Absolute Quantification of Transcription Factors Reveals Principles of Gene Regulation in Erythropoiesis.

Dynamic cellular processes such as differentiation are driven by changes in the abundances of transcription factors (TFs). However, despite years of studies, our knowledge about the protein copy number of TFs in the nucleus is limited. Here, by determining the absolute abundances of 103 TFs and co-factors during the course of human erythropoiesis, we provide a dynamic and quantitative scale for TFs in the nucleus. Furthermore, we establish the first gene regulatory network of cell fate commitment that integrates temporal protein stoichiometry data with mRNA measurements. The model revealed quantitative imbalances in TFs' cross-antagonistic relationships that underlie lineage determination. Finally, we made the surprising discovery that, in the nucleus, co-repressors are dramatically more abundant than co-activators at the protein level, but not at the RNA level, with profound implications for understanding transcriptional regulation. These analyses provide a unique quantitative framework to understand transcriptional regulation of cell differentiation in a dynamic context.

Design patterns of biological cells.

Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems.

KDM5-mediated transcriptional activation of ribosomal protein genes alters translation efficiency to regulate mitochondrial metabolism in neurons.

Genes encoding the KDM5 family of transcriptional regulators are disrupted in individuals with intellectual disability (ID). To understand the link between KDM5 and ID, we characterized five Drosophila strains harboring missense alleles analogous to those observed in patients. These alleles disrupted neuroanatomical development, cognition and other behaviors, and displayed a transcriptional signature characterized by the downregulation of many ribosomal protein genes. A similar transcriptional profile was observed in KDM5C knockout iPSC-induced human glutamatergic neurons, suggesting an evolutionarily conserved role for KDM5 proteins in regulating this class of gene. In Drosophila, reducing KDM5 changed neuronal ribosome composition, lowered the translation efficiency of mRNAs required for mitochondrial function, and altered mitochondrial metabolism. These data highlight the cellular consequences of altered KDM5-regulated transcriptional programs that could contribute to cognitive and behavioral phenotypes. Moreover, they suggest that KDM5 may be part of a broader network of proteins that influence cognition by regulating protein synthesis.

Sensitivity and activation of endoplasmic reticulum stress response and apoptosis in the perinatal sheep heart.

Although the unfolded protein response (UPR) contributes to survival by removing misfolded proteins, endoplasmic reticulum (ER) stress also activates proapoptotic pathways. Changed sensitivity to normal developmental stimuli may underlie observed cardiomyocyte apoptosis in the healthy perinatal heart. We determined in vitro sensitivity to thapsigargin in sheep cardiomyocytes from four perinatal ages. In utero cardiac activation of ER stress and apoptotic pathways was determined at these same ages. Thapsigargin-induced phosphorylation of eukaryotic initiation factor 2 (EIF2A) was decreased by 72% between 135 and 143 dGA (P = 0.0096) and remained low at 1 dPN (P = 0.0080). Conversely, thapsigargin-induced caspase cleavage was highest around the time of birth: cleaved caspase 3 was highest at 1 dPN (3.8-fold vs. 135 dGA, P = 0.0380; 7.8-fold vs. 5 dPN, P = 0.0118), cleaved caspase 7 and cleaved caspase 12 both increased between 135 and 143 dGA (25-fold and 6.9-fold respectively, both P < 0.0001) and remained elevated at 1 dPN. Induced apoptosis, measured by TdT-mediated dUTP nick-end labeling (TUNEL) assay, was highest around the time of birth (P < 0.0001). There were changes in myocardial ER stress pathway components in utero. Glucose (78 kDa)-regulated protein (GRP78) protein levels were high in the fetus and declined after birth (P < 0.0001). EIF2A phosphorylation was profoundly depressed at 1 dPN (vs. 143 dGA, P = 0.0113). In conclusion, there is dynamic regulation of ER proteostasis, ER stress, and apoptosis cascade in the perinatal heart. Apoptotic signaling is more readily activated in fetal cardiomyocytes near birth, leading to widespread caspase cleavage in the newborn heart. These pathways are important for the regulation of normal maturation in the healthy perinatal heart.NEW & NOTEWORTHY Cardiomyocyte apoptosis occurs even in the healthy, normally developing perinatal myocardium. As cardiomyocyte number is a critical contributor to heart health, the sensitivity of cardiomyocytes to endoplasmic reticulum stress leading to apoptosis is an important consideration. This study suggests that the heart has less robust protective mechanisms in response to endoplasmic reticulum stress immediately before and after birth, and that more cardiomyocyte death can be induced by stress in this period.

