Effects of swine manure dilution with lagoon effluent on microbial communities and odor formation in pit recharge systems.

Pit recharge systems (PRS) control odor by managing organic solids in swine manure. However, there needs to be more understanding of PRS's effect on the microbiome composition and its impact on odor formation. A study was conducted to understand how recharge intervals used in PRS impact manure microbiome and odor formation. Bioreactors dynamically loaded simulated recharge intervals of 14, 10, and 4 days by diluting swine manure with lagoon effluent at varying ratios. Treatment ratios tested included 10:0 (control), 7:3 (typical Korean PRS), 5:5 (enhanced PRS #1), and 2:8 (enhanced PRS #2). Manure microbial membership, chemical concentrations, and odorant concentrations were used to identify the interactions between microbiota, manure, and odor. The initial microbial community structure was controlled by dilution ratio and manure barn source material. Firmicutes and Proteobacteria were the dominant microbial phyla in manure and lagoon effluent, respectively, and significantly decreased or increased with dilution. Key microbial species were Clostridium saudiense in manure and Pseudomonas caeni in lagoon effluent. Percentages of these species declined by 8.9% or increased by 17.6%, respectively, with each unit dilution. Microbial community composition was controlled by both treatment (i.e., manure dilution ratio and barn source material) and environmental factors (i.e., solids and pH). Microbiome composition was correlated with manure odor formation profiles, but this effect was inseparable from environmental factors, which explained over 75% of the variance in odor profiles. Consequently, monitoring solids and pH in recharge waters will significantly impact odor control in PRS.

Chemical attributes, bacterial community, and antibiotic resistance genes are affected by intensive use of soil in agro-ecosystems of the Atlantic Forest, Southeastern Brazil.

Soil is one of the largest reservoirs of microbial diversity in nature. Although soil management is vital for agricultural purposes, intensive practices can have a significant impact on fertility, microbial community, and resistome. Thus, the aim of this study was to evaluate the effects of an intensive soil management system on the chemical attributes, composition and structure of prevalent bacterial communities, and presence and abundance of antimicrobial resistance genes (ARGs). The chemical characterization, bacterial diversity and relative abundance of ARGs were evaluated in soils from areas of intensive vegetable cultivation and forests. Results indicate that levels of nutrients and heavy metals were higher in soil samples from cultivated areas. Similarly, greater enrichment and diversity of bacterial genera was detected in agricultural areas. Of the 18 target ARGs evaluated, seven were detected in studied soils. The oprD gene exhibited the highest abundance among the studied genes and was the only one that showed a significantly different prevalence between areas. The oprD gene was identified only from soil of the cultivated areas. The blaSFO, erm(36), oprD and van genes, in addition to the pH, showed greater correlation with in soil of cultivated areas, which in turn exhibited higher contents of nutrients. Thus, in addition to changes in chemical attributes and in the microbial community of the soil, intensive agricultural cultivation systems cause a modification of its resistome, reinforcing the importance of the study of antimicrobial resistance in a One Health approach.

Resolving the influence of lignin on soil organic matter decomposition with mechanistic models and continental-scale data.

Confidence in model estimates of soil CO2 flux 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.

One-Week-Ahead Prediction of Cyanobacterial Harmful Algal Blooms in Iowa Lakes.

Cyanobacterial harmful algal blooms (CyanoHABs) pose serious risks to inland water resources. Despite advancements in our understanding of associated environmental factors and modeling efforts, predicting CyanoHABs remains challenging. Leveraging an integrated water quality data collection effort in Iowa lakes, this study aimed to identify factors associated with hazardous microcystin levels and develop one-week-ahead predictive classification models. Using water samples from 38 Iowa lakes collected between 2018 and 2021, feature selection was conducted considering both linear and nonlinear properties. Subsequently, we developed three model types (Neural Network, XGBoost, and Logistic Regression) with different sampling strategies using the nine selected variables (mcyA_M, TKN, % hay/pasture, pH, mcyA_M:16S, % developed, DOC, dewpoint temperature, and ortho-P). Evaluation metrics demonstrated the strong performance of the Neural Network with oversampling (ROC-AUC 0.940, accuracy 0.861, sensitivity 0.857, specificity 0.857, LR+ 5.993, and 1/LR- 5.993), as well as the XGBoost with downsampling (ROC-AUC 0.944, accuracy 0.831, sensitivity 0.928, specificity 0.833, LR+ 5.557, and 1/LR- 11.569). This study exhibited the intricacies of modeling with limited data and class imbalances, underscoring the importance of continuous monitoring and data collection to improve predictive accuracy. Also, the methodologies employed can serve as meaningful references for researchers tackling similar challenges in diverse environments.

