Biology subjects

George, J. T.

Publications and source records attributed to George, J. T..

At least 19 recordsLinked to original sources

bioRxiv2026

How optimal control of cellular cost shapes population-level tumor growth dynamics

Tumor progression is often modeled as a passive response to external therapy or immune pressure, but tumor populations may also exhibit population-level regulation of proliferation and apoptosis. We develop a continuous-time Markov decision framework in which a controlled birth--death process represents a tumor population modulating the balance between proliferation and susceptibility to apoptosis in the presence of extrinsic death pressure. We examine threshold and quadratic costs, an unbounded linear reward, and constrained linear and quadratic formulations to determine how objective structure shapes optimal policies and induced population drift. Threshold and quadratic penalties generate restoring dynamics, with transitions from growth to suppression and regions of near-neutral drift associated with regulated or near-dormant behavior. An unbounded linear reward instead produces sustained or near-neutral growth without a restoring regime. Under constraints, a linear reward expands the region of positive drift as capacity increases, whereas a quadratic reward generates restoring, logistic-like drift around an interior population scale. These results show that regulated tumor dynamics depend on how growth incentives, extrinsic death pressure, penalties, and constraints scale with population size.

cancer biology↗

bioRxiv2026

Time-dependent memory of hypoxia exposure influences tumor invasion dynamics

Cancer cells in hypoxic environments often proliferate less but exhibit enhanced migration relative to their normoxic counterparts. Recent in vitro and in silico studies have characterized the role of hypoxic memory - the ability of cancer cells to retain their hypoxic phenotype even when reoxygenated - in tumor invasion. However, the observations have been limited either to exposing cancer cells to hypoxia for a fixed duration or by assuming a fixed-time persistence of the hypoxic state upon reoxygenation independent of the duration of hypoxia exposure. Thus, time-dependent cell-state changes during hypoxia and their impact on hypoxic memory remains unclear. Here, we first analyze transcriptomic data from breast cancer samples to show that the genes upregulated at transcriptional level and hypomethylated at epigenetic level are enriched in cell invasion, indicating hypoxic memory-driven process of tumor invasion. Next, we used a computational model to investigate how the spatial-temporal dynamics of oxygen levels in a tumor drive time-dependent changes in hypoxic memory and influence tumor invasion dynamics. Our simulation results show that such dynamic hypoxic memory can drive enhanced tumor invasion over a fixed hypoxic memory by a) enriching hypoxic cell density at the tumor front, b) reducing sensitivity of hypoxic cell state to fluctuations in oxygen supply, and c) enhancing effective diffusion of hypoxic cells. Our results highlight the crucial role of dynamic hypoxic memory in shaping tumor invasion dynamics, underscoring the need to elucidate its underlying mechanisms in future studies.

systems biology↗

bioRxiv2026

A biophysical framework for accurately identifying antigen single-amino acid escape variants and corresponding variant-specific compensatory TCR sequences

The impact of single amino acid substitution on T-cell receptor (TCR) recognition is central to understanding the molecular determinants of TCR specificity and degeneracy during viral mutational escape, cancer recognition, and autoimmunity. In this study, we developed a biophysics-informed computational approach integrating experimental alanine-scan mutagenesis data from the autoimmune-associated ALWGPDPAAA peptide bound to HLA-A*02:01 together with coarse-grained structural modeling. Our approach reconstructs the energetics and structural determinants underpinning the observed loss of recognition by the diabetogenic 1E6 TCR upon single-point mutations, specifically at the critical Pro5 and Asp6 residues. Leveraging the computational models ability to incorporate multiple structural templates into binding predictions, this approach quantitatively reproduces experimentally measured affinity disruptions. Additionally, we apply our approach to identify potential compensatory interactions capable of restoring binding affinity through alternative residue interactions. This integrative computational framework contributes a strategy for inferring TCR-peptide binding energetics at the single amino acid level, guiding the rational design of peptide-based immunotherapeutics, and predicting the functional impacts of clinically relevant peptide variants.

biophysics↗

bioRxiv2025

Interconnected axes of phenotypic plasticity drive coordinated cellular behaviour and worse clinical outcomes in breast cancer

