic Prediction Among Multi-Parental Family Pools and Single Plants in Perennial Ryegrass (Lolium perenne L.).

Ryegrass single plants, bi-parental family pools, and multi-parental family pools are often genotyped, based on allele-frequencies using genotyping-by-sequencing (GBS) assays. GBS assays can be performed at low-coverage depth to reduce costs. However, reducing the coverage depth leads to a higher proportion of missing data, and leads to a reduction in accuracy when identifying the allele-frequency at each locus. As a consequence of the latter, genomic relationship matrices (GRMs) will be biased. This bias in GRMs affects variance estimates and the accuracy of GBLUP for genomic prediction (GBLUP-GP). We derived equations that describe the bias from low-coverage sequencing as an effect of binomial sampling of sequence reads, and allowed for any ploidy level of the sample considered. This allowed us to combine individual and pool genotypes in one GRM, treating pool-genotypes as a polyploid genotype, equal to the total ploidy-level of the parents of the pool. Using simulated data, we verified the magnitude of the GRM bias at different coverage depths for three different kinds of ryegrass breeding material: individual genotypes from single plants, pool-genotypes from F2 families, and pool-genotypes from synthetic varieties. To better handle missing data, we also tested imputation procedures, which are suited for analyzing allele-frequency genomic data. The relative advantages of the bias-correction and the imputation of missing data were evaluated using real data. We examined a large dataset, including single plants, F2 families, and synthetic varieties genotyped in three GBS assays, each with a different coverage depth, and evaluated them for heading date, crown rust resistance, and seed yield. Cross validations were used to test the accuracy using GBLUP approaches, demonstrating the feasibility of predicting among different breeding material. Bias-corrected GRMs proved to increase predictive accuracies when compared with standard approaches to construct GRMs. Among the imputation methods we tested, the random forest method yielded the highest predictive accuracy. The combinations of these two methods resulted in a meaningful increase of predictive ability (up to 0.09). The possibility of predicting across individuals and pools provides new opportunities for improving ryegrass breeding schemes.

Ca2+ channel clustering with insulin-containing granules is disturbed in type 2 diabetes.

Loss of first-phase insulin secretion is an early sign of developing type 2 diabetes (T2D). Ca2+ entry through voltage-gated L-type Ca2+ channels triggers exocytosis of insulin-containing granules in pancreatic ß cells and is required for the postprandial spike in insulin secretion. Using high-resolution microscopy, we have identified a subset of docked insulin granules in human ß cells and rat-derived clonal insulin 1 (INS1) cells for which localized Ca2+ influx triggers exocytosis with high probability and minimal latency. This immediately releasable pool (IRP) of granules, identified both structurally and functionally, was absent in ß cells from human T2D donors and in INS1 cells cultured in fatty acids that mimic the diabetic state. Upon arrival at the plasma membrane, IRP granules slowly associated with 15 to 20 L-type channels. We determined that recruitment depended on a direct interaction with the synaptic protein Munc13, because expression of the II-III loop of the channel, the C2 domain of Munc13-1, or of Munc13-1 with a mutated C2 domain all disrupted L-type channel clustering at granules and ablated fast exocytosis. Thus, rapid insulin secretion requires Munc13-mediated recruitment of L-type Ca2+ channels in close proximity to insulin granules. Loss of this organization underlies disturbed insulin secretion kinetics in T2D.

Accuracy of Genomic Prediction in a Commercial Perennial Ryegrass Breeding Program.

