Biology subjects

Stekel, D. J.

Publications and source records attributed to Stekel, D. J..

10 recordsLinked to original sources

bioRxiv2025

Life-Stage Transitions Drive Distinct Gut Viral Communities in Dairy Cattle

Ruminant gut microbial communities profoundly influence host health and environmental impacts, yet their viral components remain poorly characterised across developmental transitions. Here, we analysed the dairy cow gut virome across four life stages--calf, heifer, dry adult, and lactating adult. Using hybrid sequencing, we assembled 30,321 viral operational taxonomic units, including 1,338 complete genomes representing mostly novel lineages. Virome composition shifted dramatically with life stage, transitioning from low-diversity, temperate-dominated communities in calves to high-diversity, lytic-dominated communities in adults. Virome transitions paralleled but showed distinct dynamics from bacterial community development, with viral and bacterial diversity negatively correlated during drying-off. Dry cows exhibited elevated viral loads relative to their bacterial hosts. We identified 26 viral sequences targeting the methanogen Methanobrevibacter, absent in calves but present in adults. These findings reveal the dynamic nature of ruminant gut viral communities and highlight phages potential regulatory role during critical life transitions.

microbiology↗

bioRxiv2023

Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0

Microbial communities are found in all habitable environments and often occur in assemblages with self-organized spatial structures developing over time. This complexity can only be understood, predicted, and managed by combining experiments with mathematical modeling. Individual-based models are particularly suited if individual heterogeneity, local interactions, and adaptive behavior are of interest. Here we present the completely overhauled software platform, the individual-based Dynamics of Microbial Communities Simulator, iDynoMiCS 2.0, which enables researchers to specify a range of different models without having to program. Key new features and improvements are: (1) Substantially enhanced ease of use (graphical user interface, editor for model specification, unit conversions, data analysis and visualization and more). (2) Increased performance and scalability enabling simulations of up to 10 million agents in 3D biofilms. (3) Kinetics can be specified with any arithmetic function. (4) Agent properties can be assembled from orthogonal modules for pick and mix flexibility. (5) Force-based mechanical interaction framework enabling attractive forces and non-spherical agent morphologies as an alternative to the shoving algorithm. The new iDynoMiCS 2.0 has undergone intensive testing, from unit tests to a suite of increasingly complex numerical tests and the standard Benchmark 3 based on nitrifying biofilms. A second test case was based on the "biofilms promote altruism" study previously implemented in BacSim because competition outcomes are highly sensitive to the developing spatial structures due to positive feedback between cooperative individuals. We extended this case study by adding morphology to find that (i) filamentous bacteria outcompete spherical bacteria regardless of growth strategy and (ii) non-cooperating filaments outcompete cooperating filaments because filaments can escape the stronger competition between themselves. In conclusion, the new substantially improved iDynoMiCS 2.0 joins a growing number of platforms for individual-based modeling of microbial communities with specific advantages and disadvantages that we discuss, giving users a wider choice. Author summaryMicrobes are fascinating in their own right and play a tremendously important role in ecosystems. They often form complex, self-organized communities with spatial heterogeneity that is changing over time. Such complexity is challenging to understand and manage without the help of mathematical models. Individual-based models are one type of mathematical model that is particularly suited if differences between individual microbes, local interactions and adaptive behavior are important. We have developed a completely overhauled version of iDynoMiCS, a software that allows users to develop, run and analyze a wide range of individual-based models without having to program the software themselves. There are several capability enhancements and numerous small improvements, for example the ability to model different shapes of cells combined with physically realistic mechanical interactions between neighboring cells. We showcase this by simulating the competition between filaments, long chains of cells, with single cells and find that filaments outcompete single cells as they can spread quickly to new territory with higher levels of resources. Users now have a wider choice of platforms so we provide guidance on which platform might be most suitable for a given purpose.

ecology↗

bioRxiv2023

The long and short of it: Benchmarking viromics using Illumina, Nanopore and PacBio sequencing technologies

Viral metagenomics has fuelled a rapid change in our understanding of global viral diversity and ecology. Long-read sequencing and hybrid approaches that combine long and short read technologies are now being widely implemented in bacterial genomics and metagenomics. However, the use of long-read sequencing to investigate viral communities is still in its infancy. While Nanopore and PacBio technologies have been applied to viral metagenomics, it is not known to what extent different technologies will impact the reconstruction of the viral community. Thus, we constructed a mock phage community of previously sequenced phage genomes and sequenced using Illumina, Nanopore, and PacBio sequencing technologies and tested a number of different assembly approaches. When using a single sequencing technology, Illumina assemblies were the best at recovering phage genomes. Nanopore- and PacBio-only assemblies performed poorly in comparison to Illumina in both genome recovery and error rates, which both varied with the assembler used. The best Nanopore assembly had errors that manifested as SNPs and INDELs at frequencies ~4x and 120x higher than found in Illumina only assemblies respectively. While the best PacBio assemblies had SNPs at frequencies ~3.5 x and 12x higher than found in Illumina only assemblies respectively. Despite high read coverage, long-read only assemblies failed to recover a complete genome for any of the 15 phage, down sampling of reads did increase the proportion of a genome that could be assembled into a single contig. Overall the best approach was assembly by a combination of Illumina and Nanopore reads, which reduced error rates to levels comparable with short read only assemblies. When using a single technology, Illumina only was the best approach. The differences in genome recovery and error rates between technology and assembler had downstream impacts on gene prediction, viral prediction, and subsequent estimates of diversity within a sample. These findings will provide a starting point for others in the choice of reads and assembly algorithms for the analysis of viromes. Data SummaryAll reads from virome sequencing were submitted to the ENA under study PRJEB56639. The assemblies are provided via FigShare (https://figshare.com/s/2d9b5121eb421d370455). Author NotesEight Supplementary Tables and nine Supplementary Figures are available with the online version of this article.

