Qichao Tu

Reproducibility and quantitation of amplicon sequencing-based detection

To determine the reproducibility and quantitation of the amplicon sequencing-based detection approach for analyzing microbial community structure, a total of 24 microbial communities from a long-term global change experimental site were examined. Genomic DNA obtained from each community was used to amplify 16S rRNA genes with two or three barcode tags as technical replicates in the presence of a small quantity (0.1% wt/wt) of genomic DNA from Shewanella oneidensis MR-1 as the control. The technical reproducibility of the amplicon sequencing-based detection approach is quite low, with an average operational taxonomic unit (OTU) overlap of 17.2%±2.3% between two technical replicates, and 8.2%±2.3% among three technical replicates, which is most likely due to problems associated with random sampling processes. Such variations in technical replicates could have substantial effects on estimating β-diversity but less on α-diversity. A high variation was also observed in the control across different samples (for example, 66.7-fold for the forward primer), suggesting that the amplicon sequencing-based detection approach could not be quantitative. In addition, various strategies were examined to improve the comparability of amplicon sequencing data, such as increasing biological replicates, and removing singleton sequences and less-representative OTUs across biological replicates. Finally, as expected, various statistical analyses with preprocessed experimental data revealed clear differences in the composition and structure of microbial communities between warming and non-warming, or between clipping and non-clipping. Taken together, these results suggest that amplicon sequencing-based detection is useful in analyzing microbial community structure even though it is not reproducible and quantitative. However, great caution should be taken in experimental design and data interpretation when the amplicon sequencing-based detection approach is used for quantitative analysis of the β-diversity of microbial communities.

GeoChip 3.0 as a high-throughput tool for analyzing microbial community composition, structure and functional activity.

A new generation of functional gene arrays (FGAs; GeoChip 3.0) has been developed, with ∼28 000 probes covering approximately 57 000 gene variants from 292 functional gene families involved in carbon, nitrogen, phosphorus and sulfur cycles, energy metabolism, antibiotic resistance, metal resistance and organic contaminant degradation. GeoChip 3.0 also has several other distinct features, such as a common oligo reference standard (CORS) for data normalization and comparison, a software package for data management and future updating and the gyrB gene for phylogenetic analysis. Computational evaluation of probe specificity indicated that all designed probes would have a high specificity to their corresponding targets. Experimental analysis with synthesized oligonucleotides and genomic DNAs showed that only 0.0036–0.025% false-positive rates were observed, suggesting that the designed probes are highly specific under the experimental conditions examined. In addition, GeoChip 3.0 was applied to analyze soil microbial communities in a multifactor grassland ecosystem in Minnesota, USA, which showed that the structure, composition and potential activity of soil microbial communities significantly changed with the plant species diversity. As expected, GeoChip 3.0 is a high-throughput powerful tool for studying microbial community functional structure, and linking microbial communities to ecosystem processes and functioning.

