Mathematical modeling identifies clotting factor combinations that modify thrombin generation in normal and factor VIII-, IX-, or XI-deficient blood.

Background: In healthy individuals, plasma levels of clotting proteins naturally vary within a range of 50% to 150% of their mean values. We do not know how these variations modify thrombin generation. Objectives: To assess the impact of protein level variations on simulated thrombin generation in normal and factor (F)VIII-, FIX-, or FXI-deficient blood. Methods: We used a mathematical model of flow-mediated coagulation to simulate thrombin generation with all possible combinations of clotting protein variations within the normal range and for various tissue factor levels. We selected, analyzed, and ranked combinations that enhanced thrombin generation compared with baseline. Results: Protein variations most strongly affected thrombin generation at intermediate tissue factor levels. Low tissue factor levels prevented coagulation initiation, while high tissue factor levels always triggered thrombin generation. At intermediate levels, we identified protein variations that substantially modified thrombin generation. Low-normal FV shortened lag times and increased thrombin generation, whereas high-normal FV lengthened lag times and reduced thrombin generation. With severe FVIII and FIX deficiencies, low-normal tissue factor pathway inhibitor α and antithrombin amplified the effect of low-normal FV. For moderate FVIII and FIX deficiencies, high-normal tissue factor pathway inhibitor α and antithrombin enhanced the impact of high-normal FV in reducing thrombin production. In normal and FXI-deficient blood, high-normal FVIII and FIX significantly boosted thrombin generation. Conclusion: Our mathematical model predicted how variations in clotting protein levels, within the normal range, could contribute to the variability of bleeding phenotypes observed with clotting factor deficiencies. Our study generated experimentally testable hypotheses that could aid in developing new therapies toward normal hemostasis.

Multiple aspects of amyloid dynamics in vivo integrate to establish prion variant dominance in yeast.

Prion variants are self-perpetuating conformers of a single protein that assemble into amyloid fibers and confer unique phenotypic states. Multiple prion variants can arise, particularly in response to changing environments, and interact within an organism. These interactions are often competitive, with one variant establishing phenotypic dominance over the others. This dominance has been linked to the competition for non-prion state protein, which must be converted to the prion state via a nucleated polymerization mechanism. However, the intrinsic rates of conversion, determined by the conformation of the variant, cannot explain prion variant dominance, suggesting a more complex interaction. Using the yeast prion system [PSI+ ], we have determined the mechanism of dominance of the [PSI+ ]Strong variant over the [PSI+ ]Weak variant in vivo. When mixed by mating, phenotypic dominance is established in zygotes, but the two variants persist and co-exist in the lineage descended from this cell. [PSI+ ]Strong propagons, the heritable unit, are amplified at the expense of [PSI+ ]Weak propagons, through the efficient conversion of soluble Sup35 protein, as revealed by fluorescence photobleaching experiments employing variant-specific mutants of Sup35. This competition, however, is highly sensitive to the fragmentation of [PSI+ ]Strong amyloid fibers, with even transient inhibition of the fragmentation catalyst Hsp104 promoting amplification of [PSI+ ]Weak propagons. Reducing the number of [PSI+ ]Strong propagons prior to mating, similarly promotes [PSI+ ]Weak amplification and conversion of soluble Sup35, indicating that template number and conversion efficiency combine to determine dominance. Thus, prion variant dominance is not an absolute hierarchy but rather an outcome arising from the dynamic interplay between unique protein conformations and their interactions with distinct cellular proteostatic niches.

Modeling the distribution of enzymes on lipid vesicles: A novel framework for surface-mediated reactions in coagulation.

Blood coagulation is a network of biochemical reactions wherein dozens of proteins act collectively to initiate a rapid clotting response. Coagulation reactions are lipid-surface dependent, and this dependence is thought to help localize coagulation to the site of injury and enhance the association between reactants. Current mathematical models of coagulation either do not consider lipid as a variable or do not agree with experiments where lipid concentrations were varied. Since there is no analytic rate law that depends on lipid, only apparent rate constants can be derived from enzyme kinetic experiments. We developed a new mathematical framework for modeling enzymes reactions in the presence of lipid vesicles. Here the concentrations are such that only a fraction of the vesicles harbor bound enzymes and the rest remain empty. We call the lipid vesicles with and without enzyme TF:VIIa+ and TF:VIIa- lipid, respectively. Since substrate binds to both TF:VIIa+ and TF:VIIa- lipid, our model shows that excess empty lipid acts as a strong sink for substrate. We used our framework to derive an analytic rate equation and performed constrained optimization to estimate a single, global set of intrinsic rates for the enzyme-substrate pair. Results agree with experiments and reveal a critical lipid concentration where the conversion rate of the substrate is maximized, a phenomenon known as the template effect. Next, we included product inhibition of the enzyme and derived the corresponding rate equations, which enables kinetic studies of more complex reactions. Our combined experimental and mathematical study provides a general framework for uncovering the mechanisms by which lipid mediated reactions impact coagulation processes.

