TCR+/BCR+ dual-expressing cells and their associated public BCR clonotype are not enriched in type 1 diabetes.

Ahmed and colleagues recently described a novel hybrid lymphocyte expressing both a B and T cell receptor, termed double expresser (DE) cells. DE cells in blood of type 1 diabetes (T1D) subjects were present at increased numbers and enriched for a public B cell clonotype. Here, we attempted to reproduce these findings. While we could identify DE cells by flow cytometry, we found no association between DE cell frequency and T1D status. We were unable to identify the reported public B cell clone, or any similar clone, in bulk B cells or sorted DE cells from T1D subjects or controls. We also did not observe increased usage of the public clone VH or DH genes in B cells or in sorted DE cells. Taken together, our findings suggest that DE cells and their alleged public clonotype are not enriched in T1D. This Matters Arising paper is in response to Ahmed et al. (2019), published in Cell. See also the response by Ahmed et al. (2021), published in this issue.

Genetic Variation in Type 1 Diabetes Reconfigures the 3D Chromatin Organization of T Cells and Alters Gene Expression.

Genetics is a major determinant of susceptibility to autoimmune disorders. Here, we examined whether genome organization provides resilience or susceptibility to sequence variations, and how this would contribute to the molecular etiology of an autoimmune disease. We generated high-resolution maps of linear and 3D genome organization in thymocytes of NOD mice, a model of type 1 diabetes (T1D), and the diabetes-resistant C57BL/6 mice. Multi-enhancer interactions formed at genomic regions harboring genes with prominent roles in T cell development in both strains. However, diabetes risk-conferring loci coalesced enhancers and promoters in NOD, but not C57BL/6 thymocytes. 3D genome mapping of NODxC57BL/6 F1 thymocytes revealed that genomic misfolding in NOD mice is mediated in cis. Moreover, immune cells infiltrating the pancreas of humans with T1D exhibited increased expression of genes located on misfolded loci in mice. Thus, genetic variation leads to altered 3D chromatin architecture and associated changes in gene expression that may underlie autoimmune pathology.

ß-Hydroxybutyrate suppresses colorectal cancer.

Colorectal cancer (CRC) is among the most frequent forms of cancer, and new strategies for its prevention and therapy are urgently needed1. Here we identify a metabolite signalling pathway that provides actionable insights towards this goal. We perform a dietary screen in autochthonous animal models of CRC and find that ketogenic diets exhibit a strong tumour-inhibitory effect. These properties of ketogenic diets are recapitulated by the ketone body ß-hydroxybutyrate (BHB), which reduces the proliferation of colonic crypt cells and potently suppresses intestinal tumour growth. We find that BHB acts through the surface receptor Hcar2 and induces the transcriptional regulator Hopx, thereby altering gene expression and inhibiting cell proliferation. Cancer organoid assays and single-cell RNA sequencing of biopsies from patients with CRC provide evidence that elevated BHB levels and active HOPX are associated with reduced intestinal epithelial proliferation in humans. This study thus identifies a BHB-triggered pathway regulating intestinal tumorigenesis and indicates that oral or systemic interventions with a single metabolite may complement current prevention and treatment strategies for CRC.

Collapse of the hepatic gene regulatory network in the absence of FoxA factors.

The FoxA transcription factors are critical for liver development through their pioneering activity, which initiates a highly complex regulatory network thought to become progressively resistant to the loss of any individual hepatic transcription factor via mutual redundancy. To investigate the dispensability of FoxA factors for maintaining this regulatory network, we ablated all FoxA genes in the adult mouse liver. Remarkably, loss of FoxA caused rapid and massive reduction in the expression of critical liver genes. Activity of these genes was reduced back to the low levels of the fetal prehepatic endoderm stage, leading to necrosis and lethality within days. Mechanistically, we found FoxA proteins to be required for maintaining enhancer activity, chromatin accessibility, nucleosome positioning, and binding of HNF4α. Thus, the FoxA factors act continuously, guarding hepatic enhancer activity throughout adult life.

HDAC3 ensures stepwise epidermal stratification via NCoR/SMRT-reliant mechanisms independent of its histone deacetylase activity.

