Biology subjects

Scott, J. G.

Publications and source records attributed to Scott, J. G..

At least 19 recordsLinked to original sources

bioRxiv2026

Collateral sensitivities with predictive signatures emerge in a novel model of evolved chemoresistance in osteosarcoma

Osteosarcoma (OS) is the most common primary malignant bone tumor in children and adolescents. Despite aggressive multimodal therapy, including surgery and chemotherapy with methotrexate, doxorubicin, and cisplatin (MAP), outcomes for patients with relapsed or refractory disease remain poor, with no standardized second-line regimen. We developed a clinically calibrated, temporally resolved in vitro model to examine how resistance and collateral drug responses evolve during treatment with methotrexate, doxorubicin, and cisplatin (MAP). We modeled the evolution of chemotherapy resistance in vitro by exposing an OS cell line, MG63.3, to clinically relevant cycles of MAP therapy. MAP resistance and collateral drug responses were quantified over time across five independent evolutionary replicates, and three solvent-treated replicates served as controls. We complemented this phenotypic screening with transcriptomic profiling to extract predictive biomarkers of collateral drug response that could be used to personalize second-line treatment in patients with refractory OS. MAP exposure produced progressive resistance to doxorubicin and methotrexate, whereas cisplatin sensitivity remained comparatively stable. Repeated temporal screening against 12 additional drugs and combinations revealed three broad patterns of collateral response: progressive resistance, progressive sensitivity, and non-linear or stochastic change. Etoposide resistance emerged consistently, while sensitivity developed toward palifosfamide-etoposide and gemcitabine-docetaxel. Transcriptomic profiling showed dynamic, replicate-specific evolutionary trajectories rather than a single uniform resistance state. To demonstrate one approach for how our large, paired dataset could be used to translate these findings into potentially actionable clinical tools, we extracted predictive gene expression signatures of collateral drug response. By applying a dynamic, clinically inspired model of chemotherapy resistance in OS, we generated a detailed temporal mapping of collateral drug responses that highlights potential therapeutic windows and can be used inform selection of second-line agents. This approach and the resulting dataset can ultimately support personalized medicine strategies for patients with relapsed or refractory OS.

cancer biology↗

bioRxiv2026

Genomic-Adjusted Radiation Dose from Bulk RNA Sequencing for Personalized Radiotherapy

Radiotherapy is delivered to more than half of all patients with cancer yet is prescribed using uniform physical doses despite well-established interpatient variability in biological response. The genomic-adjusted radiation dose (GARD), derived from the radiosensitivity index (RSI), integrates tumor transcriptomics with radiation dose to estimate patient-specific treatment effect, and has been clinically validated as a predictor of radiotherapy benefit across diverse disease sites, including breast, lung, head and neck, glioma, sarcoma, rectal, and endometrial cancers. However, further clinical validation and deployment has been limited by reliance on microarray-based expression. Here we develop an RNA sequencing-based formulation of RSI (RSI-seq) and show that it preserves the functional properties of the original model across measurement platforms. RSI-seq maintains concordance with microarray RSI, including preservation of patient rank ordering (pooled Spearman{rho} = 0.86), and, when integrated into GARD, reproduces predicted changes in biological effect under clinically relevant dose perturbations (R2 [≥] 0.78 for {Delta}GARD in both directions). This preservation of interventional prediction is robust to expression noise and invariant to normalization strategy, enabling consistent application across RNA-seq pipelines. Application across the TCGA pan-cancer transcriptomic atlas demonstrates scalability across tumor types, with cohort medians agreeing closely with previously published microarray RSI medians (Spearman{rho} = 0.68, Pearson r = 0.85 across 20 matched cohorts). By bringing a clinically validated radiogenomic dose model into the RNA-sequencing era, RSI-seq makes biologically personalized radiotherapy directly accessible, retrospectively in existing RNA-seq cohorts and prospectively in modern clinical sequencing workflows, across the full range of tumor types treated with radiation.

