Biology subjects

Schuster, L.

Publications and source records attributed to Schuster, L..

11 recordsLinked to original sources

bioRxiv2026

Mus musculus mice self-organize existing behaviors into structured bouts as they become expert hunters.

In the wild, a mouse must flexibly perform multiple computations at once, rapidly navigating, sampling its environment, forming and using memories, and balancing internal needs. Capturing the self-paced, low-repetition, complex nature of decision making in the wild while preserving the experimental control needed to interpret its process remains a challenge. Here we show that lab mice, without food restriction, learn to forage for hidden resources cued by ambiguous sounds in a dynamic environment by spatiotemporally reorganizing their existing behavioral repertoire. Using a novel closed-loop assay, we required mice to sit still to receive informative but hard-to-localize sound cues emanating from one correct location they must find, out of 157 possible locations in a large arena. With experience, mice become efficient hunters. Efficient hunting cannot be explained by an increase in a particular behavioral module, but instead, mice reorganize existing behaviors into clustered bouts of high-quality sampling and site-checking. Looking forward, this work establishes an approach for studying the neural, molecular, and evolutionary basis of naturalistic decision-making in mice.

animal behavior and cognition↗

bioRxiv2025

Reconstructing a physiological state space via chronic jugular microdialysis in freely moving mice

Maintaining physiological homeostasis requires a complex interplay among endocrine organs, peripheral tissues, and distributed neuroendocrine control circuits, all of which are coupled through feedback loops that operate over minutes to hours. Although many physiological needs are broadcast through hormones, metabolites, and other chemical compounds circulating in the bloodstream, we rarely observe more than a few of these messengers together and at high cadence during behavior. To address this, we developed a minimally disruptive workflow to measure the free fraction of hundreds of amines and small peptides at a 7.5-minute cadence for [~]8 hrs in freely moving mice using chronic jugular microdialysis implants and chemical isotope labeling Liquid Chromatography-Mass Spectrometry. Single-compound profiles behave according to known physiology, such as purine turnover correlating with movement, delayed histamine/5-HIAA changes, and coordinated amino-acid dynamics. Our multiplexed measures enable high-dimensional analyses that uncover properties of the underlying dynamics. For example, systems-level analyses show that 10 dimensions explain over 70% of the variance in hormone/metabolite covariation, consistent with a low rank description of the physiological state space, with projections aligned to locomotion state transitions. Our work opens avenues for the discovery of hormonal dynamics, compound interactions, and their effects on behavior.

physiology↗

bioRxiv2025

InteracTor: Feature Engineering and Explainable AI for Profiling Protein Structure-Interaction-Function Relationships

Characterizing protein families structural and functional diversity is essential for understanding their biological roles. Traditional analyses often focus on primary and secondary structures, which may not fully capture complex protein interactions. Here we introduce InteracTor, a novel toolkit that extracts multimodal features from protein three-dimensional (3D) structures, including interatomic interactions like hydrogen bonds, van der Waals forces, and hydrophobic contacts. By integrating Explainable AI (XAI) techniques, we quantified the importance of the extracted features in the classification of protein structural and functional families. InteracTors interpretable features enable mechanistic insights into the determinants of protein structure, function, and dynamics, offering a transparent means to assess their predictive power within machine learning models. Interatomic interaction features extracted by InteracTor demonstrated superior predictive power for protein family classification compared to features based solely on primary or secondary structure, revealing the importance of considering specific tertiary contacts in computational protein analysis. This work provides a robust framework for future studies aiming to enhance the capabilities of models for protein function prediction and drug discovery. AUTHOR SUMMARYInteracTor is a computational toolkit designed to enhance our understanding of protein structure and function by focusing on three-dimensional (3D) structural interactions. Unlike traditional approaches that primarily rely on sequence or secondary structure data, InteracTor extracts biologically meaningful features such as hydrogen bonds, van der Waals forces, and hydrophobic contacts, which are critical for protein stability and dynamics. By integrating these features into machine learning models alongside explainable AI methods, InteracTor provides interpretable insights into how specific structural interactions influence protein behavior. Our results demonstrate that tertiary structure features significantly improve the accuracy of protein family classification compared to sequence-based methods alone, underscoring the importance of considering 3D interactions in computational protein analyses. The toolkits modular design makes it adaptable for diverse applications, including drug discovery and protein engineering. In a broader context, InteracTor bridges the gap between computational biology and practical applications in medicine and biotechnology by offering a transparent and robust framework for analyzing proteins at a molecular level. This work represents a step forward in leveraging structural data to advance predictive modeling and biological discovery.