Host tracheal and intestinal microbiomes inhibit Coccidioides growth in vitro.

Coccidioidomycosis, also known as Valley fever, is a disease caused by the fungal pathogen Coccidioides. Unfortunately, patients are often misdiagnosed with bacterial pneumonia, leading to inappropriate antibiotic treatment. The soil Bacillus subtilis-like species exhibits antagonistic properties against Coccidioides in vitro; however, the antagonistic capabilities of host microbiota against Coccidioides are unexplored. We sought to examine the potential of the tracheal and intestinal microbiomes to inhibit the growth of Coccidioides in vitro. We hypothesized that an uninterrupted lawn of microbiota obtained from antibiotic-free mice would inhibit the growth of Coccidioides, while partial in vitro depletion through antibiotic disk diffusion assays would allow a niche for fungal growth. We observed that the microbiota grown on 2×GYE (GYE) and Columbia colistin and nalidixic acid with 5% sheep's blood agar inhibited the growth of Coccidioides, but microbiota grown on chocolate agar did not. Partial depletion of the microbiota through antibiotic disk diffusion revealed diminished inhibition and comparable growth of Coccidioides to controls. To characterize the bacteria grown and identify potential candidates contributing to the inhibition of Coccidioides, 16S rRNA sequencing was performed on tracheal and intestinal agar cultures and murine lung extracts. We found that the host bacteria likely responsible for this inhibition primarily included Lactobacillus and Staphylococcus. The results of this study demonstrate the potential of the host microbiota to inhibit the growth of Coccidioides in vitro and suggest that an altered microbiome through antibiotic treatment could negatively impact effective fungal clearance and allow a niche for fungal growth in vivo. IMPORTANCE: Coccidioidomycosis is caused by a fungal pathogen that invades the host lungs, causing respiratory distress. In 2019, 20,003 cases of Valley fever were reported to the CDC. However, this number likely vastly underrepresents the true number of Valley fever cases, as many go undetected due to poor testing strategies and a lack of diagnostic models. Valley fever is also often misdiagnosed as bacterial pneumonia, resulting in 60%-80% of patients being treated with antibiotics prior to an accurate diagnosis. Misdiagnosis contributes to a growing problem of antibiotic resistance and antibiotic-induced microbiome dysbiosis; the implications for disease outcomes are currently unknown. About 5%-10% of symptomatic Valley fever patients develop chronic pulmonary disease. Valley fever causes a significant financial burden and a reduced quality of life. Little is known regarding what factors contribute to the development of chronic infections and treatments for the disease are limited.

Host tracheal and intestinal microbiomes inhibit Coccidioides growth in vitro.

Coccidioidomycosis, also known as Valley fever, is a disease caused by the fungal pathogen Coccidioides. Unfortunately, patients are often misdiagnosed with bacterial pneumonia leading to inappropriate antibiotic treatment. Soil bacteria B. subtilis-like species exhibits antagonistic properties against Coccidioides in vitro; however, the antagonistic capabilities of host microbiota against Coccidioides are unexplored. We sought to examine the potential of the tracheal and intestinal microbiomes to inhibit the growth of Coccidioides in vitro. We hypothesized that an uninterrupted lawn of microbiota obtained from antibiotic-free mice would inhibit the growth of Coccidioides while partial in vitro depletion through antibiotic disk diffusion assays would allow a niche for fungal growth. We observed that the microbiota grown on 2xGYE (GYE) and CNA w/ 5% sheep's blood agar (5%SB-CNA) inhibited the growth of Coccidioides, but that grown on chocolate agar does not. Partial depletion of the microbiota through antibiotic disk diffusion revealed that microbiota depletion leads to diminished inhibition and comparable growth of Coccidioides growth to controls. To characterize the bacteria grown and narrow down potential candidates contributing to the inhibition of Coccidioides, 16s rRNA sequencing of tracheal and intestinal agar cultures and murine lung extracts was performed. The identity of host bacteria that may be responsible for this inhibition was revealed. The results of this study demonstrate the potential of the host microbiota to inhibit the growth of Coccidioides in vitro and suggest that an altered microbiome through antibiotic treatment could negatively impact effective fungal clearance and allow a niche for fungal growth in vivo.

