intI1 gene abundance from septic tanks in Thailand using validated intI1 primers.

IMPORTANCE: Antimicrobial resistance is a global crisis, and wastewater treatment, including septic tanks, remains an important source of antimicrobial resistance (AMR) genes. The role of septic tanks in disseminating class 1 integron, and by extension AMR genes, in Thailand, where antibiotic use is unregulated remains understudied. We aimed to monitor gene abundance as a proxy to infer potential AMR from septic tanks in Thailand. We evaluated published intI1 primers due to the lack of consensus on optimal Q-PCR primers and the absence of standardization. Our findings confirmed septic tanks are a source of class 1 integron to the environment. We highlighted the significance of intI1 primer choice, in the context of interpretation of risk associated with AMR spread from septic tanks. We recommend the validated set (F3-R3) for optimal intI1 quantification toward the goal of achieving standardization across studies.

Reverse transcriptase enzyme and priming strategy affect quantification and diversity of environmental transcripts.

Reverse-transcriptase-quantitative PCR (RT-Q-PCR) and RT-PCR amplicon sequencing, provide a convenient, target-specific, high-sensitivity approach for gene expression studies and are widely used in environmental microbiology. Yet, the effectiveness and reproducibility of the reverse transcription step has not been evaluated. Therefore, we tested a combination of four commercial reverse transcriptases with two priming techniques to faithfully transcribe 16S rRNA and amoA transcripts from marine sediments. Both enzyme and priming strategy greatly affected quantification of the exact same target with differences of up to 600-fold. Furthermore, the choice of RT system significantly changed the communities recovered. For 16S rRNA, both enzyme and priming had a significant effect with enzyme having a stronger impact than priming. Inversely, for amoA only the change in priming strategy resulted in significant differences between the same samples. Specifically, more OTUs and better coverage of amoA transcripts diversity were obtained with GS priming indicating this approach was better at recovering the diversity of amoA transcripts. Moreover, sequencing of RNA mock communities revealed that, even though transcript α diversities (i.e., OTU counts within a sample) can be biased by the RT, the comparison of ß diversities (i.e., differences in OTU counts between samples) is reliable as those biases are reproducible between environments.

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.

Low-abundant but highly transcriptionally active uncharacterised Nitrosomonas drive ammonia-oxidation in the Brouage mudflat, France.

Exploring differences in nitrification within adjacent sedimentary structures of ridges and runnels on the Brouage mudflat, France, we quantified Potential Nitrification Rates (PNR) alongside amoA genes and transcripts. PNR was lower in ridges (≈1.7 fold-lower) than runnels, despite higher (≈1.8 fold-higher) ammonia-oxidizing bacteria (AOB) abundance. However, AOB were more transcriptionally active in runnels (≈1.9 fold-higher). Sequencing of amoA genes and transcripts revealed starkly contrasting profiles with transcripts from ridges and runnels dominated (≈91 % in ridges and ≈98 % in runnels) by low abundant (≈4.6 % of the DNA community in runnels and ≈0.8 % in ridges) but highly active phylotypes. The higher PNR in runnels was explained by higher abundance of this group, an uncharacterised Nitrosomonas sp. cluster. This cluster is phylogenetically similar to other active ammonia-oxidizers with worldwide distribution in coastal environments indicating its potential, but previously overlooked, contribution to ammonia oxidation globally. In contrast DNA profiles were dominated by highly abundant but low-activity clusters phylogenetically distinct from known Nitrosomonas (Nm) and Nitrosospira (Ns). This cluster is also globally distributed in coastal sediments, primarily detected as DNA, and often classified as Nitrosospira or Nitrosomonas. We therefore propose to classify this cluster as Ns/Nm. Our work indicates that low abundant but highly active AOB could be responsible for the nitrification globally, while the abundant AOB Ns/Nm may not be transcriptionally active, and as such account for the lack of correlation between rate processes and gene abundances often reported in the literature. It also raises the question as to what this seemingly inactive group is doing?

Microbial stratification and DOM removal in drinking water biofilters: Implications for enhanced performance.