SBMLDiagrams: a python package to process and visualize SBML layout and render.

SUMMARY: The systems biology markup language (SBML) is an extensible standard format for exchanging biochemical models. One of the extensions for SBML is the SBML Layout and Render package. This allows modelers to describe a biochemical model as a pathway diagram. However, up to now, there has been little support to help users easily add and retrieve such information from SBML. In this application note, we describe a new Python package called SBMLDiagrams. This package allows a user to add a layout and render information or retrieve it using a straightforward Python API. The package uses skia-python to support the rendering of the diagrams, allowing export to commons formats such as PNG or PDF. AVAILABILITY AND IMPLEMENTATION: SBMLDiagrams is publicly available and licensed under the liberal MIT open-source license. The package is available for all major platforms. The source code has been deposited at GitHub (github.com/sys-bio/SBMLDiagrams). Users can install the package using the standard pip installation mechanism: pip install SBMLDiagrams. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An automated model annotation system (AMAS) for SBML models.

MOTIVATION: Annotations of biochemical models provide details of chemical species, documentation of chemical reactions, and other essential information. Unfortunately, the vast majority of biochemical models have few, if any, annotations, or the annotations provide insufficient detail to understand the limitations of the model. The quality and quantity of annotations can be improved by developing tools that recommend annotations. For example, recommender tools have been developed for annotations of genes. Although annotating genes is conceptually similar to annotating biochemical models, there are important technical differences that make it difficult to directly apply this prior work. RESULTS: We present AMAS, a system that predicts annotations for elements of models represented in the Systems Biology Markup Language (SBML) community standard. We provide a general framework for predicting model annotations for a query element based on a database of annotated reference elements and a match score function that calculates the similarity between the query element and reference elements. The framework is instantiated to specific element types (e.g. species, reactions) by specifying the reference database (e.g. ChEBI for species) and the match score function (e.g. string similarity). We analyze the computational efficiency and prediction quality of AMAS for species and reactions in BiGG and BioModels and find that it has subsecond response times and accuracy between 80% and 95% depending on specifics of what is predicted. We have incorporated AMAS into an open-source, pip-installable Python package that can run as a command-line tool that predicts and adds annotations to species and reactions to an SBML model. AVAILABILITY AND IMPLEMENTATION: Our project is hosted at https://github.com/sys-bio/AMAS, where we provide examples, documentation, and source code files. Our source code is licensed under the MIT open-source license.

libRoadRunner 2.0: a high performance SBML simulation and analysis library.

MOTIVATION: This article presents libRoadRunner 2.0, an extensible, high-performance, cross-platform, open-source software library for the simulation and analysis of models expressed using the systems biology markup language (SBML). RESULTS: libRoadRunner is a self-contained library, able to run either as a component inside other tools via its C++, C and Python APIs, or interactively through its Python or Julia interface. libRoadRunner uses a custom just-in-time (JIT) compiler built on the widely used LLVM JIT compiler framework. It compiles SBML-specified models directly into native machine code for a large variety of processors, making it fast enough to simulate extremely large models or repeated runs in reasonable timeframes. libRoadRunner is flexible, supporting the bulk of the SBML specification (except for delay and non-linear algebraic equations) as well as several SBML extensions such as hierarchical composition and probability distributions. It offers multiple deterministic and stochastic integrators, as well as tools for steady-state, sensitivity, stability and structural analyses. AVAILABILITY AND IMPLEMENTATION: libRoadRunner binary distributions for Windows, Mac OS and Linux, Julia and Python bindings, source code and documentation are all available at https://github.com/sys-bio/roadrunner, and Python bindings are also available via pip. The source code can be compiled for the supported systems as well as in principle any system supported by LLVM-13, such as ARM-based computers like the Raspberry Pi. The library is licensed under the Apache License Version 2.0.

VSCode-Antimony: a source editor for building, analyzing, and translating antimony models.

