Biology subjects

Bahiri Elitzur, S.

Publications and source records attributed to Bahiri Elitzur, S..

3 recordsLinked to original sources

bioRxiv2024

Design of bacterial DNT sensors based on computational models

Detecting explosive compounds such as 2,4,6-trinitrotoluene (TNT) and its volatile byproduct 2,4-dinitrotoluene (DNT) is paramount for public health and environmental safety. In this study, we present the successful application of diverse computational and data analysis models toward developing a bacterial biosensor engineered to detect DNT with high sensitivity and specificity. The Escherichia coli-based biosensor harbors a plasmid-based fusion of a gene promoter acting as the sensing element to a microbial bioluminescence gene cassette as the reporter. By analyzing endogenous and heterologous promoter data under conditions of DNT exposure, a total of 367 novel variants were generated. The biosensors engineered with these modifications demonstrated a remarkable amplification of up to 4-fold change in signal intensity upon exposure to 2,4-dinitrotoluene compared to non-modified biosensors, accompanied by a decrease in the detection threshold. Our analysis suggests that the sequence features with the highest contribution to biosensor performances are DNA folding patterns and nucleotide motifs associated with DNT sensing. These computational insights guided the rational design of the biosensor, leading to significantly improved DNT detection capabilities compared to the previous biosensor strain. Our results demonstrate the effectiveness of integrating computational modeling with synthetic biology techniques to develop advanced biosensors tailored for environmental monitoring applications. A similar approach may be applied to a wide array of ecological, industrial, and medical sensing endeavors.

synthetic biology↗

bioRxiv2024

Predicting gene sequences with AI to study evolutionarily selected codon usage patterns

Selective pressure acts on the codon use, optimizing multiple, overlapping signals that are only partially understood. We trained artificial intelligence (AI) models to predict the codons given their amino acid sequence in the eukaryotes Saccharomyces cerevisiae and Schizosaccharomyces pombe and the bacteria Escherichia coli and Bacillus subtilis, to study the extent to which we can learn patterns in naturally occurring codons to improve predictions. We trained our models on a subset of the proteins, and evaluated their predictions on large, separate sets of proteins of varying lengths and expression levels. Our models significantly outperformed naive frequency-based approaches, demonstrating that there are dependencies between codons that can be learned to better predict evolutionary-selected codon usage. The prediction accuracy advantage of our models is greater for highly expressed genes and it is greater in bacteria than eukaryotes, supporting the hypothesis that there is a monotonic relationship between selective pressure for complex codon patterns and effective population size. Also, in S. cerevisiae and bacteria, our models were more accurate for longer proteins, suggesting that the AI system may have learned patterns related to co-translational folding. Gene functionality and conservation were also important determinants that affect the performance of our models. Finally, we showed that using information encoded in homologous proteins has only a minor effect on prediction accuracy, perhaps due to complex codon-usage codes in genes undergoing rapid evolution. In summary, our study employing contemporary AI methods offers a new perspective on codon usage patterns and a novel tool to optimize codon usage in endogenous and heterologous proteins. Significance statementCan one predict codon sequences used by an organism to encode a given amino acid sequence? This is difficult, because there are exponentially many codon sequences that can encode the same amino acid sequence and evolution is stochastic. Indeed, codons frequencies vary, a phenomenon known as codon-bias, yet we improve upon frequency-based predictions using contemporary AI tools that learn complex patterns and capture interactions between codons. Because our predictions are tested fairly, on cases not seen during the training process, accurate predictions suggest that these learned patterns are not random, and may be related to the evolutionary process. Thus, studying where our predictions are more accurate, is expected to reveal novel insights related to the way evolution shapes coding regions.

evolutionary biology↗

bioRxiv2020

Prokaryotic rRNA-mRNA interactions are involved in all translation steps and shape bacterial transcripts

The well-established Shine-Dalgarno model suggests that translation initiation in bacteria is regulated via base-pairing between ribosomal RNA (rRNA) and mRNA. However, little is currently known about the contribution of such interactions to the rest of the translation process and to the way bacterial transcript evolve. We used novel computational analyses and modelling of 823 bacterial genomes coupled with experiments to demonstrate that rRNA-mRNA interactions are diverse and regulate not only initiation, but all translation steps from pre-initiation to termination across the many bacterial phyla that have the Shine-Dalgarno sequence. As these interactions dictate translation efficiency, they serve as a driving evolutionary force for shaping transcripts in bacteria. We observed selection for strong rRNA-mRNA interactions in regions where such interactions are likely to enhance initiation, regulate early elongation and ensure the fidelity of translation termination. We discovered selection against strong interactions and for intermediate interactions in coding regions and present evidence that these interactions maximize elongation efficiency while also enhancing initiation by guiding free ribosomal units to the start codon. ImportancePrevious research has reported the significant influence of rRNA-mRNA interactions mainly in the initiation phase of translation. The results reported in this paper suggest that, in addition to the rRNA-mRNA interactions near the start codon that trigger initiation in bacteria, rRNA-mRNA interactions affect all sub-stages of the translation process (pre-initiation, initiation, elongation, termination). In addition, these interactions affect the way evolutionary forces shape the bacterial transcripts while considering trade-offs between the effects of different interactions across different transcript regions on translation efficacy and efficiency. Due to the centrality of the translation process, these findings are relevant to all biomedical disciplines.

systems biology↗