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.

The need for One Health systems-thinking approaches to understand multiscale dissemination of antimicrobial resistance.

Although the effects of antimicrobial resistance (AMR) are most obvious at clinical treatment failure, AMR evolution, transmission, and dispersal happen largely in environmental settings, for example within farms, waterways, livestock, and wildlife. We argue that systems-thinking, One Health approaches are crucial for tackling AMR, by understanding and predicting how anthropogenic activities interact within environmental subsystems, to drive AMR emergence and transmission. Innovative computational methods integrating big data streams (eg, from clinical, agricultural, and environmental monitoring) will accelerate our understanding of AMR, supporting decision making. There are challenges to accessing, integrating, synthesising, and interpreting such complex, multidimensional, heterogeneous datasets, including the lack of specific metrics to quantify anthropogenic AMR. Moreover, data confidentiality, geopolitical and cultural variation, surveillance gaps, and science funding cause biases, uncertainty, and gaps in AMR data and metadata. Combining systems-thinking with modelling will allow exploration, scaling-up, and extrapolation of existing data. This combination will provide vital understanding of the dynamic movement and transmission of AMR within and among environmental subsystems, and its effects across the greater system. Consequently, strategies for slowing down AMR dissemination can be modelled and compared for efficacy and cost-effectiveness.

Tracking antimicrobial resistance transmission in urban and rural communities in Bangladesh: a One Health study of genomic diversity of ESBL-producing and carbapenem-resistant Escherichia coli.

Antimicrobial resistance (AMR) poses a significant threat to global health and sustainable development goals, especially in low- and middle-income countries (LMICs). This study aimed to understand the transmission of AMR between poultry, humans, and the environment in Bangladesh using a One Health approach. We analyzed the whole genome sequences (WGS) of 117 extended-spectrum ß-lactamase-producing Escherichia coli (ESBL-Ec) isolates, with 46 being carbapenem resistant. These isolates were obtained from human (n = 20) and poultry feces (n = 12), as well as proximal environments (wastewater) (n = 85) of three different study sites, including rural households (n = 48), rural poultry farms (n = 20), and urban wet markets (n = 49). The WGS of ESBL-Ec isolates were compared with 58 clinical isolates from global databases. No significant differences in antibiotic resistance genes (ARGs) were observed in ESBL-Ec isolated from humans with and without exposure to poultry. Environmental isolates showed higher ARG diversity than human and poultry isolates. No clonal transmission between poultry and human isolates was found, but wastewater was a reservoir for ESBL-Ec for both. Except for one human isolate, all ESBL-Ec isolates were distinct from clinical isolates. Most isolates (77.8%) carried at least one plasmid replicon type, with IncFII being the most prevalent. IncFIA was predominant in human isolates, while IncFII, Col(MG828), and p0111 were common in poultry. We observed putative sharing of ARG-carrying plasmids among isolates, mainly from wastewater. However, in most cases, bacterial isolates sharing plasmids were also clonally related, suggesting clonal spread was more probable than just plasmid transfer. IMPORTANCE: Our study underscores that wastewater discharged from households and wet markets carries antibiotic-resistant organisms from both human and animal sources. Thus, direct disposal of wastewater into the environment not only threatens human health but also endangers food safety by facilitating the spread of antimicrobial resistance (AMR) to surface water, crops, vegetables, and subsequently to food-producing animals. In regions with intensive poultry production heavily reliant on the prophylactic use of antibiotics, compounded by inadequate waste management systems, such as Bangladesh, the ramifications are particularly pronounced. Wastewater serves as a pivotal juncture for the dissemination of antibiotic-resistant organisms and functions as a pathway through which strains of human and animal origin can infiltrate the environment and potentially colonize new hosts. Further research is needed to thoroughly characterize wastewater isolates/populations and understand their potential impact on interconnected environments, communities, and wildlife.

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 assembly 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 bacteriophage community of previously sequenced phage genomes and sequenced them 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 41 and 157 % higher than found in Illumina only assemblies, respectively. While the best PacBio assemblies had SNPs at frequencies 12 and 78 % higher than found in Illumina-only assemblies, respectively. Despite high-read coverage, long-read-only assemblies recovered a maximum of one complete genome from any assembly, unless reads were down-sampled prior to assembly. 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.

Modelling the impact of wastewater flows and management practices on antimicrobial resistance in dairy farms.

Dairy slurry is a major source of environmental contamination with antimicrobial resistant genes and bacteria. We developed mathematical models and conducted on-farm research to explore the impact of wastewater flows and management practices on antimicrobial resistance (AMR) in slurry. Temporal fluctuations in cephalosporin-resistant Escherichia coli were observed and attributed to farm activities, specifically the disposal of spent copper and zinc footbath into the slurry system. Our model revealed that resistance should be more frequently observed with relevant determinants encoded chromosomally rather than on plasmids, which was supported by reanalysis of sequenced genomes from the farm. Additionally, lower resistance levels were predicted in conditions with lower growth and higher death rates. The use of muck heap effluent for washing dirty channels did not explain the fluctuations in cephalosporin resistance. These results highlight farm-specific opportunities to reduce AMR pollution, beyond antibiotic use reduction, including careful disposal or recycling of waste antimicrobial metals.