MOTIVATION: Developing biochemical models in systems biology is a complex, knowledge-intensive activity. Some modelers (especially novices) benefit from model development tools with a graphical user interface. However, as with the development of complex software, text-based representations of models provide many benefits for advanced model development. At present, the tools for text-based model development are limited, typically just a textual editor that provides features such as copy, paste, find, and replace. Since these tools are not "model aware," they do not provide features for: (i) model building such as autocompletion of species names; (ii) model analysis such as hover messages that provide information about chemical species; and (iii) model translation to convert between model representations. We refer to these as BAT features. RESULTS: We present VSCode-Antimony, a tool for building, analyzing, and translating models written in the Antimony modeling language, a human readable representation of Systems Biology Markup Language (SBML) models. VSCode-Antimony is a source editor, a tool with language-aware features. For example, there is autocompletion of variable names to assist with model building, hover messages that aid in model analysis, and translation between XML and Antimony representations of SBML models. These features result from making VSCode-Antimony model-aware by incorporating several sophisticated capabilities: analysis of the Antimony grammar (e.g. to identify model symbols and their types); a query system for accessing knowledge sources for chemical species and reactions; and automatic conversion between different model representations (e.g. between Antimony and SBML). AVAILABILITY AND IMPLEMENTATION: VSCode-Antimony is available as an open source extension in the VSCode Marketplace https://marketplace.visualstudio.com/items?itemName=stevem.vscode-antimony. Source code can be found at https://github.com/sys-bio/vscode-antimony.

Rare coding variants as risk modifiers of the 22q11.2 deletion implicate postnatal cortical development in syndromic schizophrenia.

22q11.2 deletion is one of the strongest known genetic risk factors for schizophrenia. Recent whole-genome sequencing of schizophrenia cases and controls with this deletion provided an unprecedented opportunity to identify risk modifying genetic variants and investigate their contribution to the pathogenesis of schizophrenia in 22q11.2 deletion syndrome. Here, we apply a novel analytic framework that integrates gene network and phenotype data to investigate the aggregate effects of rare coding variants and identified modifier genes in this etiologically homogenous cohort (223 schizophrenia cases and 233 controls of European descent). Our analyses revealed significant additive genetic components of rare nonsynonymous variants in 110 modifier genes (adjusted P = 9.4E-04) that overall accounted for 4.6% of the variance in schizophrenia status in this cohort, of which 4.0% was independent of the common polygenic risk for schizophrenia. The modifier genes affected by rare coding variants were enriched with genes involved in synaptic function and developmental disorders. Spatiotemporal transcriptomic analyses identified an enrichment of coexpression between modifier and 22q11.2 genes in cortical brain regions from late infancy to young adulthood. Corresponding gene coexpression modules are enriched with brain-specific protein-protein interactions of SLC25A1, COMT, and PI4KA in the 22q11.2 deletion region. Overall, our study highlights the contribution of rare coding variants to the SCZ risk. They not only complement common variants in disease genetics but also pinpoint brain regions and developmental stages critical to the etiology of syndromic schizophrenia.

Generating synthetic signaling networks for in silico modeling studies.

Predictive models of signaling pathways have proven to be difficult to develop. Traditional approaches to developing mechanistic models rely on collecting experimental data and fitting a single model to that data. This approach works for simple systems but has proven unreliable for complex systems such as biological signaling networks. Thus, there is a need to develop new approaches to create predictive mechanistic models of complex systems. To meet this need, we developed a method for generating artificial signaling networks that were reasonably realistic and thus could be treated as ground truth models. These synthetic models could then be used to generate synthetic data for developing and testing algorithms designed to recover the underlying network topology and associated parameters. We defined the reaction degree and reaction distance to measure the topology of reaction networks, especially to consider enzymes. To determine whether our generated signaling networks displayed meaningful behavior, we compared them with signaling networks from the BioModels Database. This comparison indicated that our generated signaling networks had high topological similarities with BioModels signaling networks with respect to the reaction degree and distance distributions. In addition, our synthetic signaling networks had similar behavioral dynamics with respect to both steady states and oscillations, suggesting that our method generated synthetic signaling networks comparable with BioModels and thus could be useful for building network evaluation tools.

Tissue Forge: Interactive biological and biophysics simulation environment.

Tissue Forge is an open-source interactive environment for particle-based physics, chemistry and biology modeling and simulation. Tissue Forge allows users to create, simulate and explore models and virtual experiments based on soft condensed matter physics at multiple scales, from the molecular to the multicellular, using a simple, consistent interface. While Tissue Forge is designed to simplify solving problems in complex subcellular, cellular and tissue biophysics, it supports applications ranging from classic molecular dynamics to agent-based multicellular systems with dynamic populations. Tissue Forge users can build and interact with models and simulations in real-time and change simulation details during execution, or execute simulations off-screen and/or remotely in high-performance computing environments. Tissue Forge provides a growing library of built-in model components along with support for user-specified models during the development and application of custom, agent-based models. Tissue Forge includes an extensive Python API for model and simulation specification via Python scripts, an IPython console and a Jupyter Notebook, as well as C and C++ APIs for integrated applications with other software tools. Tissue Forge supports installations on 64-bit Windows, Linux and MacOS systems and is available for local installation via conda.