Phenotypic plasticity plays a key role in cancer progression and metastasis, enabling cancer cells to adapt and evolve, but precisely how distinct axes governing phenotypic plasticity interact to shape tumour progression and patient outcomes remains unclear. We investigated five major interconnected axes of plasticity in ER-positive (ER+) breast cancer: Metabolic Reprogramming, Epithelial-to-Mesenchymal Plasticity (EMP), Luminal-Basal (Lineage) Switching, Stemness, and Drug-resistance using network dynamics simulations, integrative bulk and single-cell transcriptomic analyses and patient survival analyses. We show that these axes are not independent but drive one another, forming two mutually inhibiting teams of nodes enabling specified cellular behaviour. One team (favouring high glycolysis, stem-like, basal-like, mesenchymal/hybrid and tamoxifen-resistant phenotype) was found to be associated with aggressive progression and worse survival. On the other hand, the opposing team (favouring high oxidative phosphorylation, non-stem-like, luminal-like, epithelial and tamoxifen-sensitive phenotype) correlated with better outcomes. Importantly, altering one axis of plasticity often drove coordinated responses along other axes and vice versa. Our findings establish phenotypic plasticity in cancer as a coordinated, multi-axis dynamical process, thus suggesting novel strategies to disrupt systems-level reprogramming enabling metastasis and therapeutic resistance.

systems biology↗

bioRxiv2025

A memory-driven model of phenotypic adaptation for anticipating the emergence of enzalutamide resistance in prostate cancer

While contemporary cancer treatment strategies have significantly prolonged the lives of patients, therapeutic resistance remains a predominant cause of disease progression and cancerrelated deaths. Cancer therapy often induces gene regulatory responses that promote cell survival in the face of this therapy. Herein, we sought to develop a stochastic model of the response to repeat therapeutic challenge. This model integrates reinforcement learning to account for environmental history-dependent cellular transitions and growth dynamics. When applied to prostate cancer, this memory-driven adaptive model successfully captures the experimentally-observed dynamics of drugsensitive and drug-resistant LNCaP cells under varying dosing schedules of androgen receptor blockade with enzalutamide (enza), significantly outperforming traditional transition models that lack history dependence. This performance is especially evident in the ability of our approach to robustly predict stochastic fluctuations in cancer cell population sizes across the entire disease trajectory, including subtle, later-emerging responses following initial therapy. The model was further evaluated by predicting the control of resistant cells in an enza environment by modeling inhibition of the p38/MAPK pro-survival stress axis, which was then validated experimentally. Lastly, we developed and applied a patient-calibrated model using prostate-specific antigen (PSA) data from clinical patient cohorts undergoing intermittent androgen deprivation therapy. Our model accurately predicts the PSA dynamics under repeated treatment cycles and effectively distinguishing between patients who respond and those who do not respond to treatment, thereby providing quantitative insight into prostate cancer progression. We anticipate that such adaptive modeling frameworks will be broadly useful for predicting cancer treatment outcomes and developing optimized adaptive therapeutic strategies tailored to patient-specific disease dynamics in additional cancer contexts.

evolutionary biology↗

bioRxiv2025

A phenotype-structured PDE framework for investigating the role of hypoxic memory on tumor invasion under cyclic hypoxia

Tumor growth and angiogenesis drive complex spatiotemporal variation in micro-environmental oxygen levels. Previous experimental studies have observed that cancer cells exposed to chronic hypoxia retained a phenotype characterized by enhanced migration and reduced proliferation, even after being shifted to normoxic conditions, a phenomenon which we refer to as hypoxic memory. However, because dynamic hypoxia and related hypoxic memory effects are challenging to measure experimentally, our understanding of their implications in tumor invasion is quite limited. Here, we propose a novel phenotype-structured partial differential equation modeling framework to elucidate the effects of hypoxic memory on tumor invasion along one spatial dimension in a cyclically varying hypoxic environment. We incorporated hypoxic memory by including time-dependent changes in hypoxic-to-normoxic phenotype transition rate upon continued exposure to hypoxic conditions. Our model simulations demonstrate that hypoxic memory significantly enhances tumor invasion without necessarily reducing tumor volume. This enhanced invasion was sensitive to the induction rate of hypoxic memory, but not the dilution rate. Further, shorter periods of cyclic hypoxia contributed to a more heterogeneous profile of hypoxic memory in the population, with the tumor front dominated by hypoxic cells that exhibited stronger memory. Overall, our model highlighted the complex interplay between hypoxic memory and cyclic hypoxia in shaping heterogeneous tumor invasion patterns.

