Biology subjects

Lindner, B.

Publications and source records attributed to Lindner, B..

9 recordsLinked to original sources

bioRxiv2026

Conformational Reorganization of a Processive Cellulase on Crystalline Cellulose

Enzymes that degrade insoluble polymers must couple catalytic chemistry to productive engagement with heterogeneous substrate interfaces, yet direct structural information on these interfacial states remains limited. We investigated the conformational organization of the processive cellulase Trichoderma reesei Cel7A both free in solution and bound to an insoluble cellulose substrate using small-angle neutron scattering (SANS). Use of biosynthetically deuterated bacterial cellulose allowed the scattering contribution of the cellulose-associated Cel7A holoenzyme to be selectively highlighted using SANS with contrast matching. SANS measurements performed under conditions supporting high or low catalytic activity revealed distinct differences between the free and cellulose-bound enzyme. Under poorly active conditions, the conformational distributions of free and bound Cel7A were similar. In contrast, catalytically favorable conditions produced an additional interdomain correlation in the bound enzyme, consistent with redistribution toward a substrate-selected conformational ensemble. Molecular dynamics simulations showed that free Cel7A samples can assume compact, intermediate, and extended conformations, but these solution-state ensembles did not reproduce the structural features of cellulose-bound Cel7A. These results identify productive cellulose engagement as a structurally distinct state of the Cel7A holoenzyme and support a dynamic model in which the linker and carbohydrate-binding module modulate interfacial positioning of the catalytic domain rather than functioning as a rigid mechanical unit during processive hydrolysis. More broadly, the findings suggest that heterogeneous biocatalysis depends not only on substrate binding and active-site chemistry, but also on interface-selected conformational states that determine whether binding becomes productive. The study further demonstrates how deuteration and neutron contrast variation can reveal functional macromolecular conformations directly within complex interfacial environments.

biochemistry↗

bioRxiv2025

Discovery of a Novel Chimeric Transposase-Transposon System for Advanced Genome Engineering

Transposases revolutionized the field of genetic engineering, yet the scarcity of functional systems remains a challenge. In response, we conducted a metagenomic screening and identified a novel transposase system from Acyrthosiphon pisum. Through systematic optimization, we enhanced nuclear localization, transposon composition, and created a hyperactive transposase variant to boost transposition efficiency. Intriguingly, the combined application of the newly discovered transposase with inverted terminal repeat sequences from a related pea aphid species, Aphis craccivora, further enhanced transposition activity, resulting in the first chimeric transposase system reported so far. We investigated the genomic integration events following transposition in mammalian cells, to understand the underlying mechanisms and optimize the efficiency of transgene integration. This optimized system can expedite the generation of recombinant protein producing CHO cell lines, even surpassing the hyperactive piggyBac system with regards to cell specific productivity. These findings introduce a significant addition to the field of semi-targeted transgene integration technologies, offering substantial potential for enhancing biologics manufacturing.

bioengineering↗

bioRxiv2025

Population sparseness determines strength of Hebbian plasticity for maximal memory lifetime in associative networks

The brain can efficiently learn and form memories based on limited exposure to stimuli. One key factor believed to support this ability is sparse coding, which can reduce overlap between representations and minimize interference. It is well known that increased sparseness can enhance memory capacity, yet its impact on the speed of learning remains poorly understood. Here we analyze the relationship between population sparseness and learning speed -- specifically, how the learning speed that maximizes memory capacity depends on the sparseness of the neural code, and how this in turn affects the networks maximal capacity. To this end, we study a feedforward network with Hebbian and homeostatic plasticity and a two-state synapse model. The network learns to associate binary input-output pattern pairs, where sparseness corresponds to a small fraction of active neurons per pattern. The learning speed is modeled as the probability of synaptic changes during learning. The results presented in this manuscript are based on both network simulations and an analytical theory that predicts expected memory capacity and optimal learning speed. For both perfect and noisy retrieval cues, we find that the optimal learning speed indeed increases with increasing pattern sparseness -- an effect that is more pronounced for input sparseness than for output sparseness. Interestingly, the optimal learning speed stays the same across different network sizes if the number of active units in an input pattern is kept constant. While the capacity obtained at optimal learning speed increases monotonically with output sparseness, its dependence on input sparseness is non-monotonic. Overall, we provide the first detailed investigation of the interactions between population sparseness, learning speed, and storage capacity. Our findings propose that differences in population sparseness across brain regions may underlie observed differences in how quickly those regions adapt and learn. Author summaryThe brain can efficiently learn and form memories based on limited exposure to stimuli. One key factor believed to support this ability is the way neural circuits encode and organize information about the external world. Population sparseness refers to the phenomenon in which, in many brain regions, only a small subset of neurons is active at any given time or in response to a particular stimulus. Sparse codes are believed to reduce overlap between representations and minimize interference and can thus enhance the storage capacity of a network. Here we investigate the effect of population sparseness on the speed of learning input-output associations in a network model with Hebbian learning. We find that the learning speed that yields the maximal capacity increases with increasing sparseness. The maximal capacity increases for sparser input and output representations but in the first case only up to a certain point before sparseness becomes destructive. These findings could contribute to explaining why some brain regions learn faster than others.

