Biology subjects

Dahl, A.

Publications and source records attributed to Dahl, A..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Exploring social modulation: Microglia as a key mediator of individual immune response, plasticity and pathology in App-NL-G-F mouse model of Alzheimer's disease

This study explores the influence of lifestyle on Alzheimers disease (AD) progression using App-NL-G-F mice in a complex enrichment system. Mice exhibited social deficits before plaque pathology or memory impairment, revealing a crucial link between lifestyle, behavior, and neuroinflammation. Plasma analysis indicates early inflammation and apoptosis-related changes, setting the stage for identifying markers predicting plaque manifestation. Beyond pathology, social behavior is linked to adult neurogenesis and microglia coverage, forming a dynamic connection with microglia activation. Further, sc-RNA sequencing unveiled a decrease in interferon-responsive microglia and alteration in antigen processing with enrichment. These findings underscore the beneficial impact of social housing on microglia and interconnected factors, pointing to microglia as a critical mediator of the behavior-pathology-plasticity interplay in AD. The study enhances our understanding of AD complexity and offers insights into potential therapeutic strategies, emphasizing the multifaceted nature of AD progression and the role of lifestyle in shaping its course.

neuroscience↗

bioRxiv2023

A scalable and robust variance components method reveals insights into the architecture of gene-environment interactions underlying complex traits

Understanding the contribution of gene-environment interactions (GxE) to complex trait variation can provide insights into mechanisms underlying disease risk, explain sources of heritability, and improve the accuracy of genetic risk prediction. While biobanks that collect genetic and deep phenotypic data over large numbers of individuals offer the promise of obtaining novel insights into GxE, our understanding of the architecture of GxE in complex traits remains limited. We introduce a method that can estimate the proportion of trait variance explained by GxE (GxE heritability) and additive genetic effects (additive heritability) across the genome and within specific genomic annotations. We show that our method is accurate in simulations and computationally efficient for biobank-scale datasets. We applied our method to{approx} 500, 000 common array SNPs (MAF[≥] 1%), fifty quantitative traits, and four environmental variables (smoking, sex, age, and statin usage) measured across{approx} 300, 000 unrelated white British individuals in the UK Biobank. We found 69 trait-environmental variable pairs with significant genome-wide GxE heritability (p < 0.05/200 correcting for the number of trait-E pairs tested) with an average ratio of GxE to additive heritability{approx} 6.8% that include BMI with smoking (ratio of GxE to additive heritability = 6.3 {+/-} 1.1%), WHR (waist-to-hip ratio adjusted for BMI) with sex (ratio = 19.6 {+/-} 2%), LDL cholesterol with age (ratio = 9.8 {+/-} 3.9%), and HbA1c with statin usage (ratio = 11 {+/-} 2%). Analyzing nearly 8 million common and low-frequency imputed SNPs (MAF[≥] 0.1%), we document an increase in genome-wide GxE heritability of about 28% on average over array SNPs. We partitioned GxE heritability across minor allele frequency (MAF) and local linkage disequilibrium values (LD score) of each SNP to observe that analogous to the relationship that has been observed for additive allelic effects, the magnitude of GxE allelic effects tends to increase with decreasing MAF and LD. Testing whether GxE heritability is enriched around genes that are highly expressed in specific tissues, we find significant tissue-specific enrichments that include brain-specific enrichment for BMI and Basal Metabolic Rate in the context of smoking, adipose-specific enrichment for WHR in the context of sex, and cardiovascular tissue-specific enrichment for total cholesterol in the context of age. Our analyses provide detailed insights into the architecture of GxE underlying complex traits.

genetics↗

bioRxiv2023

Genetic and brain similarity independently predict childhood anthropometrics and socioeconomic markers

Linking the developing brain with individual differences in clinical and demographic traits is challenging due to the substantial interindividual heterogeneity of brain anatomy and organization. Here we employ a novel approach that parses individual differences in both cortical thickness and common genetic variants, and assess their effects on a wide set of childhood traits. The approach uses a linear mixed model framework to obtain the unique effects of each type of similarity, as well as their covariance, with the assumption that similarity in cortical thickness may in part be driven by similarity in genetic variants. We employ this approach in a sample of 7760 unrelated children in the ABCD cohort baseline sample (mean age 9.9, 46.8% female). In general, significant associations between cortical thickness similarity and traits were limited to anthropometrics such as height (r2 = 0.11, SE = 0.01), weight (r2 = 0.12, SE = 0.01), and birth weight (r2 = 0.19, SE = 0.01), as well as markers of socioeconomic status such as local area deprivation (r2 = 0.06, SE = 0.01). Analyses of the contribution from common genetic variants to traits revealed contributions across included outcomes, albeit somewhat lower than previous reports, possibly due to the young age of the sample. No significant covariance of the effects of genetic and cortical thickness similarity was found. The present findings highlight the connection between anthropometrics as well as socioeconomic factors and the developing brain, which appear to be independent from individual differences in common genetic variants in this population-based sample. The approach provides a promising framework for analyses of neuroimaging genetics cohorts, which can be further expanded by including imaging derived phenotypes beyond cortical thickness.

