Biology subjects

Tuller, T.

Publications and source records attributed to Tuller, T..

17 recordsLinked to original sources

bioRxiv2026

Monitoring the translation initiation process in prokaryotes via large-scale examination using optimized SSU-seq technique

Translation initiation is a critical stage of protein synthesis in prokaryotes, where ribosomes assemble on mRNA to begin decoding genetic information. The interactions between ribosomal RNA (rRNA) and mRNA significantly influence this process, with features like ribosomal binding site (RBS) affecting ribosomal dynamics. However, direct experimental validation of these types of interaction is yet to be provided. In this study, we developed an optimized small sub-unit sequencing (SSU-seq) protocol for Escherichia coli, enabling the detailed examination of ribosomal complexes and subunits dynamics. Unlike Ribo-seq, which primarily maps fully assembled ribosomes, SSU-seq captures both ribosomal subunits and intact ribosomes, offering a comprehensive view of translation initiation and elongation events. By integrating experimental data with predictive models, our study advances the understanding of translation regulation in prokaryotes and underscores the utility of SSU-seq in decoding the complexities of ribosome-mRNA interactions. Our analysis reveals distinct patterns of small subunit distribution across the entire mRNAs with higher density near positions with stronger rRNA-mRNA hybridization. The results may suggest that rRNA-mRNA hybridization positions along the entire transcript facilitate efficient start codon recognition by maintaining proximity between the small subunit and the mRNA.

systems biology↗

bioRxiv2026

Rational mRNA Structure -guided Attenuation Enables Embryo-safe in-ovo NDV Vaccination and Protection

Newcastle disease virus (NDV) continues to cause major economic losses in poultry, driving many recent virulent outbreaks. Maternally derived antibodies (MDA) can interfere with the development of active immunity following vaccination in young chicks. NDV vaccine strains that are safe for day-old chicks are not safe for administration to embryos. Here, we combined a computationally guided attenuation strategy with an in-ovo vaccination and challenge study to develop and evaluate an embryo-safe NDV vaccine candidate. Using a large NDV sequence compendium, we applied a local RNA folding energy based algorithm to identify folding selected clusters within the NDV fusion protein; we introduced targeted synonymous substitutions within the first ~900 nucleotides to modulate local RNA structure while preserving the encoded protein sequence. The recombinant attenuated variant was propagated in embryonated chicken eggs to generate vaccine stocks for in-ovo vaccination. After 18 days of embryonation, the synthetic variants were well tolerated, achieving more than 97% hatchability, with no pre-challenge morbidity or mortality, while unvaccinated challenge controls showed complete mortality. Together, these data support a model in which structure-aware synonymous modifications can yield an embryo-safe NDV backbone suitable for in-ovo delivery, with protective efficacy exhibiting a clear dose window under high MDA conditions.

synthetic biology↗

bioRxiv2025

High dimensional tissue specific regulatory codes embedded in viral coding regions

Viruses co-evolve with their hosts and adapt various genomic level strategies to ensure their fitness. Previous studies have demonstrated that viruses share high dimensional silent patterns with their hosts. In this study, based on a novel computational measure, we demonstrated for the first time that this high dimensional information encodes regulatory codes related to viral tropism.

systems biology↗

bioRxiv2025

Novel Driver Mutations in GCB Lymphoma Patients That Affect Transcription Factors Binding

Mutations in the DNA can affect cancer development and progression not only by changing the amino acid chain but also by affecting different regulatory elements such as transcription factors. This study introduces a novel pipeline to identify "mutation blocks" - small genomic areas with high mutation rates that potentially influence transcription factors binding. By analyzing GCB lymphoma patient data, mutations blocks were identified that correlated with gene expression changes and were linked to transcription factor activity. These mutation blocks suggest a selection for mutations that alter gene regulation, contributing to lymphoma development. The analysis identified 56 mutation blocks in germinal center B-cell like diffuse large B-cell lymphoma (GCB DLBCL) patients genomes, affecting genes such as BCL2, MYC, SGK1, and PIM1, and linked to transcription factors including MSC, TCFL5, HOXB7, FOXP3, and ZBTB6. A machine learning model that used gene ontology data suggested further potential transcription factors-gene pairs influencing cancer. These findings highlight the role of synonymous and silent mutations in altering transcription factors binding and gene expression, offering insights into the mechanisms of GCB lymphoma.