genomics↗

bioRxiv2026

Stochastic Evolutionary Control in Heterogeneous Populations

Therapeutic resistance, which poses a central challenge in cancer and infectious disease treatment, arises from the evolutionary dynamics of heterogeneous populations as a natural consequence of the evolutionary tendency towards higher fitness. Here we introduce SHEPHERD (Stochastic Heterogeneity-informed Evolutionary Policy Hampering the Expansion of Resistance to Drugs), which integrates Wright-Fisher (WF) population genetics modeling with Markov Decision Processes (MDPs) to design optimal adaptive drug policies which reduce the fitness of evolving disease, mitigating resistance. While MDPs have been applied to evolutionary control under the strong-selection, weak-mutation (SSWM) assumption--where populations are effectively homogeneous--SHEPHERD operates beyond this regime, effectively capturing the full stochastic dynamics of genetically heterogeneous populations experiencing mutation and selection of a wide array of strengths. Using synthetic multi-drug fitness landscapes, we show the benefit of the optimized SHEPHERD protocol for in silico disease systems with three, four, and eight genotypes: the long-term mean fitness of the disease is reduced compared to any single-drug or two-drug periodic regimen. Although these examples specifically model drug resistance, we discuss the potential broad applicability of our approach to calculating strategies for controlling a wide class of evolving populations. We further analyze the sensitivity of SHEPHERD strategies to different levels of genotypic frequency discretization and temporal resolution, demonstrating robustness once moderate resolution is achieved, but strong dependence on the timing of drug updates. Although current results rely on synthetic landscapes due to the lack of empirical multi-genotype, multi-drug measurements outside the SSWM regime, our study establishes a foundation for applying MDP optimization to broad classes of evolutionary dynamics, highlighting both the opportunities and the computational challenges of controlling evolution.

evolutionary biology↗

bioRxiv2026

Bifurcation Analysis of Cancer-Immunity Cycle

Despite major advances in cancer immunotherapy, many patients still experience immune escape and disease progression. Understanding the dynamical mechanisms governing the transition between tumor control and immune escape is therefore essential for improving therapeutic strategies. Bifurcation theory and stability analysis provide a mathematical framework for explaining how gradual parameter changes can produce sudden qualitative transitions in biological systems. In the context of tumor-immune interactions, such transitions may correspond to critical thresholds separating immune-limited tumor control from immune-escape behavior. In this study, we investigate a discrete-time model of the cancer-immunity cycle applying the local stability analysis to the system equilibria based on the trace and determinant of the Jacobian matrices. The model incorporates immune suppression through a parameter representing tumor-mediated immune evasion, including mechanisms related to immune checkpoint pathways such as PD-1/PD-L1 signaling. The system exhibits a saddle-node bifurcation associated with the appearance and disappearance of nontrivial equilibria, under weak immune conditions. In addition, under strong immune conditions, the model demonstrates a distinct stability transition in which a stable spiral equilibrium (spiral sink) loses stability and becomes an unstable spiral (spiral source), resulting in the loss of stable tumor-control dynamics before equilibrium disappearance occurs. Additional mathematical analysis and numerical investigations indicate that the system does not generate stable non-equilibrium attractors such as limit cycles over the parameter ranges considered. Consequently, the stable equilibrium remains the only stable attractor in the model, emphasizing the importance of maintaining equilibrium stability for effective immune-mediated tumor suppression. Also, further parameter analyses reveal that both equilibrium existence and stability are highly sensitive near bifurcation boundaries, reflecting the delicate balance between tumor proliferation and immune activation. Overall, this work provides a mathematical framework for distinguishing equilibrium existence from effective tumor control in discrete tumor-immune systems. Beyond the specific model considered, the results highlight the importance of stability analysis in understanding immune escape and may contribute to future approaches in adaptive and personalized immunotherapy modeling.

cancer biology↗

bioRxiv2025

Leveraging Experimental Evolution to Extract Predictive Collateral Drug Response Signatures in Ewings Sarcoma