Chromatin modifiers play critical roles in epidermal development, but the functions of histone deacetylases in this context are poorly understood. The class I HDAC, HDAC3, is of particular interest because it plays divergent roles in different tissues by partnering with tissue-specific transcription factors. We found that HDAC3 is expressed broadly in embryonic epidermis and is required for its orderly stepwise stratification. HDAC3 protein stability in vivo relies on NCoR and SMRT, which function redundantly in epidermal development. However, point mutations in the NCoR and SMRT deacetylase-activating domains, which are required for HDAC3's enzymatic function, permit normal stratification, indicating that HDAC3's roles in this context are largely independent of its histone deacetylase activity. HDAC3-bound sites are significantly enriched for predicted binding motifs for critical epidermal transcription factors including AP1, GRHL, and KLF family members. Our results suggest that among these, HDAC3 operates in conjunction with KLF4 to repress inappropriate expression of Tgm1, Krt16, and Aqp3 In parallel, HDAC3 suppresses expression of inflammatory cytokines through a Rela-dependent mechanism. These data identify HDAC3 as a hub coordinating multiple aspects of epidermal barrier acquisition.

Foxa1 and Foxa2 are essential for sexual dimorphism in liver cancer.

Hepatocellular carcinoma (HCC) is sexually dimorphic in both rodents and humans, with significantly higher incidence in males, an effect that is dependent on sex hormones. The molecular mechanisms by which estrogens prevent and androgens promote liver cancer remain unclear. Here, we discover that sexually dimorphic HCC is completely reversed in Foxa1- and Foxa2-deficient mice after diethylnitrosamine-induced hepatocarcinogenesis. Coregulation of target genes by Foxa1/a2 and either the estrogen receptor (ERα) or the androgen receptor (AR) was increased during hepatocarcinogenesis in normal female or male mice, respectively, but was lost in Foxa1/2-deficient mice. Thus, both estrogen-dependent resistance to and androgen-mediated facilitation of HCC depend on Foxa1/2. Strikingly, single nucleotide polymorphisms at FOXA2 binding sites reduce binding of both FOXA2 and ERα to their targets in human liver and correlate with HCC development in women. Thus, Foxa factors and their targets are central for the sexual dimorphism of HCC.

Foxa2 and H2A.Z mediate nucleosome depletion during embryonic stem cell differentiation.

Nucleosome occupancy is fundamental for establishing chromatin architecture. However, little is known about the relationship between nucleosome dynamics and initial cell lineage specification. Here, we determine the mechanisms that control global nucleosome dynamics during embryonic stem (ES) cell differentiation into endoderm. Both nucleosome depletion and de novo occupation occur during the differentiation process, with higher overall nucleosome density after differentiation. The variant histone H2A.Z and the winged helix transcription factor Foxa2 both act to regulate nucleosome depletion and gene activation, thus promoting ES cell differentiation, whereas DNA methylation promotes nucleosome occupation and suppresses gene expression. Nucleosome depletion during ES cell differentiation is dependent on Nap1l1-coupled SWI/SNF and INO80 chromatin remodeling complexes. Thus, both epigenetic and genetic regulators cooperate to control nucleosome dynamics during ES cell fate decisions.

Termination of pre-mRNA splicing requires that the ATPase and RNA unwindase Prp43p acts on the catalytic snRNA U6.

The termination of pre-mRNA splicing functions to discard suboptimal substrates, thereby enhancing fidelity, and to release excised introns in a manner coupled to spliceosome disassembly, thereby allowing recycling. The mechanism of termination, including the RNA target of the DEAH-box ATPase Prp43p, remains ambiguous. We discovered a critical role for nucleotides at the 3' end of the catalytic U6 small nuclear RNA in splicing termination. Although conserved sequence at the 3' end is not required, 2' hydroxyls are, paralleling requirements for Prp43p biochemical activities. Although the 3' end of U6 is not required for recruiting Prp43p to the spliceosome, the 3' end cross-links directly to Prp43p in an RNA-dependent manner. Our data indicate a mechanism of splicing termination in which Prp43p translocates along U6 from the 3' end to disassemble the spliceosome and thereby release suboptimal substrates or excised introns. This mechanism reveals that the spliceosome becomes primed for termination at the same stage it becomes activated for catalysis, implying a requirement for stringent control of spliceosome activity within the cell.

The Cell Tracking Challenge: 10 years of objective benchmarking.

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.

Foxa deficiency restricts hepatitis B virus biosynthesis through epigenic silencing.