Inferring gene regulatory networks using transcriptional profiles as dynamical attractors.

Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to "static" transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach (named EA) that integrates kinetic transcription data and the theory of attractor matching to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, none of which incorporate kinetic transcriptional data, in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in Saccharomyces cerevisiae. Moreover, we demonstrate the potential of our method to predict unknown transcriptional profiles that would be produced upon genetic perturbation of the GRN governing a two-state cellular phenotypic switch in Candida albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation; the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that can accurately describe the structure and dynamics of the in vivo GRN.

Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention.

A limitation of current deep learning (DL) approaches for single-cell RNA sequencing (scRNAseq) analysis is the lack of interpretability. Moreover, existing pipelines are designed and trained for specific tasks used disjointly for different stages of analysis. We present scANNA, a novel interpretable DL model for scRNAseq studies that leverages neural attention to learn gene associations. After training, the learned gene importance (interpretability) is used to perform downstream analyses (e.g., global marker selection and cell-type classification) without retraining. ScANNA's performance is comparable to or better than state-of-the-art methods designed and trained for specific standard scRNAseq analyses even though scANNA was not trained for these tasks explicitly. ScANNA enables researchers to discover meaningful results without extensive prior knowledge or training separate task-specific models, saving time and enhancing scRNAseq analyses.

Going through phages: a computational approach to revealing the role of prophage in Staphylococcus aureus.

Prophages have important roles in virulence, antibiotic resistance, and genome evolution in Staphylococcus aureus . Rapid growth in the number of sequenced S. aureus genomes allows for an investigation of prophage sequences at an unprecedented scale. We developed a novel computational pipeline for phage discovery and annotation. We combined PhiSpy, a phage discovery tool, with VGAS and PROKKA, genome annotation tools to detect and analyse prophage sequences in nearly 10 011 S . aureus genomes, discovering thousands of putative prophage sequences with genes encoding virulence factors and antibiotic resistance. To our knowledge, this is the first large-scale application of PhiSpy on a large-scale set of genomes (10 011 S . aureus ). Determining the presence of virulence and resistance encoding genes in prophage has implications for the potential transfer of these genes/functions to other bacteria via transduction and thus can provide insight into the evolution and spread of these genes/functions between bacterial strains. While the phage we have identified may be known, these phages were not necessarily known or characterized in S. aureus and the clustering and comparison we did for phage based on their gene content is novel. Moreover, the reporting of these genes with the S. aureus genomes is novel.

A Computational Workflow for Analysis of 3' Tag-Seq Data.

RNA-sequencing (RNA-seq) is a gold-standard method to profile genome-wide changes in gene expression. RNA-seq uses high-throughput sequencing technology to quantify the amount of RNA in a biological sample. With the increasing popularity of RNA-seq, many variations on the protocol have been proposed to extract unique and relevant information from biological samples. 3' Tag-Seq (also called TagSeq, 3' Tag-RNA-Seq, and Quant-Seq 3' mRNA-Seq) is one RNA-seq variation where the 3' end of the transcript is selected and amplified to yield one copy of cDNA from each transcript in the biological sample. We present a simple, easy-to-use, and publicly available computational workflow to analyze 3' Tag-Seq data. The workflow begins by trimming sequence adapters from raw FASTQ files. The trimmed sequence reads are checked for quality using FastQC and aligned to the reference genome, and then read counts are obtained using STAR. Differential gene expression analysis is performed using DESeq2, based on differential analysis of gene count data. The outputs of this workflow are MA plots, tables of differentially expressed genes, and UpSet plots. This protocol is intended for users specifically interested in analyzing 3' Tag-Seq data, and thus normalizations based on transcript length are not performed within the workflow. Future updates to this workflow could include custom analyses based on the gene counts table as well as data visualization enhancements. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Running the 3' Tag-Seq workflow Support Protocol: Generating genome indices.

Modeling and Global Sensitivity Analysis of Strategies to Mitigate Covid-19 Transmission on a Structured College Campus.

In response to the COVID-19 pandemic, many higher educational institutions moved their courses on-line in hopes of slowing disease spread. The advent of multiple highly-effective vaccines offers the promise of a return to "normal" in-person operations, but it is not clear if-or for how long-campuses should employ non-pharmaceutical interventions such as requiring masks or capping the size of in-person courses. In this study, we develop and fine-tune a model of COVID-19 spread to UC Merced's student and faculty population. We perform a global sensitivity analysis to consider how both pharmaceutical and non-pharmaceutical interventions impact disease spread. Our work reveals that vaccines alone may not be sufficient to eradicate disease dynamics and that significant contact with an infectious surrounding community will maintain infections on-campus. Our work provides a foundation for higher-education planning allowing campuses to balance the benefits of in-person instruction with the ability to quarantine/isolate infectious individuals.

Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing.

Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. Data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially resolved data (such as spatial resolution, sensitivity, and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering, among others. However, DL models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially resolved transcriptomics while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight domains in which we anticipate transformational DL applications.

A structured model and likelihood approach to estimate yeast prion propagon replication rates and their asymmetric transmission.

Prion proteins cause a variety of fatal neurodegenerative diseases in mammals but are generally harmless to Baker's yeast (Saccharomyces cerevisiae). This makes yeast an ideal model organism for investigating the protein dynamics associated with these diseases. The rate of disease onset is related to both the replication and transmission kinetics of propagons, the transmissible agents of prion diseases. Determining the kinetic parameters of propagon replication in yeast is complicated because the number of propagons in an individual cell depends on the intracellular replication dynamics and the asymmetric division of yeast cells within a growing yeast cell colony. We present a structured population model describing the distribution and replication of prion propagons in an actively dividing population of yeast cells. We then develop a likelihood approach for estimating the propagon replication rate and their transmission bias during cell division. We first demonstrate our ability to correctly recover known kinetic parameters from simulated data, then we apply our likelihood approach to estimate the kinetic parameters for six yeast prion variants using propagon recovery data. We find that, under our modeling framework, all variants are best described by a model with an asymmetric transmission bias. This demonstrates the strength of our framework over previous formulations assuming equal partitioning of intracellular constituents during cell division.

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow.

Regulatory transcription factors control many important biological processes, including cellular differentiation, responses to environmental perturbations and stresses, and host-pathogen interactions. Determining the genome-wide binding of regulatory transcription factors to DNA is essential to understanding the function of transcription factors in these often complex biological processes. Cleavage under targets and release using nuclease (CUT&RUN) is a modern method for genome-wide mapping of in vivo protein-DNA binding interactions that is an attractive alternative to the traditional and widely used chromatin immunoprecipitation followed by sequencing (ChIP-seq) method. CUT&RUN is amenable to a higher-throughput experimental setup and has a substantially higher dynamic range with lower per-sample sequencing costs than ChIP-seq. Here, a comprehensive CUT&RUN protocol and accompanying data analysis workflow tailored for genome-wide analysis of transcription factor-DNA binding interactions in the human fungal pathogen Candida albicans are described. This detailed protocol includes all necessary experimental procedures, from epitope tagging of transcription factor-coding genes to library preparation for sequencing; additionally, it includes a customized computational workflow for CUT&RUN data analysis.

ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders.

MOTIVATION: Single-cell RNA sequencing (scRNAseq) technologies allow for measurements of gene expression at a single-cell resolution. This provides researchers with a tremendous advantage for detecting heterogeneity, delineating cellular maps or identifying rare subpopulations. However, a critical complication remains: the low number of single-cell observations due to limitations by rarity of subpopulation, tissue degradation or cost. This absence of sufficient data may cause inaccuracy or irreproducibility of downstream analysis. In this work, we present Automated Cell-Type-informed Introspective Variational Autoencoder (ACTIVA): a novel framework for generating realistic synthetic data using a single-stream adversarial variational autoencoder conditioned with cell-type information. Within a single framework, ACTIVA can enlarge existing datasets and generate specific subpopulations on demand, as opposed to two separate models [such as single-cell GAN (scGAN) and conditional scGAN (cscGAN)]. Data generation and augmentation with ACTIVA can enhance scRNAseq pipelines and analysis, such as benchmarking new algorithms, studying the accuracy of classifiers and detecting marker genes. ACTIVA will facilitate analysis of smaller datasets, potentially reducing the number of patients and animals necessary in initial studies. RESULTS: We train and evaluate models on multiple public scRNAseq datasets. In comparison to GAN-based models (scGAN and cscGAN), we demonstrate that ACTIVA generates cells that are more realistic and harder for classifiers to identify as synthetic which also have better pair-wise correlation between genes. Data augmentation with ACTIVA significantly improves classification of rare subtypes (more than 45% improvement compared with not augmenting and 4% better than cscGAN) all while reducing run-time by an order of magnitude in comparison to both models. AVAILABILITY AND IMPLEMENTATION: The codes and datasets are hosted on Zenodo (https://doi.org/10.5281/zenodo.5879639). Tutorials are available at https://github.com/SindiLab/ACTIVA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

To isolate or not to isolate: the impact of changing behavior on COVID-19 transmission.

