Biology subjects

Zimmer, C.

Publications and source records attributed to Zimmer, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Geographical variation in plasma bactericidal ability of breeding tree swallows (Tachycineta bicolor)

Immune defenses may vary across environments due to differences in life history or abiotic and biotic characteristics, but few studies have compared the strength of immune responses across broad geographical gradients. Here we investigated how bacteria killing ability (BKA), a measure of constitutive innate immune function, varies among populations of tree swallows (Tachycineta bicolor) inhabiting different environments. We hypothesized that populations living in unpredictable environments, which also have shorter breeding seasons and thus fewer opportunities to renest in the event of reproductive failure, would invest in reproduction at the expense of immunity. To test this hypothesis, we measured BKA and monitored reproductive activity in four populations located across most of the breeding range of tree swallows, in Tennessee, New York, Wyoming and Alaska. Consistent with our predictions, BKA was lowest in Alaska and Wyoming and highest in Tennessee. In most populations BKA was also lower during incubation than during nestling provisioning, and lower in males than in females. These results suggest that the high reproductive investment seen in populations experiencing more unpredictable breeding conditions may come at a cost to immune function. While reduced immune investment could increase susceptibility to parasites, this potential cost is likely offset by lower parasite pressure in these environments, limiting the need for maintaining strong constitutive innate defenses like BKA.

ecology↗

bioRxiv2026

Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria

Tuberculosis (TB) is the worldwide leading infectious killer due to a single pathogen and increasing antimicrobial resistance (AMR) makes it imperative to discover and develop new drugs with novel modes of action (MoAs) to treat TB infections. Phenotypic screening of chemical libraries has proven effective at identifying new compounds against bacterial pathogens. However, a major limitation of standard screens is their inability to uncover the MoA of hits thereby preventing targeted selection of compounds with novel MoAs. Linking drug perturbations to mutants from images could potentially enable to predict the targets of compounds that act through novel MoAs. Here, we develop a deep learning (DL)-based method to screen drug-treated Corynebacterium glutamicum (Cglu), a surrogate model for Mycobacterium tuberculosis (Mtb). Our DL model is based on a convolutional neural network architecture that takes high throughput images as input and is trained to distinguish between different MoAs. We show that our approach can robustly differentiate between the MoAs of established antibiotics and correctly recognise the MoA of antibiotics that were not previously seen by the DL model. We also show that inhibitors with the same and previously unseen MoA cluster together and apart from all other reference drugs, allowing for new MoA discovery. Importantly, we show that our model links images of chemical (drugs) and genetic (mutants) perturbations targeting similar pathways, thus paving the way towards mutant-based target prediction of compounds that act through novel MoAs, directly from high-content images. Finally, we explore the phenotypes induced by genetic disruption of pathways and demonstrate that features extracted with our DL model recover known biological relationships from high-throughput images alone using the cell cycle of Cglu as a case study, a finding with promising potential for fundamental mechanistic studies.

microbiology↗

bioRxiv2025

Organismal robustness and resilience influence the ability to cope with cold snaps

Coping with challenges often requires maintaining performance during poor conditions (robustness) and reestablishing normal performance following disturbance (resilience). Trade-offs between performance under normal and challenging conditions may generate variation in stress coping capacity, but there is little empirical data from wild populations. We studied robustness and resilience to temperature in tree swallows (Tachycineta bicolor) using a 39-year dataset that included >75,000 hours of behavioral data. Cold snaps had profound and sustained impacts on fitness; effects on nestlings were mediated by the behavioral robustness and resilience of parents. Parental robustness was predicted by developmental cold exposure and current endocrine state. As expected, trade-offs between peak performance and behavioral robustness influenced susceptibility. Selection on trade-offs may generate population differences in vulnerability to changing temperature regimes.

ecology↗

bioRxiv2025

Claustrum volume in human lifespan trajectory and effect of age, hemisphere, and sex

