Biology subjects

Balazsi, G.

Publications and source records attributed to Balazsi, G..

11 recordsLinked to original sources

bioRxiv2024

Optimizing a CRISPR-Cas13d gene circuit for tunable target RNA downregulation with minimal collateral RNA cutting

The invention of RNA-guided DNA cutting systems has revolutionized biotechnology. More recently, RNA-guided RNA cutting by Cas13d entered the scene as a highly promising alternative to RNA interference to engineer cellular transcriptomes for biotechnological and therapeutic purposes. Unfortunately, "collateral damage" by indiscriminate off-target cutting tampered enthusiasm for these systems. Yet, how collateral activity, or even RNA target reduction depends on Cas13d and guide RNA abundance has remained unclear due to the lack of expression-tuning studies to address this question. Here we use precise expression-tuning gene circuits to show that both nonspecific and specific, on-target RNA reduction depend on Cas13d and guide RNA levels, and that nonspecific RNA cutting from trans cleavage might contribute to on-target RNA reduction. Using RNA-level control techniques, we develop new Multi-Level Optimized Negative-Autoregulated Cas13d and crRNA Hybrid (MONARCH) gene circuits that achieve a high dynamic range with low basal on-target RNA reduction while minimizing collateral activity in human kidney cells and green monkey cells most frequently used in human virology. MONARCH should bring RNA-guided RNA cutting systems to the forefront, as easily applicable, programmable tools for transcriptome engineering in biotechnological and medical applications.

synthetic biology↗

bioRxiv2023

Transcriptional drift in aging cells: A global de-controller

As cells age, they undergo a remarkable global change: In transcriptional drift, hundreds of genes become overexpressed while hundreds of others become underexpressed. Using archetype modeling and Gene Ontology analysis on data from aging Caenorhabditis elegans worms, we find that the upregulated genes code for sensory proteins upstream of stress responses and downregulated genes are growth- and metabolism-related. We propose a simple mechanistic model for how such global coordination of multi-protein expression levels may be achieved by the binding of a single ligand that concentrates with age. A key implication is that a cells own responses are part of its aging process, so unlike for wear-and-tear processes, intervention might be able to modulate these effects.

biophysics↗

bioRxiv2023

A luminal intermediate cell state maintains long-term prostate homeostasis and contributes to tumorigenesis

Cellular heterogeneity poses tremendous challenges for developing cell-targeted therapies and biomarkers of clinically significant prostate cancer. The origins of this heterogeneity within normal adult and aging tissue remain unknown, leaving cellular states and transcriptional programs that allow expansions of malignant clones unidentified. To define cell states that contribute to early cancer development, we performed clonal analyses and single cell transcriptomics of normal prostate from genetically-engineered mouse models. We uncovered a luminal transcriptional state with a unique "basal-like" Wnt/p63 signaling (luminal intermediate, LumI) which contributes to the maintenance of long-term prostate homeostasis. Moreover, LumI cells greatly expand during early stages of tumorigenesis in several mouse models of prostate cancer. Genetic ablation of p63 in vivo in luminal cells reduced the formation of aggressive clones in mouse prostate tumor models. Finally, the LumI cells and Wnt signaling appear to significantly increase in human aging prostate and prostate cancer samples, highlighting the importance of this hybrid cell state for human pathologies with potential translational impact.

cancer biology↗

bioRxiv2022

Analysis of gene expression heterogeneity reveals therapeutic targets and novel regulators of metastasis

Tumor cell heterogeneity has been implicated in metastatic progression of solid tumors such as triple-negative breast cancer (TNBC), leading to resistance and recurrence. We hypothesized that genes with low cell-to-cell transcriptional variability may be effective therapeutic targets, and that analysis of variability may facilitate identification of new metastatic regulators. Here we demonstrate, using single cell RNA sequencing, that the metastasis suppressor Raf Kinase Inhibitory Protein (RKIP) reduced overall transcriptional variability in TNBC xenograft tumors. Focusing on genes with reduced variability in response to RKIP, we identified targetable gene sets such as oxidative phosphorylation and showed that metformin could inhibit RKIP-expressing but not control tumor growth. We also found many regulators of cancer progression including a novel epigenetic metastasis suppressor, KMT5C. These studies demonstrate that a metastatic regulator can alter transcriptional variability in tumors and reveal the importance of genes involved in heterogeneity as potential therapeutic targets and regulators of metastatic progression in cancer.