Therapeutic options for patients with relapsed or refractory Ewings sarcoma (EWS) remain limited. Collateral sensitivity, where resistance to one drug confers sensitivity to another, could be leveraged to optimize chemotherapy for EWS. Gene expression signatures that predict collateral sensitivity states can be used to guide treatment selection in an evolution-informed manner. We experimentally evolved resistance to first-line EWS chemotherapy in cell lines. Throughout, we measured collateral responses across a panel of anticancer drugs and quantified transcriptomic changes. Collateral drug responses varied across replicate evolutionary trajectories, but convergent states of collateral sensitivity emerged across different replicates at different times. By associating these convergent phenotypes with gene expression patterns, we derived a library of predictive signatures for numerous drugs. These signatures accurately distinguished states of collaterally sensitivity from states of collateral resistance within our dataset and were further validated in an independent EWS cell line. Our findings demonstrate that gene expression signatures can predict collateral sensitivity in EWS, providing a foundation for personalized therapeutic strategies. This approach also provides a generalizable workflow for developing predictive biomarkers to guide chemotherapy selection in patients with rare diseases that lack reliable second-line chemotherapy regimens.

cancer biology↗

bioRxiv2025

Using spatial statistics to infer game-theoretic interactions in an agent-based model of cancer cells

Drug resistance in cancer is shaped not only by evolutionary processes but also by eco-evolutionary interactions between tumor subpopulations. These interactions can support the persistence of resistant cells even in the absence of treatment, undermining standard aggressive therapies and motivating drug holiday-based approaches that leverage ecological dynamics. A key challenge in implementing such strategies is efficiently identifying interaction between drug-sensitive and drug-resistant subpopulations. Evolutionary game theory provides a framework for characterizing these interactions. We investigate whether spatial patterns in single time-point images of cell populations can reveal the underlying game theoretic interactions between sensitive and resistant cells. To achieve this goal, we develop an agent-based model in which cell reproduction is governed by local game-theoretic interactions. We compute a suite of spatial statistics on single time-point images from the agent-based model under a range of games being played between cells. We quantify the informativeness of each spatial statistic and demonstrate that a simple machine learning model can classify the type of game being played. Our findings suggest that spatial structure contains sufficient information to infer ecological interactions. This work represents a step toward clinically viable tools for identifying cell-cell interactions in tumors, supporting the development of ecologically informed cancer therapies. Author summaryDrug resistance is a major challenge in cancer treatment, often leading to relapse despite initially successful therapy. While mutations are a key driver, ecological interactions between drug-sensitive and drug-resistant cells also play a critical role. These interactions are complex and dynamic, and few molecular biomarkers exist, making them difficult to study and account for in treatment planning. We use evolutionary game theory, a framework for quantifying interactions between cells, to investigate whether it is possible to infer these interactions using just a single time-point image of the cells. We develop an agent-based model where cells reproduce based on local interactions and quantify the resulting patterns in how cells are distributed across space using a suite of spatial statistics. We find that specific interaction types produce distinct spatial patterns that are evident in these metrics, and we train a simple machine learning model to classify the interaction type based on the metrics. Our results suggest that spatial data alone can offer valuable insights into tumor dynamics, potentially enabling more informed and adaptable cancer treatments based on eco-evolutionary principles.

cancer biology↗

bioRxiv2024

Inverted topologies in sequential fitness landscapes enable evolutionary control

Adaptive populations rarely evolve in a static environment. Therefore, understanding and ultimately controlling the evolution of a population requires consideration of fluctuating selective pressures. The fitness landscape metaphor has long been used as a tool for representing the selective pressures a given environment imposes on a population. Much work has already been done to understand the dynamics of evolution on a single fitness landscape. More recently, evolution on fluctuating or sequentially applied landscapes has come to the fore of evolutionary biology. As more empirical landscapes are described, metrics for describing salient features of paired landscapes will have uses for understanding likely evolutionary dynamics. Currently, Pearson correlation coefficient and collateral sensitivity likelihoods are used to quantify topographical relatedness or dissimilarity of a pair of landscapes. Here, we introduce the edge flip fraction, a new metric for comparing landscapes, which quantifies changes in the directionality of evolution between pairs of fitness landscapes. We demonstrate that the edge flip fraction captures topographical differences in landscapes that traditional metrics may overlook which have important consequences for the trajectories of populations evolving on them. By applying this metric to both empirical and synthetic fitness landscapes, we show that it partially predicts the collateral sensitivity likelihoods and can inform the optimality of drug sequences. We show that optimal drug sequences that keep populations within lower fitness regions require shifts in evolutionary directions, which are quantified by the edge flip fraction. Edge flip fraction complements existing measures and may help researchers understand how populations evolve under changing environmental conditions, and could yield clues in the pursuit of evolutionary control.