The human claustrum is a bilateral, thin, irregularly shaped gray matter structure located between the striatum and insula. While previous research demonstrated the effect of distinct medical conditions, such as prematurity, schizophrenia, and Alzheimers disease, on claustrum function and structure, it is poorly understood how non-pathologic biological conditions effect the claustrum. This study aimed to investigate the effect of age, hemisphere, and sex on claustrum volume. We used T1-weighted 3 Tesla MRI scans of 3,474 healthy participants ranging from 1 to 80 years of age, deep learning-based automated claustrum segmentation, and a normative modeling approach to delineate lifespan trajectories of claustrum volumes for both hemispheres and sexes. Additionally, ordinary least squares regression analyses were applied to further characterize age, hemisphere, and sex effect. Lifespan analysis revealed a trajectory of rapid claustrum volume increase from infancy to adolescence ([~] 1-15 years, annual growth 39.300 mm3/year), a plateau phase from early to middle adulthood ([~] 15-40 years, annual change 0.153 mm3/year), and a subsequent decline from middle adulthood to old age ([~] 40-80 years, annual decrease 10.325 mm3/year). The right claustrum was on average larger than the left one across all ages. Finally, overall, females had larger total intracranial volume-adjusted claustrum volumes than males across the lifespan. Results demonstrate a distinct effect of age, hemisphere, and sex on claustrum volume. Data provide a comprehensive framework for sex- and hemisphere-sensitive claustrum structure lifespan trajectories relevant for studying neurodevelopmental and neurodegenerative effects on the claustrum.

neuroscience↗

bioRxiv2025

Deep learning recognises antibiotic modes of action from brightfield images

Antimicrobial resistance is a growing public health threat predicted to cause up to 10 million deaths a year by 2050. To circumvent existing bacterial resistance mechanisms, discovering antibiotics with novel modes of action (MoAs) is crucial. While growth inhibition assays can robustly identify antibiotic molecules, they miss promising compounds with subinhibitory phenotypes and do not inform on drug MoA. Microscopy-based cytological profiling of drug-treated bacteria with hand-crafted image descriptors or more recently deep learning (DL) provides complementary information on the MoA. However, current approaches are limited by the need for fluorescent labelling and drug exposure at inhibitory concentrations. It also remains unclear if cytological profiling enables the detection of drugs with novel MoAs. Here, we demonstrate an approach based on supervised DL to identify antibiotic drug MoA from microscopy images without fluorescent labelling. We train a convolutional neural network to predict treatment conditions from brightfield images of Escherichia coli exposed to reference drugs covering multiple MoAs. Our method can detect drug exposure at subinhibitory concentrations and distinguishes individual drug treatments with high accuracy (86.2%). The learned representations implicitly capture MoA-specific phenotypes, enabling perfect MoA recognition (100%) without retraining using a model trained on only 644 images. Our approach can identify the MoA of previously unseen drugs with good accuracy (77.8{+/-}3.3%), as long as the MoA is represented by at least one of the training compounds. Finally, we show that our approach can detect if a drug has a novel MoA with an area under the curve above 0.75 for five out of six MoAs, facilitating microscopy-based identification of novel classes of antibiotics. Our methods and results pave the way towards an automated pipeline for antibiotic drug discovery based on imaging and DL.

microbiology↗

bioRxiv2025

DNA-encoded Library Screening Uncovers Potent DNMT2 Inhibitors Targeting a Cryptic Allosteric Binding Site

The human RNA methyltransferase DNMT2 is thought to be involved in various pathophysiological processes, yet, a major challenge in drug targeting DNMT2 is given by the fact that current SAH-derived inhibitors have poor target selectivity and limited cellular permeability. In this study, we have performed a DNA-encoded library (DEL) screening on DNMT2 yielding five non-SAH-like hit structures, three of which feature a peptidomimetic scaffold. All DEL hits could be validated by orthogonal biophysical and biochemical assays for DNMT2 binding. At the same time, the lead structure did not interact with related methyltransferases from the DNMT and NSUN families highlighting an unmatched DNMT2-targeting selectivity profile. Subsequent crystallographic studies revealed the unique ligand binding mode including an active site loop rearrangement and the formation of a cryptic allosteric binding pocket able to modulate the enzymatic activity by non-covalent DNMT2 dimerization. Based on the crystallographic results, we performed a structure-activity relationship study around the inhibitor lead structure resulting in an optimized DNMT2 inhibitor (KD=3.04 {micro}M), which was able to reduce m5C levels in MOLM-13 tRNA. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/632061v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@1bf92b3org.highwire.dtl.DTLVardef@38498borg.highwire.dtl.DTLVardef@1645740org.highwire.dtl.DTLVardef@9c61d5_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2024

Heterogeneous, temporally consistent, and plastic brain development after preterm birth

