Biology subjects

Aguilar, B.

Publications and source records attributed to Aguilar, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Patient-Derived Glioma Models Preserve Tumor Heterogeneity and Identify Stearoyl-CoA Desaturase1 (SCD1) as a Candidate Biomarker for Precision Immunotherapy

Background: Pediatric and adult brain tumors, including glioblastoma, astrocytoma, ependymoma, and medulloblastoma, remain associated with poor prognosis despite advances in surgery, radiation, and chemotherapy. Therapeutic resistance, tumor heterogeneity, and treatment-related toxicity highlight the need for clinically relevant models that enable precision medicine and immunotherapy development. Methods: Freshly dispersed tumors (FDTs), low-passage patient-derived brain tumor (PBT) spheroid lines, and matched patient-derived xenograft (PDX) models were established from patients with primary brain tumors. Models were characterized using single-cell and bulk RNA sequencing, whole-exome sequencing, multiparameter flow cytometry, and immunohistochemistry. PBTs were compared with matched FDTs to evaluate model fidelity. Results: PBT lines were established in approximately 68% of cases and retained key patient-specific genomic alterations, including IDH1, MGMT, TP53, and PTEN, with expression of therapeutically relevant targets, including IL13R2, EGFR, HER2, WNT1, JAK1/2, and NOTCH1-4. Gene expression profiles of PBTs closely correlated with matched FDTs (R = 0.38, P = 0.0038). PBT and PDX models preserved intratumoral heterogeneity and non-clonal populations, enabling identification of therapy-resistant subclones during in vitro selection. Molecular analyses identified Stearoyl-CoA Desaturase1 (SCD1) as an overexpressed biomarker across glioma PBTs and matched patient tumors. Ingenuity Pathway Analysis identified SCD1 as an upstream regulator of EGFR-, TP53-, and CYCS-associated signaling networks implicated in tumor progression and immune suppression. Conclusions: Clinically relevant PBT and matched PDX models recapitulate molecular, transcriptional, and histopathological characteristics of primary brain tumors. These platforms provide tools for biomarker discovery, therapeutic testing, and precision immunotherapy development, while identifying SCD1 as a biomarker and therapeutic target in glioma.

cancer biology↗

bioRxiv2026

Meditope-Enabled Chimeric Antigen Receptors Facilitate Plug-and-Play Control of T Cells

Chimeric antigen receptor (CAR) T cells have transformed cancer treatment, yet challenges for achieving broader clinical success remain, including overcoming tumor antigen heterogeneity and limited T cell fitness. To address these challenges and enhance CAR T cell functionality, we leveraged meditope technology, a lock-and-key platform where Fab regions of antibodies are modified to bind a small cyclic peptide termed meditope (meP). We developed a panel of meditope-enabled Fab-based CARs (meCARs), which show selective binding to the meP and comparable activity to traditional single-chain variable fragment (scFv)-based CARs. Focusing on HER2-targeted meCARs for evaluating platform utility, we exploited the modularity of the meditope platform to detect meCAR T cells using meP-fused fluorescent agents, promote meCAR T cell expansion via meP-fused IL-15 cytokine, and broaden tumor antigen targeting through meP-fused antibodies to address tumor heterogeneity. These findings establish the meditope technology as a versatile strategy to augment CAR T cell functionality and overcome key limitations of current CAR-based therapies.

bioengineering↗

bioRxiv2026

Endothelial ACKR1 expression regulates neutrophil infiltration and breast cancer metastatic engraftment in the lung metastatic niche

The formation of the premetastatic niche prepares distant tissues for tumor cell engraftment. Endothelial cells are critical mediators of premetastatic niche formation, orchestrating extravasation of circulating tumor cells and critical pro-tumor immune cells, such as neutrophils. In mouse models of breast cancer, we show that primary tumors upregulate the non-signaling chemokine receptor ACKR1 in the endothelium of the lung premetastatic niche. ACKR1-expressing venules were found to be preferential sites of neutrophil and tumor cell localization within lung tissue. A newly generated conditional ACKR1 allele was used to show that endothelial-specific removal of ACKR1 expression significantly reduces metastatic engraftment in the lung. When ACKR1 is activated by tumor-secreted factors, endothelial ACKR1 functions to promote neutrophil recruitment within the lung parenchyma. We conclude that ACKR1 is a critical component of the endothelial response to tumors at the metastatic site of the lung, leading to neutrophil recruitment and promotion of tumor cell metastasis. SUMMARYEndothelial cells play critical roles in breast cancer metastasis. ACKR1 is upregulated in the endothelium of the lung metastatic niche in response to primary mammary tumors. Endothelial ACKR1 expression was found to promote neutrophil infiltration into the metastatic niche and support breast tumor cell metastasis to the lung.

