Biology subjects

Chiappino-Pepe, A.

Publications and source records attributed to Chiappino-Pepe, A..

10 recordsLinked to original sources

bioRxiv2024

Preventing escape and malfunction of recoded cells due to tRNA base changes

Engineering the genetic code restricts DNA transfer (cellular bioisolation) and enables new chemistries via non-standard amino acid incorporation. These distinct properties make recoded cells state-of-the-art safe technologies. However, evolutionary pressures may endanger the longevity of the recoding. Here, we reveal that recoded Escherichia coli lacking 18,214 serine codons and two tRNASer can express wild-type antibiotic resistance genes and escape up to seven orders of magnitude faster than expected. We show a two-step escape process whereby recoded cells mistranslate antibiotic resistance genes to survive until modified or mutated tRNAs reintroduce serine into unassigned codons. We developed genetic-code-sensitive kill switches that sense serine incorporation and prevent cellular escape while preserving encoding of three distinct non-standard amino acids. This work lays the foundation for the long-term controlled function of cells that incorporate new chemistries, with implications for the design, use, and biosafety of synthetic genomes in clinical and environmental applications where physical containment is insufficient.

synthetic biology↗

bioRxiv2024

Synthetic genomes unveil the effects of synonymous recoding

Engineering the genetic code of an organism provides the basis for (i) making any organism safely resistant to natural viruses and (ii) preventing genetic information flow into and out of genetically modified organisms while (iii) allowing the biosynthesis of genetically encoded unnatural polymers1-4. Achieving these three goals requires the reassignment of multiple of the 64 codons nature uses to encode proteins. However, synonymous codon replacement--recoding--is frequently lethal, and how recoding impacts fitness remains poorly explored. Here, we explore these effects using whole-genome synthesis, multiplexed directed evolution, and genome-transcriptome-translatome-proteome co-profiling on multiple recoded genomes. Using this information, we assemble a synthetic Escherichia coli genome in seven sections using only 57 codons to encode proteins. By discovering the rules responsible for the lethality of synonymous recoding and developing a data-driven multi-omics-based genome construction workflow that troubleshoots synthetic genomes, we overcome the lethal effects of 62,007 synonymous codon swaps and 11,108 additional genomic edits. We show that synonymous recoding induces transcriptional noise including new antisense RNAs, leading to drastic transcriptome and proteome perturbation. As the elimination of select codons from an organisms genetic code results in the widespread appearance of cryptic promoters, we show that synonymous codon choice may naturally evolve to minimize transcriptional noise. Our work provides the first genome-scale description of how synonymous codon changes influence organismal fitness and paves the way for the construction of functional genomes that provide genetic firewalls from natural ecosystems and safely produce biopolymers, drugs, and enzymes with an expanded chemistry.

synthetic biology↗

bioRxiv2024

Machine Learning and Directed Evolution of Base Editing Enzymes

As we enter the era of CRISPR medicines, base editors (BEs) emerged as one of the most promising tools to treat genetic associated diseases. However, unintended bystander editing beyond the target nucleotide poses a challenge to their translation into effective therapies. While many efforts have been made in the design of a universal enzyme with minimal bystander editing, the context dependent activity represents a major challenge for base editing-based therapies. In this work, we designed a sequence-specific guide RNA library with 3-extensions and detected guides that were able to reduce bystander and increase editing efficiency in a context dependent manner. The best candidate was later used for phage assisted non-continuous evolution to find a new generation of precise base editors. Simultaneously, we use protein language models trained on massive protein sequence datasets to find the evolutionarily plausible mutational patterns that can improve deaminase activity and precision. Both strategies provide a collection of precise TadA variants that not only drastically reduced bystander edits, but also was not in detriment of on-target activity. Our findings introduce a guide/enzyme parallel engineering pipeline, which lays the foundation for the development of new personalized genome editing strategies, ultimately enhancing the safety and precision of this groundbreaking technology.

synthetic biology↗

bioRxiv2024

NADPH composite index analysis quantifies the relationship between compartmentalized NADPH dynamics and growth rates in cancer cells

NADPH, a highly compartmentalized electron donor in mammalian cells, plays essential roles in cell metabolism. However, little is known about how cytosolic and mitochondrial NADPH dynamics relate to cancer cell growth rates in response to varying nutrient conditions. To address this issue, we present NADPH composite index analysis, which quantifies the relationship between compartmentalized NADPH dynamics and growth rates using genetically encoded NADPH sensors, automated image analysis pipeline, and correlation analysis. Through this analysis, we demonstrated that compartmentalized NADPH dynamics patterns were cancer cell-type dependent. Specifically, cytosolic and mitochondrial NADPH dynamics of MDA-MB-231 decreased in response to serine deprivation, while those of HCT-116 increased in response to serine or glutamine deprivation. Furthermore, by introducing a fractional contribution parameter, we correlated cytosolic and mitochondrial NADPH dynamics to growth rates. Using this parameter, we identified cancer cell lines whose growth rates were selectively inhibited by targeting cytosolic or mitochondrial NADPH metabolism. Mechanistically, we identified citrate transporter as a key mitochondrial transporter that maintains compartmentalized NADPH dynamics and growth rates. Altogether, our results present a significant advance in quantifying the relationship between compartmentalized NADPH dynamics and cancer cell growth rates, highlighting a potential of targeting compartmentalized NADPH metabolism for selective cancer cell growth inhibitions.