neuroscience↗

bioRxiv2023

Compartmentalization and synergy of osteoblasts drive bone formation in the regenerating fin

Zebrafish faithfully regenerate their fins after amputation which includes restoration of bone tissue and a component of cell plasticity. It is currently unclear how different cell populations of the regenerate divide labor to allow for efficient regenerate growth and proper patterning. Here, we studied lineage relationships of FACS-enriched epidermal, blastemal and bone forming fin regenerate cells by single cell (sc) RNA sequencing, lineage tracing, targeted osteoblast ablation and electron microscopy to show that the majority of osteoblasts in the outgrowing regenerate derive from osterix+ osteoblasts, while mmp9+ cells give rise to a limited cell number at the fin segment joints. A third population of distal blastema cells contributes to distal osteoblast progenitors, suggesting compartmentalization during appendage regeneration. Fin elongation and bone formation are carried out by distinct regenerate cell populations, and these variably depend on Fgf signaling. Ablation of osterix+ osteoblasts irreversibly impairs patterning of segment joints, and prevents bone matrix formation in the proximal regenerate. The resulting reduced regenerate length is partially compensated for by the distal regenerate which shows increased Wnt signaling activity. Surprisingly, ablation of joint cells does not abolish the formation of segment joints. Our study characterizes rare fin regenerate cell populations, indicates intricate osteoblast-blastema lineage relationships, inherent detection and compensation of impaired regeneration, and demonstrates zonation of the elongating regenerate. Furthermore, it sheds light on the variable dependence of bone formation on growth factor signaling.

developmental biology↗

bioRxiv2023

A robust model for cell type-specific interindividual variation in single-cell RNA sequencing data

The development of single-cell RNA sequencing (scRNA-seq) offers opportunities to characterize cellular heterogeneity at unprecedented resolution. Although scRNA-seq has been widely used to identify and characterize gene expression variation across cell types and cell states based on their average gene expression profiles, most studies ignore variation across individual donors. Modelling this inter-individual variation could improve statistical power to detect cell type-specific biology and inform the genes and cell types that underlying complex traits. We therefore develop a new model to detect and quantify cell type-specific variation across individuals called CTMM (Cell Type-specific linear Mixed Model). CTMM operates on cell type-specific pseudobulk expression and is fit with efficient methods that scale to hundreds of samples. We use extensive simulations to show that CTMM is powerful and unbiased in realistic settings. We also derive calibrated tests for cell type-specific interindividual variation, which is challenging given the modest sample sizes in scRNA-seq data. We apply CTMM to scRNA-seq data from human induced pluripotent stem cells to characterize the transcriptomic variation across donors as cells differentiate into endoderm. We find that almost 100% of transcriptome-wide variability between donors is differentiation stage-specific. CTMM also identifies individual genes with statistically significant stage-specific variability across samples, including 61 genes that do not have significant stage-specific mean expression. Finally, we extend CTMM to partition interindividual covariance between stages, which recapitulates the overall differentiation trajectory. Overall, CTMM is a powerful tool to characterize a novel dimension of cell type-specific biology in scRNA-seq.