cancer biology↗

bioRxiv2026

KGBN: Augmenting and optimizing logical gene regulatory networks using knowledge graphs

Logical gene regulatory network (GRN) models provide interpretable, mechanistic representations of cellular regulation and are widely used in systems biology. However, most existing models remain incomplete, context-specific, and difficult to extend to comprehensive GRNs, limiting their broader applicability to tasks such as drug-response prediction and precision medicine. Here, add some ideas about virtual twins: With AML patients, we can use comprehensive GRNs to predict drug response. We present KGBN (Knowledge Graph-augmented Boolean Network modeling), a computational workflow for systematically augmenting logical GRN models. KGBN incorporates regulatory interactions derived from curated knowledge graphs as alternative logical rules while preserving the validated structure of existing models. Rule probabilities are optimized against experimental data to represent regulatory uncertainty and achieve data-driven calibration. Applying KGBN to acute myeloid leukemia, we show that extending an existing GRN with drug-target pathways and training against ex vivo drug-response data yields mutation-specific models that recapitulate known therapeutic sensitivities and signaling dependencies, demonstrating the utility of KGBN for interpretable, context-aware GRN modeling. (Should be predict)

systems biology↗

bioRxiv2025

Overcoming myeloid-driven resistance to CAR T therapy by targeting SPP1

Chimeric antigen receptor (CAR) T cell therapy faces notable limitations in treatment of solid tumors. The suppressive tumor microenvironment (TME), characterized by complex interactions among immune and stromal cells, is gaining recognition in conferring resistance to CAR T cell therapy. Despite the abundance and diversity of macrophages in the TME, their intricate involvement in modulating responses to CAR T cell therapies remains poorly understood. Here, we conducted single-cell RNA sequencing (scRNA-seq) on tumors from 41 glioma patients undergoing IL13R2-targeted CAR T cell therapy, identifying elevated suppressive SPP1 signatures predominantly in macrophages from patients who were resistant to treatment. Further integrative scRNA-seq analysis of high-grade gliomas as well as an interferon-signaling deficient syngeneic mouse model--both resistant to CAR T therapy--demonstrated the role of congruent suppressive pathways in mediating resistance to CAR T cells and a dominant role for SPP1+ macrophages. SPP1 blockade with an anti-SPP1 antibody abrogates the suppressive TME effects and substantially prolongs survival in IFN signaling-deficient and glioma syngeneic mouse models resistant to CAR T cell therapy. These findings illuminate the role of SPP1+ macrophages in fueling a suppressive TME and driving solid tumor resistance to CAR cell therapies. Targeting SPP1 may serve as a universal strategy to reprogram immune dynamics in solid tumors mitigating resistance to CAR T cell therapies.

cancer biology↗

bioRxiv2024

Merging logical models: An application in Acute Myeloid Leukemia modeling

MotivationGene regulatory network (GRN) models provide mechanistic understanding of genetic interactions that regulate gene expression and, consequently, influence cellular behavior. Dysregulated gene expression plays a critical role in disease progression and treatment response, making GRN models a promising tool for precision medicine. While researchers have built many models to describe specific subsets of gene interactions, more comprehensive models that cover a broader range of genes are challenging to build. This necessitates the development of automated approaches for merging existing models. ResultsWe present LM-Merger, a workflow for semi-automatically merging logical GRN models. The workflow consists of five main steps: (a) model identification, (b) model standardization and annotation, (c) model verification, (d) model merging, and (d) model evaluation. We demonstrate the feasibility and benefit of this workflow with two pairs of published models pertaining to acute myeloid leukemia (AML). The integrated models were able to retain the predictive accuracy of the original models, while expanding coverage of the biological system. Notably, when applied to a new dataset, the integrated models outperformed the individual models in predicting patient response. This study highlights the potential of logical model merging to advance systems biology research and our understanding of complex diseases. Availability and implementationThe workflow and accompanying tools, including modules for model standardization, automated logical model merging, and evaluation, are available at https://github.com/IlyaLab/LogicModelMerger/.

