Biology subjects

Schmitz, D. A.

Publications and source records attributed to Schmitz, D. A..

5 recordsLinked to original sources

bioRxiv2024

TASOR expression in naive embryonic stem cells safeguards their developmental potential

The seamless transition through stages of pluripotency relies on a delicate balance between transcription factor networks and epigenetic silencing mechanisms that ensure proper regulation of the developmental program, critical for normal development. Here, we uncover the pivotal role of the transgene activation suppressor (TASOR), a component of the human silencing hub (HUSH) complex, in sustaining cell viability during the transition from naive to primed pluripotency, despite its rapid downregulation during this transition. Loss of TASOR in naive cells triggers replication stress, disrupts H3K9me3 heterochromatin formation, and compromise the transcriptional and post-transcriptional silencing of LINE-1 (L1) transposable elements (TEs), with these effects become more pronounced in primed cells. Remarkably, the survival of Tasor-knockout cells during naive to primed transition can be restored through the inhibition of cysteine-aspartic acid protease (Caspase) or deletion of mitochondrial antiviral signaling protein (MAVS). This suggests that unscheduled L1 expression activates an innate immune response, leading to programmed cell death, specifically in cells exiting naive pluripotency. Additionally, we propose that HUSH-promoted H3K9me3 in naive PSCs sets the stage for ensuing DNA methylation in primed cells, establishing long-term silencing during differentiation. Our findings shed insights on the crucial impact of epigenetic programs established in early developmental stages on subsequent phases, underscoring their significance in the developmental process.

developmental biology↗

bioRxiv2024

Single-cell trait diversity explains niche and fitness differences in aquatic microbial communities

Two fundamental questions in ecology are how biodiversity is maintained and how it affects ecosystem functioning. Until now, it has been difficult to study the above mechanisms in natural microbial communities, yet they are important drivers of biogeochemical ecosystem functions. Here, we use a new approach to define and measure biodiversity in complex lake microbial communities (Lake Cadagno, Switzerland) based on the cell-to-cell variation in multiple functionally relevant phenotypic traits. We use stable isotope probing coupled to correlative imaging using confocal laser scanning microscopy (CLSM) and nanometer-scale secondary ion mass spectrometry (NanoSIMS) to obtain morphological (size), physiological (pigments) and metabolic (carbon and nitrogen isotope uptake and sulfur content) traits for a large number of individual cells along the environmental gradient found across lake depth. We show that cell-to-cell trait variation is significantly correlated with cell densities as a proxy for ecosystem functioning, whereas genetic diversity measured at the level of 16S and 18S is not. Our single-cell analysis provides evidence for a simultaneous increase in niche partitioning (measured as increased evenness in pigment composition) and decrease in fitness differences (measured as decreased variability in sulfur content) due to light limitation and competition for nutrients in deep layers of the lake. This leads to a negative relationship between niche and fitness differences. Our results suggest that niche and fitness differences in natural microbial communities can be understood at the level of single-cell traits, providing a mechanistic understanding of the relationship between microbial diversity and ecosystem functioning.

microbiology↗

bioRxiv2024

A new protocol for multispecies bacterial infections in zebrafish and their monitoring through automated image analysis

The zebrafish Danio rerio has become a popular model host to explore disease pathology caused by infectious agents. A main advantage is its transparency at an early age, which enables live imaging of infection dynamics. While multispecies infections are common in patients, the zebrafish model is rarely used to study them, although the model would be ideal for investigating pathogen-pathogen and pathogen-host interactions. This may be due to the absence of an established multispecies infection protocol for a defined organ and the lack of suitable image analysis pipelines for automated image processing. To address these issues, we developed a protocol for establishing and tracking single and multispecies bacterial infections in the inner ear structure (otic vesicle) of the zebrafish by imaging. Subsequently, we generated an image analysis pipeline that involved deep learning for the automated segmentation of the otic vesicle, and scripts for quantifying pathogen frequencies through fluorescence intensity measures. We used Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae, three of the difficult-to-treat ESKAPE pathogens, to show that our infection protocol and image analysis pipeline work both for single pathogens and pairwise pathogen combinations. Thus, our protocols provide a comprehensive toolbox for studying single and multispecies infections in real-time in zebrafish.

microbiology↗

bioRxiv2023

Predicting bacterial interaction outcomes from monoculture growth and supernatant assays

How to derive principles of community dynamics and stability is a central question in microbial ecology. Bottom-up experiments, in which a small number of bacterial species are mixed, have become popular to address it. However, experimental setups are typically limited because co-culture experiments are labor-intensive and species are difficult to distinguish. Here, we use a 4-species bacterial community to show that information from monoculture growth and inhibitory effects induced by secreted compounds can be combined to predict the competitive rank order in the community. Specifically, integrative monoculture growth parameters allow building a preliminary competitive rank order, which is then adjusted using inhibitory effects from supernatant assays. While our procedure worked for two different media, we observed differences in species rank orders between media. We then parameterized computer simulations with our empirical data to show that higher-order species interactions largely follow the dynamics predicted from pairwise interactions with one important exception. The impact of inhibitory compounds was reduced in higher-order communities because their negative effects were spread across multiple target species. Altogether, we formulated three simple rules of how monoculture growth and supernatant assay data can be combined to establish a competitive species rank order in an experimental 4-species community.

microbiology↗

bioRxiv2022

Negative interactions and virulence differences drive the dynamics in multispecies bacterial infections

Bacterial infections are often polymicrobial, leading to intricate pathogen-pathogen and pathogen-host interactions. There is increasing interest in studying the molecular basis of pathogen interactions and how such mechanisms impact host morbidity. However, much less is known about the ecological dynamics between pathogens and how they affect virulence and host survival. Here we address these open issues by co-infecting larvae of the insect model host Galleria mellonella with one, two, three or four bacterial species, all of which are opportunistic human pathogens. We found that host mortality was always determined by the most virulent species regardless of the number of species and pathogen combinations injected. In certain combinations, the more virulent pathogen simply outgrew the less virulent pathogen. In other combinations, we found evidence for negative interactions between pathogens inside the host, whereby the more virulent pathogen typically won a competition. Taken together, our findings reveal positive associations between a pathogens growth inside the host, its competitiveness towards other pathogens, and its virulence. Beyond being generalizable across species combinations, our findings predict that treatments against polymicrobial infections should target the most virulent species to reduce host morbidity, a prediction we validated experimentally. ImportanceThere is increasing evidence that polymicrobial infections are common and that host morbidity is impacted by interactions between the co-infecting pathogens. In this study, we infected the larvae of the insect host Galleria mellonella with four opportunistic human pathogens as mono or multispecies bacterial infections in all possible combinations and followed host survival and pathogen interactions over time. We discovered that species followed the exact same rank order in terms of their virulence, their growth inside the host, and their competitive ability against co-infecting pathogens. As consequence of this consistent rank order, we found that the most virulent species determines host survival dynamics in mixed infections. Beyond revealing a generalizable predictive pattern for virulence outcomes, our findings also predict that treatments should target the most virulent species in polymicrobial infections, a prediction we validated experimentally.

microbiology↗