Role of glutamine synthetase in angiogenesis beyond glutamine synthesis.

Glutamine synthetase, encoded by the gene GLUL, is an enzyme that converts glutamate and ammonia to glutamine. It is expressed by endothelial cells, but surprisingly shows negligible glutamine-synthesizing activity in these cells at physiological glutamine levels. Here we show in mice that genetic deletion of Glul in endothelial cells impairs vessel sprouting during vascular development, whereas pharmacological blockade of glutamine synthetase suppresses angiogenesis in ocular and inflammatory skin disease while only minimally affecting healthy adult quiescent endothelial cells. This relies on the inhibition of endothelial cell migration but not proliferation. Mechanistically we show that in human umbilical vein endothelial cells GLUL knockdown reduces membrane localization and activation of the GTPase RHOJ while activating other Rho GTPases and Rho kinase, thereby inducing actin stress fibres and impeding endothelial cell motility. Inhibition of Rho kinase rescues the defect in endothelial cell migration that is induced by GLUL knockdown. Notably, glutamine synthetase palmitoylates itself and interacts with RHOJ to sustain RHOJ palmitoylation, membrane localization and activation. These findings reveal that, in addition to the known formation of glutamine, the enzyme glutamine synthetase shows unknown activity in endothelial cell migration during pathological angiogenesis through RHOJ palmitoylation.

Protein CoAlation and antioxidant function of coenzyme A in prokaryotic cells.

In all living organisms, coenzyme A (CoA) is an essential cofactor with a unique design allowing it to function as an acyl group carrier and a carbonyl-activating group in diverse biochemical reactions. It is synthesized in a highly conserved process in prokaryotes and eukaryotes that requires pantothenic acid (vitamin B5), cysteine and ATP. CoA and its thioester derivatives are involved in major metabolic pathways, allosteric interactions and the regulation of gene expression. A novel unconventional function of CoA in redox regulation has been recently discovered in mammalian cells and termed protein CoAlation. Here, we report for the first time that protein CoAlation occurs at a background level in exponentially growing bacteria and is strongly induced in response to oxidizing agents and metabolic stress. Over 12% of Staphylococcus aureus gene products were shown to be CoAlated in response to diamide-induced stress. In vitro CoAlation of S. aureus glyceraldehyde-3-phosphate dehydrogenase was found to inhibit its enzymatic activity and to protect the catalytic cysteine 151 from overoxidation by hydrogen peroxide. These findings suggest that in exponentially growing bacteria, CoA functions to generate metabolically active thioesters, while it also has the potential to act as a low-molecular-weight antioxidant in response to oxidative and metabolic stress.

Mapping the conformational free energy of aspartic acid in the gas phase and in aqueous solution.

The conformational free energy landscape of aspartic acid, a proteogenic amino acid involved in a wide variety of biological functions, was investigated as an example of the complexity that multiple rotatable bonds produce even in relatively simple molecules. To efficiently explore such a landscape, this molecule was studied in the neutral and zwitterionic forms, in the gas phase and in water solution, by means of molecular dynamics and the enhanced sampling method metadynamics with classical force-fields. Multi-dimensional free energy landscapes were reduced to bi-dimensional maps through the non-linear dimensionality reduction algorithm sketch-map to identify the energetically stable conformers and their interconnection paths. Quantum chemical calculations were then performed on the minimum free energy structures. Our procedure returned the low energy conformations observed experimentally in the gas phase with rotational spectroscopy [M. E. Sanz et al., Phys. Chem. Chem. Phys. 12, 3573 (2010)]. Moreover, it provided information on higher energy conformers not accessible to experiments and on the conformers in water. The comparison between different force-fields and quantum chemical data highlighted the importance of the underlying potential energy surface to accurately capture energy rankings. The combination of force-field based metadynamics, sketch-map analysis, and quantum chemical calculations was able to produce an exhaustive conformational exploration in a range of significant free energies that complements the experimental data. Similar protocols can be applied to larger peptides with complex conformational landscapes and would greatly benefit from the next generation of accurate force-fields.

Elucidating ligand binding and channel gating mechanisms in pentameric ligand-gated ion channels by atomistic simulations.

