Biology subjects

Vasilyeva, O. B.

Publications and source records attributed to Vasilyeva, O. B..

6 recordsLinked to original sources

bioRxiv2025

Mapping the phenotypic landscape of a transcriptional repressor using Deep Mutational Scanning and Growth-based Quantitative Sequencing

CymR is a TetR-family transcriptional repressor that recognizes a well-defined operator sequence in the promoter PcymRC. The native ligand cumate and several structurally related aromatic acids bind at an allosteric site and induce a conformational change in CymR, resulting in release from the DNA operator and de-repression of the promoter. The amino acid residues that contribute to these core functions have not been mapped, nor has the protein been subjected to extensive mutagenesis to modify its function. Here, for the first time, we integrate Deep Mutational Scanning (DMS) with Growth-based Quantitative Sequencing (GROQ-Seq) to evaluate a comprehensive phenotypic landscape of CymR variants, including single amino acid insertions and deletions. We measure this library across a concentration gradient of small molecule inducers to construct an induction curve for all library members. From this analysis, we identify amino acids throughout the protein that are essential for repressor function and discover several mutations that improve the sensitivity of CymR to the ligand perillic acid. In addition, rarely investigated insertion mutants are revealed to be a key driver of novel phenotypes, including several regions of CymR where insertions result in an inverted phenotype and the isolation of variants exhibiting an unusual band-stop phenotype. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/693801v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@121b16org.highwire.dtl.DTLVardef@b0478aorg.highwire.dtl.DTLVardef@128f2ecorg.highwire.dtl.DTLVardef@164a426_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2025

Genetically encoded RNA strand exchange circuits for programmable protein expression and computation in cells

Programmable cellular information processing could advance biomanufacturing of chemicals and medicine, and enable smart, living therapeutics and diagnostics1,2. Nucleic acids circuits based on toehold mediated strand exchange (TMSE) show tremendous potential for cellular programming due to their scalable, composable, and biocompatible parts3-6. However, these circuits are constrained primarily to in vitro applications because genetically encoding them is challenging and the principles of TMSE in cells remain unknown. Here we show the first demonstration of genetically encoded RNA strand exchange circuits, designed analogously to state-of-the-art TMSE circuits, in living cells. To elucidate the design principles of TMSE in cells, we develop toehold exchange riboregulators, which convert TMSE to protein expression, enabling precise control of protein translation rate. We find many of the design principles and parts used for TMSE in vitro transfer to Escherichia coli, allowing construction of multi-layer cascades and logic elements. We also identify caveats where strand exchange in cells differ substantially from cell-free systems, even in lysate from the same bacterial strain7, suggesting active cellular processes are involved. Our results further highlight bounds on strand exchange circuit architectures feasible in cells. We anticipate this study will lay the groundwork for developing advanced cellular circuits, bringing nucleic acid computing from the test tube to the cell8-10 and enabling new applications by connecting TMSE to gene expression. More broadly, our results have implications for RNA:RNA interactions and gene regulation in bacteria and provide a synthetic system for exploring such phenomena.

synthetic biology↗

bioRxiv2025

Experimental Evaluation of AI-Driven Protein Design Risks Using Safe Biological Proxies

Advances in machine learning are providing leaps forward for beneficial applications of protein engineering, while also raising concerns about biosecurity. Recently, Wittmann et al. described an in silico pipeline of generative AI tools to reformulate sequences of concern (SOCs) as synthetic homologs that may evade detection by biosecurity screening software (BSS) used by nucleic acid synthesis providers. Experimental testing of synthetic homologs is required to ascertain the true severity of this vulnerability. We present a generalizable framework to assess biosecurity risk consisting of testing, evaluation, validation, and verification (TEVV) of AI-assisted protein design (AIPD). We determine that common AIPD models in use at the time this study was initiated (early 2024) are not yet powerful enough to reliably rewrite the sequence of a given protein, while both maintaining activity and evading detection by BSS.

synthetic biology↗

bioRxiv2025

Epistasis in Allosteric Proteins: Can Biophysical Models Provide a Better Framework for Prediction and Understanding?