evolutionary biology↗

bioRxiv2024

Personalizing chemotherapy drug selection using a novel transcriptomic chemogram

Gene expression signatures predictive of chemotherapeutic response have the potential to greatly extend the reach of precision medicine by allowing medical providers to plan treatment regimens on an individual basis for patients with and without actionable mutations. Most published gene signatures are only capable of predicting response for individual drugs, but currently, a majority of chemotherapy regimens utilize combinations of different agents. We propose a unified framework, called the chemogram, that uses predictive gene signatures to rank the relative predicted sensitivity of different drugs for individual tumor samples. Using this approach, providers could efficiently screen against many therapeutics to identify the drugs that would fit best into a patients treatment plan at any given time. This can be easily reassessed at any point in time if treatment efficacy begins to decline due to therapeutic resistance. To demonstrate the utility of the chemogram, we first extract predictive gene signatures using a previously established method for extracting pan-cancer signatures inspired by convergent evolution. We derived 3 signatures for 3 commonly used cytotoxic drugs (cisplatin, gemcitabine, and 5-fluorouracil). We then used these signatures in our framework to predict and rank sensitivity among the drugs within individual cell lines. To assess the accuracy of our method, we compared the rank order of predicted response to the rank order of observed response (fraction of surviving cells at a standardized dose) against each of the 3 chemotherapies. Across a majority of cancer types, chemogram-generated predictions were consistently more accurate than randomized prediction rankings, as well as prediction rankings made by randomly generated gene signatures. In addition to the chemograms ability to rank relative sensitivity for any given tumor, this framework is easily scalable for any number of drugs for which a predictive signature exists. We repeated the process described above for 10 drugs and found that the accuracy of the predicted sensitivity rankings was maintained as the number of drugs in the chemograms screen increased. Our proposed framework demonstrates the ability of transcriptomic signatures to not only predict chemotherapeutic response but correctly assign rankings of drug sensitivity on an individual basis. With further validation, the chemogram could be easily integrated in a clinical setting, as it only requires gene expression data, which is less expensive than an extensive drug screen and can be performed at scale.

cancer biology↗

bioRxiv2024

Asymmetric Interactions Shape Survival During Population Range Expansions

An organism that is newly introduced into an existing population has a survival probability that is dependent on both the population density of its environment and the competition it experiences with the members of that population. Expanding populations naturally form regions of high and low density, and simultaneously experience ecological interactions both internally and at the boundary of their range. For this reason, systems of expanding populations are ideal for studying the combination of density and ecological effects. Conservation ecologists have been studying the ability of an invasive species to establish for some time, attributing success to both ecological and spatial factors. Similar behaviors have been observed in spatially structured cell populations, such as those found in cancerous tumors and bacterial biofilms. In these scenarios, invasion may be the introduction of a new mutation or bacterial species with some form of drug resistance, leading to the possibility of treatment failure. In order to gain insight into the relationship between population density and ecological interactions, we study an expanding population of interacting wild-type cells and mutant cells. We simulate these interactions in time and study the spatially dependent probability for a mutant to survive or to take over the front of the population wave (gene surfing). Additionally, we develop a mathematical model that describes this survival probability and find agreement when the payoff for the mutant is positive (corresponding to cooperation, exploitation, or commensalism). Given the types of interactions, our model provides insight into the spatial distribution of survival probability. Conversely, given a spatial distribution of survival probabilities, our model provides insight into the types of interactions that were involved to generate it.

