Biology subjects

Scott, J.

Publications and source records attributed to Scott, J..

11 recordsLinked to original sources

bioRxiv2018

Pseudomonas aeruginosa Can Inhibit Growth of Streptococcal Species via Iron Sequestration

Cystic Fibrosis (CF) is a genetic disease that causes patients to accumulate thick, dehydrated mucus in the lung and develop chronic, polymicrobial infections due to reduced mucociliary clearance. These chronic polymicrobial infections and subsequent decline in lung function are significant factors in the morbidity and mortality of CF. Pseudomonas aeruginosa and Streptococcus spp. are among the most prevalent organisms in the CF lung; the presence of P. aeruginosa correlates with lung function decline and the Streptococcus milleri group (SMG), a subgroup of the viridans streptococci, is associated with exacerbations in patients with CF. Here we characterize the interspecies interactions that occur between these two genera. We demonstrated that multiple P. aeruginosa laboratory strains and clinical CF isolates promote the growth of multiple SMG strains and oral streptococci in an in vitro coculture system. We investigated the mechanism by which P. aeruginosa enhances growth of streptococci by screening for mutants of P. aeruginosa PA14 unable to enhance Streptococcus growth, and we identified the P. aeruginosa pqsL::TnM mutant, which failed to promote growth of S. constellatus and S. sanguinis. Characterization of the P. aeruginosa {Delta}pqsL mutant revealed that this strain cannot promote Streptococcus growth. Our genetic data and growth studies support a model whereby the P. aeruginosa {Delta}pqsL mutant overproduces siderophores, and thus likely outcompetes Streptococcus sanguinis for limited iron. We propose a model whereby competition for iron represents one important means of interaction between P. aeruginosa and Streptococcus spp.\n\nImportanceCystic fibrosis (CF) lung infections are increasingly recognized for their polymicrobial nature. These polymicrobial infections may alter the biology of the organisms involved in CF-related infections, leading to changes in growth, virulence and/or antibiotic tolerance, and could thereby affect patient health and response to treatment. In this study, we demonstrate interactions between P. aeruginosa and streptococci using a coculture model, and show that one interaction between these microbes is likely competition for iron. Thus, these data indicate that one CF pathogen may influence the growth of another and add to our limited knowledge of polymicrobial interactions in the CF airway.

microbiology↗

bioRxiv2018

Hypoxia increases the tempo of evolution in the peri-necrotic niche in glioblastoma

BackgroundLow oxygen in tumours have long been associated with poor prognosis and metastatic disease, precise reasons for which remain poorly understood. Somatic evolution drives cancer progression and treatment resistance. This process is fuelled not only by genetic and epigenetic mutation, but by selection resulting from the interactions between tumour cells, normal cells and physical microenvironment. The ecological habitat tumour cells inhabit influences evolutionary dynamics but impact on tempo of evolution is less clear. MethodsWe explored this complex dialogue with a combined clinical-theoretical approach. Using an agent-based-model, we simulated proliferative hierarchy under heterogeneous oxygen availability. Predictions were compared against clinical data derived from histology samples taken from glioblastoma patients, stained to elucidate areas of hypoxia / necrosis, and p53 expression heterogeneity. ResultsSimulation results indicate cell division in hypoxic environments is effectively upregulated, and that low-oxygen niches provide new avenues for tumour cells to spread. Analysis of human data indicates cell division isnt decreased in low-oxygen regions, despite evidence of significant physiological stress. This is consistent with simulation, suggesting hypoxia is a crucible that effectively warping evolutionary velocity, making deleterious mutations more likely than in well-oxygenated regions. ConclusionsResults suggest hypoxic regions alter evolutionary tempo, driving mutations which fuel tumour heterogeneity..

cancer biology↗

bioRxiv2018

Signatures of insecticide selection in the genome of Drosophila melanogaster.

