Extending Seqenv: a taxa-centric approach to environmental annotations of 16S rDNA sequences

Understanding how the environment selects a given taxon and the diversity patterns that emerge as a result of environmental filtering can dramatically improve our ability to analyse any environment in depth as well as advancing our knowledge on how the response of different taxa can impact each other and ecosystem functions. Most of the work investigating microbial biogeography has been site-specific, and logical environmental factors, rather than geographical location, may be more influential on microbial diversity. SEQenv, a novel pipeline aiming to provide environmental annotations of sequences emerged to provide a consistent description of the environmental niches using the ENVO ontology. While the pipeline provides a list of environmental terms on the basis of sample datasets and, therefore, the annotations obtained are at the dataset level, it lacks a taxa centric approach to environmental annotation. The work here describes an extension developed to enhance the SEQenv pipeline, which provided the means to directly generate environmental annotations for taxa under different contexts. 16S rDNA amplicon datasets belonging to distinct biomes were selected to illustrate the applicability of the extended SEQenv pipeline. A literature survey of the results demonstrates the immense importance of sequence level environmental annotations by illustrating the distribution of both taxa across environments as well as the various environmental sources of a specific taxon. Significantly enhancing the SEQenv pipeline in the process, this information would be valuable to any biologist seeking to understand the various taxa present in the habitat and the environment they originated from, enabling a more thorough analysis of which lineages are abundant in certain habitats and the recovery of patterns in taxon distribution across different habitats and environmental gradients

Terminal restriction fragment length polymorphism is an “old school” reliable technique for swift microbial community screening in anaerobic digestion

The microbial community in anaerobic digestion has been analysed through microbial fingerprinting techniques, such as terminal restriction fragment length polymorphism (TRFLP), for decades. In the last decade, high-throughput 16S rRNA gene amplicon sequencing has replaced these techniques, but the time-consuming and complex nature of high-throughput techniques is a potential bottleneck for full-scale anaerobic digestion application, when monitoring community dynamics. Here, the bacterial and archaeal TRFLP profiles were compared with 16S rRNA gene amplicon profiles (Illumina platform) of 25 full-scale anaerobic digestion plants. The α-diversity analysis revealed a higher richness based on Illumina data, compared with the TRFLP data. This coincided with a clear difference in community organisation, Pareto distribution, and co-occurrence network statistics, i.e., betweenness centrality and normalised degree. The β-diversity analysis showed a similar clustering profile for the Illumina, bacterial TRFLP and archaeal TRFLP data, based on different distance measures and independent of phylogenetic identification, with pH and temperature as the two key operational parameters determining microbial community composition. The combined knowledge of temporal dynamics and projected clustering in the β-diversity profile, based on the TRFLP data, distinctly showed that TRFLP is a reliable technique for swift microbial community dynamics screening in full-scale anaerobic digestion plants

Differential ratio amplicons (Ramp) for the evaluation of RNA integrity extracted from complex environmental samples

Reliability and reproducibility of transcriptomics‐based studies are dependent on RNA integrity. In microbial ecology, microfluidics‐based techniques, such as the Ribosomal Integrity Number (RIN), targeting rRNA are currently the only approaches to evaluate RNA integrity. However, the relationship between rRNA and mRNA integrity is unknown. Here we present an integrity index, the Ratio Amplicon, Ramp, adapted from human clinical studies, to directly monitor mRNA integrity from complex environmental samples. We show, in a suite of experimental degradations of RNA extracted from sediment, that while the RIN generally reflected the degradation status of RNA the Ramp mapped mRNA degradation better. Furthermore, we examined the effect of degradation on transcript community structure by amplicon sequencing of 16S rRNA, amoA and glnA transcripts. We successfully sequenced transcripts for all three targets even from highly‐degraded RNA samples. While RNA degradation changed the community structure of the mRNA profiles, no changes were observed for the 16S rRNA transcript profiles. Since both RT‐Q‐PCR and sequencing results were obtained, even from highly degraded samples, we strongly recommend evaluating RNA integrity prior to downstream processing to ensure meaningful results. For this both the RIN and Ramp are useful, with the Ramp better evaluating mRNA integrity in this study

