Biology subjects

Walter, A.

Publications and source records attributed to Walter, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Host response and cell fates of human U937 monocytes upon infection with vancomycin-resistant E. faecium

In recent years, Enterococcus faecium has become a high-priority human opportunistic pathogen, especially due to its antibiotic and environmental resistance, promoting hospital-acquired infections. However, its interaction with host cells remains poorly understood. In this study, we investigated the interaction of four recently collected, clinical vancomycin-resistant Enterococcus faecium (VREfm) isolates with the human U937 cell line. Early after infection, we observed close bacterial association with U937 monocytes and features suggestive of internalization. We thus investigated host cell fates after washing at 2 hours post infection (hpi) to concentrate on effects induced by internalized bacteria or those with direct host cell contact. Whereas viability was not impaired at 6 hpi, moderate cell death was induced to an isolate-specific extent at 24 hpi, which was abrogated when direct host-pathogen contact as prevented via boyden chamber cell culture inserts, but was enhanced by TNF-R1 deficiency. Host mRNA sequencing revealed a consistent pattern of differentially expressed genes across all VREfm isolates, suggesting a shared innate immune and stress-related response at 6 hpi, with functionally enriched biological processes related to leukocyte activation, cytokine signaling, programmed cell death, and RNA stability. Together, these data show that VREfm triggers an early pro-inflammatory transcriptional response in U937 monocytes without immediate cytotoxicity in U937 monocytes, while late-stage isolate-specific cell death induction depends on direct host cell contact or internalization and involves TNF-R1 mediated regulation. Our findings may assist future efforts to understand how VREfm triggers host cell damage that contributes to severe inflammation and systemic infection such as persistent bacteremia.

microbiology↗

bioRxiv2026

Functional relevance of mobile and clustered CaV2.1 channels in central synapses

Reliable neurotransmitter release critically depends on the spatial relationship between voltage- gated calcium channels (VGCCs) and presynaptic release sites. Single particle tracking of endogenous CaV2.1 channels at glutamatergic synapses of hippocampal neurons revealed that apart from CaV2.1 channels aggregated in stable nanodomain clusters, a substantial fraction of Cav2.1 channels remained mobile, raising the question of whether these dispersed channels contribute to synaptic function. Mathematical modelling predicted that dispersed Cav2.1 channels cooperatively enhance release reliability. Upon repetitive stimulation, mobile CaV2.1 channels enable alternative use of release sites and thereby reduce the probability of failed presynaptic release. Both optogenetic immobilisation of CaV2.1 channels per se or activation of GABAB receptors (GABABRs) alone increase the failure rate and can lead to synaptic silencing. However, optogenetic clustering CaV2.1 channels prior to GABABR activation increases the fraction of synapses that remain active even in presence of GABABR agonist. The contribution of mobile channels to reliable neurotransmitter release is frequency-dependent and is minor at stimulation frequencies 1 Hz but becomes strong at frequencies over 10 Hz. These results demonstrate that mobile presynaptic CaV2.1 channels increase the frequency range of synaptic transmission but are particularly sensitive to metabotropic GABABR-mediated inhibition in glutamatergic hippocampal synapses.

neuroscience↗

bioRxiv2026

Staphylococcus aureus triggers isolate-specific host transcriptional responses alongside TNF-R1 regulated cell death

