A mixed blessing of influent leachate microbes in downstream biotreatment systems of a full-scale landfill leachate treatment plant.

In landfill leachate treatment plants (LLTPs), the microbiome plays a pivotal role in the decomposition of organic compounds, reduction in nutrient levels, and elimination of toxins. However, the effects of microbes in landfill leachate influents on downstream treatment systems remain poorly understood. To address this knowledge gap, we collected 23 metagenomic and 12 metatranscriptomic samples from landfill leachate and activated sludge from various treatment units in a full-scale LLTP. We successfully recovered 1,152 non-redundant metagenome-assembled genomes (MAGs), encompassing a wide taxonomic range, including 48 phyla, 95 classes, 166 orders, 247 families, 238 genera, and 1,152 species. More diverse microbes were observed in the influent leachate than in the downstream biotreatment systems, among which, an unprecedented ∼30 % of microbes with transcriptional expression migrated from the influent to the biological treatment units. Network analysis revealed that 399 shared MAGs across the four units exhibited high node centrality and degree, thus supporting enhanced interactions and increased stability