bioinformatics↗

bioRxiv2023

CD38 promotes hematopoietic stem cell dormancy via c-Fos

A subpopulation of deeply quiescent, so-called dormant hematopoietic stem cells (dHSCs) resides at the top of the hematopoietic hierarchy and serves as a reserve pool for HSCs possessing the greatest long-term blood repopulation capacity. The state of dormancy protects the HSC pool from exhaustion throughout life, however excessive dormancy may block an efficient response to hematological stresses. The mechanisms of HSC dormancy remain elusive, mainly due to the absence of surface markers that allow dHSC prompt isolation. Here, we identify CD38 as a novel surface marker for murine dHSCs that is broadly applicable. Moreover, we demonstrate that cyclic adenosine diphosphate ribose (cADPR), the product of CD38 cyclase activity, regulates the expression of the transcription factor c-Fos by increasing cytoplasmic Ca2+ concentration. Strikingly, we uncover that c-Fos drives HSCs dormancy through the induction of the cell cycle inhibitor p57Kip2. Moreover, we found that CD38 ecto-enzymatic activity at the neighboring CD38-positive cells can promote human HSC quiescence. Together, CD38/cADPR/Ca2+/cFos/p57Kip2 axis maintains HSC dormancy. Pharmacological manipulations of this pathway can provide new strategies to expand dHSCs for transplantation or to activate them during hematological stresses.

cell biology↗

bioRxiv2023

scRNAseq unravels the transcriptional network underlying zebrafish retina regeneration

In the lesioned zebrafish retina, Muller glia produce multipotent retinal progenitors that generate all retinal neurons, replacing lost cell types. To study the molecular mechanisms linking Muller glia reactivity to progenitor production and neuronal differentiation, we used single cell RNA sequencing of Muller glia, progenitors and regenerated progeny from uninjured and light-lesioned retinae. We discover an injury-induced Muller glia differentiation trajectory that leads into a cell population with a hybrid identity expressing marker genes of Muller glia and progenitors. A glial self-renewal and a neurogenic trajectory depart from the hybrid cell population. We further observe that neurogenic progenitors progressively differentiate to generate retinal ganglion cells first and bipolar cells last, similar to the events observed during retinal development. Our work provides a comprehensive description of Muller glia and progenitor transcriptional changes and fate decisions in the regenerating retina, which are key to tailor cell differentiation and replacement therapies for retinal dystrophies in humans.

developmental biology↗

bioRxiv2022

Factorizing polygenic epistasis improves prediction and uncovers biological pathways in complex traits

Epistasis is central in many domains of biology, but it has not yet proven useful for complex traits. This is partly because complex trait epistasis involves polygenic interactions that are poorly captured in current models. To address this gap, we develop a new model called Epistasis Factor Analysis (EFA). EFA assumes that polygenic epistasis can be factorized into interactions between a few Epistasis Factors (EFs), which represent latent polygenic components of the observed complex trait. The statistical goals of EFA are to improve polygenic prediction and to increase power to detect epistasis, while the biological goal is to unravel genetic effects into more-homogeneous units. We mathematically characterize EFA and use simulations to show that EFA outperforms current epistasis models when its assumptions approximately hold. Applied to predicting yeast growth rates, EFA outperforms the additive model for several traits with large epistasis heritability and uniformly outperforms the standard epistasis model. We replicate these prediction improvements in a second dataset. We then apply EFA to four previously-characterized traits in the UK Biobank and find statistically significant epistasis in all four, including two that are robust to scale transformation. Moreover, we find that the inferred EFs partly recover pre-defined biological pathways for two of the traits. Our results demonstrate that more realistic models can identify biologically and statistically meaningful epistasis in complex traits, indicating that epistasis has potential for precision medicine and characterizing the biology underlying GWAS results.

genetics↗

bioRxiv2022

Cellular senescence modulates progenitor cell expansion during axolotl limb regeneration

Axolotl limb regeneration is accompanied by the transient induction of cellular senescence within the blastema, the structure which nucleates regeneration. The precise role of this blastemal senescent cell (bSC) population, however, remains unknown. Here, through a combination of gain- and loss-of-function assays, we elucidate the functions and molecular features of cellular senescence in vivo. We demonstrate that cellular senescence plays a positive role during axolotl regeneration, by creating a pro-proliferative niche that supports progenitor cell expansion and blastema outgrowth. Senescent cells impact on their microenvironment via Wnt pathway modulation. Further, we uncover a link between Wnt signalling and senescence induction, and propose that bSC-derived Wnt signals facilitate the proliferation of neighbouring cells in part by preventing their induction into senescence. This work defines the roles of cellular senescence in regeneration of complex structures.

developmental biology↗

bioRxiv2022

Phenotype integration improves power and preserves specificity in biobank-based genetic studies of MDD

