Functional metagenomic analysis of quorum sensing signaling in a nitrifying community

Biochar inoculated with Rhodococcus biphenylivorans altered microecological regulation by promoting quorum sensing and electron transfer: Up-regulation of related genes and enhancement of phenol and ammonia degradation

Bioaugmentation is an efficient method for improving the efficiency of coking wastewater removal. Nevertheless, how different immobilization approaches affect the efficiency of bioaugmentation remains unclear, as does the corresponding mechanism. With the assistance of immobilized bioaugmentation strain Rhodococcus biphenylivorans B403, the removal of synthetic coking wastewater was investigated (drying agent, alginate agent, and absorption agent). The reactor containing the absorption agent exhibited the highest average removal efficiency of phenol (99.74 %), chemical oxygen demand (93.09 %), and NH4+-N (98.18 %). Compared to other agents, the covered extracellular polymeric substance on the absorption agent surface enhanced electron transfer and quorum sensing, and the promoted quorum sensing benefited the activated sludge stability and microbial regulation. The phytotoxicity test revealed that the wastewater's toxicity was greatly decreased in the reactor with the absorption agent, especially under high phenol concentrations. These findings showed that the absorption agent was the most suitable for wastewater treatment bioaugmentation.

Filamentous cyanobacteria and hydrophobic protein in extracellular polymeric substances facilitate algae-bacteria aggregation during partial nitrification

In algae-bacteria symbiotic wastewater treatment, the excellent settling performance of algae-bacteria aggregates is critical for biomass separation and recovery. Here, the composition of extracellular polymeric substances (EPS), microbial profiles, and functional genes of algae-bacteria aggregates were investigated at different solid retention times (SRTs) (10, 20, and 40 d) during partial nitrification in photo sequencing bioreactors (PSBRs). Results showed that SRTs greatly influenced the nitrogen transformation and the formation and morphological structure of algae-bacteria aggregates. The highest nitrite accumulation, the largest particle size (~1.54 mm) and the best settling performance were observed for the algae-bacteria aggregates in the PSBR with an SRT of 10 d, where the abundant occurrence of filamentous cyanobacteria with the highest ratio of chlorophyll a/b and the lowest EPS amount with the highest protein-to-polysaccharide ratio were observed. In particular, the EPS at 10 d of SRT contained a higher amount of protein-related hydrophobic groups and a lower ratio of α-helix/(β-sheet + random coil), indicating a looser protein structure, which might facilitate the formation and stabilization of algae-bacteria aggregates. Moreover, algal-bacterial aggregation greatly depended on the composition and evolution of filamentous cyanobacteria (unclassified _o__Oscillatoriales and Phormidium accounted for 56.29 % of the identified algae at SRT 10 d). The metagenomic analysis further revealed that functional genes related to amino acid metabolism (e.g., genes of phenylalanine, tyrosine, and tryptophan biosynthesis) were expressed at high levels within 10 d of SRT. Overall, this study demonstrates the influence of EPS structures and filamentous cyanobacteria on algae-bacteria aggregation and reveals the biological mechanisms driving photogranule structure and function.

Metagenomic profiling of antibiotic resistance genes in Red Sea brine pools

Antibiotic resistance (AR) is an alarming global health concern, causing an annual death rate of more than 35,000 deaths in the US. AR is a natural phenomenon, reported in several pristine environments. In this study, we report AR in pristine Red Sea deep brine pools. Antimicrobial resistance genes (ARGs) were detected for several drug classes with tetracycline and macrolide resistance being the most abundant. As expected, ARGs abundance increased in accordance with the level of human impact with pristine Red Sea samples having the lowest mean ARG level followed by estuary samples, while activated sludge samples showed a significantly higher ARG level. ARG hierarchical clustering grouped drug classes for which resistance was detected in Atlantis II Deep brine pool independent of the rest of the samples. ARG abundance was significantly lower in the Discovery Deep brine pool. A correlation between integrons and ARGs abundance in brine pristine samples could be detected, while insertion sequences and plasmids showed a correlation with ARGs abundance in human-impacted samples not seen in brine pristine samples. This suggests different roles of distinct mobile genetic elements (MGEs) in ARG distribution in pristine versus human-impacted sites. Additionally, we showed the presence of mobile antibiotic resistance genes in the Atlantis II brine pool as evidenced by the co-existence of integrases and plasmid replication proteins on the same contigs harboring predicted multidrug-resistant efflux pumps. This study addresses the role of non-pathogenic environmental bacteria as a silent reservoir for ARGs, and the possible horizontal gene transfer mechanism mediating ARG acquisition.

