Sampling Root-Associated Microbiome Communities of Maize (Zea mays).

The soil microbiome of maize shapes its fitness, sustainability, and productivity. Accurately sampling maize's belowground microbial communities is important for identifying and characterizing these functions. Here, we describe a protocol to sample the maize rhizosphere (including the rhizoplane and endorhizosphere) and root zone (still influential but further from the root) in a form suitable for downstream analyses like culturing and DNA extractions. Although this protocol is written with Zea mays as the focus, these methods can generally be applied to any plant with similar fibrous root systems.

Sampling the Maize (Zea mays) Leaf Microbiome.

The microbiota of maize leaves can be beneficial or detrimental to the host. Foliar diseases are the most obvious detrimental impact of the leaf microbiome, though more subtle effects of the normal (nondisease) community are an active area of research. This protocol describes two specific methodologies to sample the maize leaf microbiome: one sampling the surface (epiphyte) microbiome and one sampling the interior (endophyte) microbiome. Each method begins with collected leaf tissue and finishes with a cell suspension suitable for either isolating live microbes or extracting DNA for sequencing.

Preparation of Illumina 16s Amplicon Sequencing Libraries with Peptide Nucleic Acids (PNAs) for the Analysis of Maize-Associated Microbiomes.

One of the most common methods to survey bacterial communities is targeted amplification of the hypervariable regions of the 16s rRNA gene followed by sequencing. This protocol details Illumina library preparation of such amplicons from communities isolated from maize. We include both staggered PCR primers to improve Illumina base calling and peptide nucleic acids (PNAs) to reduce the presence of plant organelles. Primers are designed with Illumina adapter sequences for the addition of sample-specific indexes (barcodes). We also briefly discuss alternative primer sets, including ones that directly discriminate against plant organelles or that amplify different organisms (e.g., fungal internal transcribed spacer [ITS] sequences).

Sampling Maize (Zea mays) Seed Endophytes.

For most farmers, the production of maize grain is the ultimate goal of the entire field season. From the point of view of plant microbiome studies, seeds are particularly interesting in that they are the only avenue for vertical transmission of microbes from parent to offspring, though microbes can also enter maize seeds via wounds or silks. Although the presence of seed endophytes is well documented, their role, if any, in seed health and their effects on the next generation of plants are largely unknown. This protocol describes the isolation of seed endophytes. Its primary focus is properly sterilizing the seed surface, followed by grinding to release the endophytes. The end product is a cell suspension suitable for either culturing or DNA analysis.

Sampling and Analysis of the Maize Microbiome.

Maize is an important plant for both global food security and genetics research. As the importance of microorganisms to plant health is becoming clearer, there is a growing interest in understanding the relationship between maize and its associated microbiome; i.e., the collection of microorganisms living on, around, and inside the plant. The ultimate goal of this research is to use these microbial communities to support more robust and sustainable maize production. Here, we provide an overview of recent progress in the field of maize microbiome research. We discuss the major microbiome compartments (rhizosphere, phyllosphere, and endosphere) and known functions of the microbiome. We also review the methods currently available to study the maize microbiome and its functions, and discuss how to carry out maize microbiome experiments, including both a general workflow (suitable for most microbiome analyses) and maize-specific experimental considerations.

Manipulating the Maize (Zea mays) Microbiome.

Maize (Zea mays) is a multifaceted cereal grass used globally for nutrition, animal feed, food processing, and biofuels, and a model system in genetics research. Studying the maize microbiome sometimes requires its manipulation to identify the contributions of specific taxa and ecological traits (i.e., diversity, richness, network structure) to maize growth and physiology. Due to regulatory constraints on applying engineered microorganisms in field settings, greenhouse-based experimentation is often the first step for understanding the contribution of root-associated microbiota-whether natural or engineered-to plant phenotypes. In this protocol, we describe methods to inoculate maize with a specific microbiome as a tool for understanding the microbiota's influence on its host plant. The protocol involves removal of the native seed microbiome followed by inoculation of new microorganisms; separate protocols are provided for inoculations from pure culture, from soil slurry, or by mixing in live soil. These protocols cover the most common methods for manipulating the maize microbiome in soil-grown plants in the greenhouse. The methods outlined will ultimately result in rhizosphere microbial assemblages with varying degrees of microbial diversity, ranging from low diversity (individual strain and synthetic community [SynCom] inoculation) to high diversity (percent live inoculation), with the slurry inoculation method representing an "intermediate diversity" treatment.

Accessing opioid agonist treatment in prison in England and Scotland remains problematic - the views of people with lived experience.

