Biology subjects

Schreiber, F.

Publications and source records attributed to Schreiber, F..

18 recordsLinked to original sources

bioRxiv2026

Incomplete killing by antimicrobial metals generates transient antibiotic tolerance via delayed recovery in Escherichia coli

Incomplete killing is common after application of disinfectants or use of antimicrobial surfaces, leaving behind bacterial survivors with poorly characterized physiology. Here, we investigated the effects of short biocidal exposure to the common antimicrobial metals copper and silver as metal ions in suspension or under semi-dry conditions on solid metallic surfaces used against Escherichia coli. Survivors exhibited delayed regrowth and increased tolerance to bactericidal antibiotics without changes in MIC, consistent with a transient, non-genetic tolerance phenotype. When the magnitude of killing by silver or copper was matched between exposure formats, antibiotic tolerance patterns converged across metals and exposure conditions, indicating that the survivor state is governed more strongly by the severity of the primary lethal stress than by metal identity or exposure format alone. Incremental copper exposure revealed a dose- dependent increase in delayed regrowth and antibiotic tolerance but reduced tolerance to subsequent copper exposure. Post-exposure antibiotic tolerance as well as copper hypersensitivity phenotypes reverted after regrowth. Together, this supports a model of injury-linked delayed recovery rather than generalized stress priming. These findings demonstrate that incomplete disinfection can reshape survivor physiology, transiently increasing antibiotic tolerance through combined effects of selective survival acting on pre-existing phenotypic heterogeneity and acute injury-linked delayed recovery.

microbiology↗

bioRxiv2026

A growth-survival trade-off quantitatively predicts microbial selection under periodic disinfection

AO_SCPLOWBSTRACTC_SCPLOWMicrobial populations frequently experience periodic lethal stresses from natural and anthropogenic sources, including routine disinfection in clinical, industrial, and domestic environments. However, periodic disinfection can rapidly select for tolerant strains with increased survival but reduced growth during permissive conditions, creating a trade-off that shapes competitive outcomes in microbial communities. Here, we develop a mathematical framework to quantify and predict selection between microbial strains competing for growth-limiting resources under periodic disinfection. The framework is validated through a competition experiment simulating periodic disinfection with benzalkonium chloride between a wild-type Escherichia coli strain and a tolerant mutant exhibiting increased survival but decreased growth. A minimal model incorporating growth rate during permissive conditions and disinfection survival quantitatively captures selection across different experimental scenarios, including uncertainty estimates propagated from parameter variance. We further provide analytical expressions and a web-based interface to determine selection outcomes and quantify the contributions of survival and growth rate to selection. Our framework establishes a quantitative basis for predicting when periodic disinfection shifts population composition towards tolerant strains and is generalizable to other lethal stresses, including antibiotic chemotherapy and physical inactivation, thereby contributing to our understanding of the impact of periodic selective pressures on microbial competition.

microbiology↗

bioRxiv2026

User-driven development and evaluation of an agentic framework for analysis of large pathway diagrams

As biomedical knowledge keeps growing, resources storing available information multiply and grow in size and complexity. Such resources can be in the format of molecular interaction maps, which represent cellular and molecular processes under normal or pathological conditions. However, these maps can be complex and hard to navigate, especially to novice users. Large Language Models (LLMs), particularly in the form of agentic frameworks, have emerged as a promising technology to support this exploration. In this article, we describe a user-driven process of prototyping, development, and user testing of Llemy, an LLM-based system for exploring these molecular interaction maps. By involving domain experts from the very first prototyping in the form of a hackathon and collecting both fine-grained and general feedback on more refined versions, we were able to evaluate the perceived utility and quality of the developed system, in particular for summarising maps and pathways, as well as prioritise the development of future features. We recommend continued user-driven development and benchmarking to keep the community engaged. This will also facilitate the transition towards open-weight LLMs to support the needs of the open research environment in an ever-changing technology landscape.

bioinformatics↗

bioRxiv2025

Consequences of benzalkonium chloride tolerance for selection dynamics and de novo resistance evolution driven by antibiotics

