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Metagenomics has been transformative in our understanding of the diversity and function of soil microbial communities. Applying long read sequencing to whole genome shotgun metagenomics has the potential to revolutionise soil microbial ecology through improved taxonomic classification, functional characterisation and metagenome assembly. However, optimisation of robust methods for long read metagenomics of environmental samples remains undeveloped. In this study, Oxford Nanopore sequencing using samples from five commercially available soil DNA extraction kits was compared across four soil types, in order to optimise read length and reproducibility for comparative long read soil metagenomics. Average extracted DNA lengths varied considerably between kits, but longer DNA fragments did not translate consistently into read lengths. Highly variable decreases in the length of resulting reads from some kits were associated with poor classification rate and low reproducibility in microbial communities identified between technical repeats. Replicate samples from other kits showed more consistent conversion of extracted DNA fragment size into read length and resulted in more congruous microbial community representation. Furthermore, extraction kits showed significant differences in the community representation and structure they identified across all soil types. Overall, the QIAGEN DNeasy PowerSoil Pro Kit displayed the best suitability for reproducible long-read WGS metagenomic sequencing, although further optimisation of DNA purification and library preparation may enable translation of higher molecular weight DNA from other kits into longer read lengths. These findings provide a novel insight into the importance of optimising DNA extraction for achieving replicable results from long read metagenomic sequencing of environmental samples., Competing Interests: Authors declare no conflicts of interests., (Copyright © 2024 The Authors.)

Metatranscriptomic analysis of the soil microbiome has the potential to reveal molecular mechanisms that drive soil processes regulated by the microbial community. Therefore, RNA samples must be of sufficient yield and quality to robustly quantify differential gene expression. While short-read sequencing technology is often favoured for metatranscriptomics, long-read sequencing has the potential to provide several benefits over short-read technologies. The ability to resolve complete transcripts on a portable sequencing platform for a relatively low capital expenditure makes Oxford Nanopore Technology an attractive prospect for addressing many of the challenges of soil metatranscriptomics. To fully enable long-read metatranscriptomic analysis of the functional molecular pathways expressed in these diverse habitats, RNA purification methods from soil must be optimised for long-read sequencing. Here we compare RNA samples purified using five commercially available extraction kits designed for use with soil. We found that the Qiagen RNeasy PowerSoil Total RNA Kit performed the best across RNA yield, quality and purity and was robust across different soil types. We found that sufficient sequencing depth can be achieved to characterise the active community for total RNA samples using Oxford Nanopore Technology, and discuss its current limitations for differential gene expression analysis in soil studies.

Anaerobic digestion is an established method for the biological conversion of waste feedstocks to biogas and biomethane. While anaerobic digestion is an excellent waste management technique, it can be susceptible to toxins and pollutants from contaminated feedstocks, which may have a detrimental impact on a digester's efficiency and productivity. Ethylene glycol (EG) is readily used in the heat-transfer loops of anaerobic digestion facilities to maintain reactor temperature. Failure of the structural integrity of these heat transfer loops can cause EG to leak into the digester, potentially causing a decrease in the resultant gas yields. Batch fermentations were incubated with 0, 10, 100 and 500 ppm (parts per million) of EG, and analysis showed that the EG was completely metabolised by the digester microbiome. The concentrations of EG tested showed significant increases in gas yields, however there were no significant changes to the digester microbiome., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Pollen and tracheophyte spores are ubiquitous environmental indicators at local and global scales. Palynology is typically performed manually by microscopic analysis; a specialised and time-consuming task limited in taxonomical precision and sampling frequency, therefore restricting data quality used to inform climate change and pollen forecasting models. We build on the growing work using AI (artificial intelligence) for automated pollen classification to design a flexible network that can deal with the uncertainty of broad-scale environmental applications. We combined imaging flow cytometry with Guided Deep Learning to identify and accurately categorise pollen in environmental samples; here, pollen grains captured within c. 5500 Cal yr BP old lake sediments. Our network discriminates not only pollen included in training libraries to the species level but, depending on the sample, can classify previously unseen pollen to the likely phylogenetic order, family and even genus. Our approach offers valuable insights into the development of a widely transferable, rapid and accurate exploratory tool for pollen classification in 'real-world' environmental samples with improved accuracy over pure deep learning techniques. This work has the potential to revolutionise many aspects of palynology, allowing a more detailed spatial and temporal understanding of pollen in the environment with improved taxonomical resolution., (© 2023 The Authors. New Phytologist © 2023 New Phytologist Foundation.)

Atmospheric particulate matter (PM) causes 3.7 million annual deaths worldwide and potentially damages every organ in the body. The cancer-causing potential of fine particulates (PM 2.5 ) highlights the inextricable link between air quality and human health. With over half of the world's population living in cities, PM 2.5 emissions are a major concern, however, our understanding of exposure to urban PM is restricted to relatively recent (post-1990) air quality monitoring programmes. To investigate how the composition and toxicity of PM has varied within an urban region, over timescales encompassing changing patterns of industrialisation and urbanisation, we reconstructed air pollution records spanning 200 years from the sediments of urban ponds in Merseyside (NW England), a heartland of urbanisation since the Industrial Revolution. These archives of urban environmental change across the region demonstrate a key shift in PM emissions from coarse carbonaceous 'soot' that peaked during the mid-twentieth century, to finer combustion-derived PM 2.5 post-1980, mirroring changes in urban infrastructure. The evolution of urban pollution to a recent enhanced PM 2.5 signal has important implications for understanding lifetime pollution exposures for urban populations over generational timescales., (© 2023. The Author(s).)

The sediment microbiome is a demographically diverse and functionally active biosphere. Ensuring that data acquired from sediment is truly representative of the microbiome is critical to achieving robust analyses. Sample storage and the processing and timing of nucleic acid purification after environmental sample extraction may fundamentally affect the detectable microbial community and thereby significantly alter resultant data. Direct sequencing of environmental samples is increasingly commonplace due to the advent of the portable Oxford Nanopore MinION sequencing device. Here we demonstrate that storing sediment subsamples at - 20 °C or storing the cores at 4 °C for 10 weeks prior to analysis, has a significant effect on the sediment microbiome analysed using sedimentary DNA (sedDNA), especially for Alpha-, Beta- and Deltaproteobacteria species. Furthermore, these significant differences are observed regardless of sediment type. We show that the taxa which are predominantly affected by storage are Proteobacteria, and therefore recommend on-site purifications are performed to ensure an accurate representation of these taxa are observed in the microbiome. Comparisons of sedimentary RNA (sedRNA) analyses, revealed substantial differences between samples purified and sequenced immediately on-site, samples that were frozen before transportation, and cores that were stored at 4 °C prior to analysis. Our data therefore suggest that a more accurate representation of the sediment microbiome demography and functionality may be achieved by environmental sequencing as rapidly as possible to minimise confounding effects of storage., (© 2022. The Author(s).)