evolutionary biology↗

bioRxiv2024

Cell-Cell Fusion in NSCLC Confers a Fitness Benefit Under Drug Selection

Cell-cell fusion has been implicated in various physiological and pathological processes, including cancer progression. This study investigated the role of cell-cell fusion in non-small cell lung cancer (NSCLC), focusing on its contribution to chemoresistance and tumor evolution. By co-culturing drug-sensitive and drug-resistant NSCLC cell lines, we observed spontaneous cell-cell fusion events, particularly under gefitinib selection. These fused cells exhibited enhanced fitness and a higher degree of chemoresistance compared to parental lines across a panel of 12 chemotherapeutic agents. Further analysis, including fluorescence imaging and cell cycle analysis, confirmed nuclear fusion and increased DNA content in the fused cells. Bulk RNA sequencing revealed genomic heterogeneity in fused cells, including enrichment of gene sets associated with cell cycle progression and epithelial-mesenchymal transition, both of which are hallmarks of cancer. These findings demonstrate that cell-cell fusion can act as a novel source of chemotherapeutic resistance and further promote aggressive phenotypes in NSCLC, highlighting the potential of fusion as a therapeutic target.

cancer biology↗

bioRxiv2024

Frequencies of house fly proto-Y chromosomes across populations are predicted by temperature heterogeneity within populations

Sex chromosomes often differ between closely related species and can even be polymorphic within populations. Species with polygenic sex determination segregate for multiple different sex determining loci within populations, making them uniquely informative of the selection pressures that drive the evolution of sex chromosomes. The house fly (Musca domestica) is a model species for studying polygenic sex determination because male determining genes have been identified on all six of the chromosomes, which means that any chromosome can be a "proto-Y" chromosome. In addition, chromosome IV can carry a female-determining locus, making it a W chromosome. The different proto-Y chromosomes are distributed along latitudinal clines on multiple continents, their distributions can be explained by seasonality in temperature, and they have temperature-dependent effects on physiological and behavioral traits. It is not clear, however, how the clinal distributions interact with the effect of seasonality on the frequencies of house fly proto-Y chromosomes across populations. To address this question, we measured the frequencies of house fly Y and W chromosomes across nine populations in the United States of America. We confirmed the clinal distribution along the eastern coast of North America, but it is limited to the eastern coast. In contrast, annual mean daily temperature range is significantly correlated with proto-Y chromosome frequencies across the entire continent. Our results therefore suggest that temperature heterogeneity can explain the distributions of house fly proto-Y chromosomes in a way that does not depend on the cline. These results contribute to our understanding of how ecological factors affect sex chromosome evolution.

evolutionary biology↗

bioRxiv2023

The evolution of diverse antimicrobial responses in vancomycin-intermediate Staphylococcus aureus and its therapeutic implications

Staphylococcus aureus bacteremia is typically treated empirically with vancomycin, with therapy later tailored based on susceptibility results. However, these tests occur before vancomycin exposure and do not account for adaptation during empiric treatment that can alter S. aureus susceptibility to first-line drugs. To investigate these collateral drug responses, we experimentally evolved 18 methicillin-susceptible S. aureus (MSSA) populations under increasing vancomycin concentrations until they achieved intermediate resistance. Genomic sequencing revealed two distinct adaptive pathways characterized by mutations in the WalKR regulon, affecting cell wall metabolism, or rpsU, impacting translational stress responses. These pathways correlated with divergent collateral sensitivity profiles to first-line antibiotics. By developing a Collateral Response Score (CRS), we quantified the probability and magnitude of these responses, demonstrating that evolutionary dynamics critically influence resistance outcomes. Our findings suggest a probabilistic approach to antimicrobial therapy, advocating for rapid genomic diagnostics alongside susceptibility testing to better anticipate and respond to evolutionary changes. SignificanceAntibiotic treatment can influence bacterial evolution, altering the effectiveness of subsequent therapies by inducing collateral resistance or sensitivity. This study reveals that evolution toward vancomycin-intermediate resistance in the pathogen Staphylococcus aureus proceeds through at least two distinct evolutionary pathways: one characterized by alterations in cell wall metabolism and another by changes in global stress response. These adaptive trajectories result in contrasting collateral sensitivities to first-line antibiotics. By introducing the Collateral Response Score, we assess the uncertainty in these outcomes, providing a probabilistic framework to evaluate how past antibiotic exposure shapes future treatment responses. Further validation studies are needed; however, we believe that improved forecasting of pathogen evolution can enhance antibiotic stewardship, inform therapeutic decisions, and ultimately improve patient outcomes.