systems biology↗

bioRxiv2025

Regulatory network and spatial modeling reveal cooperative mechanisms of resistance and immune escape in ER+ breast cancer

Despite significant progress, the treatment of estrogen receptorpositive (ER+) breast cancer remains clinically challenging due to reversible drug resistance and immune evasion. Drug resistance often arises as cells undergo a dynamic epithelial-to-mesenchymal transition (EMT), while elevated PD-L1 levels contribute to immune escape. While these phenotypic features can variably co-occur, the impact of co-occurrence on the availability of synergistic treatment strategies remains unknown. To investigate their interplay, we constructed an ER-EMT-PD-L1 gene regulatory network and simulated these networks as coupled ordinary differential equations with biologically informed parameters, to generate steady-state expression profiles. Our study revealed that the relevant overarching network generated antagonistic epithelial and mesenchymal modules, capable of producing monostable, bistable, and tristable dynamics. We further examined the link between phenotypes and immune evasion by quantifying average PD-L1 expression, and found that epithelial-sensitive states consistently exhibited low PD-L1. In contrast, hybrid- and mesenchymal-resistant states were associated with high PD-L1, highlighting a strong coupling between EMT, resistance, and immune evasion. Extending on these network-level insights, we further used a spatially explicit agent-based model seeded with GRN-derived phenotypes to probe tumor behavior under therapeutic pressure. Simulations revealed that tumor escape required co-occurrence of therapy resistance, motility, and immune suppression, with plasticity and multistability further promoting adaptive persistence. Lastly, we identified combination therapies predicted to constrain malignant diversification and enhanced immune accessibility. Taken together, our modeling work links regulatory dynamics with tumor-level adaptation and underscores potential strategies to therapeutically reprogram cell states toward sensitivity.

bioengineering↗

bioRxiv2025

Coupled SDE-ODE Modeling of Tumor-Immune Dynamics to Infer Biomarker Release

Tumor-immune interactions are central to cancer progression and treatment response, driving cell death through immune-mediated killing and resource-limited competition. In early-stage disease or following effective treatment, cancer populations are often small and difficult to observe directly. Disease monitoring therefore relies on the detection of biomarkers such as circulating tumor DNA (ctDNA) as noisy proxies to cancer size. However, existing approaches lack robust frameworks to infer tumor burden from these signals when populations approach detection thresholds. To address this, we present a coupled deterministic-stochastic framework that links tumor-immune dynamics to biomarker release. A two-prey, one-predator Lotka-Volterra model captures interactions between immune cells and competing tumor subpopulation under shared resource constraints. Biomarker production is modeled using a linear stochastic differential equation, incorporating ctDNA release from both apoptotic death (immune-mediated) and necrotic death (due to intra-tumor competition). We derive analytical solutions for the resulting biomarker trajectories, including mean detection time under a minimal detection threshold. These results show how volatility in measured biomarker signal, disease heterogeneity, and immune pressure jointly shape signal emergence and persistence. Finally, to model situations in which the observer has access to future information--such as the terminal biomarker signal or sampling time in retrospective studies--we adopt an anticipative theoretic perspective. Using anticipative stochastic calculus, we derive path solutions to the resulting anticipating stochastic differential equation, capturing how future observations influence the inferred biomarker dynamics. This approach links the dynamics of underlying tumor-immune interactions to the corresponding detectable biomarker levels, with implications for early detection, immune monitoring, and retrospective reconstruction of disease progression.

biophysics↗

bioRxiv2025

How memory and adaptation cost shape cell phenotypic dynamics in response to fluctuating environments

Cells invariably encounter unpredictable changes in their microenvironment and adapt by orchestrating substantial alterations in their molecular states, often resulting in appreciable phenotypic changes. The timescale of molecular adaptation depends on how quickly a cell loses its molecular memory of past environmental encounters through the degradation rate of proteins unfavorable to the current environment. Concurrently, de novo synthesis of favorable biomolecules during adaptation imposes an energetic cost that impacts cellular fitness. Here, by developing a phenomenological model of intracellular processing of environmental signals and associated cell-state switching, we study the dynamical implications of cellular memory and adaptation cost on cellular responses to a changing environment. We find that while increasing cellular memory reduces cell fitness in periodic environments, counterintuitively, increasing adaptation cost can improve growth by minimizing mismatches between the environment and the cell state. Similarly, we observed a variable role of memory capacity and adaptation cost for stochastic correlated environments, with increasing memory and cost improving cellular fitness in negatively but not positively correlated environments. Lastly, we show that cellular memory and population heterogeneity in adaptation cost explained reported experimental observations in melanoma: increased population survival during drug treatment when the population was either enriched for rare cells expressing resistance marker genes or primed with low-dose drug treatment before exposure to high-dose treatment. Overall, this work establishes a foundational model for studying how cellular memory dynamics and adaptation cost drive cellular adaptation under different environmental conditions and explain complex cellular behaviors.