PURPOSE: The purpose of this paper is to examine lived experiences of opioid agonist treatment (OAT) during and immediately following release from detention in prisons in England and Scotland. DESIGN/METHODOLOGY/APPROACH: Surveys were completed by serving prisoners in both countries and by those recently released from prison (England only). The survey findings were discussed in focus groups of people with lived experience. The combined findings from the surveys and focus groups were shared with an expert group of prison OAT providers and people with lived experience with the purpose of making recommendations for more accessible and effective OAT in custodial environments and continuity of OAT on release. FINDINGS: The quality and accessibility of OAT varied considerably between establishments. It was reported to be harder to access OAT in Scottish prisons. It was often hard for people in prison to get the dosage of OAT they felt they needed and it was generally harder to access buprenorphine than methadone in English prisons. Only Scottish people in prison were aware of long-lasting forms of buprenorphine. People in English prisons had mixed experiences of the help available in prison, with no improvement recorded since a 2016 study. People in Scottish prisons were more likely to rate the help available as poor. RESEARCH LIMITATIONS/IMPLICATIONS: The number of people accessed while actually in prison (73) was reduced by the impact of the pandemic, making it more difficult to access people in prison and because some were resistant to participating on the basis that they had already been consulted for a wide variety of research projects focused on the impact of COVID. The Scottish cohort (a total of 19 individuals comprising 14 survey respondents and five focus group members) is clearly too small a number on which to base robust claims about differences in OAT provision between the English and Scottish prison systems.. PRACTICAL IMPLICATIONS: The study identifies key barriers to accessing OAT in prisons and suggests key components of more user-friendly approaches. SOCIAL IMPLICATIONS: This study provides an overview of the recent lived experiences of people accessing OAT in prison and on release and offers valuable recommendations on how to make service provision more effective and consistent. ORIGINALITY/VALUE: This study provides an overview of the recent lived experiences of people accessing OAT in prison and on release in England and Scotland and offers valuable recommendations on how to make service provision more effective and consistent.

Risk of drug-related death associated with co-prescribing of gabapentinoids and Z-drugs among people receiving opioid-agonist treatment: A national retrospective cohort study.

BACKGROUND: Prescribing of gabapentinoids and Z-drug-hypnotics has increased in the population and among people receiving opioid-agonist treatment (OAT) for opioid dependence. Evidence is mixed on whether co-prescribing of sedatives such as gabapentinoids and Z-drugs during OAT increases risk of drug-related death (DRD). METHODS: We conducted a retrospective cohort study of individuals prescribed OAT between 2011 and 2020 in Scotland. Prescribing records were linked to mortality data and other healthcare datasets (sociodemographic, comorbidity). We identified episodes of treatment with gabapentinoids/Z-drugs and used multivariable quasi-Poisson regression to model associations between co-prescription and DRD risk. RESULTS: Among 46,602 individuals with 304,783 person-years of follow-up, we found that co-prescription was common, with 25 % and 34 % ever being co-prescribed gabapentinoids and Z-drugs, respectively. Gabapentinoid exposure was strongly associated (adjusted hazard ratio (aHR)=2·18, 95 % CI=1·92, 2·46) and Z-drug exposure moderately associated (aHR=1·39, 95 % CI=1·15, 1·66) with elevated risk of DRD. Gabapentinoid exposure was associated with DRD risk on and off OAT; Z-drug exposure was less strongly associated with DRD risk when on OAT. CONCLUSIONS: Co-prescription of gabapentinoids and Z-drugs is common among OAT patients. However, co-prescription is associated with increased risk of DRD. Alternatives to prescribing sedative medications to OAT patients and/or greater monitoring - if prescribed - are needed.

Corrigendum: Mining the Utricularia gibba genome for insulator-like elements for genetic engineering.

[This corrects the article DOI: 10.3389/fpls.2023.1279231.].

Co-creating and implementing a novel pre-conference event to promote equity and inclusivity among academic researchers and people who use drugs.

This commentary draws on our experience organising a targeted networking event at the 10th International Conference on Health and Hepatitis in Substance Users, in Glasgow, Scotland in October 2022. The event, held the day before the conference, brought together people with lived and living experiences of drug use and early- and mid-career researchers on an equitable basis. We offer reflections, focussing on how the event promoted community-academic engagement from members of the respective groups. We provide recommendations for how conferences can organise to engage with people who use drugs-both those with lived and living experience and foster greater inclusion for all attendees.

Mining the Utricularia gibba genome for insulator-like elements for genetic engineering.