Metagenomic Insights into the Degradation of Resistant Starch by Human Gut Microbiota.

Several studies monitoring alterations in the community structure upon resistant starch (RS) interventions are available, although comprehensive function-based analyses are lacking. Recently, a multiomics approach based on 16S rRNA gene sequencing, metaproteomics, and metabolomics on fecal samples from individuals subjected to high and low doses of type 2 RS (RS2; 48 g and 3 g/2,500 kcal, respectively, daily for 2 weeks) in a crossover intervention experiment was performed. In the present study, we did pathway-based metagenomic analyses on samples from a subset of individuals (n = 12) from that study to obtain additional detailed insights into the functional structure at high resolution during RS2 intervention. A mechanistic framework based on obtained results is proposed where primary degradation was governed by Firmicutes, with Ruminococcus bromii as a major taxon involved, providing fermentation substrates and increased acetate concentrations for the growth of various major butyrate producers exhibiting the enzyme butyryl-coenzyme A (CoA):acetate CoA-transferase. H2-scavenging sulfite reducers and acetogens concurrently increased. Individual responses of gut microbiota were noted, where seven of the 12 participants displayed all features of the outlined pattern, whereas four individuals showed mixed behavior and one subject was unresponsive. Intervention order did not affect the outcome, emphasizing a constant substrate supply for maintaining specific functional communities.IMPORTANCE Manipulation of gut microbiota is increasingly recognized as a promising approach to reduce various noncommunicable diseases, such as obesity and type 2 diabetes. Specific dietary supplements, including resistant starches (RS), are often a focus, yet comprehensive insights into functional responses of microbiota are largely lacking. Furthermore, unresponsiveness in certain individuals is poorly understood. Our data indicate that distinct parts of microbiota work jointly to degrade RS and successively form health-promoting fermentation end products. It highlights the need to consider both primary degraders and specific more-downstream-acting bacterial groups in order to achieve desired intervention outcomes. The gained insights will assist the design of personalized treatment strategies based on an individual's microbiota.

Tackling soil diversity with the assembly of large, complex metagenomes.

The large volumes of sequencing data required to sample deeply the microbial communities of complex environments pose new challenges to sequence analysis. De novo metagenomic assembly effectively reduces the total amount of data to be analyzed but requires substantial computational resources. We combine two preassembly filtering approaches--digital normalization and partitioning--to generate previously intractable large metagenome assemblies. Using a human-gut mock community dataset, we demonstrate that these methods result in assemblies nearly identical to assemblies from unprocessed data. We then assemble two large soil metagenomes totaling 398 billion bp (equivalent to 88,000 Escherichia coli genomes) from matched Iowa corn and native prairie soils. The resulting assembled contigs could be used to identify molecular interactions and reaction networks of known metabolic pathways using the Kyoto Encyclopedia of Genes and Genomes Orthology database. Nonetheless, more than 60% of predicted proteins in assemblies could not be annotated against known databases. Many of these unknown proteins were abundant in both corn and prairie soils, highlighting the benefits of assembly for the discovery and characterization of novelty in soil biodiversity. Moreover, 80% of the sequencing data could not be assembled because of low coverage, suggesting that considerably more sequencing data are needed to characterize the functional content of soil.

Microbial activity in forest soil reflects the changes in ecosystem properties between summer and winter.