Pentameric ligand-gated ion channels (pLGICs) are important biomolecules that mediate fast synaptic transmission. Their malfunctions are linked to serious neuronal disorders and they are major pharmaceutical targets; in invertebrates, they are involved in insecticide resistance. The complexity of pLGICs and the limited crystallographic information available prevent a detailed understanding of how they function. State-of-the-art computational techniques are therefore crucial to build an accurate picture at the atomic level of the mechanisms which drive the activation of pLGICs, complementing the available experimental data. We have used a series of simulation methods, including homology modelling, ligand-protein docking, density functional theory, molecular dynamics and metadynamics, a powerful scheme for accelerating rare events, with the guidance of mutagenesis electrophysiology experiments, to explore ligand-binding mechanisms, the effects of mutations and the potential role of a proline molecular switch for the gating of the ion channels. Results for the insect RDL receptor, the GABAC receptor, the 5-HT3 receptor and the nicotinic acetylcholine receptor will be reviewed.

Insights into the binding of GABA to the insect RDL receptor from atomistic simulations: a comparison of models.

The resistance to dieldrin (RDL) receptor is an insect pentameric ligand-gated ion channel (pLGIC). It is activated by the neurotransmitter γ-aminobutyric acid (GABA) binding to its extracellular domain; hence elucidating the atomistic details of this interaction is important for understanding how the RDL receptor functions. As no high resolution structures are currently available, we built homology models of the extracellular domain of the RDL receptor using different templates, including the widely used acetylcholine binding protein and two pLGICs, the Erwinia Chrysanthemi ligand-gated ion channel (ELIC) and the more recently resolved GluCl. We then docked GABA into the selected three dimensional structures, which we used as starting points for classical molecular dynamics simulations. This allowed us to analyze in detail the behavior of GABA in the binding sites, including the hydrogen bond and cation-π interaction networks it formed, the conformers it visited and the possible role of water molecules in mediating the interactions; we also estimated the binding free energies. The models were all stable and showed common features, including interactions consistent with experimental data and similar to other pLGICs; differences could be attributed to the quality of the models, which increases with increasing sequence identity, and the use of a pLGIC template. We supplemented the molecular dynamics information with metadynamics, a rare event method, by exploring the free energy landscape of GABA binding to the RDL receptor. Overall, we show that the GluCl template provided the best models. GABA forming direct salt-bridges with Arg211 and Glu204, and cation-π interactions with an aromatic cage including Tyr109, Phe206 and Tyr254, represents a favorable binding arrangement, and the interaction with Glu204 can also be mediated by a water molecule.

Diagnostic classification of childhood cancer using multiscale transcriptomics.

The causes of pediatric cancers' distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types.

The clinical utility of integrative genomics in childhood cancer extends beyond targetable mutations.

We conducted integrative somatic-germline analyses by deeply sequencing 864 cancer-associated genes, complete genomes and transcriptomes for 300 mostly previously treated children and adolescents/young adults with cancer of poor prognosis or with rare tumors enrolled in the SickKids Cancer Sequencing (KiCS) program. Clinically actionable variants were identified in 56% of patients. Improved diagnostic accuracy led to modified management in a subset. Therapeutically targetable variants (54% of patients) were of unanticipated timing and type, with over 20% derived from the germline. Corroborating mutational signatures (SBS3/BRCAness) in patients with germline homologous recombination defects demonstrates the potential utility of PARP inhibitors. Mutational burden was significantly elevated in 9% of patients. Sequential sampling identified changes in therapeutically targetable drivers in over one-third of patients, suggesting benefit from rebiopsy for genomic analysis at the time of relapse. Comprehensive cancer genomic profiling is useful at multiple points in the care trajectory for children and adolescents/young adults with cancer, supporting its integration into early clinical management.

GABA binding to an insect GABA receptor: a molecular dynamics and mutagenesis study.

