Biology subjects

Suter, D.

Publications and source records attributed to Suter, D..

4 recordsLinked to original sources

bioRxiv2026

Grazing versus mowing: productivity and forage quality of mixtures and cultivars of perennial ryegrass

Perennial ryegrass (Lolium perenne L.) is an important grass species in temporary grasslands, where it is often sown as a mixture of multiple cultivars. In variety testing, however, candidate cultivars are evaluated as single-cultivar stands under standardised mechanical defoliation. In practice, swards are mown, grazed, or both, where grazing adds treading, selective defoliation, and heterogeneous nutrient return. Whether cultivar rankings obtained under mechanical defoliation also hold under grazing, and whether mixtures perform differently than their component cultivars, are unresolved. We compared six early-heading perennial ryegrass cultivars (three diploid, three tetraploid), and three 50:50 two-cultivar mixtures. The plots were grown under three defoliation treatments (i.e., regular cutting, simulated grazing, and grazing by dairy cows), over three years at two sites. Here we show that defoliation treatment and year affected dry matter yield (DMY) and forage quality stronger than the cultivar grown. The effect of ploidy depended on the defoliation treatment. Tetraploid cultivars had slightly higher DMY under regular cutting and simulated grazing and higher metabolisable energy throughout, whereas diploid cultivars had slightly higher DMY under grazing. Simulated grazing matched the forage quality of grazed swards, including cultivar ranking, but not the cultivar ranking for DMY. Forage quality rankings can therefore be assessed through simulated grazing, whereas DMY rankings of cultivars intended for grazing require evaluation under grazing. Diploid-tetraploid mixtures were intermediate in forage quality and, under grazing, slightly higher in DMY when compared to their individual components. Evaluating cultivars and mixtures under grazing would better reflect their relative performance in the field.

plant biology↗

bioRxiv2025

Computational Modeling of Motor-Driven Extension of Microtubule Bundles in Axons

In addition to actin assembly at the growth cone, neuronal axons elongate via interactions between microtubules and microtubule-associated proteins including dynein motor proteins and cross-linking proteins. Dynein translocates microtubules toward the growth cone to exert extensile forces for axonal outgrowth. During this process, microtubules are most likely to experience compressive loads, which can result in bending deformation on microtubules called buckling. Such buckled microtubules are impaired in their ability to bear compressive forces and may not contribute significantly to axonal outgrowth. If microtubules are interconnected by cross-linking proteins, they are less likely to be buckled and thus able to resist larger compressive loads. Despite the importance of the buckling and connectivity of microtubules, their effects on the axonal outgrowth have not been investigated to date. In this study, using an agent-based computational model, we created a microtubule bundle to simulate the microtubule system in axons. This bundle increased its length by interactions between dynein-like motors and microtubules against a mechanical load. We found that intermediate cross-linking density resulted in maximal elongation of the bundle, because microtubules were easily buckled at low cross-linking density, whereas the displacement of microtubules was inhibited at high cross-linking density. When individual microtubules were much stiffer, the bundle showed higher elongation even with lower cross-linking density, since stiff microtubules were less likely to be buckled by compressive loads. Our study provides new insights into the mechanisms driving axonal outgrowth.

biophysics↗

bioRxiv2025

RealTrace: Uncovering biological dynamics hidden under measurement noise in time-lapse microscopy data

