Showing papers by "Douglas B. Kell published in 2020"

The Biology of Lactoferrin, an Iron-Binding Protein That Can Help Defend Against Viruses and Bacteria.

Abstract: Lactoferrin is a nutrient classically found in mammalian milk. It binds iron and is transferred via a variety of receptors into and between cells, serum, bile, and cerebrospinal fluid. It has important immunological properties, and is both antibacterial and antiviral. In particular, there is evidence that it can bind to at least some of the receptors used by coronaviruses and thereby block their entry. Of importance are Heparan Sulfate Proteoglycans (HSPGs) and the host receptor angiotensin-converting enzyme 2 (ACE2), as based on other activities lactoferrin might prevent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from attaching to the host cells. Lactoferrin (and more specifically enteric-coated LF because of increased bioavailability) may consequently be of preventive and therapeutic value during the present COVID-19 pandemic.

SBML Level 3: an extensible format for the exchange and reuse of biological models

Abstract: Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

Covid-19: The Rollercoaster of Fibrin(Ogen), D-Dimer, Von Willebrand Factor, P-Selectin and Their Interactions with Endothelial Cells, Platelets and Erythrocytes.

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), also known as coronavirus disease 2019 (COVID-19)-induced infection, is strongly associated with various coagulopathies that may result in either bleeding and thrombocytopenia or hypercoagulation and thrombosis. Thrombotic and bleeding or thrombotic pathologies are significant accompaniments to acute respiratory syndrome and lung complications in COVID-19. Thrombotic events and bleeding often occur in subjects with weak constitutions, multiple risk factors and comorbidities. Of particular interest are the various circulating inflammatory coagulation biomarkers involved directly in clotting, with specific focus on fibrin(ogen), D-dimer, P-selectin and von Willebrand Factor (VWF). Central to the activity of these biomarkers are their receptors and signalling pathways on endothelial cells, platelets and erythrocytes. In this review, we discuss vascular implications of COVID-19 and relate this to circulating biomarker, endothelial, erythrocyte and platelet dysfunction. During the progression of the disease, these markers may either be within healthy levels, upregulated or eventually depleted. Most significant is that patients need to be treated early in the disease progression, when high levels of VWF, P-selectin and fibrinogen are present, with normal or slightly increased levels of D-dimer (however, D-dimer levels will rapidly increase as the disease progresses). Progression to VWF and fibrinogen depletion with high D-dimer levels and even higher P-selectin levels, followed by the cytokine storm, will be indicative of a poor prognosis. We conclude by looking at point-of-care devices and methodologies in COVID-19 management and suggest that a personalized medicine approach should be considered in the treatment of patients.

The biology of ergothioneine, an antioxidant nutraceutical.

Abstract: Ergothioneine (ERG) is an unusual thio-histidine betaine amino acid that has potent antioxidant activities. It is synthesised by a variety of microbes, especially fungi (including in mushroom fruiting bodies) and actinobacteria, but is not synthesised by plants and animals who acquire it via the soil and their diet, respectively. Animals have evolved a highly selective transporter for it, known as solute carrier family 22, member 4 (SLC22A4) in humans, signifying its importance, and ERG may even have the status of a vitamin. ERG accumulates differentially in various tissues, according to their expression of SLC22A4, favouring those such as erythrocytes that may be subject to oxidative stress. Mushroom or ERG consumption seems to provide significant prevention against oxidative stress in a large variety of systems. ERG seems to have strong cytoprotective status, and its concentration is lowered in a number of chronic inflammatory diseases. It has been passed as safe by regulatory agencies, and may have value as a nutraceutical and antioxidant more generally.

Erythrocyte, Platelet, Serum Ferritin, and P-Selectin Pathophysiology Implicated in Severe Hypercoagulation and Vascular Complications in COVID-19

