Biology subjects

Mulder, H.

Publications and source records attributed to Mulder, H..

12 recordsLinked to original sources

bioRxiv2025

Lysine Potentiates Insulin Secretion via AASS-Dependent Catabolism and Regulation of GABA Content and Signaling

Background and aimLysine is an essential amino acid with insulinotropic effects in humans. In vitro, lysine also potentiates glucose-stimulated insulin secretion (GSIS) in {beta} cell lines and rodent pancreatic islets. For decades it has been assumed that insulinotropic action of lysine is mediated by plasma membrane depolarization similar to arginine. Aminoadipate-Semialdehyde Synthase (AASS) is a mitochondrial-located bifunctional enzyme engaged in the first two steps of the lysine catabolism. Whether AASS-dependent lysine catabolism occurs in {beta} cells and whether it is required for its insulinotropic action has not been investigated. MethodsmRNA expression of lysine catabolism pathway genes was assessed in human islets from non-diabetic (ND) and type 2 diabetes (T2D) subjects. AASS was silenced in human pancreatic islets and in INS1 832/13 {beta} cells. {beta} cell metabolism and function were investigated by ELISA, extracellular flux analysis, live cell calcium imaging, transcriptomics and metabolomics analyses. ResultsExpression of genes involved in lysine catabolism, including AASS, ALDH7A1, DHTKD1 and HADH, was reduced in pancreatic islets from T2D donors. Silencing of AASS resulted in reduced lysine- and glucose-stimulated insulin secretion in human islets and INS1 832/13 {beta} cells. Surprisingly, transcriptomics and metabolomics analysis in Aass-KD {beta} cells with suppressed lysine catabolism identified reduced {gamma}-aminobutyric acid (GABA)/glutamate ratio as well as altered expression of genes implicated in GABA metabolism. This was accompanied by altered mitochondrial TCA cycle and oxidative phosphorylation (OXPHOS) activity, reflected by elevated lactate/pyruvate and reduced whole-cell ATP/ADP content as well as ATP-linked mitochondrial respiration. Glucose-and GABA-stimulated cytosolic calcium was also altered in Aass-KD {beta} cells. Strikingly, addition of GABA recovered impaired insulin secretion in Aass-KD {beta} cells. ConclusionAASS-dependent lysine catabolism is required to maintain adequate GABA shunt metabolism and signaling. In addition, lysine catabolism supports mitochondrial energy production, calcium uptake and insulin secretion. Reduced AASS-dependent lysine catabolism may contribute to {beta} cell GABA depletion and dysfunction in T2D patients.

cell biology↗

bioRxiv2024

Modelling genetic risk of β-cell dysfunction in human induced pluripotent stem cells from patients carrying the MTNR1B risk variant.

Disruptions in circadian rhythm, partly controlled by the hormone melatonin, increase the risk of type 2 diabetes (T2D). Accordingly, a variant of the gene encoding the melatonin receptor 1B (MTNR1B) is robustly associated with increased risk of T2D. This single nucleotide polymorphism (SNP; rs10830963; G-allele) is an expression quantitative trait locus (eQTL) in human pancreatic islets, conferring increased expression of MTNR1B, which is thought to perturb pancreatic {beta}-cell function. To understand this pathogenic mechanism in detail, we utilized human induced pluripotent stem cells (hiPSC), derived from individuals with T2D carrying the MTNR1B G-allele. Patient-derived fibroblasts were reprogrammed to hiPSC and single-base genome editing by CRISPR/Cas9 was employed to create isogenic lines of either the C/C or G/G genotypes (non-risk and risk, respectively). In addition, the human embryonic stem cell (hESC) line (HUES4) was subjected to genome editing to create isogenic lines of either the C/C or G/G genotypes. hiPSC and hESC were differentiated into {beta}-cells, using a 50-day 2D protocol. Single-base genome editing generated cells with the desired genotype at a success rate of >90%. Expression of stage-specific markers confirmed differentiation of both hiPSC and hESC into {beta}-cells. MTNR1B mRNA levels were consistently low in differentiated {beta}-cells, precluding quantitative analysis of gene expression. However, Western blot analysis showed higher levels of MTNR1B in differentiated {beta}-cells carrying the risk allele, consistent with rs10830963 (G-allele) being an eQTL in {beta}-cells. Insulin secretion in response to glucose and IBMX was similar between the genotypes, whereas addition of melatonin reduced secretion in G-allele carriers. We conclude that the stem cell-derived {beta}-cells are not sufficiently mature to allow determination of eQTL status at the mRNA level. However, we did observe increased MTNR1B protein and increased sensitivity of {beta}-cells from risk allele carriers (G-allele) to melatonin with regard to insulin secretion, thus supporting a functional role for the rs10830963 SNP in {beta}-cell dysfunction.

