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Abstract Understanding the interactions between plants and microorganisms can inform microbiome management to enhance crop productivity and resilience to stress. Here, we apply a genome-centric approach to identify ecologically important leaf microbiome members on replicated plots of field-grown switchgrass and miscanthus, and to quantify their activities over two growing seasons for switchgrass. We use metagenome and metatranscriptome sequencing and curate 40 medium- and high-quality metagenome-assembled-genomes (MAGs). We find that classes represented by these MAGs (Actinomycetia, Alpha- and Gamma- Proteobacteria, and Bacteroidota) are active in the late season, and upregulate transcripts for short-chain dehydrogenase, molybdopterin oxidoreductase, and polyketide cyclase. Stress-associated pathways are expressed for most MAGs, suggesting engagement with the host environment. We also detect seasonally activated biosynthetic pathways for terpenes and various non-ribosomal peptide pathways that are poorly annotated. Our findings support that leaf-associated bacterial populations are seasonally dynamic and responsive to host cues. 

Abstract Lignin is an abundant and complex plant polymer that may limit litter decomposition, yet lignin is sometimes a minor constituent of soil organic carbon (SOC). Accounting for diversity in soil characteristics might reconcile this apparent contradiction. Tracking decomposition of a lignin/litter mixture and SOC across different North American mineral soils using lab and field incubations, here we show that cumulative lignin decomposition varies 18-fold among soils and is strongly correlated with bulk litter decomposition, but not SOC decomposition. Climate legacy predicts decomposition in the lab, and impacts of nitrogen availability are minor compared with geochemical and microbial properties. Lignin decomposition increases with some metals and fungal taxa, whereas SOC decomposition decreases with metals and is weakly related with fungi. Decoupling of lignin and SOC decomposition and their contrasting biogeochemical drivers indicate that lignin is not necessarily a bottleneck for SOC decomposition and can explain variable contributions of lignin to SOC among ecosystems. 

Antimicrobial resistance (AMR) can develop in deep-pit swine manure storage when bacteria are selectively pressured by unmetabolized antibiotics. Subsequent manure application on row crops is then a source of AMR into soil and downstream runoff water. Therefore, understanding the patterns of diverse antibiotic resistance genes (ARGs) in manure among different farms is important for both interpreting the results of the detection of these genes from previous studies and for the use of these genes as bioindicators of manure borne antibiotic resistance in the environment. Previous studies of manure-associated ARGs are based on limited samples of manures. To better understand the distribution of ARGs between manures, we characterized manures from 48 geographically independent swine farms across Iowa. The objectives of this study were to characterize the distribution of ARGs among these manures and to evaluate what factors in manure management may influence the presence of ARGs in manures. Our analysis included quantification of two commonly found ARGs in swine manure, ermB and tetM . Additionally, we characterized a broader suite of 31 ARGs which allowed for simultaneous assays of the presence or absence of multiple genes. We found the company integrator had a significant effect on both ermB ( P=0.0007 ) and tetM gene concentrations ( P=0.0425 ). Our broad analysis on ARG profiles found that the tet(36) gene was broadly present in swine manures, followed by the detection of tetT , tetM , erm(35) , ermF , ermB , str , aadD , and intl3 in samples from 14 farms. Finally, we provide a comparison of methods to detect ARGs in manures, specifically comparing conventional and high-throughput qPCR and discuss their role in ARG environmental monitoring efforts. Results of this study provide insight into commonalities of ARG presence in manure holding pits and provide supporting evidence that company integrator decisions may impact ARG concentrations. 

The application of animal manures to cropland is an important nutrient recycling strategy in many parts of the world. Commonly, aggregated manure wastes contain chemical stressors including veterinary antimicrobials, heavy metals, and antimicrobial resistance genes (ARGs) that can stimulate the development and proliferation of antimicrobial resistance (AMR). While the presence of antimicrobials in manure is well-documented, the co-occurrence of other potentially impactful chemical stressors in swine manure remains underreported. This study quantifies and analyzes correlations between antimicrobials, metals, and certain ARGs present in manure samples from swine farms in Iowa, United States. Relationships between chemical stressors and different stages of swine production or feed composition are also investigated. Results revealed substantial levels of tetracyclines [up to 1,260 µg g −1 dry weight (d.w.) of manure for oxytetracycline] detected in all samples. Tiamulin, two ionophores (monensin and lasalocid), and one macrolide (tilmicosin) were detected at maximum class concentrations of 9.4, 0.547, and 0.472 µg g −1 d.w., respectively. The median relative abundances of ermB and tetM were 0.13 and 0.17 copies g −1 wet weight (w.w.) manure (normalized to 16S gene), respectively. Additionally, high levels of copper (Cu), iron (Fe), and zinc (Zn) were detected in all samples, with maximum concentrations of 887, 1,900, and 2,100 µg g −1 d.w., respectively. Notably, uranium (U) was detected in 11 samples, at concentrations up to 0.77 µg g −1 . A global analysis of AMR-stressor relationships using Spearman’s rank correlation indicates Cu, and Ba are the most positively and significantly correlated with cytotoxic anhydrotetracycline (ATC) and/or anhydrochlortetracycline (ACTC) concentrations in all tested facilities (Cu-ATC: ρ = 0.67, p = 0.0093; Cu-ACTC: ρ = 0.75, p = 0.0022; Ba-ATC: ρ = 0.84, p = 0.0002). Interestingly, ermB and tetM genes were strongly, positively correlated to each other ( ρ = 0.92, p < 0.0001), suggesting possible co-selection, despite the absence of correlation between ARGs and tetracycline concentrations. This study demonstrates the complexity of interactions between antimicrobials, metals, and ARGs in multiple manure storage pits prior to cropland application. 