Resistance to insecticides has evolved in multiple insect species, leading to increased application rates and even control failures. Understanding the genetic basis of insecticide resistance is fundamental for mitigating its impact on crop production and disease control. We performed a GWAS approach with the Drosophila Genetic Reference Panel (DGRP) to identify the mutations involved in resistance to two widely used classes of insecticides: organophosphates (OPs, parathion) and pyrethroids (deltamethrin). Most variation in parathion resistance was associated with mutations in the target gene Ace, while most variation in deltamethrin resistance was associated with mutations in Cyp6a23, a gene encoding a detoxification enzyme never previously associated with resistance. A \"nested GWAS\" further revealed the contribution of other loci: Dscam1 and trpl were implicated in resistance to parathion, but only in lines lacking Wolbachia. Cyp6a17, the paralogous gene of Cyp6a23, and CG7627, an ATP-binding cassette transporter, were implicated in deltamethrin resistance. We observed signatures of recent selective sweeps at all of these resistance loci and confirmed that the soft sweep at Ace is indeed driven by the identified resistance mutations. Analysis of allele frequencies in additional population samples revealed that most resistance mutations are segregating across the globe, but that frequencies can vary substantially among populations. Altogether, our data reveal that the widely used OP and pyrethroid insecticides imposed a strong selection pressure on natural insect populations. However, it remains unclear why, in Drosophila, resistance evolved due to changes in the target site for OPs, but due to a detoxification enzyme for pyrethroids.\n\nArticle summaryInsecticides are widely used to control pests and insect vectors of disease. In response to the strong selection pressure exerted by insecticides, resistance has evolved in most insect species. We identified few genes present in several Drosophila melanogaster natural populations implicated in the evolution of resistance against two insecticides widely used today. We identified primary and secondary genes involved in the resistance. Surprisingly, resistance evolved in the target site for one insecticide, but was associated to changes in a novel detoxification enzyme for the other insecticide.

evolutionary biology↗

bioRxiv2018

Microbiome inhibition of IRAK-4 by trimethylamine mediates metabolic and immune benefits in high-fat-diet-induced insulin resistance

The global type 2 diabetes epidemic is a major health crisis and there is a critical need for innovative strategies to fight it. Although the microbiome plays important roles in the onset of insulin resistance (IR) and low-grade inflammation, the microbial compounds regulating these phenomena remain to be discovered. Here, we reveal that the microbiome inhibits a central kinase, eliciting immune and metabolic benefits. Through a series of in vivo experiments based on choline supplementation, blocking trimethylamine (TMA) production then administering TMA, we demonstrate that TMA decouples inflammation and IR from obesity in the context of high-fat diet (HFD) feeding. Through in vitro kinome screens, we reveal TMA specifically inhibits Interleukin-1 Receptor-associated Kinase 4 (IRAK4), a central kinase integrating signals from various toll-like receptors and cytokine receptors. TMA blunts TLR4 signalling in primary human hepatocytes and peripheral blood monocytic cells, and improves mouse survival after a lipopolysaccharide-induced septic shock. Consistent with this, genetic deletion and chemical inhibition of IRAK4 result in similar metabolic and immune improvements in HFD. In summary, TMA appears to be a key microbial compound inhibiting IRAK4 and mediating metabolic and immune effects with benefits upon HFD. Thereby we highlight the critical contribution of the microbial signalling metabolome in homeostatic regulation of host disease and the emerging role of the kinome in microbial-mammalian chemical crosstalk.

systems biology↗

bioRxiv2017

sigQC: A procedural approach for standardising the evaluation of gene signatures

With the increase in next generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools, poised to make a large impact on the diagnosis, management and prognosis for a number of diseases. Increasingly, it is becoming necessary to determine whether a gene expression signature may apply to a dataset, but no standard quality control methodology exists. In this work, we introduce the first protocol, implemented in an R package sigQC, enabling a streamlined methodological and standardised approach for the quality control validation of gene signatures on independent data sets. The emphasis in this work is in showing the critical quality control steps involved in the generation of a clinically and biologically useful, transportable gene signature, including ensuring sufficient expression, variability, and autocorrelation of a signature. We demonstrate the application of the protocol in this work, showing how the outputs created from sigQC may be used for the evaluation of gene signatures on large-scale gene expression data in cancer.