Outcomes after per-oral endoscopic myotomy for Zenker's diverticula (Z-POEM) and correlation with impedance planimetry (FLIP).

INTRODUCTION: Zenker's diverticulum (ZD) is a false pulsion diverticulum of the cervical esophagus. It is typically found in older adults and manifests with dysphagia. The purpose of this study is to describe our experience with Per-oral endoscopic myotomy for Zenker's (Z-POEM) and intraoperative impedance planimetry (FLIP). METHODS: We performed a single institution retrospective review of patients undergoing Z-POEM in a prospective database between 2014 and 2022. Upper esophageal sphincter (UES) distensibility index (DI, mm2/mmHg) was measured by FLIP before and after myotomy. The primary outcome was clinical success. Secondary outcomes included technical failure, adverse events, and quality of life as assessed by the gastroesophageal health-related quality of life (GERD-HRQL), reflux severity index (RSI), and dysphagia score. A statistical analysis of DI was done with the paired t-test (p < 0.05). RESULTS: Fifty-four patients underwent Z-POEM, with FLIP measurements available in 30 cases. We achieved technical success and clinical success in 54/54 (100%) patients and 46/54 patients (85%), respectively. Three patients (6%) experienced contained leaks. Three patients were readmitted: one for aforementioned contained leak, one for dysphagia, and one post-operative pneumonia. Three patients with residual dysphagia underwent additional endoscopic procedures, all of whom had diverticula > 4 cm. Following myotomy, mean DI increased by 2.0 ± 1.7 mm2/mmHg (p < 0.001). In those with good clinical success, change in DI averaged + 1.6 ± 1.1 mm2/mmHg. Significant improvement was found in RSI and GERD-HRQL scores, but not dysphagia score. CONCLUSION: Z-POEM is a safe and feasible for treatment of ZD. We saw zero cases of intraoperative abandonment. We propose that large diverticula (> 4 cm) are a risk factor for poor outcomes and may require additional endoscopic procedures. An improvement in DI is expected after myotomy, however, the ideal range is still not known.

Efficacy and outcomes of per oral plication of the (neo)esophagus (POPE) for impaired emptying in achalasia and post-esophagectomy patients.

BACKGROUND: Per-oral plication of the (neo)esophagus (POPE) is an endoscopic procedure used to improve emptying of the defunctionalized esophagus or gastric conduit, with the hope of improving symptoms and quality of life. As this procedure has only been performed in the United States for the past 4 years, safety and efficacy have not been well established. METHODS: This is a retrospective case series for patients who underwent POPE from a single institution between 2019 and 2023. Data collected included demographics, preoperative diagnoses and treatments, imaging, endoscopic data, operative intervention, 90-day complications, and response to treatment. Quality of life and patient satisfaction data were collected by phone survey. RESULTS: Seventeen cases were identified, encompassing 13 primary procedures and 4 repeat POPEs (re-POPE). Eight patients had end-stage achalasia and 5 had impaired gastric emptying after esophagectomy with gastric conduits. Median age was 65 years and median ASA was 3, with 38.5% female patients. POPE was performed with 2-6 plication sutures in an average of 75 min. The majority of patients discharged home the same day. For the 17 procedures, there were 4 complications. Two patients required antibiotics for pneumonia, while 4 required procedural intervention. There were no deaths. Preoperative symptoms improved or resolved at initial follow up in 82.3% of patients. Four patients experienced symptom recurrence and required re-POPE, 1 with achalasia and 3 with gastric conduits. Although all achalasia patients had an "end-stage esophagus," none have required esophagectomy since the introduction of POPE. CONCLUSIONS: POPE is an endoscopic procedure that is efficacious in relieving emptying difficulties for the end-stage esophagus and gastric conduit. It may obviate the need for esophagectomy or conduit replacement. Also, it can be repeated in select patients. While the risk profile of complications is favorable compared to alternative operations, patients with gastric conduits are at higher risk.

Biosensors for the detection of chorismate and cis,cis-muconic acid in Corynebacterium glutamicum.