We used incubations of soil and stable isotope measurements to measure lignin, litter, and SOC decomposition over an 18-month lab incubation and assessed their relationships with geochemical, microbial, N-related and climatic factors across 156 mineral soils collected from 20 National Ecological Observatory Network (NEON) sites, which span broad biophysical gradients (climate, soil, and vegetation type) across North America. The soils were collected in 2019. Lignin decomposition and biogeochemical variables were also measured in an approximately 12-month field incubation. 

Confidence in model estimates of soil CO₂flux depends on assumptions regarding fundamental mechanisms that control the decomposition of litter and soil organic carbon (SOC). Multiple hypotheses have been proposed to explain the role of lignin, an abundant and complex biopolymer that may limit decomposition. We tested competing mechanisms using data‐model fusion with modified versions of the CN‐SIM model and a 571‐day laboratory incubation dataset where decomposition of litter, lignin, and SOC was measured across 80 soil samples from the National Ecological Observatory Network. We found that lignin decomposition consistently decreased over time in 65 samples, whereas in the other 15 samples, lignin decomposition subsequently increased. These “lagged‐peak” samples can be predicted by low soil pH, high extractable Mn, and fungal community composition as measured by ITS PC2 (the second principal component of an ordination of fungal ITS amplicon sequences). The highest‐performing model incorporated soil biogeochemical factors and daily dynamics of substrate availability (labile bulk litter:lignin) that jointly represented two hypotheses (C substrate limitation and co‐metabolism) previously thought to influence lignin decomposition. In contrast, models representing either hypothesis alone were biased and underestimated cumulative decomposition. Our findings reconcile competing hypotheses of lignin decomposition and suggest the need to precisely represent the role of lignin and consider soil metal and fungal characteristics to accurately estimate decomposition in Earth‐system models.

 

Advancing extensive cattle production is a major threat to biodiversity conservation in Amazonia. The dominant vegetation cover has a drastic impact on soil microbial communities, affecting their composition, structure, and ecological services. Herein, we explored relationships between land-use, soil types, and forest floor compartments on the prokaryotic metacommunity structuring in Western Amazonia. Soil samples were taken in sites under high anthropogenic pressure and distributed along a ±800 km gradient. Additionally, the litter and a root layer, characteristic of the forest environment, were sampled. DNA was extracted, and metacommunity composition and structure were assessed through 16S rRNA gene sequencing. Prokaryotic metacommunities in the bulk soil were strongly affected by pH, base and aluminum saturation, Ca + Mg concentration, the sum of bases, and silt percentage, due to land-use management and natural differences among the soil types. Higher alpha, beta, and gamma diversities were observed in sites with higher soil pH and fertility, such as pasture soils or fertile soils of the state of Acre. When taking litter and root layer communities into account, the beta diversity was significantly higher in the forest floor than in pasture bulk soil for all study regions. Our results show that the forest floor’s prokaryotic metacommunity performs a spatial turnover hitherto underestimated to the regional scale of diversity. 

ABSTRACT Here, we report our educational approach and learner evaluations of the first 5 years of the Explorations in Data Analysis for Metagenomic Advances in Microbial Ecology (EDAMAME) workshop, held annually at Michigan State University’s Kellogg Biological Station from 2014 to 2018. We hope this information will be useful for others who want to organize computing-intensive workshops and will encourage quantitative skill development among microbiologists. IMPORTANCE High-throughput sequencing and related statistical and bioinformatic analyses have become routine in microbiology in the past decade, but there are few formal training opportunities to develop these skills. A weeklong workshop can offer sufficient time for novices to become introduced to best computing practices and common workflows in sequence analysis. We report our experiences in executing such a workshop targeted to professional learners (graduate students, postdoctoral scientists, faculty, and research staff).