neuroscience↗

bioRxiv2024

Weakly nonlinear responses at low intrinsic noise levels in two types of electrosensory primary afferents

Spiking thresholds in neurons or rectification at synapses are essential for neuronal computations rendering neuronal processing inherently nonlinear. Nevertheless, linear response theory has been instrumental for understanding, for example, the impact of noise or neuronal synchrony on signal transmission, or the emergence of oscillatory activity, but is valid only at low stimulus amplitudes or large levels of intrinsic noise. At higher signal-to-noise ratios, however, nonlinear response components become relevant. Theoretical results for leaky integrate-and-fire neurons in the weakly nonlinear regime suggest strong responses at the sum of two input frequencies if one of these frequencies or their sum match the neurons baseline firing rate. We here analyze nonlinear responses in two types of primary electroreceptor afferents, the P-units of the active and the ampullary cells of the passive electrosensory system of the wave-type electric fish Apteronotus leptorhynchus of either sex. In our combined experimental and modeling approach we identify these predicted nonlinear responses in those 31 out of 172 P-units that are characterized by low intrinsic noise. In contrast, the majority (22 out of 30) ampullary cells show nonlinear responses. Our results provide experimental evidence for nonlinear responses of spike generators in the weakly nonlinear regime. We conclude that such nonlinear responses occur in any sensory neuron that operates in similar regimes particularly at near-threshold stimulus conditions. Significance statementThe generation of action potentials involves a strong threshold nonlinearity. Nevertheless, the encoding of stimuli with small amplitudes by neurons with sufficient intrinsic noise can be well described as a linear system. As the stimulus amplitude is increased, new spectral components start to appear in the so called weakly nonlinear regime. Theory predicts nonlinear interactions whenever one or the sum of two stimulus frequencies matches the neurons baseline firing rate. Indeed, we find these interactions in a large set of electrophysiological recordings from primary electroreceptive afferents of a weakly electric fish. The non-linear response components could boost sensory responses to weak signals emitted, for example, by distant conspecifics.

neuroscience↗

bioRxiv2024

Bursts boost nonlinear encoding in electroreceptor afferents

Nonlinear mechanisms are at the heart of neuronal information processing, for example to fire an action potential, the membrane voltage must exceed a threshold nonlinearity. Even though, linear encoding schemes are commonly used and often successfully describe large parts of sensory encoding nonlinear mechanisms such as thresholds and saturations are well known to be crucial to encode behaviorally relevant features in the stimulus space not captured by linear methods. Here we analyze the role of bursts in p-type electroreceptor afferents (P-units) in the weakly electric fish Apteronotus leptorhynchus. It is long known that subpopulations of these cells fire bursts of action potentials while others do not. Previous research suggests, that the non-bursting cells are better at encoding the stimulus time-course while bursting neurons are better suited to encode special features in the stimulus. We here show, based on the analysis of experimental data and modeling, that bursts affect the linear as well as the nonlinear encoding. Theoretical work predicts that in simple leaky-integrate-and-fire model neurons, two periodic stimuli interact nonlinearly when the sum of the two frequencies matches the neurons baseline firing rate as quantified by the second-order susceptibility. Indeed, such nonlinear responses have been found in non-bursting P-units when stimulated by two beats simultaneously but only in those cells, that exhibit very low levels of intrinsic noise. In this study, we found that bursts strongly enhance these nonlinear responses which may play a critical role in the detection of weak intruder signals in the presence of a strong female signal, i.e. an electrosensory cocktail party.

neuroscience↗

bioRxiv2023

Intra-ripple frequency accommodation in an inhibitory network model for hippocampal ripple oscillations

Hippocampal ripple oscillations have been implicated in important cognitive functions such as memory consolidation and planning. Multiple computational models have been proposed to explain the emergence of ripple oscillations, relying either on excitation or inhibition as the main pacemaker. Nevertheless, the generating mechanism of ripples remains unclear. An interesting dynamical feature of experimentally measured ripples, which may advance model selection, is intra-ripple frequency accommodation (IFA): a decay of the instantaneous ripple frequency over the course of a ripple event. So far, only the "bifurcation-based" inhibition-first model, which is based on delayed inhibitory synaptic coupling, has been shown to reproduce IFA. Here we use an analytical mean-field approach and numerical simulations of a leaky integrate-and-fire spiking network to explain the mechanism of IFA. We develop a drift-based approximation for the oscillation dynamics of the population rate and the mean membrane potential of interneurons under strong drive and strong inhibitory coupling. We demonstrate that IFA arises due to a speed-dependent hysteresis effect in the dynamics of the mean membrane potential, when the interneurons receive transient, sharp wave-associated excitation. We thus predict that the IFA asymmetry vanishes in the limit of slowly changing drive, but is otherwise a robust feature of the bifurcation-based inhibition-first ripple model.

neuroscience↗

bioRxiv2020

A network model of the barrel cortex combined with a differentiator detector reproduces features of the behavioral response to single-neuron stimulation.