Illumina error profiles : resolving fine-scale variation in metagenomic sequencing data

Background: Illumina’s sequencing platforms are currently the most utilised sequencing systems worldwide. The technology has rapidly evolved over recent years and provides high throughput at low costs with increasing read-lengths and true paired-end reads. However, data from any sequencing technology contains noise and our understanding of the peculiarities and sequencing errors encountered in Illumina data has lagged behind this rapid development. Results: We conducted a systematic investigation of errors and biases in Illumina data based on the largest collection of in vitro metagenomic data sets to date. We evaluated the Genome Analyzer II, HiSeq and MiSeq and tested state-of-the-art low input library preparation methods. Analysing in vitro metagenomic sequencing data allowed us to determine biases directly associated with the actual sequencing process. The position- and nucleotide-specific analysis revealed a substantial bias related to motifs (3mers preceding errors) ending in “GG”. On average the top three motifs were linked to 16 % of all substitution errors. Furthermore, a preferential incorporation of ddGTPs was recorded. We hypothesise that all of these biases are related to the engineered polymerase and ddNTPs which are intrinsic to any sequencing-by-synthesis method. We show that quality-score-based error removal strategies can on average remove 69 % of the substitution errors - however, the motif-bias remains. Conclusion: Single-nucleotide polymorphism changes in bacterial genomes can cause significant changes in phenotype, including antibiotic resistance and virulence, detecting them within metagenomes is therefore vital. Current error removal techniques are not designed to target the peculiarities encountered in Illumina sequencing data and other sequencing-by-synthesis methods, causing biases to persist and potentially affect any conclusions drawn from the data. In order to develop effective diagnostic and therapeutic approaches we need to be able to identify systematic sequencing errors and distinguish these errors from true genetic variation

Terminal restriction fragment length polymorphism is an “old school” reliable technique for swift microbial community screening in anaerobic digestion

The microbial community in anaerobic digestion has been analysed through microbial fingerprinting techniques, such as terminal restriction fragment length polymorphism (TRFLP), for decades. In the last decade, high-throughput 16S rRNA gene amplicon sequencing has replaced these techniques, but the time-consuming and complex nature of high-throughput techniques is a potential bottleneck for full-scale anaerobic digestion application, when monitoring community dynamics. Here, the bacterial and archaeal TRFLP profiles were compared with 16S rRNA gene amplicon profiles (Illumina platform) of 25 full-scale anaerobic digestion plants. The α-diversity analysis revealed a higher richness based on Illumina data, compared with the TRFLP data. This coincided with a clear difference in community organisation, Pareto distribution, and co-occurrence network statistics, i.e., betweenness centrality and normalised degree. The β-diversity analysis showed a similar clustering profile for the Illumina, bacterial TRFLP and archaeal TRFLP data, based on different distance measures and independent of phylogenetic identification, with pH and temperature as the two key operational parameters determining microbial community composition. The combined knowledge of temporal dynamics and projected clustering in the β-diversity profile, based on the TRFLP data, distinctly showed that TRFLP is a reliable technique for swift microbial community dynamics screening in full-scale anaerobic digestion plants. © 2018, The Author(s)

Systems biology approach to elucidation of contaminants biodegradation in complex samples- integration of high-resolution analytical and molecular tools