Quorum sensing responses of r-/K-strategists Nitrospira in continuous flow and sequencing batch nitrifying biofilm reactors

A better understanding of r-/K-strategists nitrifiers will help to balance the design and operation of bioprocesses for efficient pollution removal from wastewater. The objectives of study were to investigate the nitrite oxidation biokinetics, biofilm property, microbial community and quorum sensing (QS) of nitrifying biofilm in a continuously flow reactor (CFR) and a sequencing batch reactor (SBR). Results showed that nitrite-oxidizing bacteria were estimated to have a nitrite half saturation constant of 76.23 and 224.73 μM in CFR and SBR, respectively. High-throughput and metagenomic sequencing results showed that Nitrospira and Candidatus Nitrospira defluvii were the dominated nitrite-oxidizing taxa performing nitrite oxidation in both reactors. Nitrifying biofilm developed in CFR and SBR showed obviously different properties. Biofilm in SBR had an obviously higher ratio of polysaccharide and protein in extracellular polymeric substances, and higher thickness than in CFR. Metagenomics and chemical analysis revealed various types of acyl-homoserine lactone (AHL) circuit genes (e.g., luxI, lasI, hdtS) and four types of AHL signaling substances (e.g., C₆-HSL, C₈-HSL, C₁₀-HSL and 3-oxo-C₁₀-HSL) in nitrifying biofilm. The concentrations of these AHLs in biomass and water phases were obviously higher in SBR than that in CFR. Together, AHLs-based QS might affect the formation of nitrifying biofilm and thus contribute to the different biokinetics of Nitrospira in CFR and SBR. Our insights may reveal the molecular mechanism of Nitrospira for different biokinetics, and indicate the AHL association with Nitrospira adaptation to various conditions.

Long-Read Metagenome-Assembled Genomes Improve Identification of Novel Complete Biosynthetic Gene Clusters in a Complex Microbial Activated Sludge Ecosystem

Microorganisms produce a wide variety of secondary/specialized metabolites (SMs), the majority of which are yet to be discovered. These natural products play multiple roles in microbiomes and are important for microbial competition, communication, and success in the environment. SMs have been our major source of antibiotics and are used in a range of biotechnological applications. In silico mining for biosynthetic gene clusters (BGCs) encoding the production of SMs is commonly used to assess the genetic potential of organisms. However, as BGCs span tens to over 200 kb, identifying complete BGCs requires genome data that has minimal assembly gaps within the BGCs, a prerequisite that was previously only met by individually sequenced genomes. Here, we assess the performance of the currently available genome mining platform antiSMASH on 1,080 high-quality metagenome-assembled bacterial genomes (HQ MAGs) previously produced from wastewater treatment plants (WWTPs) using a combination of long-read (Oxford Nanopore) and short-read (Illumina) sequencing technologies. More than 4,200 different BGCs were identified, with 88% of these being complete. Sequence similarity clustering of the BGCs implies that the majority of this biosynthetic potential likely encodes novel compounds, and few BGCs are shared between genera. We identify BGCs in abundant and functionally relevant genera in WWTPs, suggesting a role of secondary metabolism in this ecosystem. We find that the assembly of HQ MAGs using long-read sequencing is vital to explore the genetic potential for SM production among the uncultured members of microbial communities. IMPORTANCE Cataloguing secondary metabolite (SM) potential using genome mining of metagenomic data has become the method of choice in bioprospecting for novel compounds. However, accurate biosynthetic gene cluster (BGC) detection requires unfragmented genomic assemblies, which have been technically difficult to obtain from metagenomes until very recently with new long-read technologies. Here, we determined the biosynthetic potential of activated sludge (AS), the microbial community used in resource recovery and wastewater treatment, by mining high-quality metagenome-assembled genomes generated from long-read data. We found over 4,000 BGCs, including BGCs in abundant process-critical bacteria, with no similarity to the BGCs of characterized products. We show how long-read MAGs are required to confidently assemble complete BGCs, and we determined that the AS BGCs from different studies have very little overlap, suggesting that AS is a rich source of biosynthetic potential and new bioactive compounds.

Cronos: A Machine Learning Pipeline for Description and Predictive Modeling of Microbial Communities Over Time

Microbial time-series analysis, typically, examines the abundances of individual taxa over time and attempts to assign etiology to observed patterns. This approach assumes homogeneous groups in terms of profiles and response to external effectors. These assumptions are not always fulfilled, especially in complex natural systems, like the microbiome of the human gut. It is actually established that humans with otherwise the same demographic or dietary backgrounds can have distinct microbial profiles. We suggest an alternative approach to the analysis of microbial time-series, based on the following premises: 1) microbial communities are organized in distinct clusters of similar composition at any time point, 2) these intrinsic subsets of communities could have different responses to the same external effects, and 3) the fate of the communities is largely deterministic given the same external conditions. Therefore, tracking the transition of communities, rather than individual taxa, across these states, can enhance our understanding of the ecological processes and allow the prediction of future states, by incorporating applied effects. We implement these ideas into Cronos, an analytical pipeline written in R. Cronos’ inputs are a microbial composition table (e.g., OTU table), their phylogenetic relations as a tree, and the associated metadata. Cronos detects the intrinsic microbial profile clusters on all time points, describes them in terms of composition, and records the transitions between them. Cluster assignments, combined with the provided metadata, are used to model the transitions and predict samples’ fate under various effects. We applied Cronos to available data from growing infants’ gut microbiomes, and we observe two distinct trajectories corresponding to breastfed and formula-fed infants that eventually converge to profiles resembling those of mature individuals. Cronos is freely available at https://github.com/Lagkouvardos/Cronos.