Biology subjects

Kell, D. B.

Publications and source records attributed to Kell, D. B..

At least 19 recordsLinked to original sources

bioRxiv2026

Unlocking substrate specificities of human solute carrier proteins using untargeted metabolomics

The limited understanding of transporter substrate spectra constrains our ability to interpret cell and membrane function, highlighting the need for methods that enable transporter deorphanization and characterization of promiscuous transport activities. Here, we present a Xenopus oocyte-based platform for unbiased transporter substrate discovery. Oocytes expressing heterologous solute carrier proteins (SLC) were incubated in human blood serum, a chemically complex metabolite library containing thousands of endogenous metabolites and xenobiotics, followed by paired untargeted LC-MS/MS profiling of intracellular extracts and surrounding medium to capture metabolite exchange events. Across the five human SLC transporters, viz. SLC10A2, SLC10A6, SLC13A2, SLC16A10, and SLC46A1, metabolite exchange signatures were detected, and automated feature annotation was refined by manual chromatographic peak inspection. The workflow recovered known substrates of SLC10A2 and SLC16A10 and identified additional transported metabolites with MS/MS confirmation. This method provides a scalable framework for transporter substrate profiling and prioritization of candidates for targeted validation.

systems biology↗

bioRxiv2026

An imaging flow cytometry method to study platelet-monocyte aggregates using Long COVID as a model

BackgroundLong COVID is characterised by persistent systemic inflammation and endothelial dysfunction, with increasing evidence implicating thromboinflammatory mechanisms. Platelet-monocyte aggregates (PMA) represent a sensitive marker of platelet activation and immune-vascular interactions, but their role in Long COVID remains incompletely defined. MethodsThis study quantified circulating PMA in 20 Long COVID patients and 20 healthy controls using a two-colour imaging flow cytometry assay targeting CD14 (a monocyte receptor for pathogen-associated molecular patterns, PAMPs) and CD62P (P-selectin). PMA were expressed as a percentage of total monocytes, and platelet attachment patterns were classified into single versus multiple platelet binding. Statistical analyses included Shapiro-Wilk normality testing, unpaired t-tests, Mann-Whitney U tests or two-way ANOVA as appropriate, and linear regression for correlation analysis. ResultsCirculating PMA were significantly elevated in Long COVID patients compared with controls (29.19 [20.02-37.26] vs 4.59 [2.67-7.16], p < 0.0001). Long COVID samples showed a reduced proportion of monocytes with single platelet attachment and a corresponding increase in multiple platelet binding (p < 0.0001). In controls, %PMA increased with age (p < 0.01), whereas no age association was observed in Long COVID, indicating an elevated baseline independent of age. ConclusionsLong COVID is associated with markedly increased platelet-monocyte aggregation and altered platelet attachment dynamics, consistent with sustained thromboinflammatory activity. PMA represent a sensitive cellular marker of platelet-driven immune activation and may have utility as an accessible biomarker for stratifying thromboinflammatory burden in Long COVID.

physiology↗

bioRxiv2025

Post-translational modifications within fibrinaloid microclot complexes distinguish Pre-COVID-19 Postural Orthostatic Tachycardia Syndrome, Long COVID, and Long COVID-POTS and reveal disease-specific molecular pathways