Biofiltration is a low-cost, low-energy technology that employs a biologically activated bed of porous medium to reduce the biodegradable fraction of the dissolved organic matter (DOM) pool in source water, resulting in the production of drinking water. Microbial communities at different bed depths within the biofilter play crucial roles in the degradation and removal of dissolved organic carbon (DOC), ultimately impacting its performance. However, the relationships between the composition of microbial communities inhabiting different biofilter depths and their utilisation of various DOC fractions remain poorly understood. To address this knowledge gap, we conducted an experimental study where microbial communities from the upper (i.e., top 10 cm) and lower (i.e., bottom 10 cm) sections of a 30-cm long laboratory-scale biofilter were recovered. These communities were then individually incubated for 10 days using the same source water as the biofilter influent. Our study revealed that the bottom microbial community exhibited lower diversity yet had a co-occurrence network with a higher degree of interconnections among its members compared to the top microbial community. Moreover, we established a direct correlation between the composition and network structure of the microbial communities and their ability to utilise various DOM compounds within a DOM pool. Interestingly, although the bottom microbial community had only 20 % of the total cell abundance compared to the top community at the beginning of the incubation, it utilised and hence removed approximately 60 % more total DOC from the DOM pool than the top community. While both communities rapidly utilised labile carbon fractions, such as low-molecular-weight neutrals, the utilisation of more refractory carbon fractions, like high-molecular-weight humic substances with an average molecular weight of more than ca. 1451 g/mol, was exclusive to the bottom microbial community. By employing techniques that capture microbial diversity (i.e., flow cytometry and 16S rRNA amplicon sequencing) and considering the complexities of DOM (i.e., LCOCD), our study provides novel insights into how microbial community structure could influence the microbial-mediated processes of engineering significance in drinking water production. Finally, our findings could offer the opportunity to improve biofilter performances via engineering interventions that shape the compositions of biofilter microbial communities and enhance their utilisation and removal of DOM, most notably the more classically humified and refractory DOM compound groups.

Ecological Observations Based on Functional Gene Sequencing Are Sensitive to the Amplicon Processing Method.

Until recently, the de facto method for short-read-based amplicon reconstruction was a sequence similarity threshold approach (operational taxonomic units [OTUs]). This has changed with the amplicon sequence variant (ASV) method where distributions are fitted to abundance profiles of individual genes using a noise-error model. While OTU-based approaches are still useful for 16S rRNA/18S rRNA genes, where thresholds of 97% to 99% are used, their use for functional genes is still debatable as there is no consensus on clustering thresholds. Here, we compare OTU- and ASV-based reconstruction approaches and taxonomy assignment methods, the naive Bayesian classifier (NBC) and Bayesian lowest common ancestor (BLCA) algorithm, using a functional gene data set from the microbial nitrogen-cycling community in the Brouage mudflat (France). A range of OTU similarity thresholds and ASVs were used to compare amoA (ammonia-oxidizing archaea [AOA] and ammonia-oxidizing bacteria [AOB]), nxrB, nirS, nirK, and nrfA communities between differing sedimentary structures. Significant effects of the sedimentary structure on weighted UniFrac (WUniFrac) distances were observed for AOA amoA when using ASVs, an OTU at a threshold of 97% sequence identity (OTU-97%), and OTU-85%; AOB amoA when using OTU-85%; and nirS when using ASV, OTU-90%, and OTU-85%. For AOB amoA, significant effects of the sedimentary structures on UniFrac distances were observed when using OTU-97% but not ASVs, and the inverse was found for nrfA. Interestingly, conclusions drawn for nirK and nxrB were consistent between amplicon reconstruction methods. We also show that when the sequences in the reference database are related to the environment in question, the BLCA algorithm leads to more phylogenetically relevant classifications. However, when the reference database contains sequences more dissimilar to the ones retrieved, the NBC obtains more information. IMPORTANCE Several analysis pipelines are available to microbial ecologists to process amplicon sequencing data, yet to date, there is no consensus as to the most appropriate method, and it becomes more difficult for genes that encode a specific function (functional genes). Standardized approaches need to be adopted to increase the reliability and reproducibility of environmental amplicon-sequencing-based data sets. In this paper, we argue that the recently developed ASV approach offers a better opportunity to achieve such standardization than OTUs for functional genes. We also propose a comprehensive framework for quality filtering of the sequencing reads based on protein sequence verification.