Corynebacterium glutamicum ATCC 13032 is a promising microbial chassis for industrial production of valuable compounds, including aromatic amino acids derived from the shikimate pathway. In this work, we developed two whole-cell, transcription factor based fluorescent biosensors to track cis,cis-muconic acid (ccMA) and chorismate in C. glutamicum. Chorismate is a key intermediate in the shikimate pathway from which value-added chemicals can be produced, and a shunt from the shikimate pathway can divert carbon to ccMA, a high value chemical. We transferred a ccMA-inducible transcription factor, CatM, from Acinetobacter baylyi ADP1 into C. glutamicum and screened a promoter library to isolate variants with high sensitivity and dynamic range to ccMA by providing benzoate, which is converted to ccMA intracellularly. The biosensor also detected exogenously supplied ccMA, suggesting the presence of a putative ccMA transporter in C. glutamicum, though the external ccMA concentration threshold to elicit a response was 100-fold higher than the concentration of benzoate required to do so through intracellular ccMA production. We then developed a chorismate biosensor, in which a chorismate inducible promoter regulated by natively expressed QsuR was optimized to exhibit a dose-dependent response to exogenously supplemented quinate (a chorismate precursor). A chorismate-pyruvate lyase encoding gene, ubiC, was introduced into C. glutamicum to lower the intracellular chorismate pool, which resulted in loss of dose dependence to quinate. Further, a knockout strain that blocked the conversion of quinate to chorismate also resulted in absence of dose dependence to quinate, validating that the chorismate biosensor is specific to intracellular chorismate pool. The ccMA and chorismate biosensors were dually inserted into C. glutamicum to simultaneously detect intracellularly produced chorismate and ccMA. Biosensors, such as those developed in this study, can be applied in C. glutamicum for multiplex sensing to expedite pathway design and optimization through metabolic engineering in this promising chassis organism. ONE-SENTENCE SUMMARY: High-throughput screening of promoter libraries in Corynebacterium glutamicum to establish transcription factor based biosensors for key metabolic intermediates in shikimate and ß-ketoadipate pathways.

Postinfectious Inflammation, Autoimmunity, and Obsessive-Compulsive Disorder: Sydenham Chorea, Pediatric Autoimmune Neuropsychiatric Disorder Associated with Streptococcal Infection, and Pediatric Acute-Onset Neuropsychiatric Disorder.

Postinfectious neuroinflammation has been implicated in multiple models of acute-onset obsessive-compulsive disorder including Sydenham chorea (SC), pediatric acute-onset neuropsychiatric syndrome (PANS), and pediatric autoimmune neuropsychiatric disorders associated with streptococcal infection (PANDAS). These conditions are associated with a range of autoantibodies which are thought to be triggered by infections, most notably group A streptococci (GAS). Based on animal models using huma sera, these autoantibodies are thought to cross-react with neural antigens in the basal ganglia and modulate neuronal activity and behavior. As is true for many childhood neuroinflammatory diseases and rheumatological diseases, SC, PANS, and PANDAS lack clinically available, rigorous diagnostic biomarkers and randomized clinical trials. In this review article, we outline the accumulating evidence supporting the role neuroinflammation plays in these disorders. We describe work with animal models including patient-derived anti-neuronal autoantibodies, and we outline imaging studies that show alterations in the basal ganglia. In addition, we present research on metabolites, which are helpful in deciphering functional phenotypes, and on the implication of sleep in these disorders. Finally, we encourage future researchers to collaborate across medical specialties (e.g., pediatrics, psychiatry, rheumatology, immunology, and infectious disease) in order to further research on clinical syndromes presenting with neuropsychiatric manifestations.

Publishing reproducible dynamic kinetic models.

Publishing repeatable and reproducible computational models is a crucial aspect of the scientific method in computational biology and one that is often forgotten in the rush to publish. The pressures of academic life and the lack of any reward system at institutions, granting agencies and journals means that publishing reproducible science is often either non-existent or, at best, presented in the form of an incomplete description. In the article, we will focus on repeatability and reproducibility in the systems biology field where a great many published models cannot be reproduced and in many cases even repeated. This review describes the current landscape of software tooling, model repositories, model standards and best practices for publishing repeatable and reproducible kinetic models. The review also discusses possible future remedies including working more closely with journals to help reviewers and editors ensure that published kinetic models are at minimum, repeatable. Contact: hsauro@uw.edu.

SBbadger: biochemical reaction networks with definable degree distributions.