The current view of neurodevelopment after preterm birth presents a strong paradox: diverse neurocognitive outcomes suggest heterogeneous neurodevelopment, yet numerous brain imaging studies focusing on average dysmaturation imply largely uniform aberrations across individuals1. Here we show both, spatially heterogeneous individual brain abnormality patterns (IBAPs) but with consistent underlying biological mechanisms of injury and plasticity. Using cross-sectional structural magnetic resonance imaging data from preterm neonates and longitudinal data from preterm children and adults in a normative reference framework2, we demonstrate that brain development after preterm birth is highly heterogeneous in both severity and patterns of deviations. Individual brain abnormalities were also consistent for their extent and location along the life course, associated with glial cell underpinnings, and plastic for influences of the early social environment. Thus, IBAPs of preterm birth are spatially heterogenous, temporally consistent for extent, spatial location, and cellular underpinnings, and plastic for social-environmental impacts. Our findings extend conventional views of preterm neurodevelopment, revealing a nuanced landscape of individual variation, with consistent commonalities between subjects. This integrated perspective of preterm neurodevelopment implies more targeted theranostic intervention strategies, specifically integrating brain charts2 and imaging at birth, as well as social interventions during early development3.

neuroscience↗

bioRxiv2024

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winners strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

genetics↗

bioRxiv2024

Universal dynamics of cohesin-mediated loop extrusion

Most animal genomes are partitioned into Topologically Associating Domains (TADs), created by cohesin-mediated loop extrusion and defined by convergently oriented CTCF sites. The dynamics of loop extrusion and its regulation remains poorly characterized in vivo. Here, we tracked TAD anchors in living human cells to visualize and quantify cohesin-dependent loop extrusion across multiple endogenous genomic regions. We show that TADs are dynamic structures whose anchors are brought in proximity about once per hour and for 6-19 min ([~]16% of the time). TADs are continuously subjected to extrusion by multiple cohesin complexes, extruding loops at [~]0.1 kb/s. Remarkably, despite strong differences of Hi-C patterns between the chromatin regions, their dynamics is consistent with the same density, residence time and speed of cohesin. Our results suggest that TAD dynamics is governed primarily by CTCF site location and affinity, which allows genome-wide predictive models of cohesin-dependent interactions.

genetics↗

bioRxiv2024

Deep learning-based detection of murine congenital heart defects from μCT scans

Congenital heart defects (CHD) result in high morbidity and mortality rates, but their origins are poorly understood. Mouse models of heart morphogenesis are required to study the pathological mechanisms of heart development compared to normal. In mouse fetuses, CHD can be observed and detected in 3D images obtained by thoracic micro-computed tomography (CT). However, diagnosis of CHD from CT scans is a time-consuming process that requires the experience of senior experts. An automated alternative would thus save time, empower less experienced investigators and could broaden analysis to larger numbers of samples. Here, we describe and validate an approach based on deep learning to automatically segment the heart and screen normal from malformed hearts in mouse CT scans. In an initial cohort, we collected 139 CT scans from thorax and abdomen of control and mutant perinatal mice. We trained a self-configurating neural network (nnU-Net) to segment hearts from body CT scans and validated its performance on expert segmentations, achieving a Dice coefficient of 96%. To identify malformed hearts, we developed and trained a 3D convolutional neural network (CNN) that uses segmented CT scans as inputs. Despite the relatively small training data size, our diagnosis model achieved a sensitivity, specificity (for a 0.5 threshold), and area under the curve (AUC) of 92%, 96%, and 97% respectively, as determined by 5-fold cross-validation. As further validation, we analyzed two additional cohorts that were collected after the model was trained: a prospective cohort, using the same experimental protocol as the initial cohort, and containing a subset of its genotypes, and a divergent cohort in which mice were subjected to a different treatment for heart arrest (cardioplegia) and that contained a new mouse line. Performance on the prospective cohort was excellent, with a sensitivity of 92%, a specificity of 100%, and an AUC of 100%. Performance on the divergent cohort was moderate (sensitivity: 69%, specificity: 80% and AUC: 81%), but was much improved when the model was finetuned on (a subset of) the cohort (sensitivity: 79%, specificity: 88% and AUC: 91%). These results showcase our models robustness and adaptability to technical and biological differences in the data, highlighting its usefulness for practical applications. In order to facilitate the adoption, adaptation and further improvement of these methods, we built a user-friendly Napari plugin (available at napari-hub.org/plugins/mousechd-napari) that allows users without programming skills to utilize the segmentation and diagnosis models and re-train the latter on their own data and resources. The plugin also highlights the cardiac regions used for the diagnosis. Our automatic and retrainable pipeline, which can be employed in high-throughput genetic screening, will accelerate diagnosis of heart anomalies in mice and facilitate studies of the mechanisms of CHD.

developmental biology↗

bioRxiv2024

A constricted mitochondrial morphology optimizes respiration.