systems biology↗

bioRxiv2023

A multiscale model of pulmonary micrometastasis and immune surveillance: towards cancer patient digital twins

Metastasis is the leading cause of death in patients with cancer, driving considerable scientific and clinical interest in immunosurveillance of micrometastases. We investigated this process by creating a multiscale mathematical model to study the interactions between the immune system and the progression of micrometastases in general epithelial tissue. We analyzed the parameter space of the model using high-throughput computing resources to generate over 100,000 virtual patient trajectories. We demonstrated that the model could recapitulate a wide variety of virtual patient trajectories, including uncontrolled growth, partial response, and complete immune response to tumor growth. We classified the virtual patients and identified key patient parameters with the greatest effect on the simulated immunosurveillance. We highlight the lessons derived from this analysis and their impact on the nascent field of cancer patient digital twins (CPDTs). While CPDTs could enable clinicians to systematically dissect the complexity of cancer in each individual patient and inform treatment choices, our work shows that key challenges remain before we can reach this vision. In particular, we show that there remain considerable uncertainties in immune responses, dysfunctional data stratification, and unpredictable personalized treatment. Nonetheless, we also show that in spite of these challenges, patient-specific models suggest strategies to increase control of clinically undetectable micrometastases even without complete parameter certainty.

systems biology↗

bioRxiv2023

Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an aggressive malignancy of myeloid progenitor cells characterized by successive acquisition of genetic alterations. This inherent heterogeneity poses challenges in the development of precise and effective therapies. To gain insights into the genetic influence on drug response and optimize treatment selection, we performed targeted sequencing, ex vivo drug screening, and single-cell genomic profiling on leukemia cell samples derived from AML patients. We detected genetic signatures associated with sensitivity or resistance to specific agents. By integrating large public datasets, we discovered statistical patterns of co-occurring and mutually exclusive mutations in AML. The application of single-cell genomic sequencing unveiled the co-occurrence of variants at the individual cell level, highlighting the presence of distinct sub- clones within AML patients. Machine learning models were built to predict ex vivo drug sensitivity using the genetic variants. Notably, these models demonstrated high accuracy in predicting sensitivity to some drugs, such as MEK inhibitors. Our study provides valuable resources for characterizing AML patients and predicting drug sensitivity, emphasizing the significance of considering subclonal distribution in drug response prediction. These findings provide a foundation for advancing precision medicine in AML. By tailoring treatment based on individual genetic profiles and functional testing, as well as accounting for the presence of subclones, we envision a future of improved therapeutic strategies for AML patients. One Sentence SummaryIntegrative computational and experimental analysis of mutation patterns and drug responses provide biologic insight and therapeutic guidance for patients with adult AML.

cell biology↗

bioRxiv2022

Modeling the biomechanics of cells on microcarriers in a stirred-tank bioreactor An ABM-CFD coupling approach

Highly productive and efficient growth of biomass in bioreactors is an essential bioprocess outcome in many industrial applications. In the nascent cultivated meat industry, large-scale biomass creation will be critical given the size of demand in the conventional meat and seafood sectors. However, there are many challenges that must be overcome before cultivated meat and seafood become commercially viable including cost reductions of cell culture media, bioprocess design innovation and optimization, and scaling up in the longer term. Computational modelling and simulation can help to address many of these challenges, and can be a far cheaper and faster alternative to performing physical experiments. Computer modelling can also help researchers pinpoint system interactions that matter, and guide researchers to identify those parameters that should be changed in later designs for eventual optimization. In this work, a computational model that combines agent-based modeling and computational fluid dynamics was developed to study biomass growth as a function of the operative conditions of stirred-tank bioreactors. The focus was to analyze how the mechanical stress induced by rotor speed can influence the growth of cells attached to spherical microcarriers. The computer simulation results reproduced observations from physical experiments that high rotor speeds reduce cell growth rates and induce cell death under the high mechanical stresses induced at these stir speeds. Moreover, the results suggest that modeling both cell death and cell quiescence are required to recapitulate these observations from physical experiments. These simulation outcomes are the first step towards more comprehensive models that, in combination with experimental observations, will improve our knowledge of biomass production in bioreactors for cultivated meat and other industries.