Introduction: Gene expression is often controlled via cis-regulatory elements (CREs) that modulate the production of transcripts. For multi-gene genetic engineering and synthetic biology, precise control of transcription is crucial, both to insulate the transgenes from unwanted native regulation and to prevent readthrough or cross-regulation of transgenes within a multi-gene cassette. To prevent this activity, insulator-like elements, more properly referred to as transcriptional blockers, could be inserted to separate the transgenes so that they are independently regulated. However, only a few validated insulator-like elements are available for plants, and they tend to be larger than ideal. Methods: To identify additional potential insulator-like sequences, we conducted a genome-wide analysis of Utricularia gibba (humped bladderwort), one of the smallest known plant genomes, with genes that are naturally close together. The 10 best insulator-like candidates were evaluated in vivo for insulator-like activity. Results: We identified a total of 4,656 intergenic regions with expression profiles suggesting insulator-like activity. Comparisons of these regions across 45 other plant species (representing Monocots, Asterids, and Rosids) show low levels of syntenic conservation of these regions. Genome-wide analysis of unmethylated regions (UMRs) indicates ~87% of the targeted regions are unmethylated; however, interpretation of this is complicated because U. gibba has remarkably low levels of methylation across the genome, so that large UMRs frequently extend over multiple genes and intergenic spaces. We also could not identify any conserved motifs among our selected intergenic regions or shared with existing insulator-like elements for plants. Despite this lack of conservation, however, testing of 10 selected intergenic regions for insulator-like activity found two elements on par with a previously published element (EXOB) while being significantly smaller. Discussion: Given the small number of insulator-like elements currently available for plants, our results make a significant addition to available tools. The high hit rate (2 out of 10) also implies that more useful sequences are likely present in our selected intergenic regions; additional validation work will be required to identify which will be most useful for plant genetic engineering.

kGWASflow: a modular, flexible, and reproducible Snakemake workflow for k-mers-based GWAS.

Genome-wide association studies (GWAS) have been widely used to identify genetic variation associated with complex traits. Despite its success and popularity, the traditional GWAS approach comes with a variety of limitations. For this reason, newer methods for GWAS have been developed, including the use of pan-genomes instead of a reference genome and the utilization of markers beyond single-nucleotide polymorphisms, such as structural variations and k-mers. The k-mers-based GWAS approach has especially gained attention from researchers in recent years. However, these new methodologies can be complicated and challenging to implement. Here, we present kGWASflow, a modular, user-friendly, and scalable workflow to perform GWAS using k-mers. We adopted an existing kmersGWAS method into an easier and more accessible workflow using management tools like Snakemake and Conda and eliminated the challenges caused by missing dependencies and version conflicts. kGWASflow increases the reproducibility of the kmersGWAS method by automating each step with Snakemake and using containerization tools like Docker. The workflow encompasses supplemental components such as quality control, read-trimming procedures, and generating summary statistics. kGWASflow also offers post-GWAS analysis options to identify the genomic location and context of trait-associated k-mers. kGWASflow can be applied to any organism and requires minimal programming skills. kGWASflow is freely available on GitHub (https://github.com/akcorut/kGWASflow) and Bioconda (https://anaconda.org/bioconda/kgwasflow).

2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative.

OBJECTIVES: This release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available. DATA DESCRIPTION: The datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the GxE coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data.

A perennial living mulch system fosters a more diverse and balanced soil bacterial community.

Cover crops are known to positively impact soil health, both at a physical level (through erosion control and organic matter enhancement) and at a biological level (by fostering more diverse microbial communities). However, most research in this area has been conducted in the context of annual cover crops that are terminated when the main crop is planted. We have previously demonstrated that a continuous "living mulch" cover crop system can enhance the physical and chemical aspects of soil health; In this study, we reveal its effect on the soil bacterial community and compare it to two different annual cover crops and a conventional control without cover crops. We examined the effect of a living-mulch (LM) system using perennial white clover (Trifolium pratense L), annual cereal rye (Secale cereale L.) (CR), annual crimson clover (Trifolium incarnatum L.) (CC), and a no-cover (NC) control at three time points during the 2018 growing season. 16S rRNA amplicon analysis of the soil bacterial community revealed that the community composition in cover crop systems was significantly different from the NC control, and that LM and CR accommodated more heterogeneous and even bacterial communities compared to the NC control. The difference in bacterial composition between cover crop systems appears to be partly influenced by soil nitrogen concentration and lime buffer capacity. Overall community diversity was associated with nitrogen and metal ion concentrations, and these associations were both stronger and more numerous later in the season. These results elucidate how a perennial cover crop system affects the soil bacterial community and advance our understanding of the interactions between crops, management practices, and soil microbiomes in sustainable agriculture.

Genomes to Fields 2022 Maize genotype by Environment Prediction Competition.