bioinformatics↗

bioRxiv2017

Optimal Therapy Scheduling Based on a Pair of Collaterally Sensitive Drugs

Despite major strides in the treatment of cancer, the development of drug resistance remains a major hurdle. One strategy which has been proposed to address this is the sequential application of drug therapies where resistance to one drug induces sensitivity to another drug, a concept called collateral sensitivity. The optimal timing of drug switching in these situations, however, remains unknown.\n\nTo study this, we developed a dynamical model of sequential therapy on heterogeneous tumors comprised of resistant and sensitive cells. A pair of drugs (DrugA, DrugB) are utilized and are periodically switched during therapy. Assuming resistant cells to one drug are collaterally sensitive to the opposing drug, we classified cancer cells into two groups, AR and BR, each of which is a subpopulation of cells resistant to the indicated drug and concurrently sensitive to the other, and we subsequently explored the resulting population dynamics.\n\nSpecifically, based on a system of ordinary differential equations for AR and BR, we determined that the optimal treatment strategy consists of two stages: an initial stage in which a chosen effective drug is utilized until a specific time point, T, and a second stage in which drugs are switched repeatedly, during which each drug is used for a relative duration (i.e. f{Delta}t-long for DrugA and (1 - f) {Delta}t-long for DrugB with 0 [≤] f [≤] 1 and {Delta}t [≥] 0). We prove that the optimal duration of the initial stage, in which the first drug is administered, T, is shorter than the period in which it remains effective in decreasing the total population, contrary to current clinical intuition.\n\nWe further analyzed the relationship between population makeup, [Formula], and the effect of each drug. We determine a critical ratio, which we term [Formula], at which the two drugs are equally effective. As the first stage of the optimal strategy is applied, [Formula] changes monotonically to [Formula] and then, during the second stage, remains at [Formula] thereafter.\n\nBeyond our analytic results, we explored an individual based stochastic model and presented the distribution of extinction times for the classes of solutions found. Taken together, our results suggest opportunities to improve therapy scheduling in clinical oncology.

cancer biology↗

bioRxiv2017

Collateral sensitivity is contingent on the repeatability of evolution

Antibiotic resistance represents a growing health crisis that necessitates the immediate discovery of novel treatment strategies. One such strategy is the identification of collateral sensitivities, wherein evolution under a first drug induces susceptibility to a second. Here, we report that sequential drug regimens derived from in vitro evolution experiments may have overstated therapeutic benefit, predicting a collaterally sensitive response where cross resistance ultimately occurs. We quantify the likelihood of this phenomenon by use of a mathematical model parametrised with combinatorially complete fitness landscapes for Escherichia coli. Through experimental evolution we then verify that a second drug can indeed stochastically exhibit either increased susceptibility or increased resistance when following a first. Genetic divergence is confirmed as the driver of this differential response through targeted and whole genome sequencing. Taken together, these results highlight that the success of evolutionarily-informed therapies is predicated on a rigorous probabilistic understanding of the contingencies that arise during the evolution of drug resistance.

evolutionary biology↗

bioRxiv2017

Temporally-Feathered Intensity Modulated Radiation Therapy: A technique to reduce normal tissue toxicity

PurposeIntensity modulated radiation therapy (IMRT) has allowed optimization of three-dimensional spatial radiation dose distributions permitting target coverage while reducing normal tissue toxicity. However, radiation-induced normal tissue toxicity is a major contributor to patients quality of life and often a dose-limiting factor in the definitive treatment of cancer with radiation therapy. We propose the next logical step in the evolution of IMRT using canonical radiobiological principles, optimizing the temporal dimension through which radiation therapy is delivered to further reduce radiation-induced toxicity by increased time for normal tissue recovery. We term this novel treatment planning strategy \"temporally feathered radiotherapy\" (TFRT).\n\nMethodsTFRT plans were generated as a composite of five simulated treatment plans each with altered constraints on particular hypothetical organs at risk (OARs) to be delivered sequentially. For each of these TFRT plans, OARs chosen for feathering receive higher doses while the remaining OARs receive lower doses than the standard fractional dose delivered in a conventional fractionated IMRT plan. Each TFRT plan is delivered a specific weekday, which in effect leads to a higher dose once weekly followed by four lower fractional doses to each temporally feathered OAR. We compared normal tissue toxicity between TFRT and conventional fractionated IMRT plans by using a dynamical mathematical model to describe radiation-induced tissue damage and repair over time.\n\nResultsModel-based simulations of TFRT demonstrated potential for reduced normal tissue toxicity compared to conventionally planned IMRT. The sequencing of high and low fractional doses delivered to OARs by TFRT plans suggested increased normal tissue recovery, and hence less overall radiation-induced toxicity, despite higher total doses delivered to OARs compared to conventional fractionated IMRT plans. The magnitude of toxicity reduction by TFRT planning was found to depend on the corresponding standard fractional dose of IMRT and organ-specific recovery rate of sublethal radiation-induced damage.\n\nConclusionsTFRT is a novel technique for treatment planning and optimization of therapeutic radiotherapy that considers the non-linear aspects of tissue repair to optimize toxicity profiles. Model-based simulations of TFRT to carefully conceptualized clinical cases have demonstrated potential for normal tissue toxicity reduction in a previously described dynamical model of normal tissue complication probability (NTCP).

