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.

Summary: 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 withinc.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.

Well-characterised promoter collections for synthetic biology applications are not always available in industrially relevant hosts. We developed a broadly applicable method for promoter identification in atypical microbial hosts that requires no a priori understanding of cis-regulatory element structure. This novel approach combines bioinformatic filtering with rapid empirical characterisation to expand the promoter toolkit, and uses machine learning to improve the understanding of the relationship between DNA sequence and function. Here, we apply the method in Geobacillus thermoglucosidasius, a thermophilic organism with high potential as a synthetic biology chassis for industrial applications. Bioinformatic screening of G. kaustophilus, G. stearothermophilus, G. thermodenitrificans and G. thermoglucosidasius resulted in the identification of 636 100 bp putative promoters, encompassing the genome-wide design space and lacking known transcription factor binding sites. 80 of these sequences were characterised in vivo and activities covered a 2-log range of predictable expression levels. 7 sequences were shown to function consistently regardless of the downstream coding sequence. Partition modelling identified sequence positions upstream of the canonical -35 and -10 consensus motifs that were predicted to strongly influence regulatory activity in Geobacillus, and Artificial Neural Network and Partial Least Squares regression models were derived to assess if there was a simple, forward, quantitative method for in silico prediction of promoter function. However, the models were insufficiently general to predict pre hoc promoter activity in vivo, most probably as a result of the relatively small size of the training data set as compared to the size of the modelled design space.

Vaccines induce memory B-cells that provide high affinity secondary antibody responses to identical antigens. Memory B-cells can also re-instigate affinity maturation, but how this happens against antigenic variants is poorly understood despite its potential impact on driving broadly protective immunity against pathogens such as Influenza and Dengue. We immunised mice sequentially with identical or variant Dengue-virus envelope proteins and analysed antibody and germinal-centre (GC) responses. Variant protein boosts induced GCs with a higher proportion of IgM+ B cells. The most variant protein re-stimulated GCs with the highest proportion of IgM+ cells with the most diverse, least mutated V-genes and with a slower but efficient serum antibody response. Recombinant antibodies from GC B-cells showed a higher affinity for the variant antigen than antibodies from a primary response, confirming a memory origin. This reveals a new process of antibody memory, that IgM memory cells with fewer mutations participate in secondary responses to variant antigens, demonstrating how the hierarchical structure of B-cell memory is used and indicating the potential and limits of cross-reactive antibody based immunity.

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.

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.