Abstract: Progressive respiratory failure is seen as a major cause of death in severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2)-induced infection. Relatively little is known about the associated morphologic and molecular changes in the circulation of these patients. In particular, platelet and erythrocyte pathology might result in severe vascular issues, and the manifestations may include thrombotic complications. These thrombotic pathologies may be both extrapulmonary and intrapulmonary and may be central to respiratory failure. Previously, we reported the presence of amyloid microclots in the circulation of patients with coronavirus disease 2019 (COVID-19). Here, we investigate the presence of related circulating biomarkers, including C-reactive protein (CRP), serum ferritin, and P-selectin. These biomarkers are well-known to interact with, and cause pathology to, platelets and erythrocytes. We also study the structure of platelets and erythrocytes using fluorescence microscopy (using the markers PAC-1 and CD62PE) and scanning electron microscopy. Thromboelastography and viscometry were also used to study coagulation parameters and plasma viscosity. We conclude that structural pathologies found in platelets and erythrocytes, together with spontaneously formed amyloid microclots, may be central to vascular changes observed during COVID-19 progression, including thrombotic microangiopathy, diffuse intravascular coagulation, and large-vessel thrombosis, as well as ground-glass opacities in the lungs. Consequently, this clinical snapshot of COVID-19 strongly suggests that it is also a true vascular disease and considering it as such should form an essential part of a clinical treatment regime.

L-(+)-Ergothioneine Significantly Improves the Clinical Characteristics of Preeclampsia in the Reduced Uterine Perfusion Pressure Rat Model.

Abstract: Preeclampsia is a multifactorial hypertensive disorder of pregnancy founded on abnormal placentation, and the resultant placental ischemic microenvironment is thought to play a crucial role in its ...

COVID-19: The Rollercoaster of Fibrin(ogen), D-dimer, von Willebrand Factor, P-selectin and Their Interactions with Endothelial Cells, Platelets and Erythrocytes

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), coronavirus disease 2019 (COVID-19)-induced infection is strongly associated with various coagulopathies that may result in either bleeding and thrombocytopenia or hypercoagulation and thrombosis. Thrombotic and bleeding or thrombotic pathologies are significant accompaniments to acute respiratory syndrome and lung complications in COVID-19. Thrombotic events and bleeding, often occurs in subjects with weak multiple risk factors and co-morbidities. Of particular interest are the various circulating inflammatory coagulation biomarkers involved directly in clotting, with specific focus on fibrin(ogen), D-dimer, P-selectin and von Willebrand Factor (vWF). Central to activity of these biomarkers are their receptors and signaling pathways on endothelial cells, platelets and erythrocytes. In this review, we discuss vascular implications of COVID-19, and relate this to circulating biomarker, endothelial, erythrocyte and platelet dysfunction. During the progression of the disease, these markers may either be within healthy levels, upregulated or eventually depleted. Most significant is that patients need to be treated early in the disease progression, when high levels of vWF, P-selectin and fibrinogen are present with still low levels of D-dimer. Progression to vWF and fibrinogen depletion with high D-dimer levels and even higher P-selectin levels, followed by the cytokine storm, will be indicative of a poor prognosis. We conclude by looking at point-of-care devises and methodologies in COVID-19 management and suggest that a personalized medicine approach should be considered in the treatment of patients.

DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach.

Abstract: We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties.

Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome.

Abstract: The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model. Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient’s infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased). High resolution untargeted LC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model was created. The predictors were selected for their relevant biological function and include cytosine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74 – 0.91) and 0.76 (CI 0.67 – 0.86). Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

The RESOLUTE consortium: unlocking SLC transporters for drug discovery.

Abstract: The Innovative Medicines Initiative Consortium RESOLUTE has started to develop tools and produce data sets to de-orphanize transporters in the solute carrier protein (SLC) superfamily, thereby lowering the barrier for the scientific community to explore SLCs as an attractive drug target class. The Innovative Medicines Initiative Consortium RESOLUTE has started to develop tools and produce data sets to de-orphanize transporters in the solute carrier protein (SLC) superfamily, thereby lowering the barrier for the scientific community to explore SLCs as an attractive drug target class.

Prevalence of readily detected amyloid blood clots in 'unclotted' Type 2 Diabetes Mellitus and COVID-19 plasma: a preliminary report.

Abstract: Type 2 Diabetes Mellitus (T2DM) is a well-known comorbidity to COVID-19 and coagulopathies are a common accompaniment to both T2DM and COVID-19. In addition, patients with COVID-19 are known to develop micro-clots within the lungs. The rapid detection of COVID-19 uses genotypic testing for the presence of SARS-Cov-2 virus in nasopharyngeal swabs, but it can have a poor sensitivity. A rapid, host-based physiological test that indicated clotting severity and the extent of clotting pathologies in the individual who was infected or not would be highly desirable. Platelet poor plasma (PPP) was collected and frozen. On the day of analysis, PPP samples were thawed and analysed. We show here that microclots can be detected in the native plasma of twenty COVID-19, as well as ten T2DM patients, without the addition of any clotting agent, and in particular that such clots are amyloid in nature as judged by a standard fluorogenic stain. Results were compared to ten healthy age-matched individuals. In COVID-19 plasma these microclots are significantly increased when compared to the levels in T2DM. This fluorogenic test may provide a rapid and convenient test with 100% sensitivity (P < 0.0001) and is consistent with the recognition that the early detection and prevention of such clotting can have an important role in therapy.

VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder.

Abstract: Molecular similarity is an elusive but core "unsupervised" cheminformatics concept, yet different "fingerprint" encodings of molecular structures return very different similarity values, even when using the same similarity metric. Each encoding may be of value when applied to other problems with objective or target functions, implying that a priori none are "better" than the others, nor than encoding-free metrics such as maximum common substructure (MCSS). We here introduce a novel approach to molecular similarity, in the form of a variational autoencoder (VAE). This learns the joint distribution p(z|x) where z is a latent vector and x are the (same) input/output data. It takes the form of a "bowtie"-shaped artificial neural network. In the middle is a "bottleneck layer" or latent vector in which inputs are transformed into, and represented as, a vector of numbers (encoding), with a reverse process (decoding) seeking to return the SMILES string that was the input. We train a VAE on over six million druglike molecules and natural products (including over one million in the final holdout set). The VAE vector distances provide a rapid and novel metric for molecular similarity that is both easily and rapidly calculated. We describe the method and its application to a typical similarity problem in cheminformatics.

Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently.

Abstract: The number of 'small' molecules that may be of interest to chemical biologists - chemical space - is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved 'forward' problems (have molecule, establish properties). However, we normally wish to solve the much harder generative or inverse problem (describe desired properties, find molecule). 'Deep' (machine) learning based on large-scale neural networks underpins technologies such as computer vision, natural language processing, driverless cars, and world-leading performance in games such as Go; it can also be applied to the solution of inverse problems in chemical biology. In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical space intelligently. These methods are revolutionary but require an understanding of both (bio)chemistry and computer science to be exploited to best advantage. We give a high-level (non-mathematical) background to the deep learning revolution, and set out the crucial issue for chemical biology and informatics as a two-way mapping from the discrete nature of individual molecules to the continuous but high-dimensional latent representation that may best reflect chemical space. A variety of architectures can do this; we focus on a particular type known as variational autoencoders. We then provide some examples of recent successes of these kinds of approach, and a look towards the future.

A Quantitative Survey of Bacterial Persistence in the Presence of Antibiotics: Towards Antipersister Antimicrobial Discovery.

Abstract: Background: Bacterial persistence to antibiotics relates to the phenotypic ability to survive lethal concentrations of otherwise bactericidal antibiotics. The quantitative nature of the time-kill assay, which is the sector's standard for the study of antibiotic bacterial persistence, is an invaluable asset for global, unbiased, and cross-species analyses. Methods: We compiled the results of antibiotic persistence from antibiotic-sensitive bacteria during planktonic growth. The data were extracted from a sample of 187 publications over the last 50 years. The antibiotics used in this compilation were also compared in terms of structural similarity to fluorescent molecules known to accumulate in Escherichia coli. Results: We reviewed in detail data from 54 antibiotics and 36 bacterial species. Persistence varies widely as a function of the type of antibiotic (membrane-active antibiotics admit the fewest), the nature of the growth phase and medium (persistence is less common in exponential phase and rich media), and the Gram staining of the target organism (persistence is more common in Gram positives). Some antibiotics bear strong structural similarity to fluorophores known to be taken up by E. coli, potentially allowing competitive assays. Some antibiotics also, paradoxically, seem to allow more persisters at higher antibiotic concentrations. Conclusions: We consolidated an actionable knowledge base to support a rational development of antipersister antimicrobials. Persistence is seen as a step on the pathway to antimicrobial resistance, and we found no organisms that failed to exhibit it. Novel antibiotics need to have antipersister activity. Discovery strategies should include persister-specific approaches that could find antibiotics that preferably target the membrane structure and permeability of slow-growing cells.

Is Porphyromonas gingivalis involved in Parkinson's disease?