Mitochondria assemble in a dynamic tubular network with a morphology governed by mitochondrial fusion and fission, which regulate all mitochondrial functions including oxidative phosphorylation 1-4. Yet, the link between mitochondrial morphology and respiration remains unclear 5-9. Here, we discover a previously unknown mitochondrial morphology dedicated to respiratory growth of Saccharomyces cerevisiae, which we refer to as "Ringo". The Ringo morphology is characterized by stable constrictions of mitochondrial tubules. Ringo constrictions are mediated by the yeast dynamin Dnm1 and, unlike mitochondrial fission 10-12, occur in the absence of contacts with the endoplasmic reticulum. Our data show that the Ringo morphology regulates mitochondrial DNA homeostasis during respiration to ensure stoichiometric assembly of OXPHOS complexes, demonstrating that the shape of mitochondria actively contributes to optimal respiration. One-Sentence Summary: We report a new mitochondrial morphology that actively contributes to optimal respiration in yeast.

cell biology↗

bioRxiv2023

Global neuronal activity drives cerebrospinal fluid motion mediated by brain blood volume changes in humans

In the mammalian brain, the directed motion of cerebrospinal fluid (CSF-flux) is instrumental in the distribution and removal of solutes. Changes in total cerebral blood volume (CBV) have been hypothesized to drive CSF-flux. We tested this hypothesis in two multi-modal brain imaging experiments in healthy humans, in which we drove large changes in total CBV by neuronal burst-suppression under anesthesia, or by transient global vasodilation in a hypercapnic challenge. We developed a technique to monitor CBV changes based on associated changes in total brain volume by functional MRI (fMRI) and measured cerebral blood flow by arterial spin-labeling. Relating CBV-sensitive signals to fMRI-derived measures of macroscopic CSF flow across the basal cisternae, we demonstrate that increasing total CBV extrudes CSF from the skull and decreasing CBV allows its influx. Moreover, CSF largely stagnates when CBV is stable. Together, our results establish the direct coupling between total CBV changes and CSF-flux.

neuroscience↗

bioRxiv2023

Regulation of parvalbumin interneuron plasticity by neuropeptide-encoding genes

Neuronal activity is regulated in a narrow permissive band for the proper operation of neural networks. Changes in synaptic connectivity and network activity, for example, during learning, might disturb this balance, eliciting compensatory mechanisms to maintain network function. In the neocortex, excitatory pyramidal cells and inhibitory interneurons exhibit robust forms of stabilising plasticity. However, while neuronal plasticity has been thoroughly studied in pyramidal cells, little is known about how interneurons adapt to persistent changes in their activity. Here we uncover the critical cellular and molecular mechanisms through which cortical parvalbumin-expressing (PV+) interneurons adapt to changes in their activity levels. We found that changes in the activity of PV+ interneurons drive cell-autonomous, bi-directional compensatory adjustments of the number and strength of inhibitory synapses received by these cells, specifically from other PV+ interneurons. High-throughput profiling of ribosome-associated mRNA revealed that increasing the activity of PV+ interneurons leads to the cell-autonomous upregulation of two genes encoding multiple secreted neuropeptides, Vgf and Scg2. Functional experiments demonstrated that VGF is critically required for the activity-dependent scaling of inhibitory PV+ synapses onto PV+ interneurons. Our findings reveal an instructive role for neuropeptide-encoding genes in regulating synaptic connections among PV+ interneurons in the adult mouse neocortex.

neuroscience↗

bioRxiv2023

Joint effects of social interactions and environmental challenges on physiology, internal microbiome, and reproductive performance in tree swallows (Tachycineta bicolor).

The social environment that individuals experience appears to be a particularly salient mediator of stress resilience, as the nature and valence of social interactions are often related to subsequent health, physiology, microbiota, and overall stress resilience. Relatively few studies have simultaneously manipulated the social environment and ecological challenges under natural conditions. Here, we report the results of experiments in wild tree swallows (Tachycineta bicolor) in which we manipulated both ecological challenges (predator encounters and flight efficiency reduction) and social interactions (by experimental dulling of a social signal). In two experiments conducted in separate years, we reversed the order of these treatments so that females experienced either an altered social environment followed by a challenge or vice-versa. Before, during, and after treatments were applied, we tracked breeding success, morphology and physiology (mass, corticosterone, and glucose), social interactions via an RFID sensor network, cloacal microbiome diversity, and fledging success. Overall, we found that predator exposure during the nestling period reduced the likelihood of fledging and that signal manipulation sometimes altered social interactions, but little evidence that the two categories of treatment interacted with each other. We discuss the implications of our results for understanding what types of challenges and what conditions are most likely to result in interactions between the social environment and ecological challenges.