Biocides are used in large amounts in industrial, medical, and domestic settings. Benzalkonium chloride (BAC) is a commonly used biocide, for which previous research revealed that Escherichia coli can rapidly adapt to tolerate BAC disinfection, with consequences for antibiotic susceptibility. However, the consequences of BAC tolerance for selection dynamics and resistance evolution to antibiotics remain unknown. Here, we investigated in detail the effect of BAC-tolerance in E. coli on its response upon challenge with different classes of antibiotics. Competition assays showed that subinhibitory concentrations of ciprofloxacin - but not ampicillin, colistin and gentamicin - select for the BAC-tolerant strain over the BAC-sensitive ancestor at a minimal selective concentration of 0.0013-0.0022 {micro}g/mL ciprofloxacin. In contrast, the BAC-sensitive ancestor was more likely to evolve resistance to ciprofloxacin, colistin and gentamicin than the BAC-tolerant strain when adapted to higher concentrations of antibiotics in a serial transfer laboratory evolution experiment. The observed difference in the evolvability of resistance to ciprofloxacin was partly explained by a negative epistatic interaction between the mutations conferring BAC-tolerance and a knockout mutation in the gene encoding for the outer membrane porin F. Taken together, these findings suggest that BAC-induced tolerance can be stabilized in environments containing low concentrations of ciprofloxacin, while it also constrains evolutionary pathways towards antibiotic resistance.

microbiology↗

bioRxiv2025

Interactive delineation and quantification of anatomical structure with virtual reality

A virtual environment, equipped with suitable data representations, interaction techniques, and method interfaces, can facilitate effective human-in-the-loop analytics of 3D data. Motivated by this observation, we propose Brainacle, a native editor for the interactive delineation and quantification of anatomical structures in virtual reality. We validate this in a case study, where we process the gross brain regions of 20 individual animals from six different species of African cichlid fish from the tribe Lamprologinii, endemic to Lake Tanganyika in Africa. The software presented in this article can be used to quickly visualise and obtain an overview of imaged animal tissues, visually communicate features of interest, interactively add delineations, and perform different measurements in the same 3D environment. Brainacle is made available free of charge at https://brainacle.org and on the research data repository DaRUS.

bioinformatics↗

bioRxiv2025

MeTrEx: Membrane Trajectory Explorer

BackgroundMolecular Dynamics (MD) simulations provide valuable insights into the behaviour of biomolecules, particularly in complex systems such as cellular membranes. However, the increasing volume of MD data presents significant challenges in data visualisation, exploration and analysis. ResultWe present MeTrEx (Membrane Trajectory Explorer), a Python-based software designed to enhance the exploration and analysis of MD simulation data, with a focus on ligand and membrane molecule interactions. MeTrEx offers an intuitive graphical user interface for visualisation, advanced interaction tools, and comprehensive analytical capabilities. The software also supports comparative analyses of molecular trajectories and integrates external datasets for a more holistic view. A case study analysing the interaction of Polymyxin B1 with a bacterial membrane demonstrates the tools capabilities to investigate complex molecular behaviours. ConclusionBy combining detailed molecular analysis with interactive visualisation, MeTrEx provides researchers with a powerful platform to investigate complex biomolecular systems, contributing to a deeper understanding of ligand-membrane interactions.

bioinformatics↗

bioRxiv2024

The (α, β)-k Boolean Signatures of Molecular Toxicity: Microcystin as a Case Study

BackgroundThe (, {beta})-k-Feature Set Problem is a combinatorial problem, that has been proven as alternative to typical methods for reducing the dimensionality of large datasets without compromising the performance of machine learning classifiers. ResultWe present a case study that shows that solutions of the (, {beta})-k-Feature Set Problem help to identify molecular substructures related to toxicity. The dataset investigated in this study is based on the inhibition of ser/thr-proteinphosphatases by Microcystin (MC) congeners. MC congeners are a class of structurally similar cyanobacterial toxins, which are critical to human consumption. ConclusionWe show that it is possible to identify biologically meaningful toxicity signatures by applying the (, {beta})-k feature sets on extended connectivity fingerprint representations of MC congeners. Boolean rules were derived from the feature sets to classify toxicity and can be mapped on the chemical structure, leading to insights on the absence/presence of substructures that can explain toxicity. The presented method can be applied on any other molecular data set and is therefore transferrable to other use cases.