evolutionary biology↗

bioRxiv2023

Alamar Blue assay optimization to minimize druginterference and inter-assay viability

Alamar Blue (AB) has become an increasingly popular reagent of choice for cell viability assays. We chose AB over other reagents such as MTT and Cell-Titer Glo due to its cost effectiveness and its ability to be a nondestructive assay. While analyzing the effect of osimertinib, an EGFR inhibitor on the non-small cell lung cancer cell line, PC-9, we noticed unexpected right-shifts of dose response curves as compared to the curve obtained by Cell Titer Glo assay. Here, we describe our modified AB assay method to avoid right shift right shift in dose response curve. Unlike some of the redox drugs that were reported to directly affected AB reading, osimertinib itself did not directly increase AB reading. Yet, the removal of the drug containing medium prior to AB addition eliminated falsely increased reading giving comparable dose response curve as the one determined by Cell Titer Glo assay. When a panel of 11 drugs were assessed, we found that this modified AB assay eliminated unexpected similar right shifts detected in other epidermal growth factor receptor (EGFR) inhibitors. We also found that plate-to-plate variability can be minimized by adding an appropriate concentration of rhodamine B solution to the assay plates to calibrate fluorimeter sensitivity. This calibration method also enables a continuous longitudinal assay to monitor cell growth or recovery from drug toxicity over time. Our new modified AB assay is expected to provide accurate in vitro measurement of EGFR targeted therapies.

cell biology↗

bioRxiv2023

Frequency-dependent ecological interactions increase the prevalence and shape the distribution of pre-existing drug resistance

The evolution of resistance remains one of the primary challenges for modern medicine from infectious diseases to cancers. Many of these resistance-conferring mutations often carry a substantial fitness cost in the absence of treatment. As a result, we would expect these mutants to undergo purifying selection and be rapidly driven to extinction. Nevertheless, pre-existing resistance is frequently observed from drug-resistant malaria to targeted cancer therapies in non-small cell lung cancer (NSCLC) and melanoma. Solutions to this apparent paradox have taken several forms from spatial rescue to simple mutation supply arguments. Recently, in an evolved resistant NSCLC cell line, we found that frequency-dependent ecological interactions between ancestor and resistant mutant ameliorate the cost of resistance in the absence of treatment. Here, we hypothesize that frequency-dependent ecological interactions in general play a major role in the prevalence of pre-existing resistance. We combine numerical simulations with robust analytical approximations to provide a rigorous mathematical framework for studying the effects of frequency-dependent ecological interactions on the evolutionary dynamics of pre-existing resistance. First, we find that ecological interactions significantly expand the parameter regime under which we expect to observe pre-existing resistance. Next, even when positive ecological interactions between mutants and ancestors are rare, these resistant clones provide the primary mode of evolved resistance because even weak positive interaction leads to significantly longer extinction times. We then find that even in the case where mutation supply alone is sufficient to predict pre-existing resistance, frequency-dependent ecological forces still contribute a strong evolutionary pressure that selects for increasingly positive ecological effects (negative frequency-dependent selection). Finally, we genetically engineer several of the most common clinically observed resistance mechanisms to targeted therapies in NSCLC, a treatment notorious for pre-existing resistance. We find that each engineered mutant displays a positive ecological interaction with their ancestor. As a whole, these results suggest that frequency-dependent ecological effects can play a crucial role in shaping the evolutionary dynamics of pre-existing resistance.

evolutionary biology↗

bioRxiv2023

Fitness seascapes promote genetic heterogeneity through spatiotemporally distinct mutant selection windows

Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N-allele fitness seascapes allow for N *2N-1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more accurately reflect the selection fo drug resistant genotypes. Furthermore, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine. Author SummaryDrug resistance in infectious disease and cancer is a major driver of mortality. While undergoing treatment, the population of cells in a tumor or infection may evolve the ability to grow despite the use of previously effective drugs. Researchers hypothesize that the spatial organization of these disease populations may contribute to drug resistance. In this work, we analyze how spatial gradients of drug concentration impact the evolution of drug resistance. We consider a decades-old model called the mutant selection window (MSW), which describes the drug concentration range that selects for drug-resistant cells. We show how extending this model with continuous dose-response data, which describes how different types of cells respond to drug, improves the ability of MSWs to predict evolution. This work helps us understand how the spatial organization of cells, such as the organization of blood vessels within a tumor, may promote drug resistance. In the future, we may use these methods to optimize drug dosing to prevent resistance or leverage known vulnerabilities of drug-resistant cells.

evolutionary biology↗

bioRxiv2023

Crosstalkr: An open-source R package to facilitatedrug target identification.