cell biology↗

bioRxiv2023

What makes the effect of protein mutations difficult to predict?

Protein engineering increasingly relies on machine learning models to computationally pre-screen promising novel candidates. Although machine learning approaches have proven effective, their performance on prospective screening data leaves room for improvement; prediction accuracy can vary greatly from one protein variant to the next. So far, it is unclear what characterizes variants that are associated with large prediction error. In order to establish whether structural characteristics influence predictability, we created a combinatorial variant dataset for an enzyme, that can be partitioned into subsets of variants with mutations at positions exclusively belonging to a particular structural class. By training four different variant effect prediction (VEP) models on structurally partitioned subsets of our data, we found that predictability strongly depended on all four structural characteristics we tested; buriedness, number of contact residues, proximity to the active site and presence of secondary structure elements. These same dependencies were found in various single mutation enzyme variant datasets, with effect directions being specific to the assay. Most importantly, we found that these dependencies are highly alike for all four models we tested, indicating that there are specific structure and function determinants that are insufficiently accounted for by popular existing approaches. Overall, our findings suggest that significant improvements can be made to VEP models by exploring new inductive biases and by leveraging different data modalities of protein variants, and that stratified dataset design can highlight areas of improvement for machine learning guided protein engineering.

bioinformatics↗

bioRxiv2022

Identification of a weight loss-associated causal eQTL in MTIF3 and the effects of MTIF3 deficiency on human adipocyte function

BackgroundGenetic variation at the MTIF3 (Mitochondrial Translational Initiation Factor 3) locus has been robustly associated with obesity in humans, but the functional basis behind this association is not known. MethodsHere, we applied luciferase reporter assay to map potential functional variants in the haplotype block tagged by rs1885988 and used CRISPR-Cas9 to edit the potential functional variants to confirm the regulatory effects on MTIF3 expression. We further conducted functional studies on MTIF3-deficient differentiated human white adipocyte cell line (hWAs-iCas9), generated through inducible expression of CRISPR-Cas9 combined with delivery of synthetic MTIF3-targeting guide RNA. ResultsWe demonstrate that rs67785913-centered DNA fragment (in LD with rs1885988, r2>0.8) enhances transcription in a luciferase reporter assay, and CRISPR/Cas9 edited rs67785913 CTCT cells show significantly higher MTIF3 expression than rs67785913 CT cells. Perturbed MTIF3 expression changed the expression of mitochondrial DNA-encoded genes, and reduced mitochondrial respiration, as well as altered endogenous fatty acid oxidation. Furthermore, after glucose restriction, the MTIF3 knockout cells retained more triglycerides than control cells. ConclusionsThis study demonstrates an adipocyte function-specific role of MTIF3, which originates in the maintenance of mitochondrial function, providing potential explanations for why MTIF3 genetic variation at rs67785913 is associated with body corpulence and response to weight loss interventions.

genetics↗

bioRxiv2022

NSD2 E1099K drives relapse in pediatric acute lymphoblastic leukemia by disrupting 3D chromatin organization