bioinformatics↗

bioRxiv2024

InteracTor: A new integrative feature extraction toolkit for improved characterization of protein structural properties

Understanding the structural and functional diversity of protein families is crucial for elucidating their biological roles. Traditional analyses often focus on primary and secondary structures, which include amino acid sequences and local folding patterns like alpha helices and beta sheets. However, primary and secondary structures alone may not fully represent the complex interactions within proteins. To address this limitation, we developed a new algorithm (InteracTor) to analyze proteins by extracting features from their three-dimensional (3D) structures. The toolkit extracts interatomic interaction features such as hydrogen bonds, van der Waals interactions, and hydrophobic contacts, which are crucial for understanding protein dynamics, structure, and function. Incorporating 3D structural data and interatomic interaction features provides a more comprehensive understanding of protein structure and function, potentially enhancing downstream predictive modeling capabilities. By using the extracted features in Mutual Information scoring (MI), Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and hierarchical clustering analysis as use cases, we identified clear separations among protein structural families, highlighting distinct functional aspects. Our analysis revealed that interatomic interaction features were more informative than protein secondary structure features, providing insights into potential structural and functional properties. These findings underscore the significance of considering tertiary structure in protein analysis, offering a robust framework for future studies aiming at enhancing the capabilities of models for protein function prediction and drug discovery.

bioinformatics↗

bioRxiv2024

Nucleoporin Nup153 docks the splicing machinery to the nuclear pore for efficient mRNA processing

The nuclear pore complex (NPC), composed of proteins termed nucleoporins (Nups), intercalates the nuclear envelope, and is primarily involved in protein trafficking and mRNA export. At the nuclear basket, Nups have been associated with chromatin organization and postulated to function as transcriptional hubs, working in tandem with mRNA export machinery. However, little is known about the intermediate process of RNA splicing at the NPC. Here, we used BioID to screen for interactors of basket-Nups Nup153 and TPR and discovered the enrichment of splicing proteins across all spliceosome complexes (E, A, B, B*, P). The peripheral nature of the interaction between Nup153 and selected splicing components was confirmed by in-situ proximity ligation assay and STED microscopy. The presence of splicing components at the NPC, reduced upon splicing inhibition, is partly dependent on Nup153 and functionally correlated to the splicing of Nup153-bound genes. Assessed by DamID, Nup153-bound genes ([~]500) are characterized by multiple long introns with lower-than-average GC content. Positioned at the periphery but distinct from the neighbouring lamina-associated domain (LADs) in chromatin signatures and expression levels, these genes showed Nup153-dependent splicing defect, suggesting that splicing occurs at the NPC. Altogether, our data substantiates the gene gating theory bringing transcription and export, now accompanied by speckle-distant splicing events, at the level of the NPC.

cell biology↗

bioRxiv2023

Characterizing constitutive promoters across the Proteobacteria

Although research on promoters has spanned decades, the precise prediction of promoter activity from DNA sequence remains a challenge even in model organisms. Recent literature has identified important differences in the core sequence of {sigma}70 promoters across classes of Proteobacteria as well as a lack of transferability when promoters are moved from host to host. Currently, there is a need for synthetic constitutive promoters spanning a range of expression levels in species outside of Escherichia coli. Additionally, characterization data defining behavior of the same promoter across multiple species would be extremely valuable to the field. Here, we analyzed promoter activity in three classes of Proteobacteria, which enabled us to better understand the sequence elements correlated with a strong promoter in different hosts. In doing so, we identified and characterized constitutive promoters spanning a range of expression in these species for community use and described the portability of a subset of these promoters as they were moved between hosts. These promoter libraries have broad applications as predictable genetic tools to control gene expression in diverse species (1-3). This work adds to the toolkit for gene expression in non-model bacteria and is a step towards the larger goal of accurate promoter prediction in a given host from a de novo sequence.

synthetic biology↗

bioRxiv2022

Longitudinal monitoring of maternal care and maternal neglect

Parental care is required for offspring survival, because infants require nearly-continual oversight for extensive periods. Parents must balance caretaking with their own survival, and benefit from help provided by other experienced adults. We built a system for 24/7 long-term monitoring of wild-type or oxytocin receptor knockout (OXTR-KO) mouse mothers ( dams) over litters. Some wild-type dams had high litter survival rates, but others consistently lost pups due to neglect and hypothermia. Maternal caretaking in low-pup-survival dams improved after co-housing with an experienced dam and litter, from reorganized maternal behavior including increased nest-building. In contrast, singly-housed OXTR-KOs died in childbirth and pups died due to prolonged parturition. Co-housing with a lactating female prevented OXTR-KO maternal-infant mortality, because the other female acted as a midwife by removing and cleaning pups from the pregnant dam. Thus for single mothers that continually lose litters or die in labor, maternal-infant survival is enhanced by experienced helpers.