RDL receptors are GABA-activated inhibitory Cys-loop receptors found throughout the insect CNS. They are a key target for insecticides. Here, we characterize the GABA binding site in RDL receptors using computational and electrophysiological techniques. A homology model of the extracellular domain of RDL was generated and GABA docked into the binding site. Molecular dynamics simulations predicted critical GABA binding interactions with aromatic residues F206, Y254, and Y109 and hydrophilic residues E204, S176, R111, R166, S176, and T251. These residues were mutated, expressed in Xenopus oocytes, and their functions assessed using electrophysiology. The data support the binding mechanism provided by the simulations, which predict that GABA forms many interactions with binding site residues, the most significant of which are cation-π interactions with F206 and Y254, H-bonds with E204, S205, R111, S176, T251, and ionic interactions with R111 and E204. These findings clarify the roles of a range of residues in binding GABA in the RDL receptor, and also show that molecular dynamics simulations are a useful tool to identify specific interactions in Cys-loop receptors.

Widespread hypertranscription in aggressive human cancers.

Cancers are often defined by the dysregulation of specific transcriptional programs; however, the importance of global transcriptional changes is less understood. Hypertranscription is the genome-wide increase in RNA output. Hypertranscription's prevalence, underlying drivers, and prognostic significance are undefined in primary human cancer. This is due, in part, to limitations of expression profiling methods, which assume equal RNA output between samples. Here, we developed a computational method to directly measure hypertranscription in 7494 human tumors, spanning 31 cancer types. Hypertranscription is ubiquitous across cancer, especially in aggressive disease. It defines patient subgroups with worse survival, even within well-established subtypes. Our data suggest that loss of transcriptional suppression underpins the hypertranscriptional phenotype. Single-cell analysis reveals hypertranscriptional clones, which dominate transcript production regardless of their size. Last, patients with hypertranscribed mutations have improved response to immune checkpoint therapy. Our results provide fundamental insights into gene dysregulation across human cancers and may prove useful in identifying patients who would benefit from novel therapies.

Extraction and high-throughput sequencing of oak heartwood DNA: Assessing the feasibility of genome-wide DNA methylation profiling.

Tree ring features are affected by environmental factors and therefore are the basis for dendrochronological studies to reconstruct past environmental conditions. Oak wood often provides the data for these studies because of the durability of oak heartwood and hence the availability of samples spanning long time periods of the distant past. Wood formation is regulated in part by epigenetic mechanisms such as DNA methylation. Studies of the methylation state of DNA preserved in oak heartwood thus could identify epigenetic tree ring features informing on past environmental conditions. In this study, we aimed to establish protocols for the extraction of DNA, the high-throughput sequencing of whole-genome DNA libraries (WGS) and the profiling of DNA methylation by whole-genome bisulfite sequencing (WGBS) for oak (Quercus robur) heartwood drill cores taken from the trunks of living standing trees spanning the AD 1776-2014 time period. Heartwood contains little DNA, and large amounts of phenolic compounds known to hinder the preparation of high-throughput sequencing libraries. Whole-genome and DNA methylome library preparation and sequencing consistently failed for oak heartwood samples more than 100 and 50 years of age, respectively. DNA fragmentation increased with sample age and was exacerbated by the additional bisulfite treatment step during methylome library preparation. Relative coverage of the non-repetitive portion of the oak genome was sparse. These results suggest that quantitative methylome studies of oak hardwood will likely be limited to relatively recent samples and will require a high sequencing depth to achieve sufficient genome coverage.

Lineage-defined leiomyosarcoma subtypes emerge years before diagnosis and determine patient survival.

Leiomyosarcomas (LMS) are genetically heterogeneous tumors differentiating along smooth muscle lines. Currently, LMS treatment is not informed by molecular subtyping and is associated with highly variable survival. While disease site continues to dictate clinical management, the contribution of genetic factors to LMS subtype, origins, and timing are unknown. Here we analyze 70 genomes and 130 transcriptomes of LMS, including multiple tumor regions and paired metastases. Molecular profiling highlight the very early origins of LMS. We uncover three specific subtypes of LMS that likely develop from distinct lineages of smooth muscle cells. Of these, dedifferentiated LMS with high immune infiltration and tumors primarily of gynecological origin harbor genomic dystrophin deletions and/or loss of dystrophin expression, acquire the highest burden of genomic mutation, and are associated with worse survival. Homologous recombination defects lead to genome-wide mutational signatures, and a corresponding sensitivity to PARP trappers and other DNA damage response inhibitors, suggesting a promising therapeutic strategy for LMS. Finally, by phylogenetic reconstruction, we present evidence that clones seeding lethal metastases arise decades prior to LMS diagnosis.