The implementation of genomic selection (GS) in plant breeding, so far, has been mainly evaluated in crops farmed as homogeneous varieties, and the results have been generally positive. Fewer results are available for species, such as forage grasses, that are grown as heterogenous families (developed from multiparent crosses) in which the control of the genetic variation is far more complex. Here we test the potential for implementing GS in the breeding of perennial ryegrass ( L.) using empirical data from a commercial forage breeding program. Biparental F and multiparental synthetic (SYN) families of diploid perennial ryegrass were genotyped using genotyping-by-sequencing, and phenotypes for five different traits were analyzed. Genotypes were expressed as family allele frequencies, and phenotypes were recorded as family means. Different models for genomic prediction were compared by using practically relevant cross-validation strategies. All traits showed a highly significant level of genetic variance, which could be traced using the genotyping assay. While there was significant genotype × environment (G × E) interaction for some traits, accuracies were high among F families and between biparental F and multiparental SYN families. We have demonstrated that the implementation of GS in grass breeding is now possible and presents an opportunity to make significant gains for various traits.

Estimating genomic heritabilities at the level of family-pool samples of perennial ryegrass using genotyping-by-sequencing.

KEYMESSAGE: By using the genotyping-by-sequencing method, it is feasible to characterize genomic relationships directly at the level of family pools and to estimate genomic heritabilities from phenotypes scored on family-pools in outbreeding species. Genotyping-by-sequencing (GBS) has recently become a promising approach for characterizing plant genetic diversity on a genome-wide scale. We use GBS to extend the concept of heritability beyond individuals by genotyping family-pool samples by GBS and computing genomic relationship matrices (GRMs) and genomic heritabilities directly at the level of family-pools from pool-frequencies obtained by sequencing. The concept is of interest for species where breeding and phenotyping is not done at the individual level but operates uniquely at the level of (multi-parent) families. As an example we demonstrate the approach using a set of 990 two-parent F2 families of perennial ryegrass (Lolium Perenne). The families were phenotyped as a family-unit in field plots for heading date and crown rust resistance. A total of 728 K single nucleotide polymorphism (SNP) variants were available and were divided in groups of different sequencing depths. GRMs based on GBS data showed diagonal values biased upwards at low sequencing depth, while off-diagonals were little affected by the sequencing depth. Using variants with high sequencing depth, genomic heritability for crown rust resistance was 0.33, and for heading date 0.22, and these genomic heritabilities were biased downwards when using variants with lower sequencing depth. Broad sense heritabilities were 0.61 and 0.66, respectively. Underestimation of genomic heritability at lower sequencing depth was confirmed with simulated data. We conclude that it is feasible to use GBS to describe relationships between family-pools and to estimate genomic heritability directly at the level of F2 family-pool samples, but estimates are biased at low sequencing depth.

Glucose-dependent docking and SNARE protein-mediated exocytosis in mouse pancreatic alpha-cell.

The function of alpha-cells in patients with type 2 diabetes is often disturbed; glucagon secretion is increased at hyperglycaemia, yet fails to respond to hypoglycaemia. A crucial mechanism behind the fine-tuned release of glucagon relies in the exocytotic machinery including SNARE proteins. Here, we aimed to investigate the temporal role of syntaxin 1A and SNAP-25 in mouse alpha-cell exocytosis. First, we used confocal imaging to investigate glucose dependency in the localisation of SNAP-25 and syntaxin 1A. SNAP-25 was mainly distributed in the plasma membrane at 2.8 mM glucose, whereas the syntaxin 1A distribution in the plasma membrane, as compared to the cytosolic fraction, was highest at 8.3 mM glucose. Furthermore, following inclusion of an antibody against SNAP-25 or syntaxin 1A, exocytosis evoked by a train of ten depolarisations and measured as an increase in membrane capacitance was reduced by ~50%. Closer inspection revealed a reduction in the refilling of granules from the reserve pool (RP), but also showed a decreased size of the readily releasable pool (RRP) by ~45%. Disparate from the situation in pancreatic beta-cells, the voltage-dependent Ca²âº current was not reduced, but the Ca²âº sensitivity of exocytosis decreased by the antibody against syntaxin 1A. Finally, ultrastructural analysis revealed that the number of docked granules was >2-fold higher at 16.7 mM than at 1 mM glucose. We conclude that syntaxin 1A and SNAP-25 are necessary for alpha-cell exocytosis and regulate fusion of granules belonging to both the RRP and RP without affecting the Ca²âº current.