Abstract: Porphyromonas gingivalis, a major subgingival plaque bacterium in periodontitis, has recently attracted much attention as a possible microbial driver in Alzheimer's disease In the present paper, another common neuroinflammatory disease, Parkinson's disease (PD), is discussed A recent study found major virulence factors of P gingivalis such as gingipain R1 (RgpA) and lipopolysaccharide in the blood circulation of a PD population The current review reveals how features such as systemic inflammation, hypercoagulation, presence of amyloid fibrin(ogen) in plasma, and marked ultrastructural changes in platelets, probably induced by P gingivalis, may affect the development of PD Several other clinical studies have also demonstrated an association between periodontitis and PD Even if the risk of periodontal diseases causing neurological disorders needs to be better substantiated, that should not keep us from trying to prevent them by performing careful daily dental hygiene

An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines.

Abstract: It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and that drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar. Untargeted metabolomics can provide a method for determining the differential uptake of such metabolites. Blood serum contains many thousands of molecules and provides a convenient source of biologically relevant metabolites. Our objective was to detect and identify metabolites present in serum, but to also establish a method capable of measure their uptake and secretion by different cell lines. We develop an untargeted LC-MS/MS method to detect a broad range of compounds present in human serum. We apply this to the analysis of the time course of the uptake and secretion of metabolites in serum by several human cell lines, by analysing changes in the serum that represents the extracellular phase (the ‘exometabolome’ or metabolic footprint). Our method measures some 4000–5000 metabolic features in both positive and negative electrospray ionisation modes. We show that the metabolic footprints of different cell lines differ greatly from each other. Our new, 15-min untargeted metabolome method allows for the robust and convenient measurement of differences in the uptake of serum compounds by cell lines following incubation in serum. This will enable future research to study these differences in multiple cell lines that will relate this to transporter expression, thereby advancing our knowledge of transporter substrates, both natural and xenobiotic compounds.

Effect of L-Ergothioneine on the metabolic plasma profile of the RUPP rat model of pre-eclampsia.

Abstract: Introduction Pre-eclampsia is a major cause of maternal and fetal mortality and morbidity worldwide. Its pathophysiology remains unclear, but mitochondrial dysfunction and oxidative stress have been implicated. L-Ergothioneine is a naturally occurring, water-soluble betaine, that has demonstrated antioxidant properties. Using the reduced uterine perfusion pressure (RUPP) rat model of pre-eclampsia, this study aimed to define the plasma metabolic profile following treatment with L-Ergothioneine. Methods The effect of L-Ergothioneine (ET) treatment was explored using in vivo treatment in rats: Sham control (SC, n = 5), RUPP control (RC, n = 5), Sham +ET (ST, n = 5), RUPP +ET (RT, n = 5). Differential expression of plasma metabolites were obtained using untargeted liquid chromatography coupled to mass spectrometry. Statistical analysis was performed on normalised data comparing RC to SC, RT to RC, and RT to ST. Metabolites significantly altered (FDR < 0.05) were identified through database search. Results We report significantly lower levels of L-palmitoylcarnitine in RC compared to SC, a fatty acyl substrate involved in beta-oxidation in the mitochondria. We report that a metabolite that has been associated with oxidative stress (Glutamylcysteine) was detected at significantly higher levels in RT vs RC and RT vs ST. Five metabolites associated with inflammation were significantly lower in RT vs RC and three metabolites in RT vs ST, demonstrating the anti-inflammatory effects of ET in the RUPP rat model of pre-eclampsia. Conclusions L-Ergothioneine may help preserve mitochondrial function by increasing antioxidant levels, and reducing inflammatory responses associated with pre-eclampsia. This study shows the potential of L-Ergothioneine as a treatment for pre-eclampsia.

Gingipain R1 and Lipopolysaccharide From Porphyromonas gingivalis Have Major Effects on Blood Clot Morphology and Mechanics.

Abstract: Background: Porphyromonas gingivalis and its inflammagens are associated with a number of systemic diseases, such as cardiovascular disease and type 2 diabetes (T2DM). The proteases, gingipains, have also recently been identified in the brains of Alzheimer's disease patients and in the blood of Parkinson's disease patients. Bacterial inflammagens, including lipopolysaccharides (LPSs) and various proteases in circulation, may drive systemic inflammation. Methods: Here, we investigate the effects of the bacterial products LPS from Escherichia coli and Porphyromonas gingivalis, and also the P. gingivalis gingipain [recombinant P. gingivalis gingipain R1 (RgpA)], on clot architecture and clot formation in whole blood and plasma from healthy individuals, as well as in purified fibrinogen models. Structural analysis of clots was performed using confocal microscopy, scanning electron microscopy, and AFM-Raman imaging. We use thromboelastography® (TEG®) and rheometry to compare the static and dynamic mechanical properties of clots. Results: We found that these inflammagens may interact with fibrin(ogen) and this interaction causes anomalous blood clotting. Conclusions: These techniques, in combination, provide insight into the effects of these bacterial products on cardiovascular health, and particularly clot structure and mechanics.