The NSD2 p.E1099K (EK) mutation has been shown to be enriched in patients with relapsed ALL and found to play a role in clonal fitness dependent on the underlying genetic/epigenetic landscape of the cells. To uncover 3D chromatin architecture-related mechanisms underlying drug resistance, we systematically integrated Hi-C, ATAC-seq, RNA-seq and ChIP-seq data from three B-ALL cell lines heterozygous for NSD2 EK (RS4;11, RCH-ACV, SEM) and assessed changes upon knockdown. NSD2 knockdown revealed widespread remodeling of the 3D genome, specifically in terms of compartmentalization. Systematic integration of these datasets revealed significant switches in A/B compartments with a strong bias towards B compartments upon knockdown, suggesting that NSD2 EK plays a prominent role in maintaining A compartments through enrichment of H3K36me2 epigenetic marks. In contrast, we identified few changes in intra-TAD activity suggesting that the NSD2 EK impacts transcriptional changes through a remarkable dependence on compartmentalization. Furthermore, EK-mediated reorganization of compartments highlights the existence of a common core of compacting loci shared across the three cell lines that explain previously described phenotypes as well as serve as targets for therapeutic intervention. This study offers a novel mechanism by which NSD2 EK drives clonal evolution and drug resistance.

cancer biology↗

bioRxiv2021

TIGER: The gene expression regulatory variation landscape of human pancreatic islets

GWAS have identified more than 700 genetic signals associated with type 2 diabetes (T2D). To gain insight into the underlying molecular mechanisms, we created the Translational human pancreatic Islet Genotype tissue-Expression Resource (TIGER), aggregating >500 human islet RNA-seq and genotyping datasets. We imputed genotypes using 4 reference panels and meta-analyzed cohorts to improve coverage of expression quantitative trait loci (eQTL) and developed a method to combine allele-specific expression across samples (cASE). We identified >1 million islet eQTLs (56% novel), of which 53 colocalize with T2D signals (60% novel). Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identified 8 novel cASE colocalizations, among which an SLC30A8 T2D associated variant. We make all the data available through the open-access TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translate this into therapeutic insight and precision medicine for T2D.

bioinformatics↗

bioRxiv2020

DIMT1, a regulator of ribosomal biogenesis, controls beta-cell protein synthesis, mitochondrial function and insulin secretion

We previously reported that transcription factor B1 mitochondrial (TFB1M) is involved in the pathogenesis of type 2 diabetes (T2D) owing to mitochondrial dysfunction. Here, we describe that dimethyladenosine transferase 1 homolog (DIMT1), a homologue of TFB1M, is expressed and active in pancreatic {beta}-cells. Like TFB1M, it has been implicated in control of ribosomal RNA (rRNA) but its role in {beta}-cells or T2D remains to be identified. Silencing of DIMT1 impacted mitochondrial function, leading to reduced expression of mitochondrial OXPHOS proteins, reduced oxygen consumption rate (OCR), dissipated mitochondrial membrane potential ({Delta}{Psi}m) and caused a lower rate of ATP production (mATP). In addition, DIMT1 knockdown slowed the rate of protein synthesis. In accordance with these findings, DIMT1-deficiency perturbed insulin secretion in rodent and human {beta}-cell lines. These effects are likely a result of destabilization of ribosomal assembly, involving NIN1 (RPN12) binding protein 1 homolog (NOB-1) and Pescadillo ribosomal biogenesis factor 1 (PES-1). These are two critical ribosomal subunits proteins, whose interactions were perturbed upon DIMT1-deficiency, thereby disturbing protein synthesis in {beta}-cells. Thus, we have here highlighted a role of DIMT1 in ribosomal biogenesis that perturbs protein synthesis, resulting in mitochondrial dysfunction and disrupted insulin secretion, both being potential pathogenetic factors in T2D.