Functional Molecular Ecological Networks

ABSTRACT Biodiversity and its responses to environmental changes are central issues in ecology and for society. Almost all microbial biodiversity research focuses on “species” richness and abundance but not on their interactions. Although a network approach is powerful in describing ecological interactions among species, defining the network structure in a microbial community is a great challenge. Also, although the stimulating effects of elevated CO2 (eCO2) on plant growth and primary productivity are well established, its influences on belowground microbial communities, especially microbial interactions, are poorly understood. Here, a random matrix theory (RMT)-based conceptual framework for identifying functional molecular ecological networks was developed with the high-throughput functional gene array hybridization data of soil microbial communities in a long-term grassland FACE (free air, CO2 enrichment) experiment. Our results indicate that RMT is powerful in identifying functional molecular ecological networks in microbial communities. Both functional molecular ecological networks under eCO2 and ambient CO2 (aCO2) possessed the general characteristics of complex systems such as scale free, small world, modular, and hierarchical. However, the topological structures of the functional molecular ecological networks are distinctly different between eCO2 and aCO2, at the levels of the entire communities, individual functional gene categories/groups, and functional genes/sequences, suggesting that eCO2 dramatically altered the network interactions among different microbial functional genes/populations. Such a shift in network structure is also significantly correlated with soil geochemical variables. In short, elucidating network interactions in microbial communities and their responses to environmental changes is fundamentally important for research in microbial ecology, systems microbiology, and global change. IMPORTANCE Microorganisms are the foundation of the Earths biosphere and play integral and unique roles in various ecosystem processes and functions. In an ecosystem, various microorganisms interact with each other to form complicated networks. Elucidating network interactions and their responses to environmental changes is difficult due to the lack of appropriate experimental data and an appropriate theoretical framework. This study provides a conceptual framework to construct interaction networks in microbial communities based on high-throughput functional gene array hybridization data. It also first documents that elevated carbon dioxide in the atmosphere dramatically alters the network interactions in soil microbial communities, which could have important implications in assessing the responses of ecosystems to climate change. The conceptual framework developed allows microbiologists to address research questions unapproachable previously by focusing on network interactions beyond the listing of, e.g., the number and abundance of species. Thus, this study could represent transformative research and a paradigm shift in microbial ecology. Microorganisms are the foundation of the Earths biosphere and play integral and unique roles in various ecosystem processes and functions. In an ecosystem, various microorganisms interact with each other to form complicated networks. Elucidating network interactions and their responses to environmental changes is difficult due to the lack of appropriate experimental data and an appropriate theoretical framework. This study provides a conceptual framework to construct interaction networks in microbial communities based on high-throughput functional gene array hybridization data. It also first documents that elevated carbon dioxide in the atmosphere dramatically alters the network interactions in soil microbial communities, which could have important implications in assessing the responses of ecosystems to climate change. The conceptual framework developed allows microbiologists to address research questions unapproachable previously by focusing on network interactions beyond the listing of, e.g., the number and abundance of species. Thus, this study could represent transformative research and a paradigm shift in microbial ecology.

GeoChip 4: a functional gene‐array‐based high‐throughput environmental technology for microbial community analysis

Micro‐organisms play critical roles in many important biogeochemical processes in the Earths biosphere. However, understanding and characterizing the functional capacity of microbial communities are still difficult due to the extremely diverse and often uncultivable nature of most micro‐organisms. In this study, we developed a new functional gene array, GeoChip 4, for analysing the functional diversity, composition, structure, metabolic potential/activity and dynamics of microbial communities. GeoChip 4 contained approximately 82 000 probes covering 141 995 coding sequences from 410 functional gene families related to microbial carbon (C), nitrogen (N), sulphur (S), and phosphorus (P) cycling, energy metabolism, antibiotic resistance, metal resistance/reduction, organic remediation, stress responses, bacteriophage and virulence. A total of 173 archaeal, 4138 bacterial, 404 eukaryotic and 252 viral strains were targeted, providing the ability to analyse targeted functional gene families of micro‐organisms included in all four domains. Experimental assessment using different amounts of DNA suggested that as little as 500 ng environmental DNA was required for good hybridization, and the signal intensities detected were well correlated with the DNA amount used. GeoChip 4 was then applied to study the effect of long‐term warming on soil microbial communities at a Central Oklahoma site, with results indicating that microbial communities respond to long‐term warming by enriching carbon degradation, nutrient cycling (nitrogen and phosphorous) and stress response gene families. To the best of our knowledge, GeoChip 4 is the most comprehensive functional gene array for microbial community analysis.

Soil microbial community responses to a decade of warming as revealed by comparative metagenomics.