systems biology↗

bioRxiv2025

Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens

Accurate predictions of T cell receptor (TCR) specificity remain an important open problem in immunology, with broad implications for vaccine design, optimal immunotherapy, and improved management of autoimmune diseases. However, diversity in peptide antigens and TCR sequences at the level of individual patient repertoires remains a formidable computational challenge. Here, we develop a joint experimental and computational approach for predicting the antigen specificity of clinically-derived TCR sequences. Our model is trained on a combination of experimentally pre-identified and in silico-predicted TCR-pMHC structures using AlphaFold3. We apply our structural model in the clinical setting of hematopoietic stem cell transplant (HSCT) and demonstrate that our model is able to effectively discern the specificity of previously unseen donor and patient-derived TCR sequences against tumor associated and viral antigens. Model performance was further enhanced through the integration of sequence-based clustering and structurally diverse training templates. Our results highlight the predictive capabilities of structurally guided machine learning frameworks, trained on a minority test dataset, for antigen specificity prediction on unseen TCR sequences and their potential impact on a wide range of immunological applications.

immunology↗

bioRxiv2025

Structural basis of antiphage defense by an ATPase-associated reverse transcriptase

Reverse transcriptases (RTs) have well-established roles in the replication and spread of retroviruses and retrotransposons. However, recent evidence suggests that RTs have been conscripted by cells for diverse roles in antiviral defense. Here we determine structures of a type I-A retron, which explain how RNA, DNA, RT, HNH-nuclease and four molecules of an SMC-family ATPase assemble into a 364 kDa complex that provides phage defense. We show that phage-encoded nucleases trigger degradation of the retron-associated DNA, leading to disassembly of the retron and activation of the HNH nuclease. The HNH nuclease cleaves tRNASer, stalling protein synthesis and arresting viral replication. Taken together, these data reveal diverse and paradoxical roles for RTs in the perpetuation and elimination of genetic parasites.

biochemistry↗

bioRxiv2025

Impacts of competition and phenotypic plasticity on the viability of adaptive therapy

Cancer is heterogeneous and variability in drug sensitivity is widely documented across cancer types. Adaptive therapy is an emerging modality of cancer treatment that leverages this drug resistance heterogeneity to improve therapeutic outcomes. Current standard treatments typically eliminate a large fraction of drug-sensitive cells, leading to drug-resistant relapse due to competitive release. Adaptive therapy aims to retain some drug-sensitive cells, thereby limiting resistant cell growth by ecological competition. While early clinical trials of such a strategy have shown promise, optimisation of adaptive therapy is a subject of active study. Current methods largely assume cell phenotypes to remain constant, even though cell-state transitions could permit drug-sensitive and -resistant phenotypes to interchange and thus escape therapy. We address this gap using a deterministic model of population growth, in which sensitive and resistant cells grow under competition and undergo cell-state transitions. Based on the models steady-state behaviour and temporal dynamics, we identify distinct balances of competition and phenotypic transitions that are suitable for effective adaptive versus constant dose therapy. Our data indicate that under adaptive therapy, models with cell-state transitions show a higher frequency of fluctuations than those without, suggesting that the balance between ecological competition and phenotypic transitions could determine population-level dynamical properties. Our analyses also identify key limitations of applying phenomenological models in clinical practice for therapy design and implementation, particularly when cell-state transitions are involved. These findings provide an overall perspective on the relevance of phenotypic plasticity for emerging cancer treatment strategies using population dynamics as an investigation framework. Significance StatementDrug-sensitive and -resistant cancer cells can compete with each other within the same tumour, and adaptive therapy exploits this competition to control overall tumour growth. The fact that sensitive and resistant cell types can switch phenotypes complicates the implementation of adaptive therapy. Our ODE-based theoretical ecology framework shows that asymmetric competition favouring sensitive cells generally benefits therapy outcomes, while phenotypic plasticity is usually detrimental. Our model also provides cell population-level indicators that can help predict the underlying balance between competition and plasticity. Phenomenological models are limited by whether their parameters can be reliably identified given scarce data, and our study illuminates the scope of such models in understanding cancer population dynamics and the need for mechanistic modelling.