molecular biology↗

bioRxiv2019

Mitochondrial clearance of Ca2+ controls insulin secretion

Transport of Ca2+ from the cytosol to the mitochondrial matrix of insulin-secreting pancreatic {beta}-cells facilitates nutrient-mediated insulin secretion. However, the underlying mechanism is unclear. The establishment of the molecular identity of the mitochondrial Ca2+ uniporter (MCU) and associated proteins has allowed mitochondrial Ca2+ transport to be modified in intact cells. We examined the consequences of deficiency of the accessory protein, MICU2, in rat and human insulin-secreting cell lines as well as in mouse islets. Glucose-induced mitochondrial Ca2+ elevation and inner membrane hyperpolarization were reduced, together with cytosolic ATP/ADP-ratios and insulin secretion. Insulin secretion in Micu2 knock out mice was attenuated in vitro as well as in vivo. While KCl-evoked sub-plasmalemmal Ca2+ increases were more pronounced, the global cytosolic Ca2+ response was, surprisingly, diminished in MICU2-deficient cells. These findings were supported by selective inhibition of mitochondrial Ca2+ uptake by mitochondrial depolarization. It is concluded that mitochondrial Ca2+ transport plays an additional and hitherto unrecognized role in stimulated {beta}-cells by regulating net Ca2+ entry across the plasma membrane. This is likely accounted for by clearing of sub-plasmalemmal Ca2+ levels by mitochondria located near the plasma membrane.

molecular biology↗

bioRxiv2018

Islet Gene View - a tool to facilitate islet research

Changes in the hormone-producing pancreatic islets are central culprits in type 2 diabetes (T2D) pathogenesis. Characterization of gene expression in islets how it is altered in T2D are therefore vital in understanding islet function and T2D pathogenesis. We leveraged RNA-sequencing and genome-wide genotyping in islets from 188 donors to create the Islet Gene View (IGW) platform to make this information easily accessible to the scientific community. The IGW combines expression data for a given gene with phenotypical data such as T2D status, BMI, HbA1c, insulin secretion, purity of islets, etc.), regulation of gene expression by genetic variants e.g., expression quantitative trait loci (eQTLs) and relationship with expression of islet hormones. In IGW, 285 differentially expressed genes (DEGs) were identified in T2D donors islets compared to controls. Forty percent of the DEGs showed cell-type enrichment and a large proportion of them were significantly co-expressed with islet hormone-encoding genes like glucagon (GCG, 56%), amylin (IAPP, 52%), insulin (INS, 44%) and somatostatin (SST, 24%). Inhibition of two DEGs, UNC5D and SERPINE2 impaired glucose-stimulated insulin secretion and impacted cell survival in a human beta-cell model. Significance StatementWe present Islet Gene View (IGW), a web resource facilitating information on gene expression in human pancreatic islets from organ donors easily accessible to the scientific community. In IGW, we explored RNA expression from 188 donor-islets and examined their relationship with islet phenotypes including insulin secretion and expression of genes encoding islet hormones. GWAS have shown 403 genetic variants associated with risk of type 2 diabetes (T2D) risk, however, the target genes and function of these variants in islets are largely unknown. By linking T2D risk variants to expression in islets from T2D and non-diabetic donors as well as islet phenotypes, use of IGW provided new insight into mechanisms by which variants in these loci may increase risk of T2D.

molecular biology↗

bioRxiv2018

MYCN Amplification and ATRX Mutations are Incompatible in Neuroblastoma

Aggressive cancers often have activating mutations in growth-controlling oncogenes and inactivating mutations in tumor-suppressor genes. In neuroblastoma, amplification of the MYCN oncogene and inactivation of the ATRX tumor-suppressor gene correlate with high-risk disease and poor prognosis. Here we show that ATRX mutations and MYCN amplification are mutually exclusive across all ages and stages in neuroblastoma. Using human cell lines and mouse models, we found that elevated MYCN expression and ATRX mutations are incompatible. Elevated MYCN levels promote metabolic reprogramming, mitochondrial dysfunction, reactive-oxygen species generation, and DNA-replicative stress. The combination of replicative stress caused by defects in the ATRX-histone chaperone complex and that induced by MYCN-mediated metabolic reprogramming leads to synthetic lethality. Therefore, ATRX and MYCN represent an unusual example, where inactivation of a tumor-suppressor gene and activation of an oncogene are incompatible. This synthetic lethality may eventually be exploited to improve outcomes for patients with high-risk neuroblastoma.

