Biology subjects

Reuveni, S.

Publications and source records attributed to Reuveni, S..

6 recordsLinked to original sources

bioRxiv2025

Measurements and simulations of transmembrane water exchange by diffusion NMR methods: From yeast cells to optic nerve ex vivo

Non-invasive measurement of exchange is paramount in different fields, ranging from material to biological sciences. Unaccounted exchange may even blur micro-structural or other characteristics of multi compartmental systems studied by MR methods. Despite the growing interest in diffusion-exchange studies of complex systems, comparative studies remain scarce. Most existing investigations have applied different diffusion MR methods to different biological samples under varying experimental conditions, making direct comparisons difficult. Moreover, the lack of a gold standard for exchange rate measurements further complicates efforts to validate and interpret results. To address these challenges, we employed two diffusion NMR-based methods--the constant-gradient pulsed field gradient (CG-PFG) and the recently introduced filter-exchange NMR spectroscopy (FEXSY)--to investigate apparent water exchange in yeast cells and optic nerves, both before and after fixation. We first evaluated the effect of the q-values on the extracted indices and then evaluated the repeatability and reproducibility of the measurements. The CG-PFG and FEXSY experiments were collected on the same sample to allow comparison of the results. The intracellular mean residence times (MRTs) ({tau}i) extracted from the log-linear fit of the CG-PFG NMR experiments were found to be 554 {+/-} 6 ms and 337 {+/-} 10 ms for yeast cells before and after fixation, respectively. The respective{tau} i values extracted from the FEXSY experiments before and after fixation were found to be 368 {+/-} 14 ms and 146 {+/-} 24 ms, respectively. Despite the difference in absolute values of MRTs, the same qualitative behavior is observed in the two methodologies, and both could be analyzed using the bi-compartmental Karger model. The same methodologies were then used to study exchange in the more complex porcine optic nerves. There, the bi-compartmental Karger model analysis is shown to be inadequate. Extensive Monte Carlo simulations are used to narrow down on the most possible explanation, suggesting that optic nerves are multi-compartmental systems where not all spins are free to undergo exchange. Supporting theoretical calculations point to the existence of at least one additional non-exchanging restricted compartment. Thus, a tri-compartmental model is derived and used to analyze the data. The new model fits the data significantly better and results in dramatically different exchange rates when used on white matter (WM) data: CG-PFG experiments were found to be 730 {+/-} 40 ms and 803 {+/-} 16 ms for optic nerves before and after fixation, respectively. The respective{tau} i values extracted from the FEXSY experiments before and after fixation were found to be 530 {+/-} 125 ms and 387 {+/-} 104 ms, respectively. These values are considerably lower than the values previously reported. Finally, we use simulations to show that the quantitative discrepancy between CG-PFG and FEXSY can be attributed, at least partially, to the difference in T2 values between the intra- and extracellular compartments. We thus encourage the pairing of exchange and spin-spin relaxation measurements in future studies. We end with a discussion on the current state of the diffusion-exchange, and in an attempt to put a spotlight on essential corner stones that are still missing despite the great advance of recent years: experimental standardization, method comparison, and adequate modeling.

neuroscience↗

bioRxiv2025

Inferring binding rates from enzymatic turnover time statistics

We present a method to extract enzyme-substrate binding rates from observations of enzymatic turnover times. Our approach involves segregating the short-time statistics of the turnover time distribution and inferring from it the binding rate in a general and robust manner. Beyond determining binding rates, the approach developed herein also allows us to infer: (i) the fertile collision probability p, i.e., the probability that a collision between an enzyme and a substrate will result in the formation of an enzyme-substrate complex; and (ii) the fertile catalysis probability{phi} cat, i.e., the probability that the enzyme-substrate complex will lead to the formation of a product. It has long been known that p{phi}cat << 1, indicating that most enzymes operate far from maximal efficiency, yet separating the contributions of fertile binding and catalysis was not possible in lieu of direct binding rate measurements. Our method overcomes this limitation by enabling precise inference of binding rates from turnover times, which in turn opens the door for a more detailed understanding of enzymatic efficiency.

biophysics↗

bioRxiv2024

High-order Michaelis-Menten equations allow inference of hidden kinetic parameters in enzyme catalysis

