Biology subjects

Bojesen, A. M.

Publications and source records attributed to Bojesen, A. M..

4 recordsLinked to original sources

bioRxiv2025

Integrative holo-omic data analysis predicts interactions across the host-microbiome axis

Understanding the interplay between host organisms and their microbiomes is central to the development of sustainable food systems. However, high dimensionality and spurious associations remain major obstacles to extracting meaningful biological insight from multi-omic host-associated microbiome data; a challenge further exacerbated when "holo-omic" analyses across the host-microbiome boundary is considered. Here, we show that a computational method designed for multi-omic analysis in eukaryotes can be leveraged to integrate and analyse five layers of holo-omic data from porcine hosts and their gut microbiomes. We collected caecal tissue and digesta samples during a feeding trial that tested the impact of microbiota-directed fibres (acetylated galactoglucomannan) at critical developmental stages. From 800,000 features including microbial and host genes, metagenome-assembled genomes, and metabolites from caecal tissue and digesta, we used multiset correlation and factor analysis to select the most relevant features for capturing coordinated patterns across omic layers. From these features, we predicted over 2,000 putative host-microbiome interactions based on co-occurrence. Some of them reflected previously known relationships between animal and microbiome features, such as microbial genes for carbohydrate metabolism being linked to glycoside abundances in host tissue. Other predicted co-occurrences included features that were not detected in single-omic analysis and offer new hypotheses of host-microbiome interactions that warrant future investigation. Hence, we showcase an application of holo-omic analysis that avoids common pitfalls in high-dimensional data analysis; identifies known interactions as a form of validation; and most importantly, predicts new leads for understanding host-microbiome symbiosis. ImportanceWhile study systems involving mammalian hosts and their microbiomes are inherently complex, multi- and holo-omic analyses promise to provide interpretable results with translational value for the animal production industry. Unfortunately, computational methods capable of this kind of integration are currently scarce, as most existing multi-omics approaches have been developed for analysis of data layers within a single multicellular organism. We propose to adapt existing multi-omic methods for holo-omics by combining feature selection and interaction inference. This two-step analysis approach addresses common challenges in data-driven studies and can be implemented with a variety of tools for feature selection and interaction modelling. Through this holistic approach, we show that both known and novel relationships across the holobiont axis can be identified in a data-driven manner, offering new targets for the continued study of host-microbiome interactions and the effect of dietary interventions on production animals.

bioinformatics↗

bioRxiv2025

Micro-scale spatial metagenomics: revealing high-resolution spatial biogeography of gut microbiomes

Spatial organisation is a fundamental yet poorly resolved aspect of gut microbial ecology. Conventional shotgun metagenomics provides rich functional information but relies on homogenised, macro-scale samples that obscure the micron-scale distributions critical for understanding microbial community dynamics. Here, we introduce Micro-Scale Spatial Metagenomics (MSSM), a new methodological framework that couples laser micro-dissection of tissue sections, ultra-low-input library preparation, and genome-resolved bioinformatics to reconstruct microbial communities from intestinal microsamples measuring as little as [~]500 {micro}m{superscript 2} ({approx}100 bacterial cells). We describe a fully optimised laboratory and computational pipeline that enables quantitative, strain-resolved, and functionally informed spatial profiling directly from intact gut tissue. Using chicken intestinal samples, we validated MSSM through combinatorial single-cell fluorescence in situ hybridisation (FISH) imaging and comparisons with macro-scale metagenomics, demonstrating its robustness and accuracy. MSSM captured fine-scale heterogeneity in taxonomic and functional composition across intestinal cryosections, hinting at spatially structured assemblages and segregation of metabolic capacities. Strain-level analyses uncovered coexisting Lawsonibacter lineages exhibiting distinct spatial distributions and host-specific occurrence patterns, while SNP-level microdiversity analyses showed that genetically coherent clonal populations cluster at spatial scales below [~]200 {micro}m. By enabling shotgun metagenomics at micron resolution, MSSM closes a longstanding methodological gap and provides a scalable platform for studying microbial ecosystems in situ. This approach unlocks a previously inaccessible view of microbial biogeography, offering new opportunities to investigate host-microbe and microbe-microbe interactions, and the spatial principles governing gut ecosystems. Significance statementUnderstanding how microbial communities are organised in space is essential to explaining their ecological and functional roles, yet microbiome research still relies overwhelmingly on bulk, spatially averaged measurements. We introduce micro-scale spatial metagenomics (MSSM), the first method that brings shotgun metagenomics to the microscale, enabling direct measurement of functional and taxonomic variation across regions containing as few as [~]100 cells. Unlike existing spatial approaches, MSSM reconstructs complete genomes and resolves strain-level diversity within intact tissue, allowing researchers to map metabolic potential, microdiversity, and community structure in situ. By coupling high-resolution sequencing with spatial context, MSSM reveals a previously inaccessible layer of microbial organisation, transforming how host-associated ecosystems can be studied.