BackgroundStaphylococcus aureus (S. aureus) is an increasingly recognized intracellular pathogen, yet infection outcomes vary with bacterial isolate and host cell type. The mechanisms underlying these differences remain poorly understood. This study investigates how distinct intracellular S. aureus isolates influence host signaling programs and infection outcomes by modulating cell death pathways and TNF-R1 dependent regulation of host cell fates across different human cell lines. MethodsFour S. aureus isolates were analyzed for intracellular localization using transmission electron microscopy (TEM), structured illumination microscopy (SIM), serial block-face scanning electron microscopy (SBF-SEM), and imaging flow cytometry. Transcriptional reprogramming of infected U937 monocytes was examined by mRNA sequencing. Infection outcomes were characterized and compared to A549 and SaOS-2 cell lines employing Luminex cytokine assays, flow cytometry and Western blot analysis to characterize host cell death mechanisms in both wild-type and TNF-R1 deficient backgrounds. ResultsAll S. aureus isolates localized to endolysosomal and cytosolic compartments but also peri and putatively intranuclearly, revealing an unexpected intracellular niche. In U937 monocytes, infection induced a conserved stress signature alongside isolatespecific transcriptional programs divergently affecting inflammation, metabolism, and cell fate, which was markedly attenuated in response to the chronicinfection isolate EDCC 5464. Cell death outcomes were likewise isolatedependent, involving intrinsic and extrinsic apoptosis, mitochondrial depolarization, and caspase-1 activation at distinct temporal dynamics. TNFR1 loss initially delayed but exacerbated late, isolate-independent cytotoxicity, identifying TNFR1 as a key regulator of U937 infection outcome. SaOS2 and A549 cell death was far less affected by isolate or TNF-R1 deficiency. ConclusionsThese results highlight the multilayered determinants governing intracellular S. aureus survival, non-canonical intracellular localization, and host cell susceptibility. The TNF/TNF-R1 axis is identified to critically determine regulated host defense during early infection stages in a tissue-specific manner. Together with distinct isolate-driven gene expression profiles, infection risks under TNF-targeted therapies and the contribution of S. aureus heterogeneity should be considered in the design of future host-directed treatment strategies. Plain English summaryThe bacterium Staphylococcus aureus (S. aureus) often lives harmlessly in humans but can cause severe or recurrent infections when the skin barrier is broken or the immune system is weakened. A major reason for its persistence is its ability to hide inside human cells, where it is shielded from immune attacks and antibiotics. To effectively target such bacteria, it is crucial to understand that infections vary depending on both the bacterial strain and the infected cell type. Many reasons behind these differences are still puzzling. We explored how different types of S. aureus (collected from different disease types) change how human cells respond to infection. We focused on how the different strains influence the way immune cells adjust their gene activity during infection, and how a receptor called TNF-R1 is involved in managing cell death responses. Bacteria were found not only in compartments meant to destroy them but also near and even inside the cell nucleus, an unexpected location. All strains triggered a similar stress response but also distinct patterns influencing inflammation, metabolism, and cell survival. A strain linked to chronic infection caused weaker responses, suggesting greater stealth. Cells lacking TNF-R1 initially survived longer but later showed greater damage, indicating this receptors role in infection control. In lung and bone cells, these effects were less pronounced. Concludingly, S. aureus occupies unexpected niches inside human cells and uses varying survival strategies. TNF-R1 is a key regulator of host infection responses in the analyzed immune cells, highlighting that both bacterial diversity and host factors must be considered when developing targeted treatments. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/723175v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@1b4214org.highwire.dtl.DTLVardef@18f4ee6org.highwire.dtl.DTLVardef@1851742org.highwire.dtl.DTLVardef@ba0359_HPS_FORMAT_FIGEXP M_FIG Peri- and intranuclear localization early after S. aureus uptake across host cell lines, with isolate-specific modulation of host fates and a critical role for TNF-R1 to mediate regulated death responses of U937 cells. At 2 hpi, intracellular S. aureus not only localizes in (LAMP-1 decorated) membrane-enclosed compartments or directly in the cytosol, but within invaginations of the nuclear surface and intranuclearly with or without being surrounded by a vesicular membrane in U937wt, SaOS-2wt, and A549wt cells. At 4 hpi, S. aureus triggers differential gene expression in (A) U937wt cells to an isolate-specific extent, with both unique and shared transcriptomic signatures across the four isolates, that is muted for the chronic infection isolate EDCC 5464. Apoptotic cell death is induced to an isolate-dependent extent involving extrinsic initiator caspase-8, intrinsic initiator caspase-9 (EDCC 5055 only), and variable effector caspase-3/-7 activity in the earlier stages of infection (6 hpi), which then barely increases (24 hpi) in U937wt cells. S. aureus-induced cell death and caspase activation is abolished in (B) U937{Delta}TNF-R1 at 6 hpi, but is significantly reinforced at 24 hpi with diminished isolate-specificity. Correspondingly, mitochondrial trans-membrane potential ({Delta}{Psi}m) is disrupted for all isolates upon TNF-R1 knockout, as well as caspase-1 activity, suggesting pyroptotic pathway activation at later stages of infection. (C) SaOS-2 wt cells show moderate caspase-3/-7 and -1 activation, while infection induces detachment of (D) A549wt cells with minimal caspase activation. Infection induces an isolate- and cell line-dependent cytokine release. Coloured arrows indicate the mean proportion of effector-positive cells ({uparrow} [~]20-40%, {uparrow} {uparrow} 40-60%, {uparrow} {uparrow} {uparrow} >60%) representing each S. aureus isolate. Grayed signaling arrows indicate the hypothesis by which TNF-R1 activation and internalization is required to kill lysosomal S. aureus via activation of anti-microbial enzymes and downstream regulated death pathway activation. Created with BioRender.com. C_FIG