Automating Cloning by Natural Transformation.

Abstract: Affordable and automated cloning platforms are essential to many synthetic biology studies. However, the traditional E. coli-based cloning is a major bottleneck as it requires heat shock or electroporation implemented in the robotic workflows. To overcome this problem, we explored bacterial natural transformation for automatic DNA cloning and engineering. Recombinant plasmids are efficiently generated from Gibson or overlap extension PCR (OE-PCR) products by simply adding the DNA into Acinetobacter baylyi ADP1 cultures. No DNA purification, competence induction, or special equipment is required. Up to 10,000 colonies were obtained per microgram of DNA, while the number of false positive colonies was low. We cloned and engineered 21 biosynthetic gene clusters (BGCs) of various types, with length from 1.5 to 19 kb and GC content from 35% to 72%. One of them, a nucleoside BGC, showed antibacterial activity. Furthermore, the method was easily transferred to a low-cost benchtop robot with consistent cloning efficiency. Thus, this automatic natural transformation (ANT) cloning provides an easy, robust, and affordable platform for high throughput DNA engineering.

Detection of Citrullinated Fibrin in Plasma Clots of Rheumatoid Arthritis Patients and Its Relation to Altered Structural Clot Properties, Disease-Related Inflammation and Prothrombotic Tendency.

Abstract: Aims The risk of cardiovascular events in patients with Rheumatoid Arthritis (RA) is disproportionately heightened as a result of systemic inflammation. The relative effect of autoimmune-associated citrullination on the structure and thrombotic potential of fibrin(ogen) remains unknown. We therefore compared indices of vascular function, inflammation, coagulation and fibrin clot composition in RA patients with healthy controls and evaluated parameter association with disease presence. Methods Blood samples were collected from 30 RA patients and 30 age- and gender-matched healthy volunteers. Levels of serum amyloid A (SAA), c-reactive protein (CRP), soluble intercellular adhesion molecule 1 (sICAM-1) and soluble vascular cell adhesion molecule 1 (sVCAM-1) was measured using a sandwich immunoassay. Whole blood coagulation was assessed using Thromboelastography (TEG®). Fibrin clot networks and fiber structure was investigated using Scanning Electron Microscopy. The detection and quantification of citrullination in formed fibrin clots was performed using a fluorescently labeled Citrulline monoclonal antibody with Fluorescence Wide Field Microscopy. Results Concentrations of SAA, CRP and ICAM-1 were significantly elevated in RA patients compared to controls. TEG parameters relating to coagulation initiation, rate of fibrin cross-linking, and time to reach maximum thrombus generation were attenuated in RA patients. Microscopic analysis revealed denser networks of thicker fibrin fibers in RA patients compared to controls and multiple citrullinated regions within fibrin clot structures in RA patients were present. Conclusion Our findings provide novel evidence for the citrullination of fibrin within vasculature is more prominent in RA plasma compared to control plasma and plasma is more accessible than synovial fluid. Citrullinated fibrinogen could play a role as a determinant of thrombotic risk in RA patients.

Hitchhiking into the cell.

Abstract: The mechanisms by which bioactive drugs get into their target cells is a question that has been greatly neglected. A new survey with CRISPR–Cas9 shows the widespread importance of protein transporters called solute carriers (SLCs).

Prevalence of amyloid blood clots in COVID-19 plasma

Abstract: CITATION: Pretorius, E. et al. 2020. Prevalence of amyloid blood clots in COVID-19 plasma. medRxiv, doi:10.1101/2020.07.28.20163543.

Iron Dysregulation and Inflammagens Related to Oral and Gut Health Are Central to the Development of Parkinson's Disease.