systems biology↗

bioRxiv2022

Swapped genetic code blocks viral infections and gene transfer

Removing cellular transfer RNAs (tRNAs), making their cognate codons unreadable, creates a genetic firewall that prevents viral replication and horizontal gene transfer. However, numerous viruses and mobile genetic elements encode parts of the translational apparatus, including tRNAs, potentially rendering a genetic-code-based firewall ineffective. In this paper, we show that such horizontally transferred tRNA genes can enable viral replication in Escherichia coli cells despite the genome-wide lack of three codons and the previously essential cognate tRNAs and release factor 1. By repurposing viral tRNAs, we then develop recoded cells bearing an amino-acid-swapped genetic code that reassigns two of the six serine codons to leucine during translation. This amino-acid-swapped genetic code renders cells completely resistant to viral infections by mistranslating viral proteomes and prevents the escape of synthetic genetic information by engineered reliance on serine codons to produce leucine-requiring proteins. Finally, we also repurpose the third free codon to biocontain this virus-resistant host via dependence on an amino acid not found in nature.

synthetic biology↗

bioRxiv2022

Strategies to Identify and Edit Improvements in Synthetic Genome Segments Episomally.

Genome engineering projects often utilize bacterial artificial chromosomes (BACs) to carry multi-kilobase DNA segments on low copy number vectors. However, DNA synthesis and amplification have the potential to impose mutations on the contained DNA segments that can in turn reduce or prevent viability of the final strain. Here, we describe improvements to a multiplex automated genome engineering (MAGE) protocol to improve recombineering frequency and multiplexability. This protocol was applied to recoding an Escherichia coli strain to swap out seven codons to synonymous alternatives genome-wide. Ten 44,402 to 47,179 bp de novo synthesized BAC-contained DNA segments from the recoded strain were unable to complement deletion of the corresponding 33 to 61 wild type genes using a single antibiotic resistance marker. Next-Generation Sequencing was used to identify 1-7 non-recoding mutations in essential genes per segment, and MAGE in turn proved a useful strategy to repair these mutations on the BAC-contained recoded segment when both the recoded and wild type copies of the mutated genes had to exist by necessity during the repair process. This strategy could be adapted to mutation identification and repair for other large-scale genome engineering projects, or for incorporation of small genetic engineering sites for quick protocol adjustments.

synthetic biology↗

bioRxiv2022

NICEgame: A workflow for annotating the knowledge gaps in metabolic reconstructions, using known and hypothetical reactions

Advances in medicine and biotechnology rely on the further understanding of biological processes. Despite the increasingly available types and amounts of omics data, significant biochemical knowledge gaps remain uncharacterized. Several approaches have been developed during the past years to identify missing metabolic annotations in genome-scale. However, these approaches suggest missing metabolic reactions within a limited set of already characterized metabolic capabilities. In this study, we introduce NICEgame (Network Integrated Computational Explorer for Gap Annotation of Metabolism), a workflow to characterize missing metabolic capabilities in genome-scale metabolic models using the ATLAS of Biochemistry. NICEgame suggests alternative sets of known and hypothetical reactions to resolve gaps in metabolic networks, assesses their thermodynamic feasibility, and suggests candidate genes and proteins to catalyze the introduced reactions. We use gene essentiality data use to identify metabolic gaps in the latest genome-scale model of Escherichia coli, iML1515. We apply our gap-filling approach and further enhance its genome annotation, by suggesting reactions and putative genes to resolve 46 % of the false negative gene essentiality predictions.

systems biology↗

bioRxiv2020

Database for drug metabolism and comparisons, NICEdrug.ch, aids discovery and design

The discovery of a drug requires over a decade of enormous research and financial investments--and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch database, which incorporates 250,000 bio-active molecules, and studied their metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug-drug and drug-metabolite pairs. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. Clustering based on this fingerprint in statins identified drugs for repurposing. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1,300 drugs and food molecules to target COVID-19 and explains their inhibitory mechanisms. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.

pharmacology and toxicology↗

bioRxiv2019

Genetic design automation for autonomous formation of multicellular shapes from a single cell progenitor

AO_SCPLOWBSTRACTC_SCPLOWMulti-cellular organisms originate from a single cell, ultimately giving rise to mature organisms of heterogeneous cell type composition in complex structures. Recent work in the areas of stem cell biology and tissue engineering have laid major groundwork in the ability to convert certain types of cells into other types, but there has been limited progress in the ability to control the morphology of cellular masses as they grow. Contemporary approaches to this problem have included the use of artificial scaffolds, 3D bioprinting, and complex media formulations, however, there are no existing approaches to controlling this process purely through genetics and from a single-cell starting point. Here we describe a computer-aided design approach for designing recombinase-based genetic circuits for controlling the formation of multi-cellular masses into arbitrary shapes in human cells.

synthetic biology↗

bioRxiv2019

Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.

systems biology↗