Understanding the ecology of coniferous forests is very important because these environments represent globally largest carbon sinks. Metatranscriptomics, microbial community and enzyme analyses were combined to describe the detailed role of microbial taxa in the functioning of the Picea abies-dominated coniferous forest soil in two contrasting seasons. These seasons were the summer, representing the peak of plant photosynthetic activity, and late winter, after an extended period with no photosynthate input. The results show that microbial communities were characterized by a high activity of fungi especially in litter where their contribution to microbial transcription was over 50%. Differences in abundance between summer and winter were recorded for 26-33% of bacterial genera and < 15% of fungal genera, but the transcript profiles of fungi, archaea and most bacterial phyla were significantly different among seasons. Further, the seasonal differences were larger in soil than in litter. Most importantly, fungal contribution to total microbial transcription in soil decreased from 33% in summer to 16% in winter. In particular, the activity of the abundant ectomycorrhizal fungi was reduced in winter, which indicates that plant photosynthetic production was likely one of the major drivers of changes in the functioning of microbial communities in this coniferous forest.

Scaling metagenome sequence assembly with probabilistic de Bruijn graphs.

Deep sequencing has enabled the investigation of a wide range of environmental microbial ecosystems, but the high memory requirements for de novo assembly of short-read shotgun sequencing data from these complex populations are an increasingly large practical barrier. Here we introduce a memory-efficient graph representation with which we can analyze the k-mer connectivity of metagenomic samples. The graph representation is based on a probabilistic data structure, a Bloom filter, that allows us to efficiently store assembly graphs in as little as 4 bits per k-mer, albeit inexactly. We show that this data structure accurately represents DNA assembly graphs in low memory. We apply this data structure to the problem of partitioning assembly graphs into components as a prelude to assembly, and show that this reduces the overall memory requirements for de novo assembly of metagenomes. On one soil metagenome assembly, this approach achieves a nearly 40-fold decrease in the maximum memory requirements for assembly. This probabilistic graph representation is a significant theoretical advance in storing assembly graphs and also yields immediate leverage on metagenomic assembly.

Seasonal activities of the phyllosphere microbiome of perennial crops.

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.

Microbiome differences in sugarcane and metabolically engineered oilcane accessions and their implications for bioenergy production.

Oilcane is a metabolically engineered sugarcane (Saccharum spp. hybrid) that hyper-accumulates lipids in its vegetable biomass to provide an advanced feedstock for biodiesel production. The potential impact of hyper-accumulation of lipids in vegetable biomass on microbiomes and the consequences of altered microbiomes on plant growth and lipid accumulation have not been explored so far. Here, we explore differences in the microbiome structure of different oilcane accessions and non-modified sugarcane. 16S SSU rRNA and ITS rRNA amplicon sequencing were performed to compare the characteristics of the microbiome structure from different plant compartments (leaf, stem, root, rhizosphere, and bulk soil) of four greenhouse-grown oilcane accessions and non-modified sugarcane. Significant differences were only observed in the bacterial microbiomes. In leaf and stem microbiomes, more than 90% of the entire microbiome of non-modified sugarcane and oilcane was dominated by similar core taxa. Taxa associated with Proteobacteria led to differences in the non-modified sugarcane and oilcane microbiome structure. While differences were observed between multiple accessions, accession 1566 was notable in that it was consistently observed to differ in its microbial membership than other accessions and had the lowest abundance of taxa associated with plant-growth-promoting bacteria. Accession 1566 is also unique among oilcane accessions in that it has the highest constitutive expression of the WRI1 transgene. The WRI1 transcription factor is known to contribute to significant changes in the global gene expression profile, impacting plant fatty acid biosynthesis and photomorphogenesis. This study reveals for the first time that genetically modified oilcanes associate with distinct microbiomes. Our findings suggest potential relationships between core taxa, biomass yield, and TAG in oilcane accessions and support further research on the relationship between plant genotypes and their microbiomes.

Contrasting geochemical and fungal controls on decomposition of lignin and soil carbon at continental scale.

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.

Denitrifying bioreactor microbiome: Understanding pollution swapping and potential for improved performance.