bioinformatics↗

bioRxiv2022

Selective IL13Rα2-Targeted Functionality of IL13-Ligand CARs is Enhanced by Inclusion of 4-1BB Co-Stimulation

ABSTRACTChimeric antigen receptor (CAR) T cell immunotherapy is emerging as a powerful strategy for cancer therapy; however, an important safety consideration is the potential for off-tumor recognition of normal tissue. This is particularly important as ligand-based CARs are optimized for clinical translation. Our group has developed and clinically translated an IL13(E12Y) ligand- based CAR targeting the cancer antigen IL13R2 for treatment of glioblastoma (GBM). There remains limited understanding of how IL13-ligand CAR design impacts the activity and selectivity for the intended tumor-associated target IL13R2 versus the more ubiquitous unintended target IL13R1. In this study, we functionally compared IL13(E12Y)-CARs incorporating different intracellular signaling domains, including first-generation CD3{zeta}- containing CARs (IL13{zeta}), second-generation 4-1BB- (CD137) or CD28-containing CARs (IL13- BB{zeta} or IL13-28{zeta}), and third-generation CARs containing both 4-1BB and CD28 (IL13-28BB{zeta}). In vitro co-culture assays at high tumor burden establish that 2nd generation IL13-BB{zeta} or IL13- 28{zeta} outperform first-generation IL13{zeta} and 3rd generation IL13-28BB{zeta} CAR designs, with IL13- BB{zeta} providing superior CAR proliferation and in vivo anti-tumor potency in human xenograft mouse models. IL13-28{zeta} displayed a lower threshold for antigen recognition, resulting in higher off-target IL13R1 reactivity both in vitro and in vivo. Syngeneic mouse models of GBM also demonstrate safety and anti-tumor potency of murine IL13-BB{zeta} CAR T cells delivered systemically after lymphodepletion. These findings support the use of IL13-BB{zeta} CARs for greater selective recognition of IL13R2 over IL13R1, higher proliferative potential, and superior anti-tumor responsiveness. This study exemplifies the potential of modulating factors outside the antigen targeting domain of a CAR to improve selective tumor recognition..

immunology↗

bioRxiv2022

Multicellular Reservoir Computing

AO_SCPLOWBSTRACTC_SCPLOWThe capacity of cells to process information is currently used to design cell-based tools for ecological, industrial, and biomedical applications such as detecting dangerous chemicals or for bioremediation. In most applications, individual cells are used as the information processing unit. However, single cell engineering is limited by the necessary molecular complexity and the accompanying metabolic burden of synthetic circuits. To overcome these limitations, synthetic biologists have begun engineering multicellular systems that combine cells with designed subfunctions. To further advance information processing in synthetic multicellular systems, we introduce the application of reservoir computing. Reservoir computers (RCs) approximate a temporal signal processing task via a fixed-rule dynamic network (the reservoir) with a regression-based readout. Importantly, RCs eliminate the need of network rewiring, as different tasks can be approximated with the same reservoir. Previous work has already demonstrated the capacity of single cells, as well as populations of neurons, to act as reservoirs. In this work, we extend reservoir computing in multicellular populations with the widespread mechanism of diffusion-based cell-cell signaling. As a proof-of-concept, we simulated a reservoir made of a 3D community of cells communicating via diffusible molecules and used it to approximate two benchmark signal processing tasks, computing median and parity functions from binary input signals. We demonstrate that a diffusion-based multicellular reservoir is a feasible synthetic framework for performing complex temporal computing tasks that provides a computational advantage over single cell reservoirs. We also identified a number of biological properties that can affect the computational performance of these processing systems.

systems biology↗

bioRxiv2022

Uncovering potential interventions for pancreatic cancer patients via mathematical modeling

Pancreatic Ductal Adenocarcinoma (PDAC) is widely known for its poor prognosis because it is often diagnosed when the cancer is in a later stage. We built a Boolean model to analyze the microenvironment of pancreatic cancer in order to better understand the interplay between pancreatic cancer, stellate cells, and their signaling cytokines. Specifically, we have used our model to study the impact of inducing four common mutations: KRAS, TP53, SMAD4, and CDKN2A. After implementing the various mutation combinations, we used our stochastic simulator to derive aggressiveness scores based on simulated attractor probabilities and long-term trajectory approximations. These aggression scores were then corroborated with clinical data. Moreover, we found sets of control targets that are effective among common mutations. These control sets contain nodes within both the pancreatic cancer cell and the pancreatic stellate cell, including PIP3, RAF, PIK3 and BAX in pancreatic cancer cell as well as ERK and PIK3 in the pancreatic stellate cell. Many of these nodes were found to be differentially expressed among pancreatic cancer patients in the TCGA database. Furthermore, literature suggests that many of these nodes can be targeted by drugs currently in circulation. The results herein help provide a proof of concept in the path towards personalized medicine through a means of mathematical systems biology. All data and code used for running simulations, statistical analysis, and plotting is available on a GitHub repository at https://github.com/drplaugher/PCC_Mutations.