cancer biology↗

bioRxiv2020

Bursty expression enhances reliability of messaging in gene networks

The promoter state of a gene and its expression levels are modulated by the amounts of transcription factors interacting with its regulatory regions. Hence, one may interpret a gene network as a communicating system in which the state of the promoter of a gene (the source) is communicated by the amounts of transcription factors that it expresses (the message) to modulate the state of the promoter and expression levels of another gene (the receptor). The reliability of the gene network dynamics can be quantified by the Shannons entropy of the message and the mutual information between the message and the promoter state. Here we consider a stochastic model for a binary gene and use its exact steady state solutions to calculate the entropy and mutual information. We show that a slow switching promoter having long and equally standing ON and OFF states maximizes the mutual information and reduces entropy. That is a bursty regime generating a high variance message governed by a bimodal probability distribution with peaks of the same height. Our results indicate that Shannons theory can be a powerful framework for understanding how bursty gene expression conciliates with the striking spatio-temporal precision exhibited in pattern formation of developing organisms.

systems biology↗

bioRxiv2019

Evolutionary regain of lost gene circuit function

Evolutionary reversibility - the ability to regain a lost function - is an important problem both in evolutionary and synthetic biology, where repairing natural or synthetic systems broken by evolutionary processes may be valuable. Here, we use a synthetic positive-feedback (PF) gene circuit integrated into haploid Saccharomyces cerevisiae cells to test if the population can restore lost PF function. In previous evolution experiments, mutations in a gene eliminated the fitness costs of PF activation. Since PF activation also provides drug resistance, exposing such compromised or broken mutants to both drug and inducer should create selection pressure to regain drug resistance and possibly PF function. Indeed, evolving seven PF mutant strains in the presence of drug revealed three adaptation scenarios through genomic mutations outside of the PF circuit that elevate PF basal expression, possibly by affecting transcription, translation, degradation and other fundamental cell functions. Nonfunctional mutants gained drug resistance without ever developing high expression, while quasi-functional and dysfunctional PF mutants developed high expression which then diminished, although more slowly for dysfunctional mutants where revertant clones arose. These results highlight how intracellular context, such as the growth rate, can affect regulatory network dynamics and evolutionary dynamics, which has important consequences for understanding the evolution of drug resistance and developing future synthetic biology applications.\n\nSignificance StatementNatural or synthetic genetic modules can lose their function over long-term evolution if the function is costly. How populations can evolve to restore broken functions is poorly understood. To test the reversibility of evolutionary breakdown, we use yeast cell populations with a chromosomally integrated synthetic gene circuit. In previous evolution experiments the gene circuit lost its costly function through various mutations. By exposing such mutant populations to conditions where regaining gene circuit function would be beneficial we find adaptation scenarios with or without repairing lost gene circuit function. These results are important for drug resistance or future synthetic biology applications where loss and regain of function play a significant role.

systems biology↗

bioRxiv2019

Noise-Reducing Negative-Feedback Optogenetic Circuits in Mammalian Cells

Gene autorepression is widely present in nature and is also employed in synthetic biology, partly to reduce gene expression noise in cells. Optogenetic systems have recently been employed for controlling gene expression levels in mammalian cells, but most have utilized activator-based proteins, neglecting negative feedback. Here, we engineer optogenetic negative-feedback gene circuits in mammalian cells to achieve noise-reduction for precise gene expression control. We build a toolset of these noise-reducing Light-Inducible Tuner (LITer) gene circuits using the TetR repressor fused with a Tet-Inhibitory peptide (TIP) or a degradation tag through the light-sensitive LOV2 protein domain. These LITers provide nearly a range of 4-fold gene expression control and up to five-fold noise reduction from existing optogenetic systems. Moreover, we use the LITer gene circuit architecture to control gene expression of the cancer oncogene KRAS(G12V) and study its downstream effects through phospho-ERK levels and cellular proliferation. Overall, these novel LITer optogenetic platforms should enable precise spatiotemporal perturbations for studying multicellular phenotypes in developmental biology, oncology, and other biomedical fields of research.