BackgroundPre-COVID-19 Postural orthostatic tachycardia syndrome (PC-POTS), Long COVID, and their overlap (LC-POTS) are chronic post-viral conditions marked by debilitating symptoms despite frequently normal routine laboratory results. We previously identified insoluble fibrinaloid microclot complexes (FMCs) in Long COVID. It is not known whether FMCs are also present in PC-POTS, or whether post-translational modifications (PTMs) within FMC-entrapped proteins contribute to disease mechanisms. MethodsPlatelet-poor plasma from healthy controls, PC-POTS patients (collected prior to the COVID-19 pandemic), Long COVID (without POTS) and LC-POTS patients underwent fluorescence imaging flow cytometry to quantify FMCs. Proteomic analyses were performed on insoluble protein fractions using a double trypsin digestion strategy and data-independent liquid chromatography-tandem mass spectrometry (LC-MS/MS). Differential protein abundance, PTMs, and amyloidogenicity were compared across groups. ResultsMeasured with imaging flowcytometry in objects/mL, higher levels of FMCs were present in disease groups compared to controls, although not statistically significant. Statistically significant differences potentially lay within FMC sizes and composition. Furthermore, despite only a few dysregulated proteins, FMC proteomics revealed extensive and disease-specific peptides with PTM dysregulation across coagulation, immune, and metabolic pathways. Long COVID displayed FMCs with PTMs of coagulation proteins including prominent advanced glycation end-products (AGE)- and oxidation-based modifications of fibrinogen subunits, particularly fibrinogen subunit A (FIBA), resembling diabetic glycation profiles. FMCs in PC-POTS showed fewer fibrinogen PTMs but markedly increased modifications in metabolic proteins, including oxidised apoA1 and apoB, and immune patterns with complement-related proteins (C3, C4A/B, IC1), immunoglobulin G1 (IGG1) and alpha 2 macroglobulin (A2MG). LC-POTS shared coagulation pathology with Long COVID and immune pathology with PC-POTS. Many dysregulated peptides were determined by in silco methods to be highly amyloidogenic, consistent with FMCs as {beta}-sheet-rich aggregates. Protein-level differences were minimal compared with PTM changes. ConclusionsThis study provides the first evidence that post-translational modifications (PTMs) within fibrinaloid microclots complexes (FMCs) uniquely distinguish pre-COVID-19 POTS, Long COVID, and Long COVID-POTS. Because PC-POTS samples were collected before SARS-CoV-2, their PTM patterns reflect intrinsic disease biology, allowing a clear separation from Long COVID-related changes. PTM profiling revealed pro-coagulant fibrinogen modifications in Long COVID and LC-POTS, metabolic-oxidative disruptions in Long COVID and PC-POTS, and immune dysregulation in PC-POTS and LC-POTS. None of these is detectable with routine assays, and all are independent of protein abundance. The consistent presence of amyloidogenic peptides suggests a contribution to microvascular dysfunction. These findings define disease-specific PTM landscapes and support new diagnostic and therapeutic avenues across autonomic and post-viral disorders.

physiology↗

bioRxiv2025

AmyloGram Reveals Amyloidogenic Potential in Stroke Thrombus Proteomes

BackgroundAmyloidogenic proteins play a central role in a range of pathological conditions, yet their presence in thrombi has only recently been recognized. Whether computational prediction tools can identify amyloid-forming potential in thrombus proteomes remains unclear. MethodsAmyloGram is a computational tool that estimates amyloid-forming potential based on n-gram sequence encoding and random forest classification. Using AmyloGram, we analyzed 204 proteins tagged by humans as amyloidogenic in UniProt. We then applied the same approach to proteins identified in thrombi retrieved using mechanical thrombectomy from patients with cardioembolic and atherothrombotic stroke. In addition we used AmyloGram to analyse the amyloidogenicity of 83,567 canonical human protein sequences. ResultsAmong the UniProt-annotated amyloid set, nearly all proteins received AmyloGram scores above 0.7, including 23 of the 24 human proteins. Even the lowest-scoring human protein, lysozyme (scoring 0.675), is known to form amyloid under certain conditions. In thrombi from both stroke subtypes, all detected proteins had AmyloGram scores above 0.7, suggesting a high likelihood of amyloid content. A majority of unannotated proteins also achieve AmyloGram scores exceeding 0.7. ConclusionsAmyloGram reliably identifies known amyloid-forming proteins and reveals that stroke thrombi are enriched for proteins with high amyloidogenic potential. These findings support the hypothesis that thrombus formation in stroke involves amyloid-related mechanisms and warrant further investigation using histological and functional validation.

bioinformatics↗

bioRxiv2025

Rapid accumulation of fluorophores and fast kill identify drugs with bactericidal effects against Gram-negative bacteria

Antimicrobial resistance is a massive threat, but it can take decades to develop a new antibiotic. That time could be drastically reduced if we were able to anticipate desirable properties of a chemical, such as its potential to target specific bacterial compartments. This would provide the opportunity to prioritise the development of molecules that target, for instance, the cell membrane, as this does not rely on transporters and usually results in a fast-acting bactericidal effect. We used flow cytometry and a set of fluorophores together with a group of antibiotics to discriminate between antimicrobials acting on cell membrane versus intracellularly against two Gram-negative bacteria E. coli and A. baylyi. We then chose Rhodamine 123 to screen a commercial library of chemical compounds. Using flow cytometry, several drugs present in the Prestwick library were observed to have cytotoxic effects towards E. coli. This was confirmed with growth inhibitory assays in both E. coli and A. baylyi for Pantoprazole, Theophylline and Zoledronic acid. This represents an approach to the large-scale screening of small molecules with the potential to deliver fast-acting molecules that target cell membranes in Gram-negative bacteria. 3. Impact statementThe discovery of novel antimicrobials is essential to build resilience against infectious diseases. Understanding the mechanism of action of known as well as novel compounds is equally crucial. Achieving this understanding is a long and complex process that is usually addressed on an individual basis for an antibiotic, or a class of antibiotics. Using high-throughput pipelines such as those possible with flow cytometry can help to shorten this process. We present a flow cytometry-based approach capable of determining if a given molecule has rapid antimicrobial effects that involve increasing the permeability of the cell membrane. The latter is a property that usually makes a chemical a very effective antimicrobial. 4. Data summaryThe authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

microbiology↗

bioRxiv2025

Membrane proteins ClcB, PtsI and YcaM mediate the bactericidal effects of colistin in Escherichia coli