microbiology↗

bioRxiv2022

Predicting bioactivity of antibiotic metabolites by molecular docking and dynamics

Antibiotics enter the environment through waste streams, where they can exert selective pressure for antimicrobial resistance in bacteria. However, many antibiotics are excreted as partly metabolized forms, or can be subject to partial breakdown in wastewater treatment, soil, or through natural processes in the environment. If a metabolite is bioactive, even at sub-lethal levels, and also stable in the environment, then it could provide selection pressure for resistance. (5S)-penicilloic acid of piperacillin has previously been found complexed to the binding pocket of penicillin binding protein 3 (PBP3) of Pseudomonas aeruginosa. Here, we predicted the affinities of all potentially relevant antibiotic metabolites of ten different penicillins to that target protein, using molecular docking and molecular dynamics simulations. Docking predicts that, in addition to penicilloic acid, pseudopenicillin derivatives of these penicillins, as well as 6-aminopenicillanic acid (6APA), could also bind to this target. Molecular dynamics simulations further confirmed that (5R)-pseudopenicillin and 6APA bind the target protein, in addition to (5S)-penicilloic acid. Thus, it is possible that these metabolites are bioactive, and, if stable in the environment, could be contaminants selective for antibiotic resistance. This could have considerable significance for environmental surveillance for antibiotics as a means to reduce antimicrobial resistance, because targeted mass spectrometry could be required for relevant metabolites as well as the native antibiotics.

microbiology↗

bioRxiv2022

Antimicrobial resistance in dairy slurry tanks: a critical point for measurement and control

Waste from dairy production is one of the worlds largest sources of contamination from antimicrobial resistant bacteria (ARB) and genes (ARGs). However, studies to date do not provide necessary evidence to inform antimicrobial resistance (AMR) countermeasures. We undertook a detailed, interdisciplinary, longitudinal analysis of dairy slurry waste. The slurry contained a population of ARB and ARGs, with resistances to current, historical and never-used on-farm antibiotics; resistances were associated with Gram-negative and Gram-positive bacteria and mobile elements (ISEcp1, Tn916, Tn21-family transposons). Modelling and experimental work suggested that these populations are in dynamic equilibrium, with microbial death balanced by fresh input. Consequently, storing slurry without further waste input for at least 60 days was predicted to reduce ARB spread onto land, with >99% reduction in cephalosporin resistant Escherichia coli. The model also indicated that for farms with low antibiotic use, further reductions are unlikely to reduce AMR further. We conclude that the slurry tank is a critical point for prevalence and control of AMR, and that measures to limit the spread of AMR from dairy waste should combine responsible antibiotic use, including low total quantity, avoidance of human critical antibiotics, and choosing antibiotics with shorter half-lives, coupled with appropriate slurry storage.

microbiology↗

bioRxiv2021

INfrastructure for a PHAge REference Database: Identification of large-scale biases in the current collection of phage genomes

BackgroundWith advances in sequencing technology and decreasing costs, the number of bacteriophage genomes that have been sequenced has increased markedly in the last decade. Materials and MethodsWe developed an automated retrieval and analysis system for bacteriophage genomes, INPHARED (https://github.com/RyanCook94/inphared), that provides data in a consistent format. ResultsAs of January 2021, 14,244 complete phage genomes have been sequenced. The data set is dominated by phages that infect a small number of bacterial genera, with 75% of phages isolated only on 30 bacterial genera. There is further bias with significantly more lytic phage genomes than temperate within the database, resulting in ~54% of temperate phage genomes originating from just three host genera. Within phage genomes, putative antibiotic resistance genes were found in higher frequencies in temperate phages than lytic phages. ConclusionWe provide a mechanism to reproducibly extract complete phage genomes and highlight some of the biases within this data, that underpins our current understanding of phage genomes.

microbiology↗

bioRxiv2020

Hybrid assembly of an agricultural slurry virome reveals a diverse and stable community with the potential to alter the metabolism and virulence of veterinary pathogens