biophysics↗

bioRxiv2016

MultiCellDS: a standard and a community for sharing multicellular data

Cell biology is increasingly focused on cellular heterogeneity and multicellular systems. To make the fullest use of experimental, clinical, and computational efforts, we need standardized data formats, community-curated \"public data libraries\", and tools to combine and analyze shared data. To address these needs, our multidisciplinary community created MultiCellDS (MultiCellular Data Standard): an extensible standard, a library of digital cell lines and tissue snapshots, and support software. With the help of experimentalists, clinicians, modelers, and data and library scientists, we can grow this seed into a community-owned ecosystem of shared data and tools, to the benefit of basic science, engineering, and human health.

systems biology↗

bioRxiv2016

MultiCellDS: a community-developed standard for curating microenvironment-dependent multicellular data

Exchanging and understanding scientific data and their context represents a significant barrier to advancing research, especially with respect to information siloing. Maintaining information provenance and providing data curation and quality control help overcome common concerns and barriers to the effective sharing of scientific data. To address these problems in and the unique challenges of multicellular systems, we assembled a panel composed of investigators from several disciplines to create the MultiCellular Data Standard (MultiCellDS) with a use-case driven development process. The standard includes (1) digital cell lines, which are analogous to traditional biological cell lines, to record metadata, cellular microenvironment, and cellular phenotype variables of a biological cell line, (2) digital snapshots to consistently record simulation, experimental, and clinical data for multicellular systems, and (3) collections that can logically group digital cell lines and snapshots. We have created a MultiCellular DataBase (MultiCellDB) to store digital snapshots and the 200+ digital cell lines we have generated. MultiCellDS, by having a fixed standard, enables discoverability, extensibility, maintainability, searchability, and sustainability of data, creating biological applicability and clinical utility that permits us to identify upcoming challenges to uplift biology and strategies and therapies for improving human health.

systems biology↗

bioRxiv2016

Harnessing the lymphocyte meta-phenotype to optimize adoptive cell therapy

There is an urgent need for reliable effective therapy for patients with metastatic sarcoma. Approaches that manipulate the immune system have shown promise for patients with advanced, widely disseminated malignancies. One of these approaches is adoptive cell therapy (ACT), where tumor-infiltrating lymphocytes (TIL) are isolated from the tumor, expanded ex vivo, and then transferred back to the patient. This approach has shown great promise in melanoma, leading to an objective response in approximately half of treated patients [14]. Standard protocols involve characterization of TIL populations with respect to adaptive CD4+ and CD8+ T-lymphocytes, but neglect the possible role of the innate lymphoid repertoire. Due to toxicity and the high cost associated with ACT, the IFN-{gamma} release assay is currently used as a proxy to identify suitable TIL isolates for ACT. Efforts in TIL-ACT for sarcoma, which are pre-clinical and pioneered at Moffitt Cancer Center, have shown that only a minority of the TIL cultures show tumor specific activity in ex vivo IFN-{gamma} assays. Surprisingly, internal melanoma trial data reveal a lack of correlation between IFN-{gamma} assay and clinical outcomes, highlighting the need for a more reliable proxy. We hypothesize the existence of a predictable TIL meta-phenotype that leads to optimal tumor response. Here, we describe preliminary efforts to integrate prospective and existing patient data with mathematical models to optimize the TIL meta-phenotype prior to re-injection.

cancer biology↗