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.

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.

Multi-angle meta-analysis of the gut microbiome in Autism Spectrum Disorder: a step toward understanding patient subgroups.

Observational studies have shown that the composition of the human gut microbiome in children diagnosed with Autism Spectrum Disorder (ASD) differs significantly from that of their neurotypical (NT) counterparts. Thus far, reported ASD-specific microbiome signatures have been inconsistent. To uncover reproducible signatures, we compiled 10 publicly available raw amplicon and metagenomic sequencing datasets alongside new data generated from an internal cohort (the largest ASD cohort to date), unified them with standardized pre-processing methods, and conducted a comprehensive meta-analysis of all taxa and variables detected across multiple studies. By screening metadata to test associations between the microbiome and 52 variables in multiple patient subsets and across multiple datasets, we determined that differentially abundant taxa in ASD versus NT children were dependent upon age, sex, and bowel function, thus marking these variables as potential confounders in case-control ASD studies. Several taxa, including the strains Bacteroides stercoris t__190463 and Clostridium M bolteae t__180407, and the species Granulicatella elegans and Massilioclostridium coli, exhibited differential abundance in ASD compared to NT children only after subjects with bowel dysfunction were removed. Adjusting for age, sex and bowel function resulted in adding or removing significantly differentially abundant taxa in ASD-diagnosed individuals, emphasizing the importance of collecting and controlling for these metadata. We have performed the largest (n = 690) and most comprehensive systematic analysis of ASD gut microbiome data to date. Our study demonstrated the importance of accounting for confounding variables when designing statistical comparative analyses of ASD- and NT-associated gut bacterial profiles. Mitigating these confounders identified robust microbial signatures across cohorts, signifying the importance of accounting for these factors in comparative analyses of ASD and NT-associated gut profiles. Such studies will advance the understanding of different patient groups to deliver appropriate therapeutics by identifying microbiome traits germane to the specific ASD phenotype.

MetaFunPrimer: an Environment-Specific, High-Throughput Primer Design Tool for Improved Quantification of Target Genes.

Genes belonging to the same functional group may include numerous and variable gene sequences, making characterizing and quantifying difficult. Therefore, high-throughput design tools are needed to simultaneously create primers for improved quantification of target genes. We developed MetaFunPrimer, a bioinformatic pipeline, to design primers for numerous genes of interest. This tool also enables gene target prioritization based on ranking the presence of genes in user-defined references, such as environment-specific metagenomes. Given inputs of protein and nucleotide sequences for gene targets of interest and an accompanying set of reference metagenomes or genomes, MetaFunPrimer generates primers for ranked genes of interest. To demonstrate the usage and benefits of MetaFunPrimer, a total of 78 primer pairs were designed to target observed ammonia monooxygenase subunit A (amoA) genes of ammonia-oxidizing bacteria (AOB) in 1,550 publicly available soil metagenomes. We demonstrate computationally that these amoA-AOB primers can cover 94% of the amoA-AOB genes observed in the 1,550 soil metagenomes compared with a 49% estimated coverage by previously published primers. Finally, we verified the utility of these primer sets in incubation experiments that used long-term nitrogen fertilized or unfertilized soils. High-throughput quantitative PCR (qPCR) results and statistical analyses showed significant differences in relative quantification patterns between the two soils, and subsequent absolute quantifications also confirmed that target genes enumerated by six selected primer pairs were significantly more abundant in the nitrogen-fertilized soils. This new tool gives microbial ecologists a new approach to assess functional gene abundance and related microbial community dynamics quickly and affordably. IMPORTANCE Amplification-based gene characterization allows for sensitive and specific quantification of functional genes. There is often a large diversity of genes represented for functional gene groups, and multiple primers may be necessary to target associated genes. Current primer design tools are limited to designing primers for only a few genes of interest. MetaFunPrimer allows for high-throughput primer design for various genes of interest and also allows for ranking gene targets by their presence and abundance in environmental data sets. Primers designed by this tool improve the characterization and quantification of functional genes in broad gene amplification platforms and can be powerful with high-throughput qPCR approaches.