systems biology↗

bioRxiv2021

CAR T cell therapy drives endogenous locoregional T cell dynamics in a responding patient with glioblastoma

CAR T cell therapy has transformed clinical care and management of patients with certain hematological cancers. However, it remains unclear whether the success of CAR T cell therapy relies solely on CAR T cell engagement with tumor antigen, or if it also requires the stimulation of an individual patients endogenous T cell response. Here, we performed combined analysis of longitudinal, single cell RNA and T cell receptor sequencing on glioblastoma tumors, peripheral blood (PB), and cerebrospinal fluid (CSF) from a patient with recurrent multifocal glioblastoma that underwent a remarkable response followed by recurrence on IL13RA2-targeted CAR T cell therapy (Brown et al. 2016). Single cell analysis of a tumor resected prior to CAR T cell therapy revealed the existence of an inflamed tumor microenvironment including a CD8+ cytotoxic, clonally expanded and antigen specific T cell population that disappeared in the recurrent setting. Longitudinal tracking of T cell receptors uncovered distinct T cell dynamics classes in the CSF during CAR T cell therapy. These included T cell clones with transient dynamics, representing intraventricular CAR T cell delivery and endogenous T cell recruitment from the PB into the CSF; and a group of T cells in the cerebrospinal fluid, that tracked with clonally expanded tumor resident T cells and whose dynamics contracted concomitantly with tumor volume. Our results suggest the existence of an endogenous T cell population that was invigorated by intraventricular CAR T cell infusions, and combined with the therapy to produce a complete response.

immunology↗

bioRxiv2021

SL-Cloud: A Computational Resource to Support Synthetic Lethal Interaction Discovery

Synthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, an integrated resource and framework to facilitate the prediction of context-specific SLIs by using cloud-based technologies. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the multiple comparable prediction approaches available. We demonstrate the utility of this resource by using a set of DNA damage repair genes as the basis for predicting potential SLI partners, using multiple computational strategies. Context-specific synthetic lethality potential can also be compared using the framework. We demonstrate various use cases for our cloud-based computational resource and the utility of this approach for customizable and extensible computational inference of SLIs.

bioinformatics↗

bioRxiv2020

Repurposed Drugs Block Toxin-Driven Platelet Clearance by the Hepatic Ashwell-Morell Receptor to Clear Staphylococcus aureus Bacteremia

ABSTRACTStaphylococcus aureus (SA) bloodstream infections cause high morbidity and mortality (20-30%) despite modern supportive care. In a human bacteremia cohort, development of thrombocytopenia was correlated to increased mortality and increased α-toxin expression by the pathogen. Platelet-derived antibacterial peptides are important in bloodstream defense against SA, but α-toxin decreased platelet viability, induced platelet sialidase to cause desialylation of platelet glycoproteins, and accelerated platelet clearance by the hepatic Ashwell-Morell receptor (AMR). Ticagrelor (Brilinta®), a commonly prescribed P2Y12 receptor inhibitor used post-myocardial infarction, blocked α-toxin-mediated platelet injury and resulting thrombocytopenia, thus providing protection from lethal SA infection in a murine intravenous challenge model. Genetic deletion or pharmacological inhibition of AMR stabilized platelet counts and enhanced resistance to SA infection, and the anti-influenza sialidase inhibitor oseltamivir (Tamiflu®) provided similar therapeutic benefit. Thus a “toxin-platelet-AMR” regulatory pathway plays a critical role in the pathogenesis of SA bloodstream infection, and its elucidation provides proof-of-concept for repurposing two FDA-approved drugs as adjunctive therapies to improve patient outcomes.Competing Interest StatementThe authors have declared no competing interest.View Full Text

microbiology↗

bioRxiv2020

Chlorotoxin Redirects Chimeric Antigen Receptor T Cells for Specific and Effective Targeting of Glioblastoma