Abstract: Neuronal lesions in Parkinson's disease (PD) are commonly associated with α-synuclein (α-Syn)-induced cell damage that are present both in the central and peripheral nervous systems of patients, with the enteric nervous system also being especially vulnerable. Here, we bring together evidence that the development and presence of PD depends on specific sets of interlinking factors that include neuroinflammation, systemic inflammation, α-Syn-induced cell damage, vascular dysfunction, iron dysregulation, and gut and periodontal dysbiosis. We argue that there is significant evidence that bacterial inflammagens fuel this systemic inflammation, and might be central to the development of PD. We also discuss the processes whereby bacterial inflammagens may be involved in causing nucleation of proteins, including of α-Syn. Lastly, we review evidence that iron chelation, pre-and probiotics, as well as antibiotics and faecal transplant treatment might be valuable treatments in PD. A most important consideration, however, is that these therapeutic options need to be validated and tested in randomized controlled clinical trials. However, targeting underlying mechanisms of PD, including gut dysbiosis and iron toxicity, have potentially opened up possibilities of a wide variety of novel treatments, which may relieve the characteristic motor and nonmotor deficits of PD, and may even slow the progression and/or accompanying gut-related conditions of the disease.

DeepGraphMol, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach

Abstract: We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties.

Structural Similarities between Some Common Fluorophores Used in Biology, Marketed Drugs, Endogenous Metabolites, and Natural Products.

Abstract: It is known that at least some fluorophores can act as ‘surrogate’ substrates for solute carriers (SLCs) involved in pharmaceutical drug uptake, and this promiscuity is taken to reflect at least a certain structural similarity. As part of a comprehensive study seeking the ‘natural’ substrates of ‘orphan’ transporters that also serve to take up pharmaceutical drugs into cells, we have noted that many drugs bear structural similarities to natural products. A cursory inspection of common fluorophores indicates that they too are surprisingly ‘drug-like’, and they also enter at least some cells. Some are also known to be substrates of efflux transporters. Consequently, we sought to assess the structural similarity of common fluorophores to marketed drugs, endogenous mammalian metabolites, and natural products. We used a set of some 150 fluorophores along with standard fingerprinting methods and the Tanimoto similarity metric. Results: The great majority of fluorophores tested exhibited significant similarity (Tanimoto similarity > 0.75) to at least one drug, as judged via descriptor properties (especially their aromaticity, for identifiable reasons that we explain), by molecular fingerprints, by visual inspection, and via the “quantitative estimate of drug likeness” technique. It is concluded that this set of fluorophores does overlap with a significant part of both the drug space and natural products space. Consequently, fluorophores do indeed offer a much wider opportunity than had possibly been realised to be used as surrogate uptake molecules in the competitive or trans-stimulation assay of membrane transporter activities.

An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines

Abstract: Introduction It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and that drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar. Untargeted metabolomics can provide a method for determining the differential uptake of such metabolites. Objectives Blood serum contains many thousands of molecules and provides a convenient source of biologically relevant metabolites. Our objective was to measure them. Methods We develop an untargeted LC-MS/MS method to detect a broad range of compounds present in human serum. We apply this to the analysis of the time course of the uptake and secretion of metabolites in serum by several human cell lines, by analysing changes in the serum that represents the extracellular phase (the ‘exometabolome’ or metabolic footprint). Results Our method measures some 4,000-5,000 metabolic features in both ES+ and ES− modes. We show that the metabolic footprints of different cell lines differ greatly from each other. Conclusion Our new, 15-minute untargeted metabolome method allows for the robust and convenient measurement of differences in the uptake of serum compounds by cell lines following incubation in serum, and its relation to differences in transporter expression.

A palette of fluorophores that are differentially accumulated by wild-type and mutant strains of Escherichia coli: surrogate ligands for bacterial membrane transporters

Abstract: Our previous work had demonstrated that two commonly used fluorescent dyes that were accumulated by wild-type E. coli MG1655 were accumulated differentially in single-gene knockout strains, and also that they might be used as surrogates in flow cytometric transporter assays. We summarise the desirable properties of such stains, and here survey 143 candidate dyes. We triage them eventually (on the basis of signal, accumulation levels, and cost) to a palette of 39 commercially available and affordable fluorophores that are accumulated significantly by wild-type cells of the ‘Keio’ strain BW25113, as measured flow cytometrically. Cheminformatic analyses indicate both their similarities and their (much more considerable) structural differences. We describe the effects of pH and of the efflux pump inhibitor chlorpromazine on the accumulation. Even the ‘wild-type’ MG1655 and BW25113 strains can differ significantly in their ability to take up such dyes. We illustrate the highly differential uptake of our dyes into strains with particular lesions in, or overexpressed levels of, three particular transporters or transporter components (yhjV, yihN, and tolC). The relatively small collection of dyes described offers a rapid, inexpensive, convenient and valuable approach to the assessment of microbial physiology and transporter function.