The prediction of epistasis, or the interaction between mutations, is a complex challenge impacting protein science, healthcare, and biotechnology. For allosteric proteins, the prediction of epistatic effects is further complicated by the intricate networks of conformational states and binding interactions inherent to their function. Here, we explore these issues by systematically comparing biophysical and phenomenological models to analyze mutational effects and epistasis for the lac repressor protein, LacI. Using an extensive dataset consisting of dose-response measurements for 164 LacI variants, we find that while the phenomenological Hill model provides slightly better predictive accuracy, the biophysical model fits the data more parsimoniously, with significantly less epistasis in its parameters. Our results highlight the importance of the multi-state, multi-dimensional nature of allosteric function and the potential benefits of using biophysical models for the analysis of mutational effects and epistasis.

biophysics↗

bioRxiv2025

Comparative Transcriptomic Analysis of Perfluoroalkyl Substances-Induced Responses of Exponential and Stationary Phase Escherichia coli

Per- and polyfluoroalkyl substances (PFAS) are highly stable chemical contaminants of emerging concern for human and environmental health due to their non-natural chemistry, widespread use, and environmental persistence. Despite conventional metrology, mitigation strategies, and removal technologies, the complexity of this growing problem necessitates the need for alternative approaches to tackle the immense challenges associated with complex environmental PFAS contamination. Recently, biology has emerged as an alternative approach to detect and mitigate PFAS and understand the molecular-level responses of living organisms, including microorganisms, to these compounds. However, further study is needed to understand how microorganisms in different environments and growth phases respond to PFAS. In this study, we performed RNA sequencing at mid-exponential, early stationary phase, and late stationary phase of bacterial growth to determine the global transcriptional response of a model chassis, Escherichia coli MG1655, induced by two PFAS, perfluorooctanoic acid (PFOA) and perfluorododecanoic acid (PFDoA), and equivalent non-fluorinated carboxylic acids (NFCA), octanoic acid and dodecanoic acid. Differential gene expression analysis revealed PFOA and PFDoA induced distinct changes in gene expression throughout cultivation. Specifically, we identified significant changes in expression of the formate regulon and sulfate assimilation at mid-exponential phase and ferrous iron transport, central metabolism, the molecular chaperone network, and motility processes during stationary phase. Importantly, many of these changes are not induced by NFCAs. In summary, we found PFAS induced a system-level change in gene expression, and our results expand the understanding of bacterial-PFAS interactions that could enable the development of future real-time environmental monitoring and mitigation technologies. ImportanceThe prevalence and persistence of PFAS in the environment is a growing area of concern. However, little is understood of the impacts of PFAS on the environment, particularly impacts on microorganisms that play pivotal roles in nearly every ecosystem. Thus, comprehensive measurements that provide systems-level insight into how microorganisms respond and adapt to PFAS in the environment are paramount. Here, we use RNA sequencing to study the global transcriptional response of E. coli MG1655 to two PFAS and non-fluorinated equivalent compounds across growth phases. We find that PFAS induce system-level changes in metabolic, transport, and gene regulatory pathways, providing insight into how these non-natural chemicals interact with a model bacterium. Additionally, the transcriptomic dataset associated with this work provides the community with PFSA-specific gene expression patterns and possible PFAS degradation pathways for the development of future whole-cell biosensors and mitigation efforts.

genomics↗

bioRxiv2024

Prevention of ribozyme catalysis through cDNA synthesis enables accurate RT-qPCR measurements of context-dependent ribozyme activity

Self-cleaving ribozymes are important tools in synthetic biology, biomanufacturing, and nucleic acid therapeutics. These broad applications deploy ribozymes in many genetic and environmental contexts, which can influence activity. Thus, accurate measurements of ribozyme activity across diverse contexts are crucial for validating new ribozyme sequences and ribozyme-based biotechnologies. Ribozyme activity measurements that rely on RNA extraction, such as RNA sequencing or reverse transcription-quantitative polymerase chain reaction (RT-qPCR), are generalizable to most applications and have high sensitivity. However, the activity measurement is indirect, taking place after RNA is isolated from the environment of interest and copied to DNA. So these measurements may not accurately reflect the activity in the original context. Here we develop and validate an RT-qPCR method for measuring context-dependent ribozyme activity using a set of self-cleaving RNAs for which context-dependent ribozyme cleavage is known in vitro. We find that RNA extraction and reverse transcription conditions can induce substantial ribozyme cleavage resulting in incorrect activity measurements with RT-qPCR. To restore the accuracy of the RT-qPCR measurements, we introduce an oligonucleotide into the sample preparation workflow that inhibits ribozyme activity. We then apply our method to measure ribozyme cleavage of RNAs produced in Escherichia coli (E. coli). These results have broad implications for many ribozyme measurements and technologies.

synthetic biology↗