We present here a data-driven systems biology framework to the rational design of biotechnological solutions for contaminated environments with the aim of understanding the interactions and mechanisms underpinning the role of microbial communities in the biodegradation of contaminated soils. We have considered a multi-omics approach which employs novel in silico tools to combine high-throughput sequencing data (16S rRNA amplicons) with the chemical data including high-resolution analytical data generated by comprehensive two-dimensional gas chromatography (GCxGC). To assess this approach, we have considered a matching dataset with both microbiological and chemical signatures available for samples from two former manufactured gas plant sites. On this dataset, we applied the numerical procedures informed by ecological principles (predominantly diversity measures) as well as recently published statistical approaches that give discriminatory features and their correlations by maximizing the covariances between multiple datasets on the same sample space. In particular, we have utilized sparse projection to latent discriminant analysis and its derivative to multiple datasets, an N-integration algorithm called DIABLO. Our results indicate microbial community structure dependent on the contaminated environment and unravel promising interactions of some of the microbial species with the biodegradation potential. To the best of our knowledge, this is the first study that incorporates with microbiome an unprecedented high-level distribution of hydrocarbons obtained through GC x GC

Bioreactor scalability: laboratory-scale bioreactor design influences performance, ecology, and community physiology in expanded granular sludge bed bioreactors

Studies investigating the feasibility of new, or improved, biotechnologies, such as wastewater treatment digesters, inevitably start with laboratory-scale trials. However, it is rarely determined whether laboratory-scale results reflect full-scale performance or microbial ecology. The Expanded Granular Sludge Bed (EGSB) bioreactor, which is a high-rate anaerobic digester configuration, was used as a model to address that knowledge gap in this study. Two laboratory-scale idealizations of the EGSB—a one-dimensional and a three- dimensional scale-down of a full-scale design—were built and operated in triplicate under near-identical conditions to a full-scale EGSB. The laboratory-scale bioreactors were seeded using biomass obtained from the full-scale bioreactor, and, spent water from the distillation of whisky from maize was applied as substrate at both scales. Over 70 days, bioreactor performance, microbial ecology, and microbial community physiology were monitored at various depths in the sludge-beds using 16S rRNA gene sequencing (V4 region), specific methanogenic activity (SMA) assays, and a range of physical and chemical monitoring methods. SMA assays indicated dominance of the hydrogenotrophic pathway at full-scale whilst a more balanced activity profile developed during the laboratory-scale trials. At each scale, Methanobacterium was the dominant methanogenic genus present. Bioreactor performance overall was better at laboratory-scale than full-scale. We observed that bioreactor design at laboratory-scale significantly influenced spatial distribution of microbial community physiology and taxonomy in the bioreactor sludge-bed, with 1-D bioreactor types promoting stratification of each. In the 1-D laboratory bioreactors, increased abundance of Firmicutes was associated with both granule position in the sludge bed and increased activity against acetate and ethanol as substrates. We further observed that stratification in the sludge-bed in 1-D laboratory-scale bioreactors was associated with increased richness in the underlying microbial community at species (OTU) level and improved overall performance

The active microbial community more accurately reflects the anaerobic digestion process : 16S rRNA (gene) sequencing as a predictive tool

Amplicon sequencing methods targeting the 16S rRNA gene have been used extensively to investigate microbial community composition and dynamics in anaerobic digestion. These methods successfully characterise amplicons, but do not distinguish micro-organisms that are actually responsible for the process. In this research, the archaeal and bacterial community of 48 full-scale anaerobic digestion plants was evaluated on DNA (total community) and RNA (active community) level via 16S rRNA (gene) amplicon sequencing. A significantly higher diversity on DNA compared with the RNA level was observed for archaea, but not for bacteria. Beta diversity analysis showed a significant difference in community composition between the DNA and RNA of both bacteria and archaea. This related with 25.5 and 42.3% of total OTUs for bacteria and archaea, respectively, that showed a significant difference in their DNA and RNA profiles. Similar operational parameters affected the bacterial and archaeal community, yet, the differentiating effect between DNA and RNA was much stronger for archaea. In conclusion, a clear difference in active (RNA) and total (DNA) community profiles was observed, implying the need for a combined approach to estimate community stability in anaerobic digestion