MOTIVATION: An essential step in developing computational tools for the inference, optimization and simulation of biochemical reaction networks is gauging tool performance against earlier efforts using an appropriate set of benchmarks. General strategies for the assembly of benchmark models include collection from the literature, creation via subnetwork extraction and de novo generation. However, with respect to biochemical reaction networks, these approaches and their associated tools are either poorly suited to generate models that reflect the wide range of properties found in natural biochemical networks or to do so in numbers that enable rigorous statistical analysis. RESULTS: In this work, we present SBbadger, a python-based software tool for the generation of synthetic biochemical reaction or metabolic networks with user-defined degree distributions, multiple available kinetic formalisms and a host of other definable properties. SBbadger thus enables the creation of benchmark model sets that reflect properties of biological systems and generate the kinetics and model structures typically targeted by computational analysis and inference software. Here, we detail the computational and algorithmic workflow of SBbadger, demonstrate its performance under various settings, provide sample outputs and compare it to currently available biochemical reaction network generation software. AVAILABILITY AND IMPLEMENTATION: SBbadger is implemented in Python and is freely available at https://github.com/sys-bio/SBbadger and via PyPI at https://pypi.org/project/SBbadger/. Documentation can be found at https://SBbadger.readthedocs.io. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Adapting modeling and simulation credibility standards to computational systems biology.

Computational models are increasingly used in high-impact decision making in science, engineering, and medicine. The National Aeronautics and Space Administration (NASA) uses computational models to perform complex experiments that are otherwise prohibitively expensive or require a microgravity environment. Similarly, the Food and Drug Administration (FDA) and European Medicines Agency (EMA) have began accepting models and simulations as forms of evidence for pharmaceutical and medical device approval. It is crucial that computational models meet a standard of credibility when using them in high-stakes decision making. For this reason, institutes including NASA, the FDA, and the EMA have developed standards to promote and assess the credibility of computational models and simulations. However, due to the breadth of models these institutes assess, these credibility standards are mostly qualitative and avoid making specific recommendations. On the other hand, modeling and simulation in systems biology is a narrower domain and several standards are already in place. As systems biology models increase in complexity and influence, the development of a credibility assessment system is crucial. Here we review existing standards in systems biology, credibility standards in other science, engineering, and medical fields, and propose the development of a credibility standard for systems biology models.

Jansen-de Vries syndrome: Expansion of the PPM1D clinical and phenotypic spectrum in 34 families.

Jansen-de Vries syndrome (JdVS) is a neurodevelopmental condition attributed to pathogenic variants in Exons 5 and 6 of PPM1D. As the full phenotypic spectrum and natural history remain to be defined, we describe a large cohort of children and adults with JdVS. This is a retrospective cohort study of 37 individuals from 34 families with disease-causing variants in PPM1D leading to JdVS. Clinical data were provided by treating physicians and/or families. Of the 37 individuals, 27 were male and 10 female, with median age 8.75 years (range 8 months to 62 years). Four families document autosomal dominant transmission, and 32/34 probands were diagnosed via exome sequencing. The facial gestalt, including a broad forehead and broad mouth with a thin and tented upper lip, was most recognizable between 18 and 48 months of age. Common manifestations included global developmental delay (35/36, 97%), hypotonia (25/34, 74%), short stature (14/33, 42%), constipation (22/31, 71%), and cyclic vomiting (6/35, 17%). Distinctive personality traits include a hypersocial affect (21/31, 68%) and moderate-to-severe anxiety (18/28, 64%). In conclusion, JdVS is a clinically recognizable neurodevelopmental syndrome with a characteristic personality and distinctive facial features. The association of pathogenic variants in PPM1D with cyclic vomiting bears not only medical attention but also further pathogenic and mechanistic evaluation.

The effect of collisions on the chemomechanics of ice-covered silica slabs: a molecular dynamics study.

Using molecular dynamics simulation and the REAX potential, we study the collision of two planar silica surfaces covered by water ice. Without the ice cover, the two surfaces stick at all velocities investigated (160-1800 m s-1), due to the formation of chemical bonds between the colliding surfaces. A narrow ice cover - here of thickness 2 nm - prevents the sticking above a characteristic velocity, the bouncing velocity νb. During the collision, reactions occur at the silica-water interface; in particular, water molecules are dissociated and silanols are formed at the surface of the silica slabs. Passivation of the silica surface by H atoms is of little consequence to the magnitude of vb but reduces the number of surface reactions occurring.