OBJECTIVES: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data. DATA DESCRIPTION: This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years.

Genetic mapping and prediction for novel lesion mimic in maize demonstrates quantitative effects from genetic background, environment and epistasis.

KEY MESSAGE: A novel locus was discovered on chromosome 7 associated with a lesion mimic in maize; this lesion mimic had a quantitative and heritable phenotype and was predicted better via subset genomic markers than whole genome markers across diverse environments. Lesion mimics are a phenotype of leaf micro-spotting in maize (Zea mays L.), which can be early signs of biotic or abiotic stresses. Dissecting its inheritance is helpful to understand how these loci behave across different genetic backgrounds. Here, 538 maize recombinant inbred lines (RILs) segregating for a novel lesion mimic were quantitatively phenotyped in Georgia, Texas, and Wisconsin. These RILs were derived from three bi-parental crosses using a tropical pollinator (Tx773) as the common parent crossed with three inbreds (LH195, LH82, and PB80). While this lesion mimic was heritable across three environments based on phenotypic ([Formula: see text] = 0.68) and genomic ([Formula: see text] = 0.91) data, transgressive segregation was observed. A genome-wide association study identified a single novel locus on chromosome 7 (at 70.6 Mb) also covered by a quantitative trait locus interval (69.3-71.0 Mb), explaining 11-15% of the variation, depending on the environment. One candidate gene identified in this region, Zm00001eb308070, is related to the abscisic acid pathway involving in cell death. Genomic predictions were applied to genome-wide markers (39,611 markers) contrasted with a marker subset (51 markers). Population structure explained more variation than environment in genomic prediction, but other substantial genetic background effects were additionally detected. Subset markers explained substantially less genetic variation (24.9%) for the lesion mimic than whole genome markers (55.4%) in the model, yet predicted the lesion mimic better (0.56-0.66 vs. 0.26-0.29). These results indicate this lesion mimic phenotype was less affected by environment than by epistasis and genetic background effects, which explain its transgressive segregation.

2018-2019 field seasons of the Maize Genomes to Fields (G2F) G x E project.

OBJECTIVES: This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions. DATA DESCRIPTION: Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data. The collaborators received the data before the DOI release to verify and declare that the data generated in their own locations was accurate. ReadMe and description files are available for each dataset. Previous years of evaluation are already publicly available, with common hybrids present to connect across all locations and years evaluated since this project's inception.

Effects of Inbreeding on Microbial Community Diversity of Zea mays.

Heterosis, also known as hybrid vigor, is the basis of modern maize production. The effect of heterosis on maize phenotypes has been studied for decades, but its effect on the maize-associated microbiome is much less characterized. To determine the effect of heterosis on the maize microbiome, we sequenced and compared the bacterial communities of inbred, open pollinated, and hybrid maize. Samples covered three tissue types (stalk, root, and rhizosphere) in two field experiments and one greenhouse experiment. Bacterial diversity was more affected by location and tissue type than genetic background for both within-sample (alpha) and between-sample (beta) diversity. PERMANOVA analysis similarly showed that tissue type and location had significant effects on the overall community structure, whereas the intraspecies genetic background and individual plant genotypes did not. Differential abundance analysis identified only 25 bacterial ASVs that significantly differed between inbred and hybrid maize. Predicted metagenome content was inferred with Picrust2, and it also showed a significantly larger effect of tissue and location than genetic background. Overall, these results indicate that the bacterial communities of inbred and hybrid maize are often more similar than they are different and that non-genetic effects are generally the largest influences on the maize microbiome.

Yield prediction through integration of genetic, environment, and management data through deep learning.

Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied toward this goal. Here we predict maize yield using deep neural networks, compare the efficacy of 2 model development methods, and contextualize model performance using conventional linear and machine learning models. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and best linear unbiased predictor (BLUP) models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models performed more consistently with similar average error. Optimizing deep neural network submodules for each data type improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best-performing model revealed that including interactions altered the model's sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have a limited physiological basis for influencing yield-those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for the phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.

Maize seed endophytes.

Maize is a vital global crop, and each seed (kernel) hosts an ecosystem of microbes living inside it. However, we know very little about these endophytes and what their role is in plant production and physiology. In this Microreview, I summarize the major questions around maize seed endophytes, including what organisms are present, how they get there, whether and how they transmit across generations, and how they and the plant affect each other. Although several studies touch on each of these areas, ultimately there are far more questions than answers. Future priorities for research on maize seed endophytes should include understanding what adaptations allow microbes to be seed endophytes, how the host genetics and the environment affect these communities, and how maize seed endophytes ultimately contribute to the next generation of plants.