Single cell derived mRNA signals across human kidney tumors.

Tumor cells may share some patterns of gene expression with their cell of origin, providing clues into the differentiation state and origin of cancer. Here, we study the differentiation state and cellular origin of 1300 childhood and adult kidney tumors. Using single cell mRNA reference maps of normal tissues, we quantify reference "cellular signals" in each tumor. Quantifying global differentiation, we find that childhood tumors exhibit fetal cellular signals, replacing the presumption of "fetalness" with a quantitative measure of immaturity. By contrast, in adult cancers our assessment refutes the suggestion of dedifferentiation towards a fetal state in most cases. We find an intimate connection between developmental mesenchymal populations and childhood renal tumors. We demonstrate the diagnostic potential of our approach with a case study of a cryptic renal tumor. Our findings provide a cellular definition of human renal tumors through an approach that is broadly applicable to human cancer.

Defining an Optimal Metric for the Path Collective Variables.

Path Collective Variables (PCVs) are a set of path-like variables that have been successfully used to investigate complex chemical and biological processes and compute their associated free energy surfaces and kinetics. Their current implementation relies on general, but at times inefficient, metrics (such as RMSD or DRMSD) to evaluate the distance between the instantaneous conformational state during the simulation and the reference coordinates defining the path. In this work, we present a new algorithm to construct optimal PCVs metrics as linear combinations of different CVs weighted through a spectral gap optimization procedure. The method was tested first on a simple model, trialanine peptide, in vacuo and then on a more complex path of an anticancer inhibitor binding to its pharmacological target. We also compared the results to those obtained with other path-based algorithms. We find that not only our proposed approach is able to automatically select relevant CVs for the PCVs metric but also that the resulting PCVs allow for reconstructing the associated free energy very efficiently. What is more, at difference with other path-based methods, our algorithm is able to explore nonlocally the reaction path space.

Trans- Cis Proline Switches in a Pentameric Ligand-Gated Ion Channel: How They Are Affected by and How They Affect the Biomolecular Environment.

Pentameric ligand-gated ion channels (pLGICs) are important neuroreceptors, embedded in neuronal membranes, that mediate fast synaptic transmission. The molecular details of their working mechanisms have still to be fully unravelled due to their complexity and limited structural information available. Here we focus on a potential molecular switch in a prototypical pLGIC, the serotonin-activated 5-HT3 receptor, consisting of the trans- cis isomerization of a proline at the interface between the extracellular and transmembrane domain. Mutagenesis electrophysiology experiments previously showed that if such isomerization could not take place, the channel would not open, but the hypothetical role of this mechanism as key to channel gating is still debated. We investigate this switch within the receptor with molecular dynamics and enhanced sampling simulations. We analyze how the isomerization free energy landscape is affected by the receptor environment in comparison to simplified models. Moreover, we reveal how the isomerization, in turn, affects the structural and electrostatic properties of the receptor at the extracellular-transmembrane domain interface, e.g., by tuning the ion selectivity filter.

Mutagenesis computer experiments in pentameric ligand-gated ion channels: the role of simulation tools with different resolution.

Pentameric ligand-gated ion channels (pLGICs) are an important class of widely expressed membrane neuroreceptors, which play a crucial role in fast synaptic communications and are involved in several neurological conditions. They are activated by the binding of neurotransmitters, which trigger the transmission of an electrical signal via facilitated ion flux. They can also be activated, inhibited or modulated by a number of drugs. Mutagenesis electrophysiology experiments, with natural or unnatural amino acids, have provided a large body of functional data that, together with emerging structural information from X-ray spectroscopy and cryo-electron microscopy, are helping unravel the complex working mechanisms of these neuroreceptors. Computer simulations are complementing these mutagenesis experiments, with insights at various levels of accuracy and resolution. Here, we review how a selection of computational tools, including first principles methods, classical molecular dynamics and enhanced sampling techniques, are contributing to construct a picture of how pLGICs function and can be pharmacologically targeted to treat the disorders they are responsible for.