Denitrifying woodchip bioreactors are a best management practice to reduce nitrate-nitrogen (NO3 -N) loading to surface waters from agricultural subsurface drainage. Their effectiveness has been proven in many studies, although variable results with respect to performance indicators have been observed. This paper serves the purpose of synthesizing the current state of the science in terms of the microbial community, its impact on the consistency of bioreactor performance, and its role in the production of potential harmful by-products including greenhouse gases, sulfate reduction, and methylmercury. Microbial processes other than denitrification have been observed in these bioreactor systems, including dissimilatory nitrate reduction to ammonia (DNRA) and anaerobic ammonium oxidation (anammox). Specific gene targets for denitrification, DNRA, anammox, and the production of harmful by-products are identified from bioreactor studies and other environmentally relevant systems for application in bioreactor studies. Lastly, cellulose depletion has been observed over time via increasing ligno-cellulose indices, therefore, the microbial metabolism of cellulose is an important function for bioreactor performance and management. Future work should draw from the knowledge of soil and wetland ecology to inform the study of bioreactor microbiomes.

Prairie strips' effect on transport of antimicrobial resistance indicators in poultry litter.

Poultry litter is a valuable nutrient resource for agricultural production but is also a potential source for introducing antibiotic resistance genes (ARGs) and litter-associated bacteria (LAB) to the environment. Prairie strips have been demonstrated as an effective conservation practice to improve environmental quality in agroecosystems. This research aims to assess prairie strips' potential for reducing the transport of LAB and ARGs in runoff after litter application. Plot-scale rainfall simulations were performed using a replicated block design, with soil and surface runoff samples taken during the rainfall event. Microbial taxa and ARGs were characterized in the litter, soil, and water samples. In plots with litter application, LAB and ARGs were mainly detected in runoff, with very low detection in soils. Detection of ARGs in runoff, irrespective of strip installations, is consistent with previous observations of litter as a source of antimicrobial resistance (AMR) risks. The effectiveness of prairie strips to remove LAB and ARGs varied. In two of the three prairie strip plots, fewer AMR indicators were detected relative to control plots, suggesting that the prairie strips can potentially reduce these risks. In one plot, which was also associated with increased flow rate, we observed increased AMR indicators despite the installation of a prairie strip. Our observations highlight the need to prioritize understanding of soil properties even within the same site. Although we show that prairie strips can potentially reduce AMR risks, further research is needed to better understand the influence of rainfall timing, soil, and litter characteristics.

Response of Total (DNA) and Metabolically Active (RNA) Microbial Communities in Miscanthus × Giganteus Cultivated Soil to Different Nitrogen Fertilization Rates.

Miscanthus × giganteus is a promising high-yielding perennial plant to meet growing bioenergy demands; however, the degree to which the soil microbiome affects its nitrogen cycling and subsequently, biomass yield remains unclear. In this study, we hypothesize that contributions of metabolically active soil microbial membership may be underestimated with DNA-based approaches. We assessed the response of the soil microbiome to nitrogen availability in terms of both DNA and RNA soil microbial communities from the Long-term Assessment of Miscanthus Productivity and Sustainability (LAMPS) field trial. DNA and RNA were extracted from 271 samples, and 16S small subunit (SSU) rRNA amplicon sequencing was performed to characterize microbial community structure. Significant differences were observed in the resulting soil microbiomes and were best explained by the sequencing library of origin, either DNA or RNA. Similar numbers of membership were detected in DNA and RNA microbial communities, with more than 90% of membership shared. However, the profile of dominant membership within DNA and RNA differed, with varying proportions of Actinobacteria and Proteobacteria and Firmicutes and Proteobacteria. Only RNA microbial communities showed seasonal responses to nitrogen fertilization, and these differences were associated with nitrogen-cycling bacteria. The relative abundance of bacteria associated with nitrogen cycling was 7-fold higher in RNA than in DNA, and genes associated with denitrifying bacteria were significantly enriched in RNA, suggesting that these bacteria may be underestimated with DNA-only approaches. Our findings indicate that RNA-based SSU characterization can be a significant and complementing resource for understanding the role of soil microbiomes in bioenergy crop production. IMPORTANCEMiscanthus × giganteus is a promising candidate for bioeconomy cropping systems; however, it remains unclear how the soil microbiome supplies nitrogen to this low-input crop. DNA-based techniques are used to provide community characterization, but may miss important metabolically active taxa. By analyzing both DNA- and actively transcribed RNA-based microbial communities, we found that nitrogen cycling taxa in the soil microbiome may be underestimated using only DNA-based approaches. Accurately understanding the role of microbes and how they cycle nutrients is important for the development of sustainable bioenergy crops, and RNA-based approaches are recommended as a complement to DNA approaches to better understand the microbial, plant, and management interactions.