bioinformatics↗

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↗

bioRxiv2023

Creating Aesthetically Pleasing SBGN Visualisations for Presentation and Exploration

Visualisations are widely used to present, assess, and convey data and understanding in systems biology and other fields, and graphical representations of data and information should be precise, visually engaging, informative, and aesthetically pleasing. The Systems Biology Graphical Notation (SBGN) is a standard for visual representations of processes and networks in systems biology in the form of SBGN maps. It defines graphical elements and connection rules for knowledge representation in the form of maps. Whereas the form of the map elements is defined, those map elements have to be placed in a proper way in order to obtain useful and aesthetically pleasing visualisations. Here, we present layout considerations and guidelines for SBGN maps, as well as workflows for SBGN-ED as a tool for crafting visually pleasing SBGN visualisations for presentation and exploration.

systems biology↗

bioRxiv2023

Spatially Resolved Transcriptomics Mining in 3D and Virtual Reality Environments with VR-Omics

The field of spatial transcriptomics is rapidly evolving, with increasing sample complexity, resolution, and tissue size. Yet the field lacks comprehensive solutions for automated integration and analysis of multi-slice data in either stacked (3D) or co-planar (2D) formation. To address this, we developed VR-Omics, a free, platform-agnostic software that distinctively provides end-to-end automated processing of multi-slice data through a biologist-friendly interface. Benchmarking against existing methods demonstrates VR-Omics unique strengths to perform comprehensive end-to-end analysis of multi-slice stacked data. Applied to rare paediatric cardiac rhabdomyomas, VR-Omics uncovered previously undetected dysregulated metabolic networks through co-planar slice analysis, demonstrating its potential for biological discoveries.

bioinformatics↗

bioRxiv2023

Mechanisms of low susceptibility to the disinfectant benzalkonium chloride in a multidrug-resistant environmental isolate of Aeromonas hydrophila

Excessive discharge of quaternary ammonium disinfectants such as benzalkonium chloride (BAC) into aquatic systems can trigger several physiological responses in environmental microorganisms. In this study, we isolated a low susceptible strain of Aeromonas hydrophila, namely INISA09, to from a wastewater treatment plant in Costa Rica. We characterized its phenotypic response upon exposure to three different concentrations of BAC and determined the primary mechanisms related to its resistance using genomic and proteomic tools. The genome of the strain is 4.6-Mb with 4,273 genes, and we found a propound genome rearrangement and thousands of missense mutations when compared to the reference strain A. hydrophila ATCC 7966. We identified 15,762 missense mutations mainly associated with transport, antimicrobial resistance, and outer membrane proteins. In addition, quantitative proteomic analysis revealed the significant upregulation of several efflux pumps and the downregulation of porins when the strain was exposed to three BAC concentrations. Other genes related to membrane fatty acid metabolism and redox metabolic reactions also showed an altered expression. Our findings suggest that the response of A. hydrophila INISA09 to BAC primarily occurs at the membrane level, which is BACs primary target. This study sheds light on the mechanisms of antimicrobial susceptibility in tropical aquatic environments against a widely-used disinfectant, using an environmental strain that could serve as a new study model.

microbiology↗

bioRxiv2022

A fluorescently labelled quaternary ammonium compound (NBD-DDA) to study mode-of-action and resistance mechanisms in bacteria