In the hepatis B virus (HBV) transgenic mouse model of chronic infection, the forkhead box protein A/hepatocyte nuclear factor 3 (Foxa/HNF3) family of pioneer transcription factors are required to support postnatal viral demethylation and subsequent HBV transcription and replication. Liver-specific Foxa-deficient mice with hepatic expression of only Foxa3 do not support HBV replication but display biliary epithelial hyperplasia with bridging fibrosis. However, liver-specific Foxa-deficient mice with hepatic expression of only Foxa1 or Foxa2 also successfully restrict viral transcription and replication but display only minimal alterations in liver physiology. These observations suggest that the level of Foxa activity, rather than the combination of specific Foxa genes, is a key determinant of HBV biosynthesis. Together, these findings suggest that targeting Foxa activity could lead to HBV DNA methylation and transcriptional inactivation, resulting in the resolution of chronic HBV infections that are responsible for approximately one million deaths annually worldwide. IMPORTANCE: The current absence of curative therapies capable of resolving chronic hepatis B virus (HBV) infection is a major clinical problem associated with considerable morbidity and mortality. The small viral genome limits molecular targets for drug development, suggesting that the identification of cellular factors essential for HBV biosynthesis may represent alternative targets for therapeutic intervention. Genetic Foxa deficiency in the neonatal liver of HBV transgenic mice leads to the transcriptional silencing of viral DNA by CpG methylation without affecting viability or displaying an obvious phenotype. Therefore, limiting liver Foxa activity therapeutically may lead to the methylation of viral covalently closed circular DNA (cccDNA), resulting in its transcriptional silencing and ultimately the resolution of chronic HBV infection.

Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study.

BACKGROUND: Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS 2.1) and the standard of care in multidisciplinary routine practice at scale. METHODS: In this international, paired, non-inferiority, confirmatory study, we trained and externally validated an AI system (developed within an international consortium) for detecting Gleason grade group 2 or greater cancers using a retrospective cohort of 10 207 MRI examinations from 9129 patients. Of these examinations, 9207 cases from three centres (11 sites) based in the Netherlands were used for training and tuning, and 1000 cases from four centres (12 sites) based in the Netherlands and Norway were used for testing. In parallel, we facilitated a multireader, multicase observer study with 62 radiologists (45 centres in 20 countries; median 7 [IQR 5-10] years of experience in reading prostate MRI) using PI-RADS (2.1) on 400 paired MRI examinations from the testing cohort. Primary endpoints were the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) of the AI system in comparison with that of all readers using PI-RADS (2.1) and in comparison with that of the historical radiology readings made during multidisciplinary routine practice (ie, the standard of care with the aid of patient history and peer consultation). Histopathology and at least 3 years (median 5 [IQR 4-6] years) of follow-up were used to establish the reference standard. The statistical analysis plan was prespecified with a primary hypothesis of non-inferiority (considering a margin of 0·05) and a secondary hypothesis of superiority towards the AI system, if non-inferiority was confirmed. This study was registered at ClinicalTrials.gov, NCT05489341. FINDINGS: Of the 10 207 examinations included from Jan 1, 2012, through Dec 31, 2021, 2440 cases had histologically confirmed Gleason grade group 2 or greater prostate cancer. In the subset of 400 testing cases in which the AI system was compared with the radiologists participating in the reader study, the AI system showed a statistically superior and non-inferior AUROC of 0·91 (95% CI 0·87-0·94; p<0·0001), in comparison to the pool of 62 radiologists with an AUROC of 0·86 (0·83-0·89), with a lower boundary of the two-sided 95% Wald CI for the difference in AUROC of 0·02. At the mean PI-RADS 3 or greater operating point of all readers, the AI system detected 6·8% more cases with Gleason grade group 2 or greater cancers at the same specificity (57·7%, 95% CI 51·6-63·3), or 50·4% fewer false-positive results and 20·0% fewer cases with Gleason grade group 1 cancers at the same sensitivity (89·4%, 95% CI 85·3-92·9). In all 1000 testing cases where the AI system was compared with the radiology readings made during multidisciplinary practice, non-inferiority was not confirmed, as the AI system showed lower specificity (68·9% [95% CI 65·3-72·4] vs 69·0% [65·5-72·5]) at the same sensitivity (96·1%, 94·0-98·2) as the PI-RADS 3 or greater operating point. The lower boundary of the two-sided 95% Wald CI for the difference in specificity (-0·04) was greater than the non-inferiority margin (-0·05) and a p value below the significance threshold was reached (p<0·001). INTERPRETATION: An AI system was superior to radiologists using PI-RADS (2.1), on average, at detecting clinically significant prostate cancer and comparable to the standard of care. Such a system shows the potential to be a supportive tool within a primary diagnostic setting, with several associated benefits for patients and radiologists. Prospective validation is needed to test clinical applicability of this system. FUNDING: Health~Holland and EU Horizon 2020.

Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study.

BACKGROUND: The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. METHODS: In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. FINDINGS: In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p<0·0001). INTERPRETATION: Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation. FUNDING: Deutsche Forschungsgemeinschaft (German Research Foundation) and an Else Kröner Clinician Scientist Endowed Professorship by the Else Kröner Fresenius Foundation.

In vivo tracing of the Cytokeratin 14 lineages using self-cleaving guide RNAs and CRISPR/Cas9.

The current gold-standard for genetic lineage tracing in transgenic mice is based on cell-type specific expression of Cre recombinase. As an alternative, we developed a cell-type specific CRISPR/spCas9 system for lineage tracing. This method relies on RNA polymerase II promoter driven self-cleaving guide RNAs (scgRNA) to achieve tissue-specificity. To demonstrate proof-of-principle for this approach a transgenic mouse was generated harbouring a knock-in of a scgRNA into the Cytokeratin 14 (Krt14) locus. Krt14 expression marks the stem cells of squamous epithelium in the skin and oral mucosa. The scgRNA targets a Stop cassette preceding a fluorescent reporter in the Ai9-tdtomato mouse. Ai9-tdtomato reporter mice harbouring this allele along with a spCas9 transgene demonstrated precise marking of the Krt14 lineage. We conclude that RNA polymerase II promoter driven scgRNAs enable the use of CRISPR/spCas9 for genetic lineage tracing.

Evaluation of clinical parameters and biomarkers in older, untreated mantle cell lymphoma patients receiving bendamustine-rituximab.

Mantle cell lymphoma (MCL) is clinically and biologically heterogeneous. While various prognostic features have been proposed, none currently impact therapy selection, particularly in older patients, for whom treatment is primarily dictated by age and comorbidities. Herein, we undertook a comprehensive comparison of clinicopathological features in a cohort of patients 60 years and older, uniformly treated with bendamustine and rituximab, with a median survival of >8 years. The strongest prognostic indicators in this cohort were a high-risk call by a simplified MCL international prognostic index (s-MIPI) (HR: 3.32, 95% CI: 1.65-6.68 compared to low risk), a high-risk call by MCL35 (HR: 10.34, 95% CI: 2.37-45.20 compared to low risk) and blastoid cytology (HR: 4.21, 95% CR: 1.92-9.22 compared to classic). Patients called high risk by both the s-MIPI and MCL35 had the most dismal prognosis (HR: 11.58, 95% CI: 4.10-32.72), while those with high risk by either had a moderate but clinically relevant prognosis (HR: 2.95, 95% CI: 1.49-5.82). A robust assay to assess proliferation, such as MCL35, along with stringent guidelines for cytological evaluation of MCL, in combination with MIPI, may be a strong path to risk-stratify older MCL patients in future clinical trials.

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.

Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.

Weakly Supervised MRI Slice-Level Deep Learning Classification of Prostate Cancer Approximates Full Voxel- and Slice-Level Annotation: Effect of Increasing Training Set Size.