The Atypical Fibrin Fibre Network in Rheumatoid Arthritis and its Relation to Autoimmunity, Inflammation and Thrombosis

Abstract: Objective: The risk of cardiovascular events in patients with RA is disproportionately heightened as a result of systemic inflammation. The relative effect of autoimmune-associated citrullination on the structure and thrombotic potential of fibrin(ogen) remains unknown. We therefore compared indices of vascular function, inflammation, coagulation and fibrin clot composition in RA patients with healthy controls and evaluated inter-parameter relationships. Methods. Blood samples were collected from 30 RA patients and 25 age- and gender-matched healthy volunteers. Levels of SAA, CRP, ICAM-1 and VCAM-1 was measured using a sandwich immunoassay. Whole blood coagulation was assessed using Thromboelastography. Fibrin clot networks and fiber structure was investigated using Scanning Electron Microscopy. The detection and quantification of citrullination in formed fibrin clots were performed using a fluorescently labeled Citrulline monoclonal antibody with Confocal Microscopy. Results. Concentrations of SAA, CRP and ICAM-1 were significantly elevated in RA patients compared to controls. TEG parameters relating to coagulation initiation (R and K), rate of fibrin cross-linking (α-Angle), and time to reach maximum thrombus generation (TMRTG) were attenuated in RA patients. Parameters relating to clot strength (MA, MRTG, TGG) did not statistically differ between RA and controls. Logistic regression modelling revealed stronger association between acute phase reactants (CRP, SAA) with TEG parameters than endothelial function markers. Microscopic analysis revealed denser networks of thicker fibrin fibers in RA patients compared to controls [median (interquartile range) 214 (170-285) vs 120 (100-144) nm respectively, p

VAE-Sim: a novel molecular similarity measure based on a variational autoencoder

Abstract: Molecular similarity is an elusive but core unsupervised cheminformatics concept, yet different fingerprint encodings of molecular structures return very different similarity values even when using the same similarity metric Each encoding may be of value when applied to other problems with objective or target functions, implying that a priori none is better than the others, nor than encoding-free metrics such as maximum common substructure (MCSS) We here introduce a novel approach to molecular similarity, in the form of a variational autoencoder (VAE) This learns the joint distribution p(z|x) where z is a latent vector and x are the (same) input/output data It takes the form of a bowtie-shaped artificial neural network In the middle is a bottleneck layer or latent vector in which inputs are transformed into, and represented as, a vector of numbers (encoding), with a reverse process (decoding) seeking to return the SMILES string that was the input We train a VAE on over 6 million druglike molecules and natural products (including over one million in the final holdout set) The VAE vector distances provide a rapid and novel metric for molecular similarity that is both easily and rapidly calculated We describe the method and its application to a typical similarity problem in cheminformatics

Why most transporter mutations that cause antibiotic resistance are to efflux pumps rather than to import transporters

Abstract: Genotypic microbial resistance to antibiotics with intracellular targets commonly arises from mutations that increase the activities of transporters (pumps) that cause the efflux of intracellular antibiotics. A priori it is not obvious why this is so much more common than are mutations that simply inhibit the activity of uptake transporters for the antibiotics. We analyse quantitatively a mathematical model consisting of one generic equilibrative transporter and one generic concentrative uptake transporter (representing any number of each), together with one generic efflux transporter. The initial conditions are designed to give an internal concentration of the antibiotic that is three times the minimum inhibitory concentration (MIC). The effect of varying the activity of each transporter type 100-fold is dramatically asymmetric, in that lowering the activities of individual uptake transporters has comparatively little effect on internal concentrations of the antibiotic. By contrast, increasing the activity of the efflux transporter lowers the internal antibiotic concentration to levels far below the MIC. Essentially, these phenomena occur because inhibiting individual influx transporters allows others to ‘take up the slack’, whereas increasing the activity of the generic efflux transporter cannot easily be compensated. The findings imply strongly that inhibiting efflux transporters is a much better approach for fighting antimicrobial resistance than is stimulating import transporters. This has obvious implications for the development of strategies to combat the development of microbial resistance to antibiotics and possibly also cancer therapeutics in human.