Quaternary ammonium compounds (QACs) are widely used as active agents in disinfectants, antiseptics, and preservatives. Despite being in use since the 1940s, there remain multiple open questions regarding their detailed mode-of-action and the mechanisms, including phenotypic heterogeneity, that can make bacteria less susceptible to QACs. To facilitate mode-of-action studies, we synthesized a fluorescent analogue of the quaternary ammonium compound benzalkonium chloride, namely N-dodecyl-N,N-dimethyl-[2-[(4-nitro-2,1,3-benzoxadiazol-7-yl)amino]ethyl]azanium-iodide (NBD-DDA). NBD-DDA is readily detected by flow cytometry and fluorescence microscopy with standard GFP/FITC-settings, making it suitable for molecular and single-cell studies. NBD-DDA was then used to investigate resistance mechanisms which can be heterogeneous among individual bacterial cells. Our results reveal that the antimicrobial activity of NBD-DDA against E. coli, S. aureus and P. aeruginosa is comparable to that of benzalkonium chloride (BAC), a widely used QAC. Characteristic time-kill kinetics and increased tolerance of a BAC tolerant E. coli strain against NBD-DDA suggest that the mode of action of NBD-DDA is similar to that of BAC. Leveraging these findings and NBD-DDAs fluorescent properties, we show that reduced cellular adsorption is responsible for the evolved BAC tolerance in the BAC tolerant E. coli strain. As revealed by confocal laser scanning microscopy (CLSM), NBD-DDA is preferentially localized in the cell envelope of E. coli, which is a primary target of BAC and other QACs. Overall, NBD-DDAs antimicrobial activity, its fluorescent properties, and its ease of detection render it a powerful tool to study the mode-of-action and the resistance mechanisms of QACs in bacteria.

microbiology↗

bioRxiv2022

Time-kill kinetics reveal heterogeneous tolerance to disinfectants

Disinfection is an important strategy to limit the spread of infections. Failure of disinfection may facilitate evolution of resistance against disinfectants and antibiotics through the processes of cross-resistance and co-resistance. The best possible outcome of disinfection minimizes the number of surviving bacteria and the chance for resistance evolution. Resistance describes the ability to grow in previously inhibitory concentrations of an antimicrobial, whereas tolerance is associated with enhanced survival of lethal doses. Individual bacteria from the same population can display considerable heterogeneity in their ability to survive treatment (i.e. tolerance) with antimicrobials, which can result in unexpected treatment failure. Here, we investigated how phenotypic heterogeneity affects the ability of E. coli to survive treatment with six different substances commonly used as active substances in disinfectants, preservatives and antiseptics. A mathematical model which assumes that phenotypic heterogeneity underlies the observed disinfection kinetics was used to infer whether time-kill kinetics were caused by a tolerant subpopulation. The analysis identified bimodal kill kinetics for benzalkonium chloride (BAC), didecyldimethylammonium chloride (DDAC), and isopropanol (Iso). In contrast, kill kinetics by chlorhexidine (CHX), glutaraldehyde (GTA), and hydrogen peroxide (H2O2) were best explained by unimodal kill kinetics underpinned by a broad distribution of tolerance times for CHX as opposed to a narrow distribution of tolerance times for GTA and H2O2. These findings have implications for the risk of disinfection failure, with potential consequences for the evolution of antimicrobial resistance and tolerance.

microbiology↗

bioRxiv2020

COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms

We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

systems biology↗

bioRxiv2020

Development of caecaloids to study host-pathogen interactions: new insights into immunoregulatory functions of Trichuris muris extracellular vesicles in the caecum

The caecum, an intestinal appendage in the junction of the small and large intestines, displays a unique epithelium that serves as an exclusive niche for a range of pathogens including whipworms (Trichuris spp). While protocols to grow organoids from small intestine (enteroids) and colon (colonoids) exist, the conditions to culture organoids from the caecum have yet to be described. Here, we report methods to grow, differentiate and characterise mouse adult stem cell-derived caecal organoids, termed caecaloids. We compare the cellular composition of caecaloids to that of enteroids identifying differences in intestinal epithelial cell (IEC) populations that mimic those found in the caecum and small intestine. The remarkable similarity in the IECs composition and spatial conformation of caecaloids and their tissue of origin enables their use as an in vitro model to study host interactions with important caecal pathogens. Thus, exploiting this system we investigated the responses of caecal IECs to extracellular vesicles (EVs) secreted/excreted by the intracellular helminth Trichuris muris. Our findings reveal novel immunoregulatory effects of whipworm EVs on the caecal epithelium, including the downregulation of responses to nucleic acid recognition and type-I interferon (IFN) signalling.