Bacteria can be killed very effectively by targeting their first line of protection. The cell membranes (outer and inner membranes) of Gram-negative bacteria are directly targeted by antibiotics, such as polymyxins, via electrostatic interactions with their lipopolysaccharide (LPS) fraction. The downstream effects - preceding the cell death - of the disruptions of the bacterial membranes upon the intercalation of these antibiotics are unknown. By screening a set of E. coli membrane protein knockouts, three membrane transporters were shown to mediate the growth-inhibitory effects of colistin. This was corroborated with growth assays on gain-of-function strains, cytotoxic assays and in vivo infections in an invertebrate animal model. This is first-time evidence that the disruption caused to membrane proteins, such as the chloride channel ClcB, the sugar transport system component PtsI and the hypothetical Glutamate:GABA antiporter YcaM, is part of the cytotoxic pathway that follows or is concomitant to the electrostatic intercalations of polymyxins with the Gram-negative bacteria membrane.

microbiology↗

bioRxiv2024

The proteome content of blood clots observed under different conditions: successful role in predicting clot amyloid(ogenicity)

A recent analysis compared the proteome of (i) blood clots seen in two diseases - sepsis and long COVID - when blood was known to have clotted into an amyloid microclot form (as judged by staining with the fluorogenic amyloid stain thioflavin T) with (ii) that of those non-amy-loid clots considered to have formed normally. Such fibrinaloid microclots are also relatively resistant to fibrinolysis. The proteins that the amyloid microclots contained differed markedly both from the soluble proteome of typical plasma and that of normal clots, and also between the disease studies (an acute syndrome in the form of sepsis in an ITU and a chronic disease represented by Long COVID). Many proteins in the amyloid microclots were low in concentration in plasma and were effectively accumulated into the fibres, whereas many other abundant plasma proteins were excluded. The proteins found in the microclots associated with the diseases also tended to be themselves amyloidogenic. We here ask effectively the inverse question. This is: can the clot proteome tell us whether the clots associated with a particular disease contained proteins that are observed uniquely (or are highly over-represented) in known amyloid clots relative to normal clots, and thus were in fact amyloid in nature? The answer is in the affirmative in a variety of major coagulopathies, viz. venous thromboembolism, pulmonary embolism, deep vein thrombosis, various cardiac issues, and ischaemic stroke. Galectin-3-binding protein and thrombospondin-1 seem to be especially widely associated with amyloid-type clots, and the latter has indeed been shown to be incorporated into growing fibrin fibres. These may consequently provide useful biomarkers with a mechanistic basis.

bioinformatics↗

bioRxiv2024

Proteomic evidence for amyloidogenic cross-seeding in fibrinaloid microclots

In classical amyloidoses, amyloid fibres form through the nucleation and accretion of protein monomers, with protofibrils and fibrils exhibiting a cross-{beta} motif of parallel or antiparallel {beta}-sheets oriented perpendicular to the fibre direction. These protofibrils and fibrils can intertwine to form mature amyloid fibres. Similar phenomena occur in blood from individuals with circulating inflammatory molecules (also those originating from viruses and bacteria). In the presence of inflammagens, pathological clotting can occur, that results in an anomalous amyloid form termed fibrinaloid microclots. Previous proteomic analyses of these microclots have shown the presence of non-fibrin(ogen) proteins, suggesting a more complex mechanism than simple entrapment. We provide evidence against a simple entrapment model, noting that clot pores are too large and centrifugation would have removed weakly bound proteins. Instead, we explore whether co-aggregation into amyloid fibres may involve axial (multiple proteins within the same fibril), lateral (single-protein fibrils contributing to a fibre), or both types of integration. Our analysis of proteomic data from fibrinaloid microclots in different diseases shows no significant overlap with the normal plasma proteome and no correlation between plasma protein abundance and presence in microclots. Notably, abundant plasma proteins like -2-macroglobulin, fibronectin, and transthyretin are absent from microclots, while less abundant proteins such as adiponectin, periostin, and von Willebrand Factor are well represented. Using bioinformatic tools including AmyloGram and AnuPP, we found that proteins entrapped in fibrinaloid microclots exhibit high amyloidogenic tendencies, suggesting their integration as cross-{beta} elements into amyloid structures. This integration likely contributes to the microclots resistance to proteolysis. Our findings underscore the role of cross-seeding in fibrinaloid microclot formation and highlight the need for further investigation into their structural properties and implications in thrombotic and amyloid diseases. These insights provide a foundation for developing novel diagnostic and therapeutic strategies targeting amyloidogenic cross-seeding in blood clotting disorders.