Comparison of microbial communities in replicated woodchip bioreactors.

Denitrification in woodchip bioreactors is a microbial process, but the effects of variations in bioreactors operation on microbial community structure are not well understood. Here, our goals were to understand hydraulic retention time (HRT) as a factor that influences woodchip bioreactor microbial community variation and structure in replicated field bioreactors and to evaluate relationships between microbial community membership and marker genes for denitrification. We used a combination of quantitative polymerase chain reaction of nirS, nirK, nosZI, and nosZII and 16S rRNA amplicon sequencing to characterize the microbial communities of nine pilot-scale woodchip bioreactors located at Iowa State University. Our results showed dynamic microbial communities but with persistent taxa between two sampling years and three HRTs. Similarities between functional gene copy numbers across sampling year and HRT indicate that the potential for denitrification is conserved despite differences in the microbial communities. These results are evidence that there are specific and persistent taxa within replicated bioreactors. Woodchip bioreactor microbial community membership is recommended to be the focus of future studies to better understand the relationship between microbial community functions and bioreactor management.

The Human Health Implications of Antibiotic Resistance in Environmental Isolates from Two Nebraska Watersheds.

One Health field-based approaches are needed to connect the occurrence of antibiotics present in the environment with the presence of antibiotic resistance genes (ARGs) in Gram-negative bacteria that confer resistance to antibiotics important in for both veterinary and human health. Water samples from two Nebraska watersheds influenced by wastewater effluent and agricultural runoff were tested for the presence of antibiotics used in veterinary and human medicine. The water samples were also cultured to identify the bacteria present. Of those bacteria isolated, the Gram-negative rods capable of causing human infections had antimicrobial susceptibility testing and whole-genome sequencing (WGS) performed to identify ARGs present. Of the 211 bacterial isolates identified, 37 belonged to pathogenic genera known to cause human infections. Genes conferring resistance to beta-lactams, aminoglycosides, fosfomycins, and quinolones were the most frequently detected ARGs associated with horizontal gene transfer (HGT) in the watersheds. WGS also suggest recent HGT events involving ARGs transferred between watershed isolates and bacteria of human and animal origins. The results of this study demonstrate the linkage of antibiotics and bacterial ARGs present in the environment with potential human and/or veterinary health impacts. IMPORTANCE One health is a transdisciplinary approach to achieve optimal health for humans, animals, plants and their shared environment, recognizing the interconnected nature of health in these domains. Field based research is needed to connect the occurrence of antibiotics used in veterinary medicine and human health with the presence of antibiotic resistance genes (ARGs). In this study, the presence of antibiotics, bacteria and ARGs was determined in two watersheds in Nebraska, one with agricultural inputs and the other with both agricultural and wastewater inputs. The results presented in this study provide evidence of transfer of highly mobile ARG between environment, clinical, and animal-associated bacteria.

Diversity of Antibiotic Resistance genes and Transfer Elements-Quantitative Monitoring (DARTE-QM): a method for detection of antimicrobial resistance in environmental samples.

