A branching model of lineage differentiation underpinning the neurogenic potential of enteric glia.

Glial cells have been proposed as a source of neural progenitors, but the mechanisms underpinning the neurogenic potential of adult glia are not known. Using single cell transcriptomic profiling, we show that enteric glial cells represent a cell state attained by autonomic neural crest cells as they transition along a linear differentiation trajectory that allows them to retain neurogenic potential while acquiring mature glial functions. Key neurogenic loci in early enteric nervous system progenitors remain in open chromatin configuration in mature enteric glia, thus facilitating neuronal differentiation under appropriate conditions. Molecular profiling and gene targeting of enteric glial cells in a cell culture model of enteric neurogenesis and a gut injury model demonstrate that neuronal differentiation of glia is driven by transcriptional programs employed in vivo by early progenitors. Our work provides mechanistic insight into the regulatory landscape underpinning the development of intestinal neural circuits and generates a platform for advancing glial cells as therapeutic agents for the treatment of neural deficits.

An HNF1α truncation associated with maturity-onset diabetes of the young impairs pancreatic progenitor differentiation by antagonizing HNF1ß function.

The HNF1αp291fsinsC truncation is the most common mutation associated with maturity-onset diabetes of the young 3 (MODY3). Although shown to impair HNF1α signaling, the mechanism by which HNF1αp291fsinsC causes MODY3 is not fully understood. Here we use MODY3 patient and CRISPR/Cas9-engineered human induced pluripotent stem cells (hiPSCs) grown as 3D organoids to investigate how HNF1αp291fsinsC affects hiPSC differentiation during pancreatic development. HNF1αp291fsinsC hiPSCs shows reduced pancreatic progenitor and ß cell differentiation. Mechanistically, HNF1αp291fsinsC interacts with HNF1ß and inhibits its function, and disrupting this interaction partially rescues HNF1ß-dependent transcription. HNF1ß overexpression in the HNF1αp291fsinsC patient organoid line increases PDX1+ progenitors, while HNF1ß overexpression in the HNF1αp291fsinsC patient iPSC line partially rescues ß cell differentiation. Our study highlights the capability of pancreas progenitor-derived organoids to model disease in vitro. Additionally, it uncovers an HNF1ß-mediated mechanism linked to HNF1α truncation that affects progenitor differentiation and could explain the clinical heterogeneity observed in MODY3 patients.

Plating human iPSC lines on micropatterned substrates reveals role for ITGB1 nsSNV in endoderm formation.

Quantitative analysis of human induced pluripotent stem cell (iPSC) lines from healthy donors is a powerful tool for uncovering the relationship between genetic variants and cellular behavior. We previously identified rare, deleterious non-synonymous single nucleotide variants (nsSNVs) in cell adhesion genes that are associated with outlier iPSC phenotypes in the pluripotent state. Here, we generated micropatterned colonies of iPSCs to test whether nsSNVs influence patterning of radially ordered germ layers. Using a custom-built image analysis pipeline, we quantified the differentiation phenotypes of 13 iPSC lines that harbor nsSNVs in genes related to cell adhesion or germ layer development. All iPSC lines differentiated into the three germ layers; however, there was donor-specific variation in germ layer patterning. We identified one line that presented an outlier phenotype of expanded endodermal differentiation, which was associated with a nsSNV in ITGB1. Our study establishes a platform for investigating the impact of nsSNVs on differentiation.

Regulation of intestinal immunity and tissue repair by enteric glia.

Tissue maintenance and repair depend on the integrated activity of multiple cell types1. Whereas the contributions of epithelial2,3, immune4,5 and stromal cells6,7 in intestinal tissue integrity are well understood, the role of intrinsic neuroglia networks remains largely unknown. Here we uncover important roles of enteric glial cells (EGCs) in intestinal homeostasis, immunity and tissue repair. We demonstrate that infection of mice with Heligmosomoides polygyrus leads to enteric gliosis and the upregulation of an interferon gamma (IFNγ) gene signature. IFNγ-dependent gene modules were also induced in EGCs from patients with inflammatory bowel disease8. Single-cell transcriptomics analysis of the tunica muscularis showed that glia-specific abrogation of IFNγ signalling leads to tissue-wide activation of pro-inflammatory transcriptional programs. Furthermore, disruption of the IFNγ-EGC signalling axis enhanced the inflammatory and granulomatous response of the tunica muscularis to helminths. Mechanistically, we show that the upregulation of Cxcl10 is an early immediate response of EGCs to IFNγ signalling and provide evidence that this chemokine and the downstream amplification of IFNγ signalling in the tunica muscularis are required for a measured inflammatory response to helminths and resolution of the granulomatous pathology. Our study demonstrates that IFNγ signalling in enteric glia is central to intestinal homeostasis and reveals critical roles of the IFNγ-EGC-CXCL10 axis in immune response and tissue repair after infectious challenge.

