Biology subjects

Weaver, D. T.

Publications and source records attributed to Weaver, D. T..

6 recordsLinked to original sources

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

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↗

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

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↗

bioRxiv2019

Network potential identifies therapeutic miRNA cocktails in Ewings Sarcoma

MicroRNA (miRNA)-based therapies are an emerging class of targeted therapeutics with many potential applications. Ewing Sarcoma patients could benefit dramatically from personalized miRNA therapy due to inter-patient heterogeneity and a lack of druggable (to this point) targets. However, because of the broad effects miRNAs may have on different cells and tissues, trials of miRNA therapies have struggled due to severe toxicity and unanticipated immune response. In order to overcome this hurdle, a network science-based approach is well-equipped to evaluate and identify miRNA candidates and combinations of candidates for the repression of key oncogenic targets while avoiding repression of essential housekeeping genes. We first characterized 6 Ewing sarcoma cell lines using mRNA sequencing. We then estimated a measure of tumor state, which we term network potential, based on both the mRNA gene expression and the underlying protein-protein interaction network in the tumor. Next, we ranked mRNA targets based on their contribution to network potential. We then identified miRNAs and combinations of miRNAs that preferentially act to repress mRNA targets with the greatest influence on network potential. Our analysis identified TRIM25, APP, ELAV1, RNF4, and HNRNPL as ideal mRNA targets for Ewing sarcoma therapy. Using predicted miRNA-mRNA target mappings, we identified miR-3613-3p, let-7a-3p, miR-300, miR-424-5p, and let-7b-3p as candidate optimal miRNAs for preferential repression of these targets. Ultimately, our work, as exemplified in the case of Ewing sarcoma, describes a novel pipeline by which personalized miRNA cocktails can be designed to maximally perturb gene networks contributing to cancer progression. Author SummaryPrecision medicine in cancer aims to find the right treatment, for the right patient, at the right time. Substantial variation between patient tumors, even of the same disease site, has limited the application of precision medicine in the clinic. In this study, we present novel computational tools for the identification of targets for cancer therapy using widely available sequencing data. We used a network-science based approach that leveraged multiple types of omic data to identify functionally relevant disease targets. Further, we developed algorithms to identify potential miRNA-based therapies that inhibit these predicted disease targets. We applied this pipeline to a novel Ewing Sarcoma transcriptomics data-set as well as publicly available patient data from the St. Jude Cloud. We identified a number of promising therapeutic targets for this rare disease, including EWSR1, the proposed driver of Ewing Sarcoma development. These novel data and methods will provide researchers with new tools for the development of precision medicine treatments in a variety of cancer systems.

systems biology↗

bioRxiv2019

FAK activity sustains intrinsic and acquired ovarian cancer resistance to platinum chemotherapy

Gene copy number changes, cancer stem cell (CSC) increases, and platinum chemotherapy resistance contribute to poor prognosis in patients with recurrent high grade serous ovarian cancer (HGSOC). CSC phenotypes involving Wnt-{beta}-catenin and aldehyde dehydrogenase activities, platinum resistance, and tumor initiating frequency are here associated with spontaneous genetic gains, including genes encoding KRAS, MYC and FAK, in a new murine model of ovarian cancer (KMF). Noncanonical FAK signaling was sufficient to sustain human and KMF tumorsphere proliferation, CSC survival, and platinum resistance. Increased FAK tyrosine phosphorylation occurred in HGSOC patient tumors surviving neo-adjuvant platinum and paclitaxel chemotherapy and platinum resistant tumorspheres acquired FAK dependence for growth. Importantly, combining a pharmacologic FAK inhibitor with platinum overcame chemoresistance and triggered apoptosis in vitro and in vivo. Knockout, rescue, genomic and transcriptomic analyses collectively identified more than 400 genes regulated along a FAK/{beta}-catenin/Myc axis impacting stemness and DNA repair in HGSOC, with 66 genes gained in a majority of Cancer Genome Atlas samples. Together, these results support combinatorial testing of FAK inhibitors for the treatment of recurrent ovarian cancer.\n\nGraphical Summary\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC=\"FIGDIR/small/594184_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (52K):\norg.highwire.dtl.DTLVardef@20c737org.highwire.dtl.DTLVardef@1f714faorg.highwire.dtl.DTLVardef@823560org.highwire.dtl.DTLVardef@7e6219_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LIHigh grade serous ovarian carcinoma tumors contain PTK2 (FAK) 8q24.3 gains associated with prognostic differences.\nC_LIO_LIKMF, a new murine ovarian cancer model with K-Ras, Myc, and FAK gene gains and intrinsic platinum resistance.\nC_LIO_LIFAK activation in tumors surviving platinum chemotherapy promotes cancer stem cell survival.\nC_LIO_LIFAK facilitates a {beta}-catenin-Myc signaling axis controlling gene expression supporting platinum resistance.\nC_LIO_LIFAK activity is essential for KMF tumor growth and is a targetable cellular adaptation of platinum resistance.\nC_LI

cancer biology↗