Biobanks often contain several phenotypes relevant to a given disorder, and researchers face complex tradeoffs between shallow phenotypes (high sample size, low specificity and sensitivity) and deep phenotypes (low sample size, high specificity and sensitivity). Here, we study an extreme case: Major Depressive Disorder (MDD) in UK Biobank. Previous studies found that shallow and deep MDD phenotypes have qualitatively distinct genetic architectures, but it remains unclear which are optimal for scientific study or clinical prediction. We propose a new framework to get the best of both worlds by integrating together information across hundreds of MDD-relevant phenotypes. First, we use phenotype imputation to increase sample size for the deepest available MDD phenotype, which dramatically improves GWAS power (increases #loci ~10 fold) and PRS accuracy (increases R2 ~2 fold). Further, we show the genetic architecture of the imputed phenotype remains specific to MDD using genetic correlation, PRS prediction in external clinical cohorts, and a novel PRS-based pleiotropy metric. We also develop a complementary approach to improve specificity of GWAS on shallow MDD phenotypes by adjusting for phenome-wide PCs. Finally, we study phenotype integration at the level of GWAS summary statistics, which can increase GWAS and PRS power but introduces non-MDD-specific signals. Our work provides a simple and scalable recipe to improve genetic studies in large biobanks by combining the sample size of shallow phenotypes with the sensitivity and specificity of deep phenotypes.

genetics↗

bioRxiv2022

Deep Learning-based Phenotype Imputation on Population-scale Biobank Data Increases Genetic Discoveries

Biobanks that collect deep phenotypic and genomic data across large numbers of individuals have emerged as a key resource for human genetic research. However, phenotypes acquired as part of Biobanks are often missing across many individuals, limiting the utility of these datasets. The ability to accurately impute or "fill-in" missing phenotypes is critical to harness the power of population-scale Biobank datasets. We propose AutoComplete, a deep learning-based imputation method which can accurately impute missing phenotypes in population-scale Biobank datasets. When applied to collections of phenotypes measured across {approx} 300K individuals from the UK Biobank, AutoComplete improved imputation accuracy over existing 2 methods (average improvement in r2 of 18% for all phenotypes and 42% for binary phenotypes). We explored the utility of phenotype imputation for improving the power of genome-wide association studies (GWAS) by applying our method to a group of five clinically relevant traits with an average missigness rate of 83% (67% to 94%) leading to an an increase in effective sample size of {approx}2-fold on average (0.5 to 3.3-fold across the phenotypes). GWAS on the resulting imputed phenotypes led to an increase in the total number of loci significantly associated to the traits from four to 129. Our results demonstrate the utility of deep-learning based imputation to increase power for genetic discoveries in existing biobank data sets.

bioinformatics↗

bioRxiv2022

INFLAMMATION INDUCED DOWNREGULATION OF SUCCINATE DEHYDROGENASE REGULATES ADRENOCORTICAL FUNCTION

The hypothalamus-pituitary-adrenal (HPA) axis is activated in response to inflammation leading to increased production of anti-inflammatory glucocorticoids by the adrenal cortex, thereby representing an endogenous feedback loop. However, severe inflammation reduces the responsiveness of the adrenal gland to adrenocorticotropic hormone (ACTH) although the underlying mechanisms are poorly understood. Here, we show by transcriptomic, proteomic and metabolomic analyses that LPS-induced systemic inflammation triggers profound metabolic changes in steroidogenic adrenocortical cells, including downregulation of the TCA cycle and oxidative phosphorylation. Inflammation disrupts the TCA cycle at the level of succinate dehydrogenase (SDH) leading to succinate accumulation and disturbed steroidogenesis. Mechanistically, IL-1{beta} reduces SDHB expression through upregulation of DNA methyltransferase 1 (DNMT1) and methylation of the SDHB promoter. Consequently, increased succinate levels impair oxidative phosphorylation and ATP synthesis, leading to reduced steroidogenesis. Together, we demonstrate that the IL-1{beta}-DNMT1-SDHB-succinate axis disrupts steroidogenesis. Our findings not only provide a mechanistic explanation for the adrenal dysfunction in severe inflammation but also a potential target for therapeutic intervention.

immunology↗

bioRxiv2022

Cross-trait assortative mating is widespread and inflates genetic correlation estimates