microbiology↗

bioRxiv2026

Deep learning enables direct HLA typing from immunopeptidomics data

The immune system eliminates malignant and infected cells through T-cell-mediated recognition of peptides presented by human leukocyte antigen molecules. Mass spectrometry-based immunopeptidomics enables unbiased identification of naturally presented HLA-restricted peptides and has become central to the development of T-cell-based immunotherapies. However, immunopeptidomics data reflects the combined peptide presentation of multiple HLA alleles, and determining which allotypes are represented in this multi-allelic complexity remains an unmet computational challenge. Here, we introduce immunotype, a deep learning-based ensemble predictor for HLA class I allotyping directly from immunopeptidomics data. Immunotype integrates peptide and HLA protein sequence information through transformer encoders and a graph neural network, complemented by a curated mono-allelic reference of known peptide-HLA binding preferences. Immunotype achieves an overall accuracy of 87.2% at protein-level resolution across diverse tissues and thereby enables rapid, cost-effective HLA typing of large-scale immunopeptidomics datasets.

bioinformatics↗

bioRxiv2026

A Functional Metabolomics Framework to Track Microbiome Drug Metabolism

Understanding how gut microbes transform drugs, and how this influences microbiome composition and function, remains a key question to better understand the efficacy and side effects of pharmaceuticals. To accelerate the discovery of microbiome-derived drug metabolites, we developed a functional metabolomics workflow that combines the use of synthetic microbial communities (SynComs) with a time-series resolved molecular networking approach and advanced computational metabolite annotation. We demonstrate how this framework can be used to illuminate chemical transformation dynamics in a gut SynCom (Com20) with 50 clinical drugs. Our results highlight a multitude of drug metabolites, including multi-step metabolic cascades, some of which correlated to shifts in microbial taxa, suggesting functional links between microbiome composition and biochemical transformations. Our computational data analysis workflow is publicly available through the GNPS2 ecosystem at chemprop.gnps2.org, which can be used to prioritize biotransformations and other (bio)chemical reactions in various biological and abiotic systems.

systems biology↗

bioRxiv2025

From Canopy Images to Organ-Level Disease Assessments: A Scalable Approach to Measure Quantitative Resistance in the Field

Breeding for quantitative, polygenic resistance is widely considered the most durable, cost-effective, and environmentally safe approach to crop disease control. However, progress in resistance breeding is hindered by the limited capability of current approaches to measure highly quantitative disease phenotypes under field conditions with high precision and sufficient throughput. Here, we present an imaging protocol and a modular image processing pipeline that enables wheat disease detection and severity estimation directly from very-high-resolution canopy imagery, eliminating the need for physical interaction with the monitored plants as required in previously proposed sensor-based methods capable of symptom-level diagnosis. The pipeline combines deep-learning-based semantic segmentation, keypoint detection, and depth estimation to diagnose and quantify disease symptoms and extract the analyzable reference plant surfaces for severity estimation. By leveraging estimated relative depth and analyzing image texture, well-focused areas with sufficient quality were accurately segmented. Despite the challenging nature of canopy images and frequent symptom ambiguity, symptom detection and segmentation models trained on a new dataset reached a similar performance as already described in more simplified scenarios where detached, flattened leaves were analyzed. Plot-level severity estimates of Septoria Tritici Blotch, a major wheat disease, obtained using the new method and a precise but more laborious reference method were highly correlated (Pearson R = 0.83) across a range of morphologically contrasting cultivars. Validation of the new method on data collected by different operators at different sites demonstrated the robustness of the approach. The ability of the method to process imagery acquired in a contact-free manner can enable deployment on autonomous ground vehicles, paving the way for automated, scalable phenotype acquisition.