Pathogenic missense protein variants affect different functional pathways and proteomic features than healthy population variants.

Missense variants are present amongst the healthy population, but some of them are causative of human diseases. A classification of variants associated with "healthy" or "diseased" states is therefore not always straightforward. A deeper understanding of the nature of missense variants in health and disease, the cellular processes they may affect, and the general molecular principles which underlie these differences is essential to offer mechanistic explanations of the true impact of pathogenic variants. Here, we have formalised a statistical framework which enables robust probabilistic quantification of variant enrichment across full-length proteins, their domains, and 3D structure-defined regions. Using this framework, we validate and extend previously reported trends of variant enrichment in different protein structural regions (surface/core/interface). By examining the association of variant enrichment with available functional pathways and transcriptomic and proteomic (protein half-life, thermal stability, abundance) data, we have mined a rich set of molecular features which distinguish between pathogenic and population variants: Pathogenic variants mainly affect proteins involved in cell proliferation and nucleotide processing and are enriched in more abundant proteins. Additionally, rare population variants display features closer to common than pathogenic variants. We validate the association between these molecular features and variant pathogenicity by comparing against existing in silico variant impact annotations. This study provides molecular details into how different proteins exhibit resilience and/or sensitivity towards missense variants and provides the rationale to prioritise variant-enriched proteins and protein domains for therapeutic targeting and development. The ZoomVar database, which we created for this study, is available at fraternalilab.kcl.ac.uk/ZoomVar. It allows users to programmatically annotate missense variants with protein structural information and to calculate variant enrichment in different protein structural regions.

Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks.

Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein-protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein-Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named 'short loop commonality' to measure indirect PPIs occurring via common SLM interactions. This detects 'modules' of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR-Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

Identifying Extrinsic versus Intrinsic Drivers of Variation in Cell Behavior in Human iPSC Lines from Healthy Donors.

Large cohorts of human induced pluripotent stem cells (iPSCs) from healthy donors are a potentially powerful tool for investigating the relationship between genetic variants and cellular behavior. Here, we integrate high content imaging of cell shape, proliferation, and other phenotypes with gene expression and DNA sequence datasets from over 100 human iPSC lines. By applying a dimensionality reduction approach, Probabilistic Estimation of Expression Residuals (PEER), we extracted factors that captured the effects of intrinsic (genetic concordance between different cell lines from the same donor) and extrinsic (cell responses to different fibronectin concentrations) conditions. We identify genes that correlate in expression with intrinsic and extrinsic PEER factors and associate outlier cell behavior with genes containing rare deleterious non-synonymous SNVs. Our study, thus, establishes a strategy for examining the genetic basis of inter-individual variability in cell behavior.

Genetic variants and protein-protein interactions: a multidimensional network-centric view.

We review recent progress in the mapping of genetic variants to proteins, in the context of their interactions, as measured from experiments and/or computational predictions. Such variants can impact on the molecular mechanisms underlying an interaction and its stability. We highlight recent work which relies on the effective use of protein-protein interaction networks (PPINs), integrated with 3D structural information, for evaluating disease-associated variants. Furthermore, we discuss how the integration of multiple layers of biological information, in the context of PPINs, can improve the interpretation of genetic variants and inspire new therapeutic strategies.

TITINdb-a computational tool to assess titin's role as a disease gene.

SUMMARY: Large numbers of rare and unique titin missense variants have been discovered in both healthy and disease cohorts, thus the correct classification of variants as pathogenic or non-pathogenic has become imperative. Due to titin's large size (363 coding exons), current web applications are unable to map titin variants to domain structures. Here, we present a web application, TITINdb, which integrates titin structure, variant, sequence and isoform information, along with pre-computed predictions of the impact of non-synonymous single nucleotide variants, to facilitate the correct classification of titin variants. AVAILABILITY AND IMPLEMENTATION: TITINdb can be freely accessed at http://fraternalilab.kcl.ac.uk/TITINdb. CONTACT: franca.fraternali@kcl.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.