physiology↗

bioRxiv2022

Developmental stage-dependent effects of perceived predation risk on physiology and fledging success of tree swallows (Tachycineta bicolor)

The risk of predation directly affects physiology, behavior, and fitness of wild birds. Social interactions with conspecifics may affect how individuals respond to stressors such as predators. Strong social connections could help individuals recover from a stressful experience; however, competitive interactions also have the potential to exacerbate stress. Few studies have investigated the interaction between environmental stressors and the social landscape in wild bird populations. Here, we experimentally simulated predation attempts on breeding female tree swallows (Tachicyneta bicolor). At the same time, we manipulated female breast plumage color, a key social signal. Simulated predation events on tree swallows negatively affected their nestlings condition, telomere lengths, and fledging success. However, the effects of experimental manipulations were timing-dependent: simulated predation during the early nestling period was more detrimental than "predation" during incubation. Contrary to our expectations, manipulation of the social environment did not affect the response of tree swallows to simulated predation. However, manipulating female plumage during the nestling period did affect nestling size, indicating an effect of the social environment on reproductive success. Our data demonstrate that transient stressors on breeding female birds can have carry-over effects on their nestlings, some of which may be long-lasting.

ecology↗

bioRxiv2022

Discovering genomic regions associated with the phenotypic differentiation of European local pig breeds

BackgroundIntensive selection of modern pig breeds resulted in genetic improvement of productive traits while local pig breeds remained less performant. As they have been bred in extensive systems, they have adapted to specifical environmental conditions resulting in a rich genotypic and phenotypic diversity. This study is based on European local pig breeds genetically characterized using DNA-pool sequencing data and phenotypically characterized using breed level phenotypes related to stature, fatness, growth and reproductive performance traits. These data were analyzed using a dedicated approach to detect selection signatures linked to phenotypic traits in order to uncover potential candidate genes that may be under adaptation to specific environments. ResultsGenetic data analysis of European pig breeds revealed four main axes of genetic variation represented by Iberian and modern breeds (i.e. Large White, Landrace, and Duroc). In addition, breeds clustered according to their geographical origin, for example French Gascon and Basque breeds, Italian Apulo Calabrese and Casertana breeds, Spanish Iberian and Portuguese Alentejano breeds. Principal component analysis of phenotypic data distinguished between larger and leaner breeds with better growth potential and reproductive performance on one hand and breeds that were smaller, fatter, and had low growth and reproductive efficiency on the other hand. Linking selection signatures with phenotype identified 16 significant genomic regions associated with stature, 24 with fatness, 2 with growth and 192 with reproduction. Among them, several regions contained candidate genes with possible biological effect on stature, fatness, growth and reproduction performance traits. For example, strong associations were found for stature in two regions containing the ANXA4 and ANTXR1 genes, for fatness containing the DNMT3A and POMC genes and for reproductive performance containing the HSD17B7 gene. ConclusionsThe present study on European local pig breeds used a dedicated approach for searching selection signatures supported by phenotypic data at the breed level to identify potential candidate genes that may have adapted to different living environments and production systems. Results can be useful to define conservation programs of local pig breeds.

genetics↗

bioRxiv2021

ShareLoc - an open platform for sharing localization microscopy data

Novel insights and more powerful analytical tools can emerge from the reanalysis of existing data sets, especially via machine learning methods. Despite the widespread use of single molecule localization microscopy (SMLM) for super-resolution bioimaging, the underlying data are often not publicly accessible. We developed ShareLoc (https://shareloc.xyz), an open platform designed to enable sharing, easy visualization and reanalysis of SMLM data. We discuss its features and show how data sharing can improve the performance and robustness of SMLM image reconstruction by deep learning.

bioinformatics↗

bioRxiv2021

Molecular organization and mechanics of single vimentin filaments revealed by super-resolution imaging

Intermediate filaments (IF) are involved in key cellular functions including polarization, migration, and protection against large deformations. These functions are related to their remarkable ability to extend without breaking, a capacity that should be determined by the molecular organization of subunits within filaments. However, this structure-mechanics relationship remains poorly understood at the molecular level. Here, using super-resolution microscopy (SRM), we show that vimentin filaments exhibit a ~49 nm axial repeat both in cells and in vitro. As unit-length-filaments (ULFs) were measured at ~59 nm, this demonstrates a partial overlap of ULFs during filament assembly. Using an SRM-compatible stretching device, we also provide evidence that the extensibility of vimentin is due to the unfolding of its subunits and not to their sliding, thus establishing a direct link between the structural organization and its mechanical properties. Overall, our results pave the way for future studies of IF assembly, mechanical and structural properties in cells.

biophysics↗