systems biology↗

bioRxiv2024

Feature Selection Enhances Peptide Binding Predictions for TCR-Specific Interactions

T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for advancing immunotherapy, vaccine design, and understanding mechanisms of autoimmune disorders. This study presents a novel theoretical method that explores the impact of feature selection techniques on enhancing the predictive accuracy of peptide binding models tailored for specific TCRs. To evaluate the universality of our approach across different TCR systems, we utilized a dataset that includes peptide libraries tested against three distinct murine TCRs. A broad range of physicochemical properties, including amino acid composition, dipeptide composition, and tripeptide features, were integrated into the machine learning-based feature selection framework to identify key features contributing to binding affinity. Our analysis reveals that leveraging optimized feature subsets not only simplifies the model complexity but also enhances predictive performance, enabling more precise identification of TCR-peptide interactions. The results of our feature selection method are consistent with findings from hybrid approaches that utilize both sequence and structural data as input as well as experimental data. Our theoretical approach highlights the role of feature selection in peptide-TCR interactions, providing a powerful tool for uncovering the molecular mechanisms of the T-cell response and assisting in the design of more advanced targeted therapeutics.

biophysics↗

bioRxiv2024

Computational modeling of cancer cell metabolism along the catabolic-anabolic axes

Abnormal metabolism is a hallmark of cancer. Initially recognized through the observation of aerobic glycolysis in cancer nearly a century ago. Also, we now know that mitochondrial respiration is also used by cancer for progression and metastasis. However, it remains largely unclear the mechanisms by which cancer cells mix and match different metabolic modalities (oxidative/reductive) and leverage various metabolic ingredients (glucose, fatty acids, glutamine) to meet their bioenergetic and biosynthetic needs. Here, we formulate a phenotypic model for cancer metabolism by coupling master gene regulators (AMPK, HIF-1, Myc) with key metabolic substrates (glucose, fatty acid, and glutamine). The model predicts that cancer cells can acquire four metabolic phenotypes: a catabolic phenotype characterized by vigorous oxidative processes - O, an anabolic phenotype characterized by pronounced reductive activities - W, and two complementary hybrid metabolic states - one exhibiting both high catabolic and high anabolic activity - W/O, and the other relying mainly on glutamine oxidation - Q. Using this framework, we quantified gene and metabolic pathway activity respectively by developing scoring metrics based on gene expression. We validated the model-predicted gene-metabolic pathway association and the characterization of the four metabolic phenotypes by analyzing RNA-seq data of tumor samples from TCGA. Strikingly, carcinoma samples exhibiting hybrid metabolic phenotypes are often associated with the worst survival outcomes relative to other metabolic phenotypes. Our mathematical model and scoring metrics serve as a platform to quantify cancer metabolism and study how cancer cells adapt their metabolism upon perturbations, which ultimately could facilitate an effective treatment targeting cancer metabolic plasticity. Statement of significanceWe present a theoretical framework that integrates both catabolic and anabolic modes of cancer metabolism, considering the complex interplay between glucose, fatty acids, and glutamine, by coupling genetic regulation with metabolic pathways. Our work characterizes four main metabolic phenotypes in cancer - OXPHOS, glycolysis, hybrid, glutamine-dominant and demonstrates the critical role of Myc on glutamine metabolism in all four phenotypes. The characterization of metabolism can guide our evaluation of patient survival across cancer types.

systems biology↗

bioRxiv2024

How Modulation of the Tumor Microenvironment Drives Cancer Immune Escape Dynamics