While chimeric antigen receptor (CAR) T cells have demonstrated antitumor activity against glioblastoma (GBM), tumor heterogeneity remains a critical challenge. To more effectively target heterogeneous GBMs, we report the development of a novel peptide-based CAR exploiting the GBM-binding potential of chlorotoxin (CLTX). CLTX bound a greater proportion of tumor cells than GBM-associated antigens EGFR, HER2 and IL13R2. CAR T cells bearing CLTX as the targeting domain (CLTX-CAR), mediated potent in vitro and in vivo anti-GBM activity, and efficiently targeted tumors lacking expression of other GBM-associated antigens. Importantly, CLTX-CAR T cells exhibited no observable off-target effector activity against normal cells, or when adoptively transferred into mice. Effective targeting by CLTX-CAR T cells required cell surface expression of matrix metalloproteinase-2 (MMP-2). Our results are the first demonstration of a peptide toxin utilized as a CAR targeting domain, expanding the repertoire of tumor-selective CAR T cells with the potential to reduce antigen escape. One Sentence SummaryChimeric antigen receptors incorporating chlorotoxin as the tumor targeting domain recognize and kill glioblastoma with high specificity and potency.

immunology↗

bioRxiv2019

An Integrated Multiscale, Multicellular Skin Model

Skin is our primary barrier to the outside world, protecting us from physical, biological and chemical threats. Developing innovative products that preserve and improve skin barrier function requires a thorough understanding of the mechanisms underlying barrier response to topical applications. In many fields, computer simulations already facilitate understanding, thus accelerating innovation. Simulations of software models allow scientists to test hypothesized mechanisms by comparing predicted results to physical observations. They also enable virtual product optimization, without physical experiments, once mechanisms have been validated. The physical accessibility and abundant knowledge of skin structure makes it a prime candidate for computational modeling. In this article, we describe a computational multiscale multicellular skin model used to simulate growth and response of the epidermal barrier. The model integrates several modeling styles and mathematical frameworks including ordinary differential equations, partial differential equations, discrete agent-based modeling and discrete element methods. Specifically, to capture cell biology and physical transport, we combined four distinct sub-models from existing literature. We also implemented methods for elastic biomechanics. Our software implementation of the model is compatible with the high-performance computing simulation platform Biocellion. The integrated model recapitulates barrier formation, homeostasis and response to environmental, chemical and mechanical perturbation. This work exemplifies methodology for integrating models of vastly different styles. The methodology enables us to effectively build on existing knowledge and produce "whole-system" tissue models capable of displaying emergent properties. It also illustrates the inherent technical difficulties associated with the mounting complexity of describing biological systems at high fidelity. Among the challenges are validation of the science, the mathematical representations approximating the science and the software implementing these representations. Responsibility for a discrepancy observed between in silico and in vitro results may as easily lie at one of these three levels as at another, demanding that any sustainable modeling endeavor engage expertise from biology, mathematics and computing.

systems biology↗

bioRxiv2019

Intricate genetic programs controlling dormancy in Mycobacterium tuberculosis

Mycobacterium tuberculosis (MTB), responsible for the deadliest infectious disease worldwide, displays the remarkable ability to transition in and out of dormancy, a hallmark of the pathogens capacity to evade the immune system and opportunistically exploit immunocompromised individuals. Uncovering the gene regulatory programs that underlie the dramatic phenotypic shifts in MTB during disease latency and reactivation has posed an extraordinary challenge. We developed a novel experimental system to precisely control dissolved oxygen levels in MTB cultures in order to capture the chain of transcriptional events that unfold as MTB transitions into and out of hypoxia-induced dormancy. Using a comprehensive genome-wide transcription factor binding location map and insights from network topology analysis, we identified regulatory circuits that deterministically drive sequential transitions across six transcriptionally and functionally distinct states encompassing more than three-fifths of the MTB genome. The architecture of the genetic programs explains the transcriptional dynamics underlying synchronous entry of cells into a dormant state that is primed to infect the host upon encountering favorable conditions.\n\nOne Sentence SummaryHigh-resolution transcriptional time-course reveals six-state genetic program that enables MTB to enter and exit hypoxia-induced dormancy.

systems biology↗