In the last few decades, interest in graph-based analysis of biological networks has grown substantially. Protein-protein interaction networks are one of the most common biological networks, and represent the molecular relationships between every known protein and every other known protein. Integration of these interactomic data into bioinformatic pipelines may increase the translational potential of discoveries made through analysis of multi-omic datasets. Crosstalkr provides a unified toolkit for drug target and disease subnetwork identification, two of the most common uses of protein protein interaction networks. First, crosstalkr enables users to download and leverage high-quality protein-protein interaction networks from online repositories. Users can then filter these large networks into manageable subnetworks using a variety of methods. For example, network filtration can be done using random walks with restarts, starting at the user-provided seed proteins. Affinity scores from a given random walk with restarts are compared to a bootstrapped null distribution to assess statistical significance. Random walks are implemented using sparse matrix multiplication to facilitate fast execution. Next, users can perform in-silico repression experiments to assess the relative importance of nodes in their network. At this step, users can supply protein or gene expression data to make node rankings more meaningful. The default behavior evaluates the human interactome. However, users can evaluate more than 1000 non-human protein-protein interaction networks as a result of integration with StringDB. It is a free, open-source R package designed to allow users to integrate functional analysis using the protein-protein interaction network into existing bioinformatic pipelines. A beta version of crosstalkr available on CRAN (https://cran.rstudio.com/web/packages/crosstalkr/index.html).

bioinformatics↗

bioRxiv2023

Cuticular profiling of insecticide resistant Aedes aegypti

Insecticides have made great strides in reducing the global burden of vector-borne disease. Nonetheless, serious public health concerns remain because insecticide-resistant vector populations continue to spread globally. To circumvent insecticide resistance, it is essential to understand all contributing mechanisms. Contact-based insecticides are absorbed through the insect cuticle, which is comprised mainly of chitin polysaccharides, cuticular proteins, hydrocarbons, and phenolic biopolymers sclerotin and melanin. Cuticle interface alterations can slow or prevent insecticide penetration in a phenomenon referred to as cuticular resistance. Cuticular resistance characterization of the yellow fever mosquito, Aedes aegypti, is lacking. In the current study, we utilized solid-state Nuclear Magnetic Resonance (ssNMR) spectroscopy, gas chromatography/mass spectrometry (GC-MS), and transmission electron microscopy (TEM) to gain insights into the cuticle composition of congenic cytochrome P450 monooxygenase insecticide resistant and susceptible Ae. aegypti. No differences in cuticular hydrocarbon content or phenolic biopolymer deposition were found. In contrast, we observed cuticle thickness of insecticide resistant Ae. aegypti increased over time and exhibited higher polysaccharide abundance. Moreover, we found these local cuticular changes correlated with global metabolic differences in the whole mosquito, suggesting the existence of novel cuticular resistance mechanisms in this major disease vector.

biochemistry↗

bioRxiv2023

Reinforcement Learning informs optimal treatmentstrategies to limit antibiotic resistance.

Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 {beta} -lactam antibiotics with which to treat the simulated E. coli population, we demonstrate that RL agents outperform two naive treatment paradigms at minimizing the population fitness over time. We also show that RL agents approach the performance of the optimal drug cycling policy. Even when stochastic noise is introduced to the measurements of population fitness, we show that RL agents are capable of maintaining evolving populations at lower growth rates compared to controls. We further tested our approach in arbitrary fitness landscapes of up to 1024 genotypes. We show that minimization of population fitness using drug cycles is not limited by increasing genome size. Our work represents a proof-of-concept for using AI to control complex evolutionary processes.

microbiology↗