Metastatic disease is the leading cause of cancer-related death, despite recent advances in therapeutic interventions. Prior modeling approaches have accounted for the adaptive immune systems role in combating tumors, which has led to the development of stochastic models that explain cancer immunoediting and tumor-immune co-evolution. However, cancer immunemediated dormancy, wherein the adaptive immune system maintains a micrometastatic population by keeping its growth in check, remains poorly understood. Immune-mediated dormancy can significantly delay the emergence (and therefore detection) of metastasis. An improved quantitative understanding of this process will thereby improve our ability to identify and treat cancer during the micrometastatic period. Here, we introduce a generalized stochastic model that incorporates the dynamic effects of immunomodulation within the tumor microenvironment on T cell-mediated cancer killing. This broad class of nonlinear birth-death model can account for a variety of cytotoxic T cell immunosuppressive effects, including regulatory T cells, cancer-associated fibroblasts, and myeloid-derived suppressor cells. We develop analytic expressions for the likelihood and mean time of immune escape. We also develop a method for identifying a corresponding diffusion approximation applicable to estimating population dynamics across a wide range of nonlinear birth-death processes. Lastly, we apply our model to estimate the nature and extent of immunomodulation that best explains the timing of disease recurrence in bladder and breast cancer patients. Our findings quantify the effects that stochastic tumor-immune interaction dynamics can play in the timing and likelihood of disease progression. Our analytical approximations provide a method of studying population escape in other ecological contexts involving nonlinear transition rates.

cancer biology↗

bioRxiv2024

Integration of Kinetic Data into Affinity-Driven Models for Improved T Cell-Antigen Specificity Prediction

T cell receptor (TCR) and peptide-major histocompatibility complex (pMHC) interactions that result in T cell activation are complex and have been distinguished by their equilibrium affinity and kinetic profiles. While prior affinity-based models can successfully predict meaningful TCR-pMHC interactions in many cases, they occasionally fail at identifying TCR-pMHC interactions with low binding affinity. This study analyzes TCR-pMHC systems for which empirical kinetic and affinity data exist and prior affinity-based predictions fail. We identify a criteria for TCR-pMHC systems with available kinetic information where the introduction of a correction factor improves energybased model predictions. This kinetic correction factor offers a means to refine existing models with additional data and offers molecular insights to help reconcile previously conflicting reports concerning the influence of TCR-pMHC binding kinetics and affinity on T cell activation.

biophysics↗

bioRxiv2024

Distinct evolutionary patterns of tumor immune escape and elimination determined by ECM architectures

Cancer progression remains a significant clinical challenge. Phenotypic adaptation by tumor cells results in disease hetero-geneity, which drives treatment resistance and immune escape. T cell immunotherapy, while effective at treating some cancer subtypes, can also fail due to limits on tumor immunogenicity or T cell recognition. For example, one potential contributor to immune escape involves the density and alignment of the extracellular matrix (ECM) surrounding tumors, also known as Tumor-Associated Collagen Signature (TACS). However, the specific mechanisms by which aligned fibers contribute to decreased patient survival rates have not yet been decoupled. Here, we developed our EVO-ACT (EVOlutionary Agent-based Cancer T cell interaction) model to study how TACS affects tumor evolution and dynamic tumor-T cell interactions. We identified a variety of TACS-specific dynamical features that influence T cell infiltration, cancer immunoediting, and ultimate immune escape. Our model demonstrates how TACS and phenotypic adaptation together explain overall survival trends in breast cancer.

biophysics↗

bioRxiv2024

De novo gene synthesis by an antiviral reverse transcriptase

Bacteria defend themselves from viral infection using diverse immune systems, many of which sense and target foreign nucleic acids. Defense-associated reverse transcriptase (DRT) systems provide an intriguing counterpoint to this immune strategy by instead leveraging DNA synthesis, but the identities and functions of their DNA products remain largely unknown. Here we show that DRT2 systems execute an unprecedented immunity mechanism that involves de novo gene synthesis via rolling-circle reverse transcription of a non-coding RNA (ncRNA). Unbiased profiling of RT-associated RNA and DNA ligands in DRT2-expressing cells revealed that reverse transcription generates concatenated cDNA repeats through programmed template jumping on the ncRNA. The presence of phage then triggers second-strand cDNA synthesis, leading to the production of long double-stranded DNA. Remarkably, this DNA product is efficiently transcribed, generating messenger RNAs that encode a stop codon-less, never-ending ORF (neo) whose translation causes potent growth arrest. Phylogenetic analyses and screening of diverse DRT2 homologs further revealed broad conservation of rolling-circle reverse transcription and Neo protein function. Our work highlights an elegant expansion of genome coding potential through RNA-templated gene creation, and challenges conventional paradigms of genetic information encoded along the one-dimensional axis of genomic DNA. One-Sentence SummaryBacterial reverse transcriptases synthesize extrachromosomal genes via rolling-circle amplification to confer potent antiviral immunity.

molecular biology↗