Improved detection of mcyA genes and their phylogenetic origins in harmful algal blooms.

Microcystins, a group of cyanotoxins produced by cyanobacterial strains, have become a significant microbial hazard to human and animal health due to increases in the frequency and intensity of cyanobacterial harmful algal blooms (CyanoHABs). Many studies have explored the correlation between microcystin concentrations and abundances of toxin-producing genes (e.g., mcyA genes) measured using quantitative PCR, and discrepancies between toxin concentrations and gene abundances are often observed. In this study, the results show that these discrepancies are at least partially due to primer sets that do not capture the phylogenetic diversity of naturally present toxin-producers. We designed three novel primer gene probes based on known mcyA genes to improve the detection and quantification of these genes in environmental samples. These primers were shown to improve the identification of mcyA genes compared to previously published primers in freshwater metagenomes, cyanobacterial isolates, and lake water samples. Unlike previously published primers, our primer sets could selectively amplify and resolve Microcystis, Anabaena, and Planktothrix mcyA genes. In lake water samples, abundance estimations of mcyA genes were found to correlate strongly with microcystin concentrations. Based on our results, these primers offer significant improvements over previously published probes to accurately identify and quantify mcyA genes in the environment. There is an increasing need to develop models based on microbial information and environmental factors to predict CyanoHABs, and improved primers will play an important role in aiding monitoring efforts to collect reliable and consistent data on toxicity risks.

Toward Antibiotic Stewardship: Route of Antibiotic Administration Impacts the Microbiota and Resistance Gene Diversity in Swine Feces.

Oral antibiotics are a critical tool for fighting bacterial infections, yet their use can have negative consequences, such as the disturbance of healthy gut bacterial communities and the dissemination of antibiotic residues in feces. Altering antibiotic administration route may limit negative impacts on intestinal microbiota and reduce selective pressure for antimicrobial resistance genes (ARG) persistence and mobility. Thus, a study was performed in pigs to evaluate route of therapeutic oxytetracycline (oxytet) administration, an antibiotic commonly used in the U.S. swine industry, on intestinal microbial diversity and ARG abundance. Given that oral antibiotics would be in direct contact with intestinal bacteria, we hypothesized that oral administration would cause a major shift in intestinal bacterial community structure when compared to injected antibiotic. We further postulated that the impact would extend to the diversity and abundance of ARG in swine feces. At approximately 3 weeks-of-age, piglets were separated into three groups (n = 21-22 per group) with two groups receiving oxytet (one via injection and the second via feed) and a third non-medicated group. Oxytet levels in the plasma indicated injected antibiotic resulted in a spike 1 day after administration, which decreased over time, though oxytet was still detected in plasma 14 days after injection. Conversely, in-feed oxytet delivery resulted in lower but less variable oxytet levels in circulation and high concentrations in feces. Similar trends were observed in microbial community changes regardless of route of oxytet administration; however, the impact on the microbial community was more pronounced at all time points and in all samples with in-feed administration. Fecal ARG abundance was increased with in-feed administration over injected, with genes for tetracycline and aminoglycoside resistance enriched specifically in the feces of the in-feed group. Sequencing of plasmid-enriched samples revealed multiple genetic contexts for the resistance genes detected and highlighted the potential role of small plasmids in the movement of antibiotic resistance genes. The findings are informative for disease management in food animals, but also manure management and antibiotic therapy in human medicine for improved antibiotic stewardship.

A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data.

Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.

RefSoil+: a Reference Database for Genes and Traits of Soil Plasmids.