cancer biology↗

bioRxiv2017

Perturbed Mitochondrial Metabolism in Islets from Donors with Type-2 Diabetes

There is a preponderance for genes involved in {beta}-cell function among gene variants associated with future risk of type-2 diabetes (T2D). {beta}-cell function is controlled by metabolism of glucose, yielding signals triggering and amplifying insulin secretion. Perturbed {beta}-cell metabolism is a likely, albeit not proven, cause of T2D. We profiled metabolites in islets from T2D and non-diabetic donors, and found altered levels of mitochondrial metabolites in T2D. Analysis of genes encoding proteins localized to mitochondria (MitoCarta) by RNA-seq in an extended sample of islets revealed genes whose expression was associated with glycaemia- and/or BMI. Expression of two of these, -methylacyl-CoA racemase (AMACR) and methylmalonyl-CoA mutase (MUT), was influenced by genetic variation (cis-eQTL). Silencing of AMACR and MUT in insulin-secreting cells reduced hormone secretion by 40-50%. In conclusion, by linking the metabolome to the transcriptome, we showed that perturbed mitochondrial metabolism is a feature of {beta}-cell dysfunction in T2D.\n\n[Supplementary material is available for this article.]

cell biology↗

bioRxiv2017

Clustering of adult-onset diabetes into novel subgroups guides therapy and improves prediction of outcome

BackgroundDiabetes is presently classified into two main forms, type 1 (T1D) and type 2 diabetes (T2D), but especially T2D is highly heterogeneous. A refined classification could provide a powerful tool individualize treatment regimes and identify individuals with increased risk of complications already at diagnosis.\n\nMethodsWe applied data-driven cluster analysis (k-means and hierarchical clustering) in newly diagnosed diabetic patients (N=8,980) from the Swedish ANDIS (All New Diabetics in Scania) cohort, using five variables (GAD-antibodies, BMI, HbA1c, HOMA2-B and HOMA2-IR), and related to prospective data on development of complications and prescription of medication from patient records. Replication was performed in three independent cohorts: the Scania Diabetes Registry (SDR, N=1466), ANDIU (All New Diabetics in Uppsala, N=844) and DIREVA (Diabetes Registry Vaasa, N=3485). Cox regression and logistic regression was used to compare time to medication, time to reaching the treatment goal and risk of diabetic complications and genetic associations.\n\nFindingsWe identified 5 replicable clusters of diabetes patients, with significantly different patient characteristics and risk of diabetic complications. Particularly, individuals in the most insulin-resistant cluster 3 had significantly higher risk of diabetic kidney disease, but had been prescribed similar diabetes treatment compared to the less susceptible individuals in clusters 4 and 5. The insulin deficient cluster 2 had the highest risk of retinopathy. In support of the clustering, genetic associations to the clusters differed from those seen in traditional T2D.\n\nInterpretationWe could stratify patients into five subgroups predicting disease progression and development of diabetic complications more precisely than the current classification. This new substratificationn may help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes.\n\nFundingThe funders of the study had no role in study design, data collection, analysis, interpretation or writing of the report.\n\nResearch in contextEvidence before this study\n\nThe current diabetes classification into T1D and T2D relies primarily on presence (T1D) or absence (T2D) of autoantibodies against pancreatic islet beta cell autoantigens and age at diagnosis (earlier for T1D). With this approach 75-85% of patients are classified as T2D. A third subgroup, Latent Autoimmune Diabetes in Adults (LADA,<10%), is defined by presence of autoantibodies against glutamate decarboxylase (GADA) with onset in adult age. In addition, several rare monogenic forms of diabetes have been described, including Maturity Onset Diabetes of the Young (MODY) and neonatal diabetes. This information is provided by national guidelines (ADA,WHO, IDF, Diabetes UK etc) but has not been much updated during the past 20 years and very few attempts have been made to explore heterogeneity of T2D. A topological analysis of potential T2D subgroups using electronic health records was published in 2015 but this information has not been implemented in the clinic.\n\nAdded value of this study\n\nHere we applied a data-driven cluster analysis of 5 simple variables measured at diagnosis in 4 independent cohorts of newly-diagnosed diabetic patients (N=14755) and identified 5 replicable clusters of diabetes patients, with significantly different patient characteristics and risk of diabetic complications. Particularly, individuals in the most insulin-resistant cluster 3 had significantly higher risk of diabetic kidney disease.\n\nImplications of the available evidence\n\nThis new sub-stratification may help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes

epidemiology↗