Biology subjects

Poulsen, L. L.

Publications and source records attributed to Poulsen, L. L..

5 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

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

Comparison of genomic assembly and annotation based on two clones of avian pathogenic Escherichia coli

Methods for assembly and annotation of whole genomic sequences were compared for six strains of avian pathogenic Escherichia coli (APEC). Two vertically transferred E. coli clones, represented by three isolates all belonging to pulse field genome electrophoresis (PFGE) type 65- sequence type (ST)95 and three isolates belonging to PFGE type 47- ST131, were selected for Illumina short read sequencing. There was no significant difference between SPAdes and CLC Genomic Workbench for benchmark parameters to assemble the short reads. The six strains were also sequenced by long read sequencing (Nanopore) and these reads were hybrid assembled with the short reads. Unicycler provided a lower number of contigs and higher N50 compared to Flye. No significant differences between total length of genomes were obtained from the four assemblers. At least 2.1 and 0.9% of coding gene sequences (CDSs) annotated with RAST and PROKKA, respectively were wrongly annotated. The errors were most often associated to CDS of shorter length (< 150 nt) with functions such as transposases, mobile genetic elements or being hypothetical. The investigation points out the importance of controlling automatic annotations and suggest further work to improve annotations in strains not belonging to the K12 or B lineages.

bioinformatics↗

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↗

bioRxiv2020

Evaluation of a new probiotic concept for broilers

Probiotics were introduced as a spray directly in the hatcher when chickens started to leave the eggs which potentially could reduce the horizontal transmission and colonization with pathogenic bacteria. The single introduction of probiotics could limit the cost compared to multiple introductions with feed and/or water. A mixture of five probiotic strains belonging to Escherichia coli, Enterococcus faecalis, Lactobacillus agilis and Lactobacillus rhamnosus was tested with two independent flocks of broilers (Ross 308). For each experiment, a comparison was made to an untreated control flock on the same farm. At day 14 of production the probiotic strains were re-isolated from ileum of euthanized chickens. The first week mortality was slightly increased in the probiotic flock (0.42%) compared to the control (0.35%) in experiment 1, however, it was higher in the control flock (1.45%) compared to the probiotic flock (1.12%) in experiment 2. The average weight of chickens that could be slaughtered for consumption was increased by 3.5% in the probiotic flocks compared to the control flocks, resulting in a 1.9% higher total weight of slaughtered chickens in the probiotics treated flocks compared to the control as a mean of the two experiments. The number of condemned animals was within the normal range for the production system and could not directly be related to effects of probiotics. Although one probiotic strain of E. coli was isolated from dead animals, the probiotics did not affect the proportion of chickens which died due to E. coli during the first week compared to the control. Primary audienceplant managers, veterinarians, nutritionists

microbiology↗