Exploring Cryptic Pockets Formation in Targets of Pharmaceutical Interest with SWISH.

Cryptic (hidden) pockets are sites that are not visible on unliganded target proteins' structures and only become apparent when a ligand binds. They might provide a valid alternative to classical binding sites in otherwise "undruggable" targets, but their hidden nature makes it difficult to use standard structure-based or computer-aided drug discovery approaches. Our group recently developed a Hamiltonian replica-exchange method (sampling water interfaces through scaled Hamiltonians or SWISH) that improves the sampling of hydrophobic cavities by scaling the interactions between water molecules and protein atoms. Here, we discuss further improvements to SWISH and its combination with fragment probe simulations. We tested the robustness and general applicability of the improved approach in a variety of pharmaceutically relevant targets. The chosen proteins: NPC2, p38α, LfrR, and hPNMT, represent a set of diversified and interesting targets harboring nontrivial cryptic binding sites. In all cases, the updated version of our algorithm efficiently explored the cryptic sites.

A Multiscale Simulation Approach to Modeling Drug-Protein Binding Kinetics.

Drug-target binding kinetics has recently emerged as a sometimes critical determinant of in vivo efficacy and toxicity. Its rational optimization to improve potency or reduce side effects of drugs is, however, extremely difficult. Molecular simulations can play a crucial role in identifying features and properties of small ligands and their protein targets affecting the binding kinetics, but significant challenges include the long time scales involved in (un)binding events and the limited accuracy of empirical atomistic force fields (lacking, e.g., changes in electronic polarization). In an effort to overcome these hurdles, we propose a method that combines state-of-the-art enhanced sampling simulations and quantum mechanics/molecular mechanics (QM/MM) calculations at the BLYP/VDZ level to compute association free energy profiles and characterize the binding kinetics in terms of structure and dynamics of the transition state ensemble. We test our combined approach on the binding of the anticancer drug Imatinib to Src kinase, a well-characterized target for cancer therapy with a complex binding mechanism involving significant conformational changes. The results indicate significant changes in polarization along the binding pathways, which affect the predicted binding kinetics. This is likely to be of widespread importance in binding of ligands to protein targets.

The Free Energy Landscape of GABA Binding to a Pentameric Ligand-Gated Ion Channel and Its Disruption by Mutations.

Pentameric ligand-gated ion channels (pLGICs) of the Cys-loop superfamily are important neuroreceptors that mediate fast synaptic transmission. They are activated by the binding of a neurotransmitter, but the details of this process are still not fully understood. As a prototypical pLGIC, here we choose the insect resistance to dieldrin (RDL) receptor involved in resistance to insecticides and investigate the binding of the neurotransmitter GABA to its extracellular domain at the atomistic level. We achieve this by means of µ-sec funnel-metadynamics simulations, which efficiently enhance the sampling of bound and unbound states by using a funnel-shaped restraining potential to limit the exploration in the solvent. We reveal the sequence of events in the binding process from the capture of GABA from the solvent to its pinning between the charged residues Arg111 and Glu204 in the binding pocket. We characterize the associated free energy landscapes in the wild-type RDL receptor and in two mutant forms, where the key residues Arg111 and Glu204 are mutated to Ala. Experimentally these mutations produce nonfunctional channels, which is reflected in the reduced ligand binding affinities due to the loss of essential interactions. We also analyze the dynamical behavior of the crucial loop C, whose opening allows the access of GABA to the binding site and closure locks the ligand into the protein. The RDL receptor shares structural and functional features with other pLGICs; hence, our work outlines a valuable protocol to study the binding of ligands to pLGICs beyond conventional docking and molecular dynamics techniques.

Tunable heat transfer with smart nanofluids.

Strongly thermophilic nanofluids are able to transfer either small or large quantities of heat when subjected to a stable temperature difference. We investigate the bistability diagram of the heat transferred by this class of nanofluids. We show that bistability can be exploited to obtain a controlled switching between a conductive and a convective regime of heat transfer, so as to achieve a controlled modulation of the heat flux.