biochemistry↗

bioRxiv2024

Demonstrating Soft X-Ray Tomography in the lab for correlative cryogenic biological imaging using X-rays and light microscopy

Soft X-ray tomography (SXT) enables native-contrast three-dimensional (3D) imaging of fully hydrated, cryogenically preserved biological samples, revealing ultrastructural details without the need for staining, embedding, or sectioning. Traditionally available only at synchrotron facilities, recent advances in laser-driven plasma sources have led to the development of compact soft X-ray microscopes, such as the SXT-100. The SXT-100 achieves imaging resolutions down to 54 nm full-pitch, with tomograms acquired in 30 minutes to two hours. Integrated with an epifluorescence microscope, the SXT-100 facilitates correlative workflows by bridging fluorescence and electron microscopy while preserving the structural integrity of vitrified samples. We demonstrate the capabilities of the SXT-100 through various use cases, including imaging Euglena gracilis, Saccharomyces cerevisiae yeast cells, and nanoparticles in mammalian cells. The relatively short tomogram acquisition times, the virtually non-destructive nature of soft X-ray tomography, and its quantitative imaging capabilities underscore its potential as a powerful tool for advanced biological imaging. Future developments promise enhanced throughput and deeper integration with emerging correlative imaging modalities, and a wider variety of sample types including tissue.

biophysics↗

bioRxiv2024

The FIP 1.0 Data Set: Highly Resolved Annotated Image Time Series of 4,000 Wheat Plots Grown in Six Years

BackgroundUnderstanding genotype-environment interactions of plants is crucial for crop improvement, yet limited by the scarcity of quality phenotyping data. This data note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data. FindingsWe provide time series data for more than 4,000 wheat plots, including aligned high-resolution image sequences totaling more than 153,000 aligned images across six years. Measurement data for eight key wheat traits is included, namely canopy cover values, plant heights, wheat head counts, senescence ratings, heading date, final plant height, grain yield, and protein content. Genetic marker information and environmental data complement the time series. Data quality is demonstrated through heritability analyses and genomic prediction models, achieving accuracies aligned with previous research. ConclusionsThis extensive data set offers opportunities for advancing crop modeling and phenotyping techniques, enabling researchers to develop novel approaches for understanding genotype-environment interactions, analyzing growth dynamics, and predicting crop performance. By making this resource publicly available, we aim to accelerate research in climate-adaptive agriculture and foster collaboration between plant science and machine learning communities.

plant biology↗

bioRxiv2024

DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies

Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies now offer potential advantages in terms of repeat mapping and variant phasing. We present DeepSomatic, a deep learning method for detecting somatic SNVs and insertions and deletions (indels) from both short-read and long-read data, with modes for whole-genome and exome sequencing, and able to run on tumor-normal, tumor-only, and with FFPE-prepared samples. To help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available a dataset of five matched tumor-normal cell line pairs sequenced with Illumina, PacBio HiFi, and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples and technologies (short-read and long-read), DeepSomatic consistently outperforms existing callers, particularly for indels.

bioinformatics↗

bioRxiv2024

Mapping the genetic landscape establishing a tumor immune microenvironment favorable for anti-PD-1 response in mice and humans

Identifying host genetic factors modulating immune checkpoint inhibitor (ICI) efficacy has been experimentally challenging because of variations in both host and tumor genomes, differences in the microbiome, and patient life exposures. Utilizing the Collaborative Cross (CC) multi-parent mouse genetic resource population, we developed an approach that fixes the tumor genomic configuration while varying host genetics. With this approach, we discovered that response to anti-PD-1 (aPD1) immunotherapy was significantly heritable in four distinct murine tumor models (H2 between 0.18-0.40). For the MC38 colorectal carcinoma system (H2 = 0.40), we mapped four significant ICI response quantitative trait loci (QTL) localized to mouse chromosomes (mChr) 5, 9, 15 and 17, and identified significant epistatic interactions between specific QTL pairs. Differentially expressed genes within these QTL were highly enriched for immune genes and pathways mediating allograft rejection and graft vs host disease. Using a cross species analytical approach, we found a core network of 48 genes within the four QTLs that showed significant prognostic value for overall survival in aPD1 treated human cohorts that outperformed all other existing validated immunotherapy biomarkers, especially in human tumors of the previously defined immune subtype 4. Functional blockade of two top candidate immune targets within the 48 gene network, GM-CSF and high affinity IL-2/IL-15 signaling, completely abrogated the MC38 tumor transcriptional response to aPD1 therapy in vivo. Thus, we have established a powerful cross species in vivo platform capable of uncovering host genetic factors that establish the tumor immune microenvironment configuration propitious for ICI response.