The observation of genetic correlations between disparate traits has been interpreted as evidence of widespread pleiotropy, altered theories of human genetic architecture, and spurred considerable research activity across the natural and social sciences. Here, we introduce cross-trait assortative mating (xAM) as an alternative explanation for observed genetic correlations. We observe that xAM is common across a broad array of phenotypes and that phenotypic cross-mate correlation estimates are strongly associated with genetic correlation estimates (R2 = 76%). Then, we present theoretical and simulation-based results demonstrating that, under xAM, genetic correlation estimators yield significant estimates even for traits with entirely distinct genetic bases. We demonstrate that existing xAM plausibly accounts for substantial fractions of genetic correlation estimates in two large samples (N = 827,960). For example, previously reported genetic correlation estimates between many pairs of psychiatric disorders are fully consistent with xAM alone. Finally, we provide evidence for a history of xAM at the genetic level using a novel approach based on cross-trait even/odd chromosome polygenic score correlations. Together, our results demonstrate that previous reports have likely overestimated the true genetic similarity between many phenotypes.

genetics↗

bioRxiv2021

Predictive framework of macrophage activation

Macrophages populate every organ during homeostasis and disease, displaying features of tissue imprinting and heterogeneous activation. The disjointed picture of macrophage biology that emerged from these observations are a barrier for integration across models or with in vitro macrophage activation paradigms. We set out to contextualize macrophage heterogeneity across mouse tissues and inflammatory conditions, specifically aiming to define a common framework of macrophage activation. We built a predictive model with which we mapped the activation of macrophages across 12 tissues and 25 biological conditions, finding a striking commonality and finite number of transcriptional profiles, which we modelled as defined stages along four conserved activation paths. We verified this model with adoptive cell transfer experiments and identified transient RELM[a] expression as a feature of macrophage tissue engraftment. We propose that this integrative approach of macrophage classification allows the establishment of a common predictive framework of macrophage activation in inflammation and homeostasis. One Sentence SummaryWe propose an integrative approach of macrophage classification that allows the establishment of a common framework of macrophage activation in inflammation and homeostasis.

immunology↗

bioRxiv2021

Fast and powerful statistical method for context-specific QTL mapping in multi-context genomic studies

Context-specific eQTLs mediate genetic risk for complex diseases. However, limitations in current methods for identifying these eQTLs have hindered their comprehensive characterization and downstream interpretation of disease-associated variants. Here, we introduce FastGxC, a method to efficiently and powerfully map context-specific eQTLs by leveraging the correlation structure in genomic studies with repeated sampling, e.g., single-cell RNA-seq studies. Using simulations, we demonstrate that FastGxC is up to nine times more powerful and 106 times faster than existing approaches, reducing computation time from years to minutes. We applied FastGxC to bulk multi-tissue (N=698) and single-cell PBMC (N=1,218) RNA-seq datasets, generating comprehensive tissue- and cell-type-specific eQTL maps. These eQTLs exhibited up to four-fold enrichment in open chromatin regions from matched contexts and were twice as enriched as standard context-specific eQTLs, highlighting their biological relevance. Furthermore, we examined the relationship between context-specific eQTLs and complex human traits and diseases. FastGxC improved precision in identifying relevant contexts for each trait by three-fold and expanded candidate causal genes by 25% in cell types and 6% in tissues compared to standard eQTLs. In summary, FastGxC provides a powerful framework for mapping context-specific eQTLs, advancing our understanding of gene regulatory mechanisms underlying complex human traits and diseases.

genomics↗

bioRxiv2021

Loss of SETD1B results in the redistribution of genomic H3K4me3 in the oocyte

Histone 3 lysine 4 trimethylation (H3K4me3) is an epigenetic mark found at gene promoters and CpG islands. H3K4me3 is essential for mammalian development, yet mechanisms underlying its genomic targeting are poorly understood. H3K4me3 methyltransferases SETD1B and MLL2 are essential for oogenesis. We investigated changes in H3K4me3 in Setd1b conditional knockout (cKO) oocytes using ultra-low input ChIP-seq, with comparisons to DNA methylation and gene expression analyses. H3K4me3 was redistributed in Setd1b cKO oocytes showing losses at active gene promoters associated with downregulated gene expression. Remarkably, many regions also gained H3K4me3, in particular those that were DNA hypomethylated, transcriptionally inactive and CpG-rich, which are hallmarks of MLL2 targets. Consequently, loss of SETD1B disrupts the balance between MLL2 and de novo DNA methyltransferases in determining the epigenetic landscape during oogenesis. Our work reveals two distinct, complementary mechanisms of genomic targeting of H3K4me3 in oogenesis, with SETD1B linked to gene expression and MLL2 to CpG content. Graphical AbstractIn oogenesis, SETD1B and CXXC1 target H3K4me3 to actively transcribed gene promoters, while MLL2 targets transcriptionally inactive regions based on underlying CpG composition (upper panel). When SETD1B is ablated, H3K4me3 is lost at a subset of active promoters, resulting in downregulation of transcription (lower panel). Loss of SETD1B alters the activity of MLL2, permitting MLL2 to deposit H3K4me3 at CpG-rich regions, many of which should otherwise be DNA methylated. Thus, it is evident that MLL2 and de novo DNMTs compete for genomic occupancy late in oogenesis, and loss of SETD1B disrupts the balance of these mechanisms. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