Plasmids harbor transferable genes that contribute to the functional repertoire of microbial communities, yet their contributions to metagenomes are often overlooked. Environmental plasmids have the potential to spread antibiotic resistance to clinical microbial strains. In soils, high microbiome diversity and high variability in plasmid characteristics present a challenge for studying plasmids. To improve the understanding of soil plasmids, we present RefSoil+, a database containing plasmid sequences from 922 soil microorganisms. Soil plasmids were larger than other described plasmids, which is a trait associated with plasmid mobility. There was a weak relationship between chromosome size and plasmid size and no relationship between chromosome size and plasmid number, suggesting that these genomic traits are independent in soil. We used RefSoil+ to inform the distributions of antibiotic resistance genes among soil microorganisms compared to those among nonsoil microorganisms. Soil-associated plasmids, but not chromosomes, had fewer antibiotic resistance genes than other microorganisms. These data suggest that soils may offer limited opportunity for plasmid-mediated transfer of described antibiotic resistance genes. RefSoil+ can serve as a reference for the diversity, composition, and host associations of plasmid-borne functional genes in soil, a utility that will be enhanced as the database expands. Our study improves the understanding of soil plasmids and provides a resource for assessing the dynamics of the genes that they carry, especially genes conferring antibiotic resistances. IMPORTANCE Soil-associated plasmids have the potential to transfer antibiotic resistance genes from environmental to clinical microbial strains, which is a public health concern. A specific resource is needed to aggregate the knowledge of soil plasmid characteristics so that the content, host associations, and dynamics of antibiotic resistance genes can be assessed and then tracked between the environment and the clinic. Here, we present RefSoil+, a database of soil-associated plasmids. RefSoil+ presents a contemporary snapshot of antibiotic resistance genes in soil that can serve as a reference as novel plasmids and transferred antibiotic resistances are discovered. Our study broadens our understanding of plasmids in soil and provides a community resource of important plasmid-associated genes, including antibiotic resistance genes.

Strategies for Building Computing Skills To Support Microbiome Analysis: a Five-Year Perspective from the EDAMAME Workshop.

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).

Characterizing the soil microbiome and quantifying antibiotic resistance gene dynamics in agricultural soil following swine CAFO manure application.

As agriculture industrializes, concentrated animal feeding operations (CAFOs) are becoming more common. Feces from CAFOs is often used as fertilizer on fields. However, little is known about the effects manure has on the soil microbiome, which is an important aspect of soil health and fertility. In addition, due to the subtherapeutic levels of antibiotics necessary to keep the animals healthy, CAFO manure has elevated levels of antibiotic resistant bacteria. Using 16s rRNA high-throughput sequencing and qPCR, this study sought to determine the impact of swine CAFO manure application on both the soil microbiome and abundance of select antibiotic resistance genes (ARGs) and mobile element genes (erm(B), erm(C), sul1, str(B), intI1, IncW repA) in agricultural soil over the fall and spring seasons. We found the manure community to be distinct from the soil community, with a majority of bacteria belonging to Bacteroidetes and Firmicutes. The soil samples had more diverse communities dominated by Acidobacteria, Actinobacteria, Proteobacteria, Verrucomicrobia, and unclassified bacteria. We observed significant differences in the soil microbiome between all time points, except between the spring samples. However, by tracking manure associated taxa, we found the addition of the manure microbiome to be a minor driver of the shift. Of the measured genes, manure application only significantly increased the abundance of erm(B) and erm(C) which remained elevated in the spring. These results suggest bacteria in the manure do not survive well in soil and that ARG dynamics in soil following manure application vary by resistance gene.

A meta-analysis of global fungal distribution reveals climate-driven patterns.

The evolutionary and environmental factors that shape fungal biogeography are incompletely understood. Here, we assemble a large dataset consisting of previously generated mycobiome data linked to specific geographical locations across the world. We use this dataset to describe the distribution of fungal taxa and to look for correlations with different environmental factors such as climate, soil and vegetation variables. Our meta-study identifies climate as an important driver of different aspects of fungal biogeography, including the global distribution of common fungi as well as the composition and diversity of fungal communities. In our analysis, fungal diversity is concentrated at high latitudes, in contrast with the opposite pattern previously shown for plants and other organisms. Mycorrhizal fungi appear to have narrower climatic tolerances than pathogenic fungi. We speculate that climate change could affect ecosystem functioning because of the narrow climatic tolerances of key fungal taxa.

Practical implications of erythromycin resistance gene diversity on surveillance and monitoring of resistance.