cancer biology↗

bioRxiv2024

Towards high throughput in-field detection and quantification of wheat foliar diseases with deeplearning

1Reliable, quantitative information on the presence and severity of crop diseases is critical for site-specific crop management and resistance breeding. Successful analysis of leaves under naturally variable lighting, presenting multiple disorders, and across phenological stages is a critical step towards high-throughput disease assessments directly in the field. Here, we present a dataset comprising 422 high resolution images of flattened leaves captured under variable outdoor lighting with polygon annotations of leaves, leaf necrosis and insect damage as well as point annotations of Septoria tritici blotch (STB) fruiting bodies (pycnidia) and rust pustules. Based on this dataset, we demonstrate the capability of deep learning for keypoint detection of pycnidia (F 1 = 0.76) and rust pustules (F 1 = 0.77) combined with semantic segmentation of leaves (IoU = 0.96), leaf necrosis (IoU = 0.77) and insect damage(IoU = 0.69) to reliably detect and quantify the presence of STB, leaf rusts, and insect damage under natural outdoor conditions. An analysis of intra- and inter-annotator agreement on selected images demonstrated that the proposed method achieved a performance close to that of annotators in the majority of the scenarios. We validated the generalization capabilities of the proposed method by testing it on images of unstructured canopies acquired directly in the field and with-out manual interaction with single leaves. The corresponding imaging procedure can be adapted to support automated data acquisition. Model predictions were in good agreement with visual assessments of in-focus regions in these images, despite the presence of new challenges such as variable orientation of leaves and more complex lighting. This underscores the principle feasibility of diagnosing and quantifying the severity of foliar diseases under field conditions using the proposed imaging setup and image processing methods. By demonstrating the ability to diagnose and quantify the severity of multiple diseases in highly natural complex scenarios, we lay out the groundwork for a significantly more efficient, non-invasive in-field analysis of foliar diseases that can support resistance breeding and the implementation of core principles of precision agriculture.

plant biology↗

bioRxiv2023

Temporal resolution trumps spectral resolution in UAV-based monitoring of canopy senescence dynamics

Senescence is a dynamic process that is affected by many environmental, genetic, and physiological factors. Quantifying this process is important for breeding wheat varieties with high yield and of high quality. We present a method that allows up-scaling of the state of the art method - visual scoring - by using image sequences acquired from Unmanned Aerial Vehicles (UAV). This reduces measurement time and environmental changes during the measurement as well as rater bias. We compared the potential of a widely used multispectral sensor and a cheaper high-resolution RGB camera to track the dynamics of senescence. A UAV each was equipped with one of these sensors and used to measure canopy reflectance throughout the senescence process that lasted several weeks, for more than 400 winter wheat cultivars across three field seasons. Multiple spectral and RGB indices were calculated at the experimental plot level and used to model the dynamics of senescence. Model fits were further processed to extract key time points of the senescence phase. By comparing the results of the two sensors with each other and with the visual evaluation, respectively, we show that both sensors allow monitoring of senescence dynamics and measure key time points of the phase with a precision close to that of more sophisticated proximal sensing approaches. Optimal timing of measurements proved to be more important than the choice of sensor, confirming that timely and frequent measurements should be prioritized over more expensive sensors that provide a higher spectral resolution.

plant biology↗

bioRxiv2023

Thermal imaging can reveal variation in stay-green functionality of wheat canopies under temperate conditions