microbiology↗

bioRxiv2025

New high accuracy diagnostics for avian Aspergillus fumigatus infection using Nanopore methylation sequencing of host cell-free DNA and machine learning prediction

Avian aspergillosis is a detrimental fungal infection affecting wild and domestic birds yet sensitive antemortem diagnostics for early clinical infections are lacking. Here we present new diagnostics for Aspergillus fumigatus (Af) infection developed from cell-free DNA (cfDNA) methylation markers. Broiler chickens were experimentally infected with either Af, a non-Af agent (Escherichia coli or Gallibacterium anatis) or assigned as controls. Oxford Nanopore (ONT) sequencing was performed on serum cfDNA (n = 124), and machine learning (ML) models were trained on infection-specific markers. Three tests were developed: A High Accuracy test for best performance (sensitivity: 100%, specificity: 89.2%) and robustness (ROC-AUC: 0.92) as well as Fast- and In situ tests for rapid turnaround and methylation PCR. Diagnostic accuracies were 92.3%, 82.7%, and 73.1%, respectively. In conclusion, new tests using on ML- and host cfDNA methylation markers demonstrated high diagnostic performance comparable to microbial cfDNA (mcfDNA) tests but without concern for environmental contamination. Key highlightsO_LIWe present three new high accuracy diagnostic tests for Aspergillus fumigatus infection in chickens that use methylation markers from serum cell-free DNA (cfDNA). C_LIO_LIDifferentially methylated cfDNA regions (DMRs) were detected by Oxford Nanopore sequencing (ONT) in experimentally infected chickens and used as markers to train machine learning (ML) models for development of three diagnostic tests. C_LIO_LIThe highest accuracy was found with 83 markers of 10 kilobases (KB) using the glmnet algorithm for the ML model, which classified 92.3% blinded samples correctly. C_LIO_LIA Fast test designed for cheap <1h sequencing using adaptive sampling could correctly classify 82.7% samples with 22 markers using a random forest (rf) model. C_LIO_LIAn In situ test with only four markers, envisioned for use in a simple methylation-specific PCR (MSP-PCR) assay, could correctly classify 73.1% blinded samples. C_LIO_LIReference values with associated probabilities of infection were calculated for each of the three tests and are presented for further evaluation. C_LI

microbiology↗

bioRxiv2024

Deep learning from videography as a tool for measuring infection in poultry

Poultry farming is threatened by regular outbreaks of Escherichia coli (E. coli) that lead to significant economic losses and public health risks. However, traditional surveillance methods often lack sensitivity and scalability. Early detection of infected poultry using minimally invasive procedures is thus essential for preventing epidemics. To that end, we leverage recent advancements in computer vision, employing deep learning-based tracking to detect behavioural changes associated with E. coli infection in a case-control trial comprising two groups of 20 broiler chickens: (1) a healthy control group and (2) a group infected with a pathogenic E. coli field strain from the poultry industry. More specifically, kinematic features derived from deep learning-based tracking data revealed markedly reduced activity in the challenged group compared to the negative control. These findings were validated by lower mean optical flow in the infected flock, suggesting reduced movement and activity, and post-mortem physiological markers of inflammation which confirmed the severity of infection in the challenged group. Overall, this study demonstrates that deep learning-based tracking offers a promising solution for real-time monitoring and early infection detection in poultry farming, with the potential to help reduce economic losses and mitigate public health risks associated with infectious disease outbreaks in poultry.

animal behavior and cognition↗