immunology↗

bioRxiv2020

An integrated model system to gain mechanistic insights into biofilm formation and antimicrobial resistance development in Pseudomonas aeruginosa MPAO1

Pseudomonas aeruginosa MPAO1 is the parental strain of the widely utilized transposon mutant collection for this important clinical pathogen. Here, we validate a model system to identify genes involved in biofilm growth and antibiotic resistance. Our model employs a genomics-driven workflow to assemble the complete MPAO1 genome, identify unique and conserved genes by comparative genomics with the PAO1 reference strain and missed genes by proteogenomics. Among over 200 unique MPAO1 genes, we identified six general essential genes that were overlooked when mapping public Tn-seq datasets against PAO1, including an antitoxin. Genomic data were integrated with phenotypic data from an experimental workflow using a user-friendly, soft lithography-based microfluidic flow chamber for biofilm growth. Experiments conducted across three laboratories delivered reproducible data on P. aeruginosa biofilms and validated both known and novel genes involved in biofilm growth and antibiotic resistance identified in screens of the mutant collection. Differential protein expression data from planktonic cells versus biofilm confirmed upregulation of candidates known to affect biofilm formation, of structural and secreted proteins of type six secretion systems, and provided proteogenomic evidence for some missed MPAO1 genes. This integrated, broadly applicable model promises to improve the mechanistic understanding of biofilm formation, antimicrobial tolerance and resistance evolution.

systems biology↗

bioRxiv2019

Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI across Sites

Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, and are a marker of cerebral small vessel disease. Most studies use time-consuming and subjective visual scoring to assess these structures. Recently, automated methods to quantify enlarged perivascular spaces have been proposed. Most of these methods have been evaluated only in high resolution scans acquired in controlled research settings. We evaluate and compare two recently published automated methods for the quantification of enlarged perivascular spaces in 76 clinical scans acquired from 9 different scanners. Both methods are neural networks trained on high resolution research scans and are applied without fine-tuning the networks parameters. By adapting the preprocessing of clinical scans, regions of interest similar to those computed from research scans can be processed. The first method estimates only the number of PVS, while the second method estimates simultaneously also a high resolution attention map that can be used to detect and segment PVS. The Pearson correlations between visual and automated scores of enlarged perivascular spaces were higher with the second method. With this method, in the centrum semiovale, the correlation was similar to the inter-rater agreement, and also similar to the performance in high resolution research scans. Results were slightly lower than the inter-rater agreement for the hippocampi, and noticeably lower in the basal ganglia. By computing attention maps, we show that the neural networks focus on the enlarged perivascular spaces. Assessing the burden of said structures in the centrum semiovale with the automated scores reached a satisfying performance, could be implemented in the clinic and, e.g., help predict the bleeding risk related to cerebral amyloid angiopathy.

neuroscience↗

bioRxiv2018

Diagnostic host gene signature to accurately distinguish enteric fever from other febrile diseases

Misdiagnosis of enteric fever is a major global health problem resulting in patient mismanagement, antimicrobial misuse and inaccurate disease burden estimates. Applying a machine-learning algorithm to host gene expression profiles, we identified a diagnostic signature which could accurately distinguish culture-confirmed enteric fever cases from other febrile illnesses (AUROC<95%). Applying this signature to a culture-negative suspected enteric fever cohort in Nepal identified a further 12.6% as likely true cases. Our analysis highlights the power of data-driven approaches to identify host-response patterns for the diagnosis of febrile illnesses. Expression signatures were validated using qPCR highlighting their utility as PCR-based diagnostic for use in endemic settings.

molecular biology↗