Canopy temperature (CT) is often interpreted as representing leaf activity traits such as photosynthetic rates, gas exchange rates, or stomatal conductance. Accordingly, CT measurements may provide a basis for high throughput assessments of the productivity of wheat canopies during early grain filling, which would allow distinguishing functional from dysfunctional stay-green. However, whereas the usefulness of CT as a fast surrogate measure of sustained vigor under soil drying is well established, its potential to quantify leaf activity traits under high-yielding conditions is less clear. To better understand sensitivity limits of CT measurements under high yielding conditions, we generated within-genotype variability in stay-green functionality by means of differential short-term pre-anthesis canopy shading that modified the sink:source balance. We quantified the effects of these modifications on stay-green properties through a combination of gold standard physiological measurements of leaf activity and newly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation. In parallel, we monitored CT by means of a pole-mounted thermal camera that delivered continuous, ultra-high temporal resolution CT data. Our results show that differences in leaf activity stemming from differences in stay-green functionality translate into measurable differences in CT in the absence of major confounding factors. Differences amounted to approximately 0.8{degrees}C and 1.5{degrees}C for a very high-yielding source-limited genotype, and a medium-yielding sink-limited genotype, respectively. The gradual nature of the effects of shading on CT during the stay-green phase underscore the importance of a high measurement frequency and a time-integrated analysis of CT, whilst modest effect sizes confirm the importance of restricting screenings to a limited range of morphological and phenological diversity.

plant biology↗

bioRxiv2023

Field phenotyping reveals the importance of including cultivar-specific per se temperature response in phenology modeling

Predicting plant development, a longstanding goal in plant physiology, involves two interwoven components: continuous growth and the progression of growth stages (phenology). Current models, like thermal time, assume species-level growth responses to temperature. We challenge this assumption, suggesting that cultivar-specific temperature responses significantly affect phenology. To investigate, we collected field-based growth and phenology data in winter wheat and soybean over multiple years. We used diverse models, from linear to neural networks, to assess growth responses to temperature at various trait and covariate levels. Cultivar-specific non-linear models best explained phenology-related cultivar-environment interactions. With cultivar-specific models, additional relations to other stressors than temperature were found. The availability of the presented field phenotyping tools allows incorporating cultivar-specific temperature response functions in future plant physiology studies, which will deepen our understanding of key factors that influence plant development. Consequently, this work has implications for crop breeding and cultivation under adverse climatic conditions.

physiology↗

bioRxiv2023

Wall teichoic acid substitution with glucose governs phage susceptibility of Staphylococcus epidermidis

The species- and clone-specific susceptibility of Staphylococcus cells for bacteriophages is governed by the structures and glycosylation patterns of wall teichoic acid (WTA) glycopolymers. The glycocodes of phage-WTA interaction in the opportunistic pathogen Staphylococcus epidermidis and in other coagulase-negative staphylococci (CoNS) have remained unknown. We report a new S. epidermidis WTA glycosyltransferase TagE whose deletion confers resistance to siphoviruses such as {Phi}E72 but enables binding of otherwise unbound podoviruses. S. epidermidis glycerolphosphate WTA was found to be modified with glucose in a tagE-dependent manner. TagE is encoded together with the enzymes PgcA and GtaB providing uridine diphosphate-activated glucose. {Phi}E72 transduced several other CoNS species encoding TagE homologs suggesting that WTA glycosylation via TagE is a frequent trait among CoNS that permits inter-species horizontal gene transfer. Our study unravels a crucial mechanism of phage-Staphylococcus interaction and of horizontal gene transfer and it will help in the design of anti-staphylococcal phage therapies. ImportancePhages are highly specific for certain bacterial hosts, and some can transduce DNA even across species boundaries. How phages recognize cognate host cells remains incompletely understood. Phages infecting members of the genus Staphylococcus bind to wall teichoic acid (WTA) glycopolymers with highly variable structures and glycosylation patterns. How WTA is glycosylated in the opportunistic pathogen Staphylococcus epidermidis and in other coagulase-negative Staphylococcus (CoNS) species has remained unknown. We describe that S. epidermidis glycosylates its WTA backbone with glucose and we identify a cluster of three genes, responsible for glucose activation and transfer to WTA. Their inactivation strongly alters phage susceptibility patterns, yielding resistance to siphoviruses but susceptibility to podoviruses. Many different CoNS species with related glycosylation genes can exchange DNA via siphovirus {Phi}E72 suggesting that glucose-modified WTA is crucial for interspecies horizontal gene transfer. Our finding will help to develop antibacterial phage therapies and unravel routes of genetic exchange.

