Biology subjects

Kugler, H.

Publications and source records attributed to Kugler, H..

6 recordsLinked to original sources

bioRxiv2026

Acute stress induces circuit-specific alterations in mesolimbic and nigrostriatal inhibitory transmission and potentiates operant learning

Acute stress can facilitate learning about stimuli that predict rewarding outcomes, yet whether stress similarly potentiates acquisition of reward-directed actions remains less well understood. Reward learning broadly depends on dopamine signaling within mesolimbic and nigrostriatal pathways. Dopamine transmission in the mesolimbic system is traditionally associated with cue-reward learning, whereas nigrostriatal dopamine signaling has been implicated in movement vigor and the acquisition of instrumental actions. Stress can alter dopamine transmission within these circuits and thereby influence reward learning. Previous work demonstrated that acute stress downregulates the potassium-chloride cotransporter KCC2 in ventral tegmental area GABA neurons. These stress-induced adaptations in inhibitory transmission enhance mesolimbic dopamine signaling and potentiate associative learning. Here, we show that prior exposure to acute restraint stress facilitates acquisition of operant sucrose self-administration in male and female rats. Enhanced learning was associated with increased temporal coincidence of GABA release events onto dopamine neurons and increased excitability of GABAergic inputs, alterations previously linked to enhanced dopamine signaling. These stress-induced adaptations exhibited marked circuit specificity within mesolimbic and nigrostriatal systems, selectively affecting inhibitory transmission onto dopamine neurons projecting to the nucleus accumbens lateral shell and dorsomedial striatum. Importantly, pharmacological enhancement of KCC2 function with CLP290 normalized inhibitory transmission within these pathways and attenuated stress-induced potentiation of operant learning. Together, these findings identify circuit-specific alterations in midbrain inhibitory signaling induced by acute stress that contribute to enhanced reward learning.

neuroscience↗

bioRxiv2026

In-cell structural insights into fungal ER stress responses

Approximately a third of a cells proteome is folded in the endoplasmic reticulum (ER). The accumulation of misfolded proteins in the ER results in ER stress. The unfolded protein response (UPR) has evolved to attenuate ER stress by increasing the cells folding capacity and reducing the folding load in the ER. In metazoans, the latter is notably achieved through mRNA translation inhibition. However, S. cerevisiae lacks the corresponding pathways, and its reliance on translational downregulation in response to ER stress remains largely unexplored. In this study we combined cryo-focused ion beam milling, cryo-electron tomography and subtomogram averaging to investigate the effect of ER stress on the translational machinery of S. cerevisiae. At the ultrastructural level, we describe changes in organelle morphology, confirming ER volume expansion and the activation of autophagy. Further structural analysis reveals that ER stress triggers an increase in the relative abundance of inactive 80S ribosomes (up to 25% of the total), both in the cytosol and at the ER. Finally, we visualise the binding of the elongation factors eEF2, eIF5A and eEF3 to inactive ribosomes. Our work provides evidence that ER stress in S. cerevisiae leads to modest translation inhibition, which might contribute to alleviating ER stress.

molecular biology↗

bioRxiv2025

Characterizing Gene Regulatory Network Ensembles in Kidney Injury and Repair

The inference of gene regulatory networks (GRNs) from single-cell RNAseq data allows for mechanistic characterization of the different cell states and their dynamics in complex biological processes. While numerous algorithms have been proposed to infer GRNs from single-cell transcriptomic data, multiple network solutions may explain the same dataset, posing a challenge for biologically meaningful interpretation. Here, we use the Reasoning Engine for Interaction Networks (RE:IN), a computational tool based on formal reasoning, to characterize GRN ensembles in the context of acute kidney injury (AKI). To this end, we applied RE:IN to a single-cell RNAseq dataset from a mouse ischemiareperfusion injury (IRI) model, focusing on distinct proximal tubule cell states related to kidney injury and repair. We first created an Abstract Boolean Net-work (ABN) model for the kidney using RE:IN and synthesized an ensemble of consistent network solutions. Then, we visualized the ensemble in latent space using Principal Components Analysis (PCA) and discovered four distinct GRN families compatible with the input gene expression and regulatory constraints. Finally, we identified two specific network substructures that discern between the four different network families. This study provides a methodology for characterizing and interpreting GRN heterogeneity in complex processes such as tissue development, disease, and repair.

systems biology↗

bioRxiv2021

Modeling the C. elegans Germline Stem Cell Genetic Network using Automated Reasoning

Computational methods and tools are a powerful complementary approach to experimental work for studying regulatory interactions in living cells and systems. We demonstrate the use of formal reasoning methods as applied to the Caenorhabditis elegans germ line, which is an accessible model system for stem cell research. The dynamics of the underlying genetic networks and their potential regulatory interactions are key for understanding mechanisms that control cellular decision-making between stem cells and differentiation. We model the "stem cell fate" versus entry into the "meiotic development" pathway decision circuit in the young adult germ line based on an extensive study of published experimental data and known/hypothesized genetic interactions. We apply a formal reasoning framework to derive predictive networks for control of differentiation. Using this approach we simultaneously specify many possible scenarios and experiments together with potential genetic interactions, and synthesize genetic networks consistent with all encoded experimental observations. In silico analysis of knock-down and overexpression experiments within our model recapitulate published phenotypes of mutant animals and can be applied to make predictions on cellular decision-making. This work lays a foundation for developing realistic whole tissue models of the C. elegans germ line where each cell in the model will execute a synthesized genetic network.

systems biology↗

bioRxiv2020

Embryonic stem cells commit to differentiation by symmetric divisions following a variable lag period

Mouse embryonic stem (ES) cells are derived from the epiblast of the preimplantation embryo and retain the capacity to give rise to all embryo lineages. ES cells can be released into differentiation from a near-homogeneous maintenance condition. Exit from the ES cell state can be accurately monitored using the Rex1-GFPd2 transgenic reporter, providing a powerful system for examining a mammalian cell fate transition. Here, we performed live-cell imaging and tracking of ES cells during entry into differentiation for 48 hours in defined conditions. We observed a greater cell surface area and a modest shortening of the cell cycle prior to exit and subsequently a reduction in cell size and increase in motility. We did not see any instance of cells regaining ES cell identity, consistent with unidirectional developmental progression. Transition occurred asynchronously across the population but genealogical tracking revealed a high correlation in cell-cycle length and Rex1-GFPd2 expression between daughter cells. A population dynamics model was consistent with symmetric divisions during exit from naive pluripotency. Collapse of ES cell identity occurred acutely in individual cells but after a variable delay. The variation in lag period can extend up to three generations, creating marked population asynchrony.

developmental biology↗

bioRxiv2018

Formal Analysis of Network Motifs

A recurring set of small sub-networks have been identified as the building blocks of biological networks across diverse organisms. These network motifs have been associated with certain dynamical behaviors and define key modules that are important for understanding complex biological programs. Besides studying the properties of motifs in isolation, existing algorithms often evaluate the occurrence frequency of a specific motif in a given biological network compared to that in random networks of similar structure. However, it remains challenging to relate the structure of motifs to the observed and expected behavior of the larger network. Indeed, even the precise structure of these biological networks remains largely unknown. Previously, we developed a formal reasoning approach enabling the synthesis of biological networks capable of reproducing some experimentally observed behavior. Here, we extend this approach to allow reasoning about the requirement for specific network motifs as a way of explaining how these behaviors arise. We illustrate the approach by analyzing the motifs involved in sign-sensitive delay and pulse generation. We demonstrate the scalability and biological relevance of the approach by revealing the requirement for certain motifs in the network governing stem cell pluripotency.

systems biology↗