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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.

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. The enteric nervous system encompasses the gut-intrinsic neuroglial networks that regulate gastrointestinal functions. Based on single-cell analysis, the authors propose a model in which neurogenic differentiation paths branch from a gliogenic trajectory.

Tissue maintenance and repair depend on the integrated activity of multiple cell types 1 . Whereas the contributions of epithelial 2 , 3 , immune 4 , 5 and stromal cells 6 , 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 disease 8 . 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. Enteric glial cells have tissue-wide immunoregulatory roles through the upregulation of IFNγ-dependent genes both at steady state and after parasite infection, promoting immune homeostasis and CXCL10-mediated tissue repair after pathogen-induced intestinal damage in mice.

Differentiation of multipotential progenitor cells is a key process in the development of any multi-cellular organism and often continues throughout its life. It is often assumed that a bi-potential progenitor develops along a (relatively) straight trajectory until it reaches a decision point where the trajectory bifurcates. At this point one of two directions is chosen, each direction representing the unfolding of a new transcriptional programme. However, we have lacked quantitative means for testing this model. Accordingly, we have developed the R package TrajectoryGeometry. Applying this to published data we find several examples where, rather than bifurcate, developmental pathways branch. That is, the bipotential progenitor develops along a relatively straight trajectory leading to one of its potential fates. A second relatively straight trajectory branches off from this towards the other potential fate. In this sense only cells that branch off to follow the second trajectory make a 'decision'. Our methods give precise descriptions of the genes and cellular pathways involved in these trajectories. We speculate that branching may be the more common behaviour and may have advantages from a control-theoretic viewpoint.

Differentiation of multipotential progenitor cells is a key process in the development of any multi-cellular organism and often continues throughout its life. It is often assumed that a bi-potential progenitor develops along a (relatively) straight trajectory until it reaches a decision point where the trajectory bifurcates. At this point one of two directions is chosen, each direction representing the unfolding of a new transcriptomic programme. However, we have lacked quantitative means for testing this model. Accordingly, we have developed the R package TrajectoryGeometry. Applying this to published data we find several examples where, rather than bifurcate, developmental pathways branch. That is, the bipotential progenitor develops along a relatively straight trajectory leading to one of its potential fates. A second relatively straight trajectory branches off from this towards the other potential fate. In this sense only cells that branch off to follow the second trajectory make a “decision”. Our methods give precise descriptions of the genes and cellular pathways involved in these trajectories. We speculate that branching may be the more common behaviour and may have advantages from a control-theoretic viewpoint.Competing Interest StatementThe authors have declared no competing interest.

The genomic revolution has brought about large advances in the identification of disease-associated variants. However, despite the recent explosion of genetic data, the problem of missing heritability persists. Variants with low penetrance remain difficult to identify, as do variants which are rare or unique to a single individual. To fully understand disease mechanisms and design targeted therapies, the molecular mechanisms underlying the pathogenic effects of such variants must be clarified. A prime example of missense variants which are difficult to classify is provided by those which localise the Titin gene, a number of which are associated with titinopathies. Due to titin's large size, even the majority of healthy individuals possess one or more rare titin missense variants. This results in the paradox that rare titin variants are commonly found; therefore, pathogenicity cannot be inferred from frequency alone. To address this issue we have created a web application, TITINdb (http://fraternalilab.kcl.ac.uk/TITINdb/), which integrates structural, variant, sequence and isoform information along with precomputed in-silico analyses, in order to facilitate the prioritisation of variants for further wet-lab investigation. Recently available databases allow access to missense variant data on an unprecedented scale. We sought to harness this information to better understand the characteristics of variants associated with health and disease, through a large scale-analysis of population variants from the gnomAD database, as well as disease-associated variants (ClinVar) and somatic cancer-associated variants (COSMIC). Here we established that variants from each data set target distinct functional pathways and proteomics features. In order to accomplish this analysis, we created a database, web interface and REST API, ZoomVar (http://fraternalilab.kcl.ac.uk/ZoomVar/), to allow for the mapping of variants to a 3D integrated protein-protein interaction network and calculation of the regional enrichment of missense variants. Despite the multitude of features which are able to segregate deleterious from neutral missense variants, a number of problem cases remain. This motivated us to investigate whether features extracted from molecular dynamics simulations could improve predictions of variant deleteriousness. To accomplish this we constructed a dataset of rare population and deleterious titin variants, and created machine-learning (random forest) based models of variant impact. We show that dynamicsbased features are able to segregate the majority of disease-associated titin variants from population variants. Ultimately, we believe a collaborative framework for the sharing of mutant and wildtype trajectories must be set up; both to enable investigation into the possible benefits of using dynamics-based features, and to harness their power.

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.

Linear thresholding systems have been used as a model of neural activation and more recently proposed as a model of gene regulation. Here we exhibit linear thresholding systems whose dynamics produce surprisingly long cycles.

Linear thresholding systems have been used as a model of neural activation and have more recently been proposed as a model of gene activation. Deterministic linear thresholding systems can be turned into non-deterministic systems by the introduction of noise. Under mild conditions on the noise, we show that the deterministic model can be deduced from the probabilities of the non-deterministic model.

How neurogenesis and gliogenesis are coordinated during development and why mature glial cells often share properties with neuroectodermal progenitors remains unclear. Here, we have used single cell RNA sequencing to map the regulatory landscape of neuronal and glial differentiation in the mammalian enteric nervous system (ENS). Our analysis indicates that neurogenic trajectories branch directly from a linear gliogenic axis defined by autonomic neural crest cells adopting sequential states as they progressively lose their strong neurogenic bias and acquire properties of adult enteric glia. We identify gene modules associated with transcriptional programs driving enteric neurogenesis and cell state transitions along the gliogenic axis. By comparing the chromatin accessibility profile of autonomic neural crest and adult enteric glia we provide evidence that the latter maintain an epigenetic memory of their neurogenic past. Finally, we demonstrate that adult enteric glia maintain neurogenic potential and are capable of generating enteric neurons in certain contexts by activating transcriptional programs employed by early ENS progenitors. Our studies uncover a novel configuration of enteric neurogenesis and gliogenesis that enables the coordinate development of ENS lineages and provides a mechanistic explanation for the ability of enteric glia to be functionally integrated into the adult intestine and simultaneously maintain attributes of early ENS progenitors.