BackgroundViruses are the most abundant biological entities on Earth, known to be crucial components of microbial ecosystems. However, there is little information on the viral community within agricultural waste. There are currently ~2.7 million dairy cattle in the UK producing 7-8% of their own bodyweight in manure daily, and 28 million tonnes annually. To avoid pollution of UK freshwaters, manure must be stored and spread in accordance with guidelines set by DEFRA. Manures are used as fertiliser, and widely spread over crop fields, yet little is known about their microbial composition. We analysed the virome of agricultural slurry over a five-month period using short and long-read sequencing. ResultsHybrid sequencing uncovered more high-quality viral genomes than long or short-reads alone; yielding 7,682 vOTUs, 174 of which were complete viral genomes. The slurry virome was highly diverse and dominated by lytic bacteriophage, the majority of which represent novel genera (~98%). Despite constant influx and efflux of slurry, the composition and diversity of the slurry virome was extremely stable over time, with 55% of vOTUs detected in all samples over a five-month period. Functional annotation revealed a diverse and abundant range of auxiliary metabolic genes and novel features present in the community. Including the agriculturally relevant virulence factor VapE, which was widely distributed across different phage genera that were predicted to infect several hosts. Furthermore, we identified an abundance of phage-encoded diversity-generating retroelements, which were previously thought to be rare on lytic viral genomes. Additionally, we identified a group of crAssphages, including lineages that were previously thought only to be found in the human gut. ConclusionsThe cattle slurry virome is complex, diverse and dominated by novel genera, many of which are not recovered using long or short-reads alone. Phages were found to encode a wide range of AMGs that are not constrained to particular groups or predicted hosts, including virulence determinants and putative ARGs. The application of agricultural slurry to land may therefore be a driver of bacterial virulence and antimicrobial resistance in the environment.

microbiology↗

bioRxiv2020

Towards a general model for predicting minimal metal concentrations co-selecting for antibiotic resistance plasmids

Many antibiotic resistance genes co-occur with resistance genes for transition metals, such as copper, zinc, or mercury. In some environments, a positive correlation between high metal concentration and high abundance of antibiotic resistance genes has been observed, suggesting co-selection due to metal presence. Of particular concern is the use of copper and zinc in animal husbandry, leading to potential co-selection for antibiotic resistance in animal gut microbiomes, slurry, manure, or amended soils. For antibiotics, predicted no effect concentrations have been derived from laboratory measured minimum inhibitory concentrations and some minimal selective concentrations have been investigated in environmental settings. However, minimal co-selection concentrations for metals are difficult to identify. Here, we use mathematical modelling to provide a general mechanistic framework to predict minimal co-selective concentrations for metals, given knowledge of their toxicity at different concentrations. We apply the method to copper (Cu), Zinc (Zn), mercury (Hg), lead (Pb) and silver (Ag), predicting their minimum co-selective concentrations in mg/L (Cu: 5.5, Zn: 1.6, Hg: 0.0156, Pb: 21.5, Ag: 0.152). To exemplify use of these thresholds, we consider metal concentrations from slurry and slurry-amended soil from a UK dairy farm that uses copper and zinc as additives for feed and antimicrobial footbath: the slurry is predicted to be co-selective, but not the slurry-amended soil. This modelling framework could be used as the basis for defining standards to mitigate risks of antimicrobial resistance applicable to a wide range of environments, including manure, slurry and other waste streams.

microbiology↗

bioRxiv2017

DirtyGenes: significance testing for gene or bacterial population composition changes using the Dirichlet distribution

High throughput sequencing, and quantitative polymerase chain reaction (qPCR), can detect changes in bacterial communities or the genes that they carry, between different environments or treatments. These methods are applied widely to microbiomes in humans, animals, soil and water; an important application is for changes in antimicrobial resistance genes (ARGs). However, at present, there is no statistical method to determine whether observed changes in the overall composition are significant, or result from random variations between samples. Therefore researchers are limited to graphical descriptions. We describe a novel statistical method to determine whether or not observed differences in bacterial populations or their genes are significant. It can be used with data from shotgun metagenomics, 16S characterisation or qPCR. It can also be used for experimental design, to calculate the number of samples needed in future experiments. We show its application to two example data sets. The first is published data on bacterial communities and ARGs in the environment, in which we show that there are significant changes in both ARG and community composition. The second is a new data set on seasonality in bacterial communities and ARGs in hooves from four sheep. While the observed differences are not significant, we show that a minimum group size of eight sheep in a future experiment would provide sufficient power to observe significant changes, should the already observed changes be true. This method has broad uses for statistical testing and experimental design in experiments on changing microbiomes, including for studies on antimicrobial resistance.

bioinformatics↗

bioRxiv2017

Reconstructing Promoter Activity From Lux Bioluminescent Reporters

The bacterial Lux system is used as a gene expression reporter. It is fast, sensitive and non-destructive, enabling high frequency measurements. Originally developed for bacterial cells, it has been adapted for eukaryotic cells, and can be used for whole cell biosensors, or in real time with live animals without the need for slaughter. However, correct interpretation of bioluminescent data is limited: the bioluminescence is different from gene expression because of nonlinear molecular and enzyme dynamics of the Lux system. We have developed a modelling approach that, for the first time, allows users of Lux assays to infer gene transcription levels from the light output. We show examples where a decrease in bioluminescence would be better interpreted as a switching off of the promoter, or where an increase in bioluminescence would be better interpreted as a longer period of gene expression. This approach could benefit all users of Lux technology.

bioinformatics↗