The stimulation of a single neuron in the rat somatosensory cortex can elicit a behavioral response. The probability of a behavioral response does not depend appreciably on the duration or intensity of a constant stimulation, whereas the response probability increases significantly upon injection of an irregular current. Biological mechanisms that can potentially suppress a constant input signal are present in the dynamics of both neurons and synapses and seem ideal candidates to explain these experimental findings. Here, we study a large network of integrate-and-fire neurons with several salient features of neuronal populations in the rat barrel cortex. The model includes cellular spike-frequency adaptation, experimentally constrained numbers and types of chemical synapses endowed with short-term plasticity, and gap junctions. Numerical simulations of this model indicate that cellular and synaptic adaptation mechanisms alone may not be sufficient to account for the experimental results if the local network activity is read out by an integrator. However, a differentiator circuit can detect the single-cell stimulation with a reliability that barely depends on the length or intensity of the stimulus, but that increases when an irregular signal is used. This finding is in accordance with the experimental results obtained for the stimulation of a regularly-spiking excitatory cell. Author summaryIt is widely assumed that only a large group of neurons can encode a stimulus or control behavior. This tenet of neuroscience has been challenged by experiments in which stimulating a single cortical neuron has had a measurable effect on an animals behavior. Recently, theoretical studies have explored how a single-neuron stimulation could be detected in a large recurrent network. However, these studies missed essential biological mechanisms of cortical networks and are unable to explain more recent experiments in the barrel cortex. Here, to describe the stimulated brain area, we propose and study a network model endowed with many important biological features of the barrel cortex. Importantly, we also investigate different readout mechanisms, i.e. ways in which the stimulation effects can propagate to other brain areas. We show that a readout network which tracks rapid variations in the local network activity is in agreement with the experiments. Our model demonstrates a possible mechanism for how the stimulation of a single neuron translates into a signal at the population level, which is taken as a proxy of the animals response. Our results illustrate the power of spiking neural networks to properly describe the effects of a single neurons activity.

neuroscience↗

bioRxiv2018

Gap junctions set the speed and nucleation rate of stage I retinal waves

Spontaneous waves in the developing retina are essential in the formation of the retinotopic mapping in the visual system. From experiments in rabbits, it is known that the earliest type of retinal waves (stage I) is nucleated spontaneously, propagates at a speed of 451{+/-}91 m/sec and relies on gap junction coupling between ganglion cells. Because gap junctions (electrical synapses) have short integration times, it has been argued that they cannot set the low speed of stage I retinal waves. Here, we present a theoretical study of a two-dimensional neural network of the ganglion cell layer with gap junction coupling and intrinsic noise. We demonstrate that this model can explain observed nucleation rates as well as the comparatively slow propagation speed of the waves. From the interaction between two coupled neurons, we estimate the wave speed in the model network. Furthermore, using simulations of small networks of neurons (N[≤]260), we estimate the nucleation rate in form of an Arrhenius escape rate. These results allow for informed simulations of a realistically sized network, yielding values of the gap junction coupling and the intrinsic noise level that are in a physiologically plausible range.\n\nAuthor summaryRetinal waves are a prominent example of spontaneous activity that is observed in neuronal systems of many different species during development. Spatio-temporally correlated bursts travel across the retina at a few hundred m/sec to facilitate the maturation of the underlying neuronal circuits. Even at the earliest stage, in which the network merely consists of ganglion cells coupled by electric synapses (gap junctions), it is unclear which mechanisms are responsible for wave nucleation and transmission speed. We propose a model of gap-junction coupled noisy neurons, in which waves emerge from rare stochastic fluctuations in single cells and the waves transmission speed is set by the latency of the burst onset in response to gap-junction currents between neighboring cells.

neuroscience↗

bioRxiv2017

Circuit and cellular mechanisms facilitate the transformation from dense to sparse coding in the insect olfactory system

Transformations between sensory representations are shaped by neural mechanisms at the cellular and the circuit level. In the insect olfactory system encoding of odor information undergoes a transition from a dense spatio-temporal population code in the antennal lobe to a sparse code in the mushroom body. However, the exact mechanisms shaping odor representations and their role in sensory processing are incompletely identified. Here, we investigate the transformation from dense to sparse odor representations in a spiking model of the insect olfactory system, focusing on two ubiquitous neural mechanisms: spike-frequency adaptation at the cellular level and lateral inhibition at the circuit level. We find that cellular adaptation is essential for sparse representations in time (temporal sparseness), while lateral inhibition regulates sparseness in the neuronal space (population sparseness). The interplay of both mechanisms shapes dynamical odor representations, which are optimized for discrimination of odors during stimulus onset and offset. In addition, we find that odor identity is stored on a prolonged time scale in the adaptation levels but not in the spiking activity of the principal cells of the mushroom body, providing a testable hypothesis for the location of the so-called odor trace.

neuroscience↗