microbiology↗

bioRxiv2023

Combining high-resolution imaging, deep learning, and dynamic modelling to separate disease and senescence in wheat canopies

Maintenance of sufficient healthy green leaf area after anthesis is key to ensuring an adequate assimilate supply for grain filling. Tightly regulated age-related physiological senescence and various biotic and abiotic stressors drive overall greenness decay dynamics under field conditions. Besides direct effects on green leaf area in terms of leaf damage, stressors often anticipate or accelerate physiological senescence, which may multiply their negative impact on grain filling. Here, we present an image processing methodology that enables the monitoring of chlorosis and necrosis separately for ears and shoots (stems + leaves) based on deep learning models for semantic segmentation and color properties of vegetation. A vegetation segmentation model was trained using semi-synthetic training data generated using image composition and generative adversarial neural networks, which greatly reduced the risk of annotation uncertainties and annotation effort. Application of the models to image time-series revealed temporal patterns of greenness decay as well as the relative contributions of chlorosis and necrosis. Image-based estimation of greenness decay dynamics was highly correlated with scoring-based estimations (r {approx} 0.9). Contrasting patterns were observed for plots with different levels of foliar diseases, particularly septoria tritici blotch. Our results suggest that tracking the chlorotic and necrotic fractions separately may enable (i) a separate quantification of the contribution of biotic stress and physiological senescence on overall green leaf area dynamics and (ii) investigation of the elusive interaction between biotic stress and physiological senescence. The potentially high-throughput nature of our methodology paves the way to conducting genetic studies of disease resistance and tolerance.

plant biology↗

bioRxiv2023

Microbiome modulates immunotherapy response in cutaneous squamous cell carcinoma

The gut microbiome is increasingly recognized to alter cancer risk, progression, and response to treatments such as immunotherapy, especially in cutaneous melanoma. However, whether the microbiome influences immune checkpoint inhibitor (ICI) immunotherapy response to non-melanoma skin cancer has not yet been defined. As squamous cell carcinomas (SCC) are in closest proximity to the skin microbiome, we hypothesized that the skin microbiome, which regulates cutaneous immunity, might affect SCC-associated anti-PD1 immunotherapy treatment response. We used ultraviolet radiation to induce SCC in SKH1 hairless mice. We then treated the mice with broad-band antibiotics to deplete the microbiome, followed by colonization by candidate skin and gut bacteria or persistent antibiotic treatment, all in parallel with ICI treatment. We longitudinally monitored skin and gut microbiome dynamics by 16S rRNA gene sequencing, and tumor burden by periodic tumor measurements and histologic assessment. Our study revealed that antibiotics-induced abrogation of the microbiome reduced tumor burden, suggesting a functional role of the microbiome in non-melanoma skin cancer therapy response.

microbiology↗

bioRxiv2022

Effect of unequal vaccination coverage and migration on long-term pathogen evolution in a metapopulation

Worldwide inequalities in vaccine availability are expected to affect the spread and spatial distribution of infectious diseases. It is unclear, however, how spatial variation in vaccination coverage can affect the long-term evolution of pathogens. Here we use an analytical model and numerical simulations to analyse the influence of different imperfect vaccines on the potential evolution of pathogen virulence in a two-population model where vaccination coverage varies between populations. We focus on four vaccines, with different modes of action on the life-cycle of a pathogen infecting two host populations coupled by migration. We show that, for vaccines that reduce infection risk or transmissibility, spatial heterogeneity has little effect on pathogen prevalence and host mortality, and no effect on the evolution of pathogen virulence. In contrast, vaccines that reduce pathogen virulence can select for more virulent pathogens and may lead to the coexistence of different pathogen strains, depending on the degree of spatial heterogeneity in the metapopulation. This heterogeneity is driven by two parameters: pathogen migration and the difference in the vaccination rate between the two populations. We show that vaccines that only reduce pathogen virulence select mainly for a single pathogen strategy in the long term while vaccines that reduce both transmission and virulence can favor the coexistence of two pathogen genotypes. We discuss the implications and potential extensions of our analysis.

evolutionary biology↗