neuroscience↗

bioRxiv2022

Interactive Neurorobotics: Behavioral and Neural Dynamics of Agent Interactions

Interactive neurorobotics is a subfield which characterizes brain responses evoked during interaction with a robot, and their relationship with the behavioral responses. Gathering rich neural and behavioral data from humans or animals responding to agents can act as a scaffold for the design process of future social robots. The goals of this research can be broadly broken down into two categories. The first, seeks to directly study how organisms respond to artificial agents in contrast to biological or inanimate ones. The second, uses the novel affordances of the robotic platforms to investigate complex phenomena, such as responses to multisensory stimuli during minimally structured interactions, that would be difficult to capture with classical experimental setups. Here we argue that to realize the full potential of the approach, both goals must be integrated through methodological design that is informed by a deep understanding of the model system, as well as engineering and analytical considerations. We then propose a general framework for such experiments that emphasizes naturalistic interactions combined with multimodal observations and complementary analysis pipelines that are necessary to render a holistic picture of the data for the purpose of informing robotic design principles. Finally, we demonstrate this approach with an exemplar rat-robot social interaction task which included simultaneous multi-agent tracking and neural recordings.

animal behavior and cognition↗

bioRxiv2021

Host-symbiont stress response to lack-of-sulfide in the giant ciliate mutualism

The thiotrophic mutualism between the sulfur-oxidizing, chemoautotrophic (thiotrophic) bacterial ectosymbiont Candidatus Thiobius zoothamnicola and the giant ciliate Zoothamnium niveum thrives in a variety of shallow-water marine environments with highly fluctuating sulfide emission. To persist over time both partners must reproduce and ensure symbiont transmission prior cessation of sulfide, fueling the symbionts carbon fixation and host nourishment. We experimentally investigated the response of this mutualism to waning of sulfide. We found that colonies followed the r-strategy and released initially present but also newly produced macrozooids until death. A fraction of middle-sized longer-lived colonies were particularly proficient in producing and releasing swarmers. The symbionts on the colonies proliferated less and became larger and more rod-shaped under oxic conditions compared to symbionts from freshly collected colonies exposed to sulfide and oxygen. The symbiont monolayer was highly disturbed with epigrowth of other microbes and loss of symbionts that were subsequently found in the experimental seawater. We conclude that both partners response to cessation of sulfide emission was remarkably fast. The colony experienced death within two days but host reproduction through swarmers carrying the symbiont ensured the continuation of the association.

ecology↗

bioRxiv2019

Oxytocin Neurons Enable Social Transmission of Maternal Behavior

Maternal care is profoundly important for mammalian survival, and non-biological parents can express it after experience with infants. One critical molecular signal for maternal behavior is oxytocin, a hormone centrally released by hypothalamic paraventricular nucleus (PVN). Oxytocin enables plasticity within the auditory cortex, a necessary step for responding to infant vocalizations. To determine how this change occurs during natural experience, we continuously monitored homecage behavior of female virgin mice co-housed for days with an experienced mother and litter, synchronized with recordings from virgin PVN cells, including from oxytocin neurons. Mothers engaged virgins in maternal care by ensuring their nest presence, and demonstrated maternal behavior in self-generated pup retrieval episodes. These social interactions activated virgin PVN and gated behaviorally-relevant cortical plasticity for pup vocalizations. Thus rodents can acquire maternal behavior by social transmission, and our results describe a mechanism for adapting brains of adult caregivers to infant needs via endogenous oxytocin. One Sentence SummaryMother mice help co-housed virgins become maternal by enacting specific behaviors that activate virgin oxytocin neurons.

neuroscience↗

bioRxiv2016

Pooled CRISPR screening with single-cell transcriptome read-out

CRISPR-based genetic screens have revolutionized the search for new gene functions and biological mechanisms. However, widely used pooled screens are limited to simple read-outs of cell proliferation or the production of a selectable marker protein. Arrayed screens allow for more complex molecular read-outs such as transcriptome profiling, but they provide much lower throughput. Here we demonstrate CRISPR genome editing together with single-cell RNA sequencing as a new screening paradigm that combines key advantages of pooled and arrayed screens. This approach allowed us to link guide-RNA expression to the associated transcriptome responses in thousands of single cells using a straightforward and broadly applicable screening workflow.

genomics↗