Use of antibiotics in human and animal medicine has applied selective pressure for the global dissemination of antibiotic-resistant bacteria. Therefore, it is of interest to develop strategies to mitigate the continued amplification and transmission of resistance genes in environmental reservoirs such as farms, hospitals and watersheds. However, the efficacy of mitigation strategies is difficult to evaluate because it is unclear which resistance genes are important to monitor, and which primers to use to detect those genes. Here, we evaluated the diversity of one type of macrolide antibiotic resistance gene (erm) in one type of environment (manure) to determine which primers would be most informative to use in a mitigation study of that environment. We analyzed all known erm genes and assessed the ability of previously published erm primers to detect the diversity. The results showed that all known erm resistance genes group into 66 clusters, and 25 of these clusters (40%) can be targeted with primers found in the literature. These primers can target 74%-85% of the erm gene diversity in the manures analyzed.

The LacI family protein GlyR3 co-regulates the celC operon and manB in Clostridium thermocellum.

BACKGROUND: Clostridium thermocellum utilizes a wide variety of free and cellulosomal cellulases and accessory enzymes to hydrolyze polysaccharides present in complex substrates. To date only a few studies have unveiled the details by which the expression of these cellulases are regulated. Recent studies have described the auto regulation of the celC operon and determined that the celC-glyR3-licA gene cluster and nearby manB-celT gene cluster are co-transcribed as polycistronic mRNA. RESULTS: In this paper, we demonstrate that the GlyR3 protein mediates the regulation of manB. We first identify putative GlyR3 binding sites within or just upstream of the coding regions of manB and celT. Using an electrophoretic mobility shift assay (EMSA), we determined that a higher concentration of GlyR3 is required to effectively bind to the putative manB site in comparison to the celC site. Neither the putative celT site nor random DNA significantly binds GlyR3. While laminaribiose interfered with GlyR3 binding to the celC binding site, binding to the manB site was unaffected. In the presence of laminaribiose, in vivo transcription of the celC-glyR3-licA gene cluster increases, while manB expression is repressed, compared to in the absence of laminaribiose, consistent with the results from the EMSA. An in vitro transcription assay demonstrated that GlyR3 and laminaribiose interactions were responsible for the observed patters of in vivo transcription. CONCLUSIONS: Together these results reveal a mechanism by which manB is expressed at low concentrations of GlyR3 but repressed at high concentrations. In this way, C. thermocellum is able to co-regulate both the celC and manB gene clusters in response to the availability of ß-1,3-polysaccharides in its environment.

Identification of Soil Microbes Capable of Utilizing Cellobiosan.

Approximately 100 million tons of anhydrosugars, such as levoglucosan and cellobiosan, are produced through biomass burning every year. These sugars are also produced through fast pyrolysis, the controlled thermal depolymerization of biomass. While the microbial pathways associated with levoglucosan utilization have been characterized, there is little known about cellobiosan utilization. Here we describe the isolation and characterization of six cellobiosan-utilizing microbes from soil samples. Each of these organisms is capable of using both cellobiosan and levoglucosan as sole carbon source, though both minimal and rich media cellobiosan supported significantly higher biomass production than levoglucosan. Ribosomal sequencing was used to identify the closest reported match for these organisms: Sphingobacterium multivorum, Acinetobacter oleivorans JC3-1, Enterobacter sp SJZ-6, and Microbacterium sps FXJ8.207 and 203 and a fungal species Cryptococcus sp. The commercially-acquired Enterobacter cloacae DSM 16657 showed growth on levoglucosan and cellobiosan, supporting our isolate identification. Analysis of an existing database of 16S rRNA amplicons from Iowa soil samples confirmed the representation of our five bacterial isolates and four previously-reported levoglucosan-utilizing bacterial isolates in other soil samples and provided insight into their population distributions. Phylogenetic analysis of the 16S rRNA and 18S rRNA of strains previously reported to utilize levoglucosan and our newfound isolates showed that the organisms isolated in this study are distinct from previously described anhydrosugar-utilizing microbial species.