synthetic biology↗

bioRxiv2019

Optimality of the spontaneous prophage induction rate

Lysogens are bacterial cells that have survived after being infected by bacterial viruses called bacteriophages. Instead of being killed by the virus, the infected cell survives by integrating the viral DNA into its own genome. This is only possible with "temperate" bacteriophages which do not always lyse their host to reproduce, but sometimes replicate passively using the lysogenic pathway. After an infection resulting in lysogeny, the lysogen continues to grow and divide normally, seemingly unaffected by the integrated viral genome which is now referred to as a prophage. However, the prophage can have an impact on the hosts phenotype and overall fitness in certain environments. This makes competition between the lysogen and its nonlysogen counterpart possible because both cells have different genomes and potentially different growth rates. Additionally, the prophages within the lysogens are capable of spontaneously reverting back to the lytic pathway via spontaneous prophage induction (SPI), causing death of the lysogen and the release of new progeny phages. These new phages can then lyse or lysogenize other susceptible nonlysogens, thereby impacting the competition between lysogens and nonlysogens. In a scenario with differing growth rates, it is not clear whether SPI would be beneficial or detrimental to the lysogens since it directly causes cell death but also attacks nonlysogenic competitiors, either lysing or lysogenizing them. In this work we study the evolutionary dynamics of a mixture of lysogens and nonlysogens and derive general conditions on the rate of SPI resulting in lysogens displacing nonlysogens. We show that there exists an optimal SPI rate, and apply the model to bacteriophage{lambda} . We find that the model can explain why the experimentally measured SPI rate for phage{lambda} is so low. We also investigate the impact of stochasticity and conclude that even at low copy numbers the SPI rate can still be fairly low while still providing an advantage to the lysogens.

evolutionary biology↗

bioRxiv2018

Backward evolution from gene network dynamics

Gene expression is controlled by regulator genes that together with effector genes form gene regulatory networks. How mutation in the genes comprising gene regulatory networks influences cell population dynamics has not been adequately investigated. In this study, we develop mathematical models to study how a mutation in a regulator gene that reaches the effector gene with a time delay affects short-term and long-term population growth. Using theory and experiment, we find a paradoxical outcome of evolution where a mutation in a regulator gene leads to an interaction between gene regulatory network and population dynamics, causing in certain cases a permanent decrease in population fitness in a constant environment.\n\nSignificance StatementThe properties of a cell are largely the products of its proteins, synthesized at rates depending on the regulation of protein coding genes. Single-cell measurements show that genetically identical cells can differ radically in their protein levels, partially due to the random production and degradation of proteins. It is currently unknown how mutants arise and spread in populations affected by such biological variability. We use computer simulations and evolution experiments to study how a mutant spreads in a population that carries a synthetic drug resistance gene network. Our results show for the first time a paradoxical outcome of evolution, where an initially beneficial mutation can interact with gene regulatory network dynamics and cause a permanent decrease in population fitness in the same environment.

evolutionary biology↗

bioRxiv2018

Evolutionary trade-offs between unicellularity and multicellularity in budding yeast

Multicellular organisms appeared on Earth through several independent major evolutionary transitions. Are such transitions reversible? Addressing this fundamental question entails understanding the benefits and costs of multicellularity versus unicellularity. For example, some wild yeast strains form multicellular clumps, which might be beneficial in stressful conditions, but this has been untested. Here we show that unicellular yeast evolves from clump-forming ancestors by propagating samples from suspension after larger clumps have settled. Unicellular yeast strains differed from their clumping ancestors mainly by mutations in the AMN1 (Antagonist of Mitotic exit Network) gene. Ancestral yeast clumps were more resistant to freeze/thaw, hydrogen peroxide, and ethanol stressors than their unicellular counterparts, while unicellularity was advantageous without stress. These findings inform mathematical models, jointly suggesting a trade-off between the benefits and downsides of multicellularity, causing bet-hedging by regulated phenotype switching as a survival strategy in unexpected stress.

evolutionary biology↗

bioRxiv2017

Functional effects of heating and cooling gene networks

Everyday existence and survival of most organisms requires coping with temperature changes, which involves gene regulatory networks both as subjects and agents of cellular protection. Yet, how temperature affects gene network function remains unclear, partly because natural gene networks are complex and incompletely characterized. Here, we study how heating and cooling affect the function of single genes and well-characterized synthetic gene circuits in Saccharomyces cerevisiae. We found nontrivial, nonmonotone temperature-dependent gene expression trends at non-growth-optimal temperatures. In addition, heating caused unusual bimodality in the negative-feedback gene circuit expression and shifts upward the bimodal regime for the positive feedback gene circuit. Mathematical models incorporating temperature-dependent growth rates and Arrhenius scaling of reaction rates captured the effects of cooling, but not those of heating. Molecular dynamics simulations revealed that heating alters the conformational dynamics and allows DNA-binding of the TetR transcriptional repressor, fully explaining the experimental results for the negative-feedback gene circuit. Overall, we uncover how temperature shifts may corrupt gene networks, which may aid future designs of temperature-robust synthetic gene circuits.

synthetic biology↗