developmental biology↗

bioRxiv2020

Multi-omics profiling of living human pancreatic islet donors reveals heterogeneous beta cell trajectories toward type 2 diabetes

Existing studies do not sufficiently describe the molecular changes of pancreatic islet beta cells leading to their deficient insulin secretion in type 2 diabetes (T2D). Here we address this deficiency with a comprehensive multi-omics analysis of metabolically profiled pancreatectomized living human donors stratified along the glycemic continuum from normoglycemia to T2D. Islet pools isolated from surgical samples by laser-capture microdissection had remarkably heterogeneous transcriptomic and proteomic profiles in diabetics, but not in non-diabetic controls. Transcriptomics analysis of this unique cohort revealed islet genes already dysregulated in prediabetic individuals with impaired glucose tolerance. Our findings demonstrate a progressive but disharmonic remodeling of mature beta cells, challenging current hypotheses of linear trajectories toward precursor or trans-differentiation stages in T2D. Further, integration of islet transcriptomics and pre-operative blood plasma lipidomics data enabled us to define the relative importance of gene co-expression modules and lipids positively or negatively associated with HbA1c levels, pointing to potential prognostic markers.

bioinformatics↗

bioRxiv2020

MLL1 is required for maintenance of intestinal stem cells and the expression of the cell adhesion molecule JAML

Epigenetic control is crucial for lineage-specific gene expression that creates cellular identity during mammalian development and in adult organism. Histone 3 lysine 4 methylation (H3K4) is a universal epigenetic mark. Mixed lineage leukemia (MLL1) is the founding member of the mammalian family of H3K4 methyltransferases. It was originally discovered as the main gene mutated in early onset leukemias and then found to be required for hematopoietic stem cell development and maintenance. However, the roles of MLL1 in non-hematopoietic tissues remain largely unexplored. To bypass hematopoietic lethality, we used bone marrow transplantation and conditional mutagenesis to discover that the most overt phenotype in Mll1-mutant mice is intestinal failure. Loss of MLL1 is accompanied by a differentiation bias towards the secretory lineage with increased numbers of goblet cells. MLL1 is expressed in intestinal stem cells (ISCs) and transit amplifying (TA) cells but at reduced levels in Paneth cells and not in the villus. MLL1 is required for the maintenance of intestinal stem cells (ISCs) and proliferation in the crypt. Transcriptome analysis implicate MLL1-dependent expression in ISCs of several transcription factors including Pitx2, Gata4, Foxa1 and Onecut2, and also a cell adhesion molecule, Jaml. Reactive transcriptome changes in Paneth cells and organoids imply that JAML plays a key role in the crypt stem cell niche. All known postnatal functions of MLL1 relate to stem cell maintenance and lineage decisions thereby highlighting the suggestion that MLL1 is a master stem cell regulator. Author SummaryThe ability of adult stem cells to produce functional progenies through differentiation is critical to maintain function and integrity of organs. A fundamental challenge is to identify factors that control the transition from self-renewal to the differentiated state. Epigenetic factors amongst others can fullfill such a role. Methylation of histone 3 on lysine 4 (H3K4) is a posttranslational epigenetic modification that is associated with actively transcribed genes. In mammals, this epigenetic mark is catalyzed by one of six H3K4 methyltransferases, including the founding member of the family, MLL1. MLL1 is important for the precise functioning of the hematopoietic stem cell compartment. This raises the possibility of similar functions in other adult stem cell compartments. Due to its intense self-renewal kinetics and its simple repetitive architecture, the intestinal epithelium serves as a prime model for studying adult stem cells. We demonstrate that MLL1 controls intestinal stem cell proliferation and differentiation. Additionally, transcriptome analysis suggests a pertubation in the close interaction between intestinal stem cells and neighbouring Paneth cells through loss of junction adhesion molecule like (JAML). Our work sheds new light on the function of MLL1 for the control of intestinal stem cell identity.

genetics↗