Effective monitoring of antibiotic resistance genes and their dissemination in environmental ecosystems has been hindered by the cost and efficiency of methods available for the task. We developed the Diversity of Antibiotic Resistance genes and Transfer Elements-Quantitative Monitoring (DARTE-QM), a method implementing TruSeq high-throughput sequencing to simultaneously sequence thousands of antibiotic resistant gene targets representing a full-spectrum of antibiotic resistance classes common to environmental systems. In this study, we demonstrated DARTE-QM by screening 662 antibiotic resistance genes within complex environmental samples originated from manure, soil, and livestock feces, in addition to a mock-community reference to assess sensitivity and specificity. DARTE-QM offers a new approach to studying antibiotic resistance in environmental microbiomes, showing advantages in efficiency and the ability to scale for many samples. This method provides a means of data acquisition that will alleviate some of the obstacles that many researchers in this area currently face.

Impact of Swine and Cattle Manure Treatment on the Microbial Composition and Resistome of Soil and Drainage Water.

Evaluating potential environmental and clinical impacts of industrial antibiotic use is critical in mitigating the spread of antimicrobial resistance. Using soil columns to simulate field application of swine or cattle manure and subsequent rain events, and a targeted qPCR-based approach, we tracked resistance genes from source manures and identified important differences in antimicrobial resistance gene transport and enrichment over time in the soil and water of artificially drained cropland. The source manures had distinct microbial community and resistance gene profiles, and these differences were also reflected in the soil columns after manure application. Antibiotic resistance genes (ARGs) were only significantly enriched in effluent samples following the first rain event (day 11) for both soil types compared to the control columns, illustrating the high background level of resistance present in the control soils chosen. For swine, the genes tetQ, tet(36), tet44, tetM, sul2 and ant(6)-ib persisted in the soil columns, whereas tetO, strB and sul1 persisted in effluent samples. Conversely, for cattle manure sul2 and strB persisted in both soil and effluent. The distinct temporal dynamics of ARG distribution between soil and effluent water for each manure type can be used to inform potential mitigation strategies in the future.

Antimicrobial resistance in integrated agroecosystems: State of the science and future opportunities.

As the Journal of Environmental Quality (JEQ) celebrates 50 years of publication, the division of environmental microbiology is one of the newest additions to the journal. During this time, significant advances in understanding of the interconnected microbial community and impact of the microbiome on natural and designed environmental systems have occurred. In this review, we highlight the intractable challenge of antimicrobial resistance (AMR) on humans, animals, and the environment, with particular emphasis on the role of integrated agroecosystems and by highlighting contributions published in JEQ. From early studies of phenotypic resistance of indicator organisms in waters systems to current calls for integrating AMR assessment across "One Health," publications in JEQ have advanced our understanding of AMR. As we reflect on the state of the science, we emphasize future opportunities. First, integration of phenotypic and molecular tools for assessing environmental spread of AMR and human health risk continues to be an urgent research need for a one health approach to AMR. Second, monitoring AMR levels in manure is recommended to understand inputs and potential spread through agroecosystems. Third, baseline knowledge of AMR levels is important to realize the impact of manure inputs on water quality and public health risk; this can be achieved through background monitoring or identifying the source-related genes or organisms. And finally, conservation practices designed to meet nutrient reduction goals should be explored for AMR reduction potential.

Identification and Evolution of Cas9 tracrRNAs.

Clustered regularly interspaced palindromic repeats (CRISPR)-associated (Cas)9 transactivating CRISPR RNAs (tracrRNAs) form distinct structures essential for target recognition and cleavage and dictate exchangeability between orthologous proteins. As noncoding RNAs that are often apart from the CRISPR array, their identification can be arduous. In this article, a new bioinformatic method for the detection of Cas9 tracrRNAs is presented. The approach utilizes a covariance model based on both sequence homology and predicted secondary structure to locate tracrRNAs. This method predicts a tracrRNA for 98% of CRISPR-Cas9 systems identified by us. To ensure accuracy, we also benchmark our approach against biochemically vetted tracrRNAs finding false-positive and false-negative rates of 5.5% and 7.1%, respectively. Finally, the association between Cas9 amino acid sequence-based phylogeny and tracrRNA secondary structure is evaluated, revealing strong evidence that secondary structure is evolutionarily conserved among Cas9 lineages. Altogether, our findings provide insight into Cas9 tracrRNA evolution and efforts to characterize the tracrRNA of Cas9 systems.