Single-molecule measurements provide a platform for investigating the dynamical properties of enzymatic reactions. To this end, the single-molecule Michaelis-Menten equation was instrumental as it asserts that the first moment of the enzymatic turnover time depends linearly on the reciprocal of the substrate concentration. This, in turn, provides robust and convenient means to determine the maximal turnover rate and the Michaelis-Menten constant. Yet, the information provided by these parameters is incomplete and does not allow full characterization of enzyme kinetics at the single molecule level. Here we show that the missing kinetic information can be accessed via a set of high-order Michaelis-Menten equations that we derive. These equations capture universal linear relations between the reciprocal of the substrate concentration and distinguished combinations of turnover time moments, essentially generalizing the Michaelis-Menten equation to moments of any order. We demonstrate how key observables such as the lifetime of the enzyme-substrate complex, the rate of substrate-enzyme binding, and the probability of successful product formation, can all be inferred using these high-order Michaelis-Menten equations.

biophysics↗

bioRxiv2020

Growth-laws and invariants from ribosome biogenesis in lower Eukarya

Eukarya and Bacteria are the most evolutionarily distant domains of life, which is reflected by differences in their cellular structure and physiology. For example, Eukarya feature membrane-bound organelles such as nuclei and mitochondria, whereas Bacteria have none. The greater complexity of Eukarya renders them difficult to study from both an experimental and theoretical perspective. However, encouraged by a recent experimental result showing that budding yeast (a unicellular eukaryote) obeys the same proportionality between ribosomal proteome fractions and cellular growth rates as Bacteria, we derive a set of relations describing eukaryotic growth from first principles of ribosome biogenesis. We recover the observed ribosomal protein proportionality, and then continue to obtain two growth-laws for the number of RNA polymerases synthesizing ribosomal RNA per ribosome in the cell. These growth-laws, in turn, reveal two invariants of eukaryotic growth, i.e. quantities predicted to be conserved by Eukarya regardless of growth conditions. The invariants, which are the first of their kind for Eukarya, clarify the coordination of transcription and translation kinetics as required by ribosome biogenesis, and link these kinetic parameters to cellular physiology. We demonstrate application of the relations to the yeast S. cerevisiae and find the predictions to be in good agreement with currently available data. We then outline methods to quantitatively deduce several unknown kinetic and physiological parameters. The analysis is not specific to S. cerevisiae and can be extended to other lower (unicellular) Eukarya when data become available. The relations may also have relevance to certain cancer cells which, like bacteria and yeast, exhibit rapid cell proliferation and ribosome biogenesis.

biophysics↗

bioRxiv2018

Multisite phosphorylation regulates phenotypic variability in antibiotic tolerance

Isogenic populations of cells exhibit phenotypic variability that has specific physiological consequences. For example, individual bacteria within a population can differ in their sensitivity to an antibiotic, but whether this variability can be regulated or is generally an unavoidable consequence of stochastic fluctuations is unclear. We observed that a bacterial stress response gene, the (p)ppGpp synthetase sasA, exhibits high levels of extrinsic noise in expression, suggestive of a regulatory process. We traced this variability to the convergence of two signaling systems that together control the multisite phosphorylation of a transcription factor, an event largely unexplored in bacteria, This regulatory intersection between a Ser/Thr kinase and a prototypical two component system is crucial for controlling the appearance of outliers, rare cells with unusually high levels of sasA expression. Additionally, by examining the full distributions of gene expression we calculated the contribution of the additional Ser/Thr kinase-dependent phosphorylation in setting the relative abundance of cells with a given a level of SasA. We then created a predictive model for the probability of a given cell surviving antibiotic treatment as a function of sasA expression. Therefore, our data show that multisite phosphorylation can be used to strongly regulate variations in phenotypes across a bacterial population.

microbiology↗

bioRxiv2016

Single-enzyme approach predicts natural emergence of inhibitor-activator duality

The classical theory of enzymatic inhibition aims to quantitatively describe the effect of certain molecules--called inhibitors--on the progression of enzymatic reactions, but growing signs indicate that it must be revised to keep pace with the single-molecule revolution that is sweeping through the sciences. Here, we take the single enzyme perspective and rebuild the theory of enzymatic inhibition from the bottom up. We find that accounting for multi-conformational enzyme structure and intrinsic randomness cannot undermine the validity of classical results in the case of competitive inhibition; but that it should strongly change our view on the uncompetitive and mixed modes of inhibition. There, stochastic fluctuations on the single-enzyme level could give rise to inhibitor-activator duality--a phenomenon in which, under some conditions, the introduction of a molecule whose binding shuts down enzymatic catalysis will counter intuitively work to facilitate product formation. We state--in terms of experimentally measurable quantities--a mathematical condition for the emergence of inhibitor-activator duality, and propose that it could explain why certain molecules that act as inhibitors when substrate concentrations are high elicit a non-monotonic dose response when substrate concentrations are low. The fundamental and practical implications of our findings are thoroughly discussed.

biophysics↗