BACKGROUND: The COVID-19 pandemic has caused more than 25 million cases and 800 thousand deaths worldwide to date. In early days of the pandemic, neither vaccines nor therapeutic drugs were available for this novel coronavirus. All measures to prevent the spread of COVID-19 are thus based on reducing contact between infected and susceptible individuals. Most of these measures such as quarantine and self-isolation require voluntary compliance by the population. However, humans may act in their (perceived) self-interest only. METHODS: We construct a mathematical model of COVID-19 transmission with quarantine and hospitalization coupled with a dynamic game model of adaptive human behavior. Susceptible and infected individuals adopt various behavioral strategies based on perceived prevalence and burden of the disease and sensitivity to isolation measures, and they evolve their strategies using a social learning algorithm (imitation dynamics). RESULTS: This results in complex interplay between the epidemiological model, which affects success of different strategies, and the game-theoretic behavioral model, which in turn affects the spread of the disease. We found that the second wave of the pandemic, which has been observed in the US, can be attributed to rational behavior of susceptible individuals, and that multiple waves of the pandemic are possible if the rate of social learning of infected individuals is sufficiently high. CONCLUSIONS: To reduce the burden of the disease on the society, it is necessary to incentivize such altruistic behavior by infected individuals as voluntary self-isolation.

Rules of Engagement: A Guide to Developing Agent-Based Models.

Agent-based models (ABM), also called individual-based models, first appeared several decades ago with the promise of nearly real-time simulations of active, autonomous individuals such as animals or objects. The goal of ABMs is to represent a population of individuals (agents) interacting with one another and their environment. Because of their flexible framework, ABMs have been widely applied to study systems in engineering, economics, ecology, and biology. This chapter is intended to guide the users in the development of an agent-based model by discussing conceptual issues, implementation, and pitfalls of ABMs from first principles. As a case study, we consider an ABM of the multi-scale dynamics of cellular interactions in a microbial community. We develop a lattice-free agent-based model of individual cells whose actions of growth, movement, and division are influenced by both their individual processes (cell cycle) and their contact with other cells (adhesion and contact inhibition).

A field guide to cultivating computational biology.

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

AddTag, a two-step approach with supporting software package that facilitates CRISPR/Cas-mediated precision genome editing.

CRISPR/Cas-induced genome editing is a powerful tool for genetic engineering, however, targeting constraints limit which loci are editable with this method. Since the length of a DNA sequence impacts the likelihood it overlaps a unique target site, precision editing of small genomic features with CRISPR/Cas remains an obstacle. We introduce a two-step genome editing strategy that virtually eliminates CRISPR/Cas targeting constraints and facilitates precision genome editing of elements as short as a single base-pair at virtually any locus in any organism that supports CRISPR/Cas-induced genome editing. Our two-step approach first replaces the locus of interest with an "AddTag" sequence, which is subsequently replaced with any engineered sequence, and thus circumvents the need for direct overlap with a unique CRISPR/Cas target site. In this study, we demonstrate the feasibility of our approach by editing transcription factor binding sites within Candida albicans that could not be targeted directly using the traditional gene-editing approach. We also demonstrate the utility of the AddTag approach for combinatorial genome editing and gene complementation analysis, and we present a software package that automates the design of AddTag editing.

Distribution of ß-Lactamase Genes in Clinical Isolates from California Central Valley Hospital Deviates from the United States Nationwide Trends.

The evolution and dissemination of antibiotic resistance genes throughout the world are clearly affected by the selection and migration of resistant bacteria. However, the relative contributions of selection and migration at a local scale have not been fully explored. We sought to identify which of these factors has the strongest effect through comparisons of antibiotic resistance gene abundance between a distinct location and its surroundings over an extended period of six years. In this work, we used two repositories of extended spectrum ß-lactamase (ESBL)-producing isolates collected since 2013 from patients at Dignity Health Mercy Medical Center (DHMMC) in Merced, California, USA, and a nationwide database compiled from clinical isolate genomes reported by the National Center for Biotechnology Information (NCBI) since 2013. We analyzed the stability of average resistance gene frequencies over the years since collection of these clinical isolates began for each repository. We then compared the frequencies of resistance genes in the DHMMC collection with the averages of the nationwide frequencies. We found DHMMC gene frequencies are stable over time and differ significantly from nationwide frequencies throughout the period of time we examined. Our results suggest that local selective pressures are a more important influence on the population structure of resistance genes in bacterial populations than migration. This, in turn, indicates the potential for antibiotic resistance to be controlled at a regional level, making it easier to limit the spread through local stewardship.