One of the most powerful approaches for identifying the mechanisms underlying complex biological phenomena is not just to measure bulk populations, or even take single-cell snapshots, but to directly track the behaviors of single cells in time. Indeed, in recent years there has been a steep rise in the use of time-lapse microscopy with fluorescent reporters to quantify single-cell time dynamics in many different biological systems. However, there is a major challenge with the analysis of such data. Since biological changes are inherently small on short time scales, much of the true biological dynamics is hidden under unavoidable measurement noise, but we do not know what functional form the time dynamics may take, and smoothing over neighboring time points simply replaces the true dynamics with a time average. Here, we present RealTrace, a Bayesian method that rigorously solves this challenge using only the assumption that, while true biological fluctuations are correlated on short time scales, measurement errors are uncorrelated. We show that this assumption can be formalized in a Bayesian model using maximum entropy process priors and that this model can be accurately solved by recursively approximating the non-linear dynamics over short time intervals. Given raw input cell size and fluorescence measurements, RealTrace calculates the estimated true sizes, total GFP contents, instantaneous growth rates, and volumic GFP production rates across all cells and time, together with rigorous error bars on all these quantities. We demonstrate the broad applicability of RealTrace by analyzing time-lapse microscopy datasets from E. coli bacteria, mouse embryonic stem cells, and entire C. elegans embryos. To exemplify the subtle dynamical features RealTrace can uncover, we perform an in-depth study of E. coli cells carrying fluorescent reporters for constitutive and ribosomal promoters, across multiple growth conditions. We find growth rates of single cells substantially fluctuate in time and, as the average growth rate decreases, these fluctuations systematically increase in amplitude and duration. Both growth rate and gene expression vary systematically across the cell cycle, but while the pattern of growth rate variation is condition-independent, the systematic variation in gene expression across the cell cycle is both condition and promoter-dependent. Finally, under a sudden change in nutrients, single cells exhibit highly consistent transient changes in growth rate and gene expression. While gene expression rates settle into a new steady-state surprisingly rapidly, the growth rate shows a dramatic drop followed by an overshoot that takes several cell cycles to settle down. All these observations give fundamental new insights into the single-cell physiology of bacteria. RealTrace is a highly versatile method that accurately removes measurement noise from any fluorescence time-lapse microscopy data, enabling the identification and quantification of subtle features in the biological dynamics that would otherwise be hidden under measurement noise.

bioinformatics↗

bioRxiv2018

Single-molecule dynamics and genome-wide transcriptomics reveal that NF-kB (p65)-DNA binding times can be decoupled from transcriptional activation

Transcription factors (TFs) regulate gene expression in both prokaryotes and eukaryotes by recognizing and binding to specific DNA promoter sequences. In higher eukaryotes, it remains unclear how the duration of TF binding to DNA relates to downstream transcriptional output. Here, we address this question for the transcriptional activator NF-{kappa}B (p65), by live-cell single molecule imaging of TF-DNA binding kinetics and genome-wide quantification of p65-mediated transcription. We used mutants of p65, perturbing either the DNA binding domain (DBD) or the protein-protein transactivation domain (TAD). We found that p65-DNA binding time was predominantly determined by its DBD and directly correlated with its transcriptional output as long as the TAD is intact. Surprisingly, mutation or deletion of the TAD did not modify p65-DNA binding stability, suggesting that the p65 TAD generally contributes neither to the assembly of an \"enhanceosome,\" nor to the active removal of p65 from putative specific binding sites. However, TAD removal did reduce p65-mediated transcriptional activation, indicating that protein-protein interactions act to translate the long-lived p65-DNA binding into productive transcription.\n\nAuthor SummaryTo control transcription of a certain gene or a group of genes, both eukaryotes and prokaryotes express specialized proteins, transcription factors (TFs). During gene activation, TFs bind gene promotor sequences to recruit the transcriptional machinery including DNA polymerase II. TFs are often multi-subunit proteins containing a DNA-binding domain (DBD) as well as a protein-protein interaction interface. It was suggested that the duration of a TF-DNA binding event 1) depends on these two subunits and 2) dictates the outcome, i.e. the amount of mRNA produced from an activated gene. We set out to address these hypotheses using the transcriptional activator NF-{kappa}B (p65) as well as a number of mutants affecting different functional subunits. Using a combination of live-cell microscopy and RNA sequencing, we show that p65 DNA-binding time indeed correlates with the transcriptional output, but that this relationship depends on, and hence can be uncoupled by altering, the protein-protein interaction capacity. Our results suggest that, while p65 DNA binding times are dominated by the DBD, a transcriptional output can only be achieved with a functional protein-protein interaction subunit.

genetics↗