ABSTRACT Soil microbial communities are extremely complex, being composed of thousands of low-abundance species (<0.1% of total). How such complex communities respond to natural or human-induced fluctuations, including major perturbations such as global climate change, remains poorly understood, severely limiting our predictive ability for soil ecosystem functioning and resilience. In this study, we compared 12 whole-community shotgun metagenomic data sets from a grassland soil in the Midwestern United States, half representing soil that had undergone infrared warming by 2°C for 10 years, which simulated the effects of climate change, and the other half representing the adjacent soil that received no warming and thus, served as controls. Our analyses revealed that the heated communities showed significant shifts in composition and predicted metabolism, and these shifts were community wide as opposed to being attributable to a few taxa. Key metabolic pathways related to carbon turnover, such as cellulose degradation (∼13%) and CO2 production (∼10%), and to nitrogen cycling, including denitrification (∼12%), were enriched under warming, which was consistent with independent physicochemical measurements. These community shifts were interlinked, in part, with higher primary productivity of the aboveground plant communities stimulated by warming, revealing that most of the additional, plant-derived soil carbon was likely respired by microbial activity. Warming also enriched for a higher abundance of sporulation genes and genomes with higher G+C content. Collectively, our results indicate that microbial communities of temperate grassland soils play important roles in mediating feedback responses to climate change and advance the understanding of the molecular mechanisms of community adaptation to environmental perturbations.

Network succession reveals the importance of competition in response to emulsified vegetable oil amendment for uranium bioremediation

Discerning network interactions among different species/populations in microbial communities has evoked substantial interests in recent years, but little information is available about temporal dynamics of microbial network interactions in response to environmental perturbations. Here, we modified the random matrix theory-based network approach to discern network succession in groundwater microbial communities in response to emulsified vegetable oil (EVO) amendment for uranium bioremediation. Groundwater microbial communities from one control and seven monitor wells were analysed with a functional gene array (GeoChip 3.0), and functional molecular ecological networks (fMENs) at different time points were reconstructed. Our results showed that the network interactions were dramatically altered by EVO amendment. Dynamic and resilient succession was evident: fairly simple at the initial stage (Day 0), increasingly complex at the middle period (Days 4, 17, 31), most complex at Day 80, and then decreasingly complex at a later stage (140-269 days). Unlike previous studies in other habitats, negative interactions predominated in a time-series fMEN, suggesting strong competition among different microbial species in the groundwater systems after EVO injection. Particularly, several keystone sulfate-reducing bacteria showed strong negative interactions with their network neighbours. These results provide mechanistic understanding of the decreased phylogenetic diversity during environmental perturbations.

Strain/species identification in metagenomes using genome-specific markers

Shotgun metagenome sequencing has become a fast, cheap and high-throughput technology for characterizing microbial communities in complex environments and human body sites. However, accurate identification of microorganisms at the strain/species level remains extremely challenging. We present a novel k-mer-based approach, termed GSMer, that identifies genome-specific markers (GSMs) from currently sequenced microbial genomes, which were then used for strain/species-level identification in metagenomes. Using 5390 sequenced microbial genomes, 8 770 321 50-mer strain-specific and 11 736 360 species-specific GSMs were identified for 4088 strains and 2005 species (4933 strains), respectively. The GSMs were first evaluated against mock community metagenomes, recently sequenced genomes and real metagenomes from different body sites, suggesting that the identified GSMs were specific to their targeting genomes. Sensitivity evaluation against synthetic metagenomes with different coverage suggested that 50 GSMs per strain were sufficient to identify most microbial strains with ≥0.25× coverage, and 10% of selected GSMs in a database should be detected for confident positive callings. Application of GSMs identified 45 and 74 microbial strains/species significantly associated with type 2 diabetes patients and obese/lean individuals from corresponding gastrointestinal tract metagenomes, respectively. Our result agreed with previous studies but provided strain-level information. The approach can be directly applied to identify microbial strains/species from raw metagenomes, without the effort of complex data pre-processing.

Correlation of genomic and physiological traits of Thermoanaerobacter species with biofuel yields.