biochemistry↗

bioRxiv2024

Automated microscopic measurement of fibrinaloid microclots and their degradation by nattokinase, the main natto protease

Nattokinase, from the Japanese fermented food natto, is a protease with fibrinolytic activity that can thus degrade conventional blood clots. In some cases, however, including in Long COVID, fibrinogen can polymerise into an anomalous amyloid form to create clots that are resistant to normal fibrinolysis and that we refer to as fibrinaloid microclots. These can be detected with the fluorogenic stain thioflavin T. We describe an automated microscopic technique for the quantification of fibrinaloid microclot formation, which also allows the kinetics of their formation and aggregation to be recorded. We also here show that recombinant nattokinase is effective at degrading the fibrinaloid microclots in vitro. This adds to the otherwise largely anecdotal evidence, that we review, that nattokinase might be anticipated to have value as part of therapeutic treatments for individuals with Long COVID and related disorders that involve fibrinaloid microclots.

biochemistry↗

bioRxiv2023

Hyperparameter optimisation in differential evolution using Summed Local Difference Strings, a rugged but easily calculated landscape for combinatorial search problems

We analyse the effectiveness of differential evolution hyperparameters in large-scale search problems, i.e. those with very many variables or vector elements, using a novel objective function that is easily calculated from the vector/string itself. The objective function is simply the sum of the differences between adjacent elements. For both binary and real-valued elements whose smallest and largest values are min and max in a vector of length N, the value of the objective function ranges between 0 and (N-1) x (max-min) and can thus easily be normalised if desired. This provides for a conveniently rugged landscape. Using this we assess how effectively search varies with both the values of fixed hyperparameters for Differential Evolution and the string length. String length, population size and generations for computational iterations have been studied. Finally, a neural network is trained by systematically varying three hyper-parameters, viz population (NP), mutation factor (F) and crossover rate (CR), and two output target variables are collected (a) median and (b) maximum cost function values from 10-trial experiments. This neural system is then tested on an extended range of data points generated by varying the three parameters on a finer scale to predict both median and maximum function costs. The results obtained from the machine learning model have been validated with actual runs using Pearsons coefficient to justify the reliability to motivate the use of machine learning techniques over grid search for hyper-parameter search for numerical optimisation algorithms. The performance has also been compared with SMAC3 and OPTUNA in addition to grid search and random search.

bioinformatics↗

bioRxiv2021

MassGenie: a transformer-based deep learning method for identifying small molecules from their mass spectra

The inverse problem of mass spectrometric molecular identification ( given a mass spectrum, calculate the molecule whence it came) is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem ( calculate a small molecules likely fragmentation and hence at least some of its mass spectrum from its structure alone) is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the translation a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the true molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are similar to the top hit. In addition to using the top hits directly, we can produce a rank order of these by round-tripping candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. The ability to create and to learn millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.

bioinformatics↗

bioRxiv2020

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

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.

pharmacology and toxicology↗

bioRxiv2020

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

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.

microbiology↗

bioRxiv2020

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

IntroductionIt 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. ObjectivesBlood serum contains many thousands of molecules and provides a convenient source of biologically relevant metabolites. Our objective was to measure them. MethodsWe 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). ResultsOur 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. ConclusionOur 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.

biochemistry↗

bioRxiv2020

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

ObjectiveThe 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. MethodsBlood 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. ResultsConcentrations 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 (-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<0.0001, Odds ratio=22.7). Detection of multiple citrullinated regions within fibrin clot structures in RA patients, which was less prevalent in control samples (p<0.05, OR=2.2). ConclusionPatients with active RA display a coagulation profile that is dissimilar to general findings associated with other inflammatory conditions. The alteration of protein structures by autoimmune linked citrullination could play a role in determining the structure of fibrin and the potential of conferring a heightened thrombotic risk in RA patients.

biochemistry↗

bioRxiv2020

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

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.

bioinformatics↗

bioRxiv2020

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

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.

microbiology↗

bioRxiv2019

Structural similarities between some common fluorophores used in biology and marketed drugs, endogenous metabolites, and natural products

BackgroundIt 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.\n\nResultsThe 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 a significant part of both 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.

bioinformatics↗