BACKGROUND: Weakly supervised learning promises reduced annotation effort while maintaining performance. PURPOSE: To compare weakly supervised training with full slice-wise annotated training of a deep convolutional classification network (CNN) for prostate cancer (PC). STUDY TYPE: Retrospective. SUBJECTS: One thousand four hundred eighty-nine consecutive institutional prostate MRI examinations from men with suspicion for PC (65 ± 8 years) between January 2015 and November 2020 were split into training (N = 794, enriched with 204 PROSTATEx examinations) and test set (N = 695). FIELD STRENGTH/SEQUENCE: 1.5 and 3T, T2-weighted turbo-spin-echo and diffusion-weighted echo-planar imaging. ASSESSMENT: Histopathological ground truth was provided by targeted and extended systematic biopsy. Reference training was performed using slice-level annotation (SLA) and compared to iterative training utilizing patient-level annotations (PLAs) with supervised feedback of CNN estimates into the next training iteration at three incremental training set sizes (N = 200, 500, 998). Model performance was assessed by comparing specificity at fixed sensitivity of 0.97 [254/262] emulating PI-RADS ≥ 3, and 0.88-0.90 [231-236/262] emulating PI-RADS ≥ 4 decisions. STATISTICAL TESTS: Receiver operating characteristic (ROC) and area under the curve (AUC) was compared using DeLong and Obuchowski test. Sensitivity and specificity were compared using McNemar test. Statistical significance threshold was P = 0.05. RESULTS: Test set (N = 695) ROC-AUC performance of SLA (trained with 200/500/998 exams) was 0.75/0.80/0.83, respectively. PLA achieved lower ROC-AUC of 0.64/0.72/0.78. Both increased performance significantly with increasing training set size. ROC-AUC for SLA at 500 exams was comparable to PLA at 998 exams (P = 0.28). ROC-AUC was significantly different between SLA and PLA at same training set sizes, however the ROC-AUC difference decreased significantly from 200 to 998 training exams. Emulating PI-RADS ≥ 3 decisions, difference between PLA specificity of 0.12 [51/433] and SLA specificity of 0.13 [55/433] became undetectable (P = 1.0) at 998 exams. Emulating PI-RADS ≥ 4 decisions, at 998 exams, SLA specificity of 0.51 [221/433] remained higher than PLA specificity at 0.39 [170/433]. However, PLA specificity at 998 exams became comparable to SLA specificity of 0.37 [159/433] at 200 exams (P = 0.70). DATA CONCLUSION: Weakly supervised training of a classification CNN using patient-level-only annotation had lower performance compared to training with slice-wise annotations, but improved significantly faster with additional training data. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Author Correction: Subepithelial telocytes are an important source of Wnts that supports intestinal crypts.

Change history: In this Letter, the surname of author Efi E. Massasa was misspelled 'Massassa'. This error has been corrected online.

Subepithelial telocytes are an important source of Wnts that supports intestinal crypts.

Tissues that undergo rapid cellular turnover, such as the mammalian haematopoietic system or the intestinal epithelium, are dependent on stem and progenitor cells that proliferate to provide differentiated cells to maintain organismal health. Stem and progenitor cells, in turn, are thought to rely on signals and growth factors provided by local niche cells to support their function and self-renewal. Several cell types have been hypothesized to provide the signals required for the proliferation and differentiation of the intestinal stem cells in intestinal crypts1-6. Here we identify subepithelial telocytes as an important source of Wnt proteins, without which intestinal stem cells cannot proliferate and support epithelial renewal. Telocytes are large but rare mesenchymal cells that are marked by expression of FOXL1 and form a subepithelial plexus that extends from the stomach to the colon. While supporting the entire epithelium, FOXL1+ telocytes compartmentalize the production of Wnt ligands and inhibitors to enable localized pathway activation. Conditional genetic ablation of porcupine (Porcn), which is required for functional maturation of all Wnt proteins, in mouse FOXL1+ telocytes causes rapid cessation of Wnt signalling to intestinal crypts, followed by loss of proliferation of stem and transit amplifying cells and impaired epithelial renewal. Thus, FOXL1+ telocytes are an important source of niche signals to intestinal stem cells.

The Pioneer Transcription Factor FoxA Maintains an Accessible Nucleosome Configuration at Enhancers for Tissue-Specific Gene Activation.

Nuclear DNA wraps around core histones to form nucleosomes, which restricts the binding of transcription factors to gene regulatory sequences. Pioneer transcription factors can bind DNA sites on nucleosomes and initiate gene regulatory events, often leading to the local opening of chromatin. However, the nucleosomal configuration of open chromatin and the basis for its regulation is unclear. We combined low and high levels of micrococcal nuclease (MNase) digestion along with core histone mapping to assess the nucleosomal configuration at enhancers and promoters in mouse liver. We find that MNase-accessible nucleosomes, bound by transcription factors, are retained more at liver-specific enhancers than at promoters and ubiquitous enhancers. The pioneer factor FoxA displaces linker histone H1, thereby keeping enhancer nucleosomes accessible in chromatin and allowing other liver-specific transcription factors to bind and stimulate transcription. Thus, nucleosomes are not exclusively repressive to gene regulation when they are retained with, and exposed by, pioneer factors.