ABSTRACT Thermophilic anaerobic noncellulolytic Thermoanaerobacter species are of great biotechnological importance in cellulosic ethanol production due to their ability to produce high ethanol yields by simultaneous fermentation of hexose and pentose. Understanding the genome structure of these species is critical to improving and implementing these bacteria for possible biotechnological use in consolidated bioprocessing schemes (CBP) for cellulosic ethanol production. Here we describe a comparative genome analysis of two ethanologenic bacteria, Thermoanaerobacter sp. X514 and Thermoanaerobacter pseudethanolicus 39E. Compared to 39E, X514 has several unique key characteristics important to cellulosic biotechnology, including additional alcohol dehydrogenases and xylose transporters, modifications to pentose metabolism, and a complete vitamin B12 biosynthesis pathway. Experimental results from growth, metabolic flux, and microarray gene expression analyses support genome sequencing-based predictions which help to explain the distinct differences in ethanol production between these strains. The availability of whole-genome sequence and comparative genomic analyses will aid in engineering and optimizing Thermoanaerobacter strains for viable CBP strategies.

The Thermoanaerobacter Glycobiome Reveals Mechanisms of Pentose and Hexose Co-Utilization in Bacteria

Thermoanaerobic bacteria are of interest in cellulosic-biofuel production, due to their simultaneous pentose and hexose utilization (co-utilization) and thermophilic nature. In this study, we experimentally reconstructed the structure and dynamics of the first genome-wide carbon utilization network of thermoanaerobes. The network uncovers numerous novel pathways and identifies previously unrecognized but crucial pathway interactions and the associated key junctions. First, glucose, xylose, fructose, and cellobiose catabolism are each featured in distinct functional modules; the transport systems of hexose and pentose are apparently both regulated by transcriptional antiterminators of the BglG family, which is consistent with pentose and hexose co-utilization. Second, glucose and xylose modules cooperate in that the activity of the former promotes the activity of the latter via activating xylose transport and catabolism, while xylose delays cell lysis by sustaining coenzyme and ion metabolism. Third, the vitamin B12 pathway appears to promote ethanologenesis through ethanolamine and 1, 2-propanediol, while the arginine deiminase pathway probably contributes to cell survival in stationary phase. Moreover, by experimentally validating the distinct yet collaborative nature of glucose and xylose catabolism, we demonstrated that these novel network-derived features can be rationally exploited for product-yield enhancement via optimized timing and balanced loading of the carbon supply in a substrate-specific manner. Thus, this thermoanaerobic glycobiome reveals novel genetic features in carbon catabolism that may have immediate industrial implications and provides novel strategies and targets for fermentation and genome engineering.

Preliminary analysis of salivary microbiome and their potential roles in oral lichen planus.

Several studies have explored the origin and development mechanism of oral lichen planus (OLP) with limited attention to the role of bacteria in the progression of this common oral disease. Here we utilized MiSeq sequencing of 16S rRNA gene amplicons to identify complex oral microbiota associated with OLP from saliva samples of two subtypes (reticular and erosive) of OLP patients and healthy controls. Our analyses indicated that the overall structure of the salivary microbiome was not significantly affected by disease status. However, we did observe evident variations in abundance for several taxonomic groups in OLP. Porphyromonas and Solobacterium showed significantly higher relative abundances, whereas Haemophilus, Corynebacterium, Cellulosimicrobium and Campylobacter showed lower abundances in OLP patients, as compared with healthy controls. In addition, we explored specific microbial co-occurrence patterns in OLP, and revealed significantly fewer linkers of Streptococcus comprising species in erosive OLP. Furthermore, the disease severity and immune dysregulation were also genus-associated, including with Porphyromonas that correlated to disease scores and salivary levels of interleukin (IL)-17 and IL-23. Overall, this study provides a general description of oral microbiome in OLP, and it will be useful for further investigation of their potential roles in the initiation and immune modulation of OLP.