systems biology↗

bioRxiv2025

AI-directed gene fusing prolongs the evolutionary half-life of synthetic gene circuits

Evolutionary instability is a persistent challenge in synthetic biology, often leading to the loss of heterologous gene expression over time. Here, we present STABLES, a novel gene fusion strategy that links a gene of interest (GOI) to an essential endogenous gene (EG), with a "leaky" stop codon in between. This ensures both selective pressure against deleterious mutations and high expression of the GOI. By leveraging a machine learning (ML) framework, we predict optimal GOI-EG pairs based on bioinformatic and biophysical features, identify linkers likely to minimize protein misfolding, and optimize DNA sequences for stability and expression. Experimental validation in Saccharomyces cerevisiae demonstrated significant improvements in stability and productivity for fluorescent proteins and human proinsulin. The results highlight a scalable, adaptable and organism-agnostic method to enhance the evolutionary stability of engineered strains, with broad implications for industrial biotechnology and synthetic biology.

synthetic biology↗

bioRxiv2025

Computational design of toehold switches in eukaryotes and prokaryotes for efficient post-transcriptional control

The potential to design RNA molecules implementing translational regulated synthetic networks in eukaryotic cells can be harnessed to ramp up the development of various applications in biotechnology, synthetic biology, agriculture and personalized precision therapeutics. In prokaryotes, RNA based translation activation Toehold switches were demonstrated to achieve high fold change in throughput of a desired expression gene in response to different levels of both exogenous and endogenous target trigger RNA molecules. However, low fold changes were reported for such switches applying similar design architecture for eukaryotes. We developed Triggate, a computational pipeline which implements different algorithms for the efficient design and generation of higher on/off ratio optimized toehold switches in prokaryotes and eukaryotes. We also present here a new design architecture for eukaryotic toehold switches which is leaner and simpler. Using our tool and new architecture we report results with a much higher on/off ratio (up to around 13) than achieved before for mRNA toehold switches for eukaryotes. Triggate is available as a web application at http://www.cs.tau.ac.il/~tamirtul/Triggate.

synthetic biology↗

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

A tool for CRISPR-Cas9 gRNA evaluation based on computational models of gene expression

CRISPR based technologies have revolutionized all biomedical fields as it enables efficient genomic editing. These technologies are often used to silence genes by inducing mutations that are expected to nullify their expression. To this end, dozens of computational tools have been developed to design gRNAs, CRISPRs gene-targeting molecular guide, with high cutting efficiency and no off-target effect. However, these tools do not consider the induced mutations effect on the genes expression, which is the actual objective that should be optimized. This fact can often lead to failures in the design, as an efficient cutting of the DNA does not ensure the desired effect in protein production. Therefore, we developed EXPosition, a computational tool for gRNA design. It is the first tool designed to improve the true objective of using CRISPR: the effect it has on gene expression. To this end, we used predictive deep-learning models for the relevant gene expression steps: transcription, splicing, and translation initiation. We validated our tool by demonstrating that it can classify sites as "silencing" or "non-silencing" better than models that consider only the cutting efficiency. We believe that this tool will significantly improve both the efficiency and accuracy of genome editing endeavors. EXPosition is available at http://www.cs.tau.ac.il/~tamirtul/EXPosition.

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↗

bioRxiv2022

Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity

The success of the human body in fighting SARS-CoV-2 infection relies on lymphocytes and their antigen receptors. Identifying and characterizing clinically relevant receptors is of utmost importance. We report here the application of a machine learning approach, utilizing B cell receptor repertoire sequencing data from severely and mildly infected individuals with SARS-CoV-2 compared with uninfected controls. In contrast to previous studies, our approach successfully stratifies non-infected from infected individuals, as well as disease level of severity. The features that drive this classification are based on somatic hypermutation patterns, and point to alterations in the somatic hypermutation process in COVID-19 patients. These features may be used to build and adapt therapeutic strategies to COVID-19, in particular to quantitatively assess potential diagnostic and therapeutic antibodies. These results constitute a proof of concept for future epidemiological challenges.

immunology↗

bioRxiv2022

eIF3 mRNA selectivity profiling reveals eIF3k as a cancer-relevant regulator of ribosome content

eIF3, whose subunits are frequently overexpressed in cancer, regulates mRNA translation from initiation to termination, but mRNA-selective functions of individual subunits remain poorly defined. Using multi-omic profiling upon acute depletion of eIF3 subunits, we observed that while eIF3a, b, e, and f markedly differed in their impact on eIF3 holo-complex formation and translation, they were each required for cancer cell proliferation and tumor growth. Remarkably, eIF3k showed the opposite pattern with depletion promoting global translation, cell proliferation, tumor growth, and stress resistance through repressing the synthesis of ribosomal proteins, especially RPS15A. Whereas ectopic expression of RPS15A mimicked the anabolic effects of eIF3k depletion, disruption of eIF3 binding to the 5-UTR of RSP15A mRNA negated them. Supported by mathematical modeling, our data uncovers eIF3k-l as a mRNA-specific module which, through controlling RPS15A translation, serves as a rheostat of ribosome content to secure spare translational capacity that can be mobilized during stress.

cell biology↗

bioRxiv2022

Translation in the cell under fierce competition for shared resources: a mathematical model

During the process of translation the mRNAs in the cell "compete" for shared resources like tRNA molecules and ribosomes. This creates an indirect and intricate coupling between the mRNAs. For example, if ribosomal "traffic jams" evolve on some mRNA then the abundance of free ribosomes may decrease leading to lower initiation rates in the other mRNAs. When the shared resources are abundant the coupling between mRNAs due to this competition is weak. However, when the resources are scarce, e.g., when the pool of free ribosomes is starved, the competition may have a dramatic effect on the dynamics of translation in the cell. This scenario may be relevant for example under stress conditions or during a high yield viral infection, where the viral mRNAs "hijack" components of the translation machinery. Fierce competition for shared resources may also take place in synthetic or engineered systems such as cell free systems or in the case of high-throughput heteroglougs gene expression. We study this scenario using a mathematical model that includes a network of m ribosome flow models (RFMs) interconnected via a pool of free ribosomes. Each RFM is a non-linear dynamical model for ribosome flow along a single mRNA molecule, and the interconnection via the pool encapsulates the competition for shared resources. We analyze the case where m is large, i.e., a there is a large number of mRNAs. This implies that many ribosomes are attached to the mRNAs and thus the pool is starved. Our model allows quantitative and qualitative analysis of the network steady state when the pool is starved. Our analysis results show that adding an mRNA to the network always decreases the steady state pool density. This makes sense, as every new mRNA "consumes" ribosomes. We also show that adding an mRNA has an intricate effect on the total protein production in the network: on the one-hand, the new mRNA produces new proteins. On the other-hand, the other mRNAs produce less proteins, as the pool that feeds these mRNAs now has a smaller abundance of ribosomes. Our analysis yields an explicit bound for the total production rate of the network when the number of RFMIOs is very large. In particular, we analyze how the total density of ribosomes in the network bounds the total production rate. This bound demonstrates that when the number of mRNAs increases, the marginal utility of adding another mRNA diminishes, and the total protein production rate saturates to a limiting value. We demonstrate our analysis approach using an example of producing insulin in a cell free system.

systems 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↗

bioRxiv2020

Exploring Potential Signals of Selection for Disordered Residues in Naturally Occurring Prokaryotic and Eukaryotic Proteins

Intrinsically disordered proteins (IDPs) were recognized as an important class of proteins in all domains of life for their functional importance. However, how nature has shaped the disorder potential of prokaryotic and eukaryotic proteins is still not clearly known. Randomly generated sequences are free of any selective constraints thus these sequences are commonly used as null models. Considering different types of random protein models here we seek to understand how disorder potential of natural eukaryotic and prokaryotic proteins differs from random sequences. Comparing proteome-wide disorder content between real and random sequences of 12 model organisms we noticed that while in eukaryotes natural sequences tend to be more disordered than random sequences prokaryotes follow an opposite trend. By analyzing position-wise disorder profile, here we showed that there is a general trend of higher disorder near the N and C-terminal regions of eukaryotic proteins as compared to the random models; however, either no or a weak such trend was found in prokaryotic proteins. Moreover here we showed that this preference is not due to the biases either in the amino acid or nucleotide composition or other factors at the respective sites. Instead, these regions were found to be endowed with a higher fraction of protein-protein binding sites suggesting their functional importance. Here, we proposed various explanations for this pattern such as improving the efficiency of protein-protein interaction, ribosome movement, and post-translational modification, etc. However, further studies are needed to clearly understand the biophysical mechanisms causing the trend.

bioinformatics↗

bioRxiv2020

mRNA secondary structure stability regulates bacterial translation insulation and re-initiation

In bacteria, translation re-initiation is crucial for synthesizing proteins encoded by genes that are organized into operons. The mechanisms regulating translation re-initiation remain, however, poorly understood. We now describe the ribosome termination structure (RTS), a conserved and stable mRNA secondary structure precisely localized downstream of stop codons, which serves as the main factor governing re-initiation efficiency in a synthetic Escherichia coli operon. We further report that in 95% of 128 analyzed bacterial genomes representing all phyla, this structure is selectively depleted when re-initiation is advantageous yet selectively enriched so as to insulate translation when re-initiation is deleterious.

molecular biology↗

bioRxiv2018

Solving the riddle of the evolution of Shine-Dalgarno based translation in chloroplasts

The chloroplast, a photosynthetic organelle found in all plant and algae species, originates from an ancient event in which a cyanobacterium was engulfed by a larger eukaryote. Thus, modern chloroplasts still harbor a bacterial-like genome and carry out all stages of gene expression, including mRNA translation by a 70S ribosome. However, the Shine-Dalgarno model, which predominantly regulates translation initiation by base-pairing between the ribosomal RNA and the mRNA in model bacteria genera, was reported to have ambiguous effects on chloroplast gene expression. Here we show that while the Shine-Dalgarno motif is clearly conserved in proteobacterial mRNAs, its general absence from chloroplast mRNAs is observed in cyanobacteria as well, promoting the idea that the evolutionary process of reducing the centrality of the Shine-Dalgarno mechanism began well before plastid endosymbiosis. As plastid ribosomal RNA anti-Shine-Dalgarno elements are highly similar to their bacterial counterparts, these sites alone cannot explain the decline in plastid Shine-Dalgarno generality. However, by computational simulation we show that upstream point mutations modulate the local structure of ribosomal RNA in chloroplasts, creating significantly tighter structures around the anti-Shine-Dalgarno locus, which in-turn reduce the probability of ribosome binding via the Shine-Dalgarno mechanism. To validate our model, we expressed a mCherry gene harboring a Shine-Dalgarno motif in the Chlamydomonas reinhardtii chloroplast. We show that co-expressing it with a 16S ribosomal RNA, modified according to our model, significantly enhances its expression compared to co-expression with an endogenous 16S gene.\n\nSignificance statementChloroplasts are fascinating intracellular organelles which have evolved from an ancient cyanobacterium engulfed by a larger eukaryote. Surprisingly, the canonical mechanism regulating bacterial translation initiation - Shine-Dalgarno - has been shown to play a reduced role in chloroplasts. Here, we show that mutations upstream from the anti-Shine-Dalgarno element decrease the probability of spontaneous ribosome binding by modulating the secondary structure of the ribosomal RNA. These mutations constitute a regulatory step which acclimates the Shine-Dalgarno mechanism to the translational regulation regime of chloroplasts. Interestingly, we show that these chloroplast features occur in modern cyanobacteria as well, promoting the idea that they have evolved prior to endosymbiosis.

plant biology↗

bioRxiv2018

Doppler-Based Algorithm for Mapping Cardiac Rotors by Induced Temperature Perturbations

Electrogram-guided ablation for mapping of abnormal atrial activity has become increasingly popular in clinical applications. However, current methods have several limitations, and none have been shown to increase the ablation procedure success rate more than empirical ablation procedures. Here we present a new approach to identify arrhythmogenic sources as targets for ablation. Based on our previous findings that rotor drifting can be characterized by a local temperature gradient within the tissue, this article describes an innovative induced temperature technique which exploits the fact that rotor drifting produces Doppler shifts in the dominant frequency as measured at stationary locations. A mathematical algorithm is detailed to solve the inverse problem, reconstruct the drift trajectory, and predict the rotor origin location. Mathematical modeling and computer simulations demonstrate the feasibility of the new approach for rotors and focal source, two well-known arrhythmogenic sources of irregular conduction. Performance was extensively investigated for different numbers of electrodes and varied SNRs. Random conditions were also taken into account, since the electrodes array position and the initial location of the rotor pivot can impact the outcomes. By using temperature perturbation and employing the Doppler algorithm, the rotor drift trajectory and the origin region is shown to be estimated. We consider ways in which this technique can be extended to differentiate between rotors and ectopic activity. Future experimental and clinical validations should lead to potential use in ablation procedures and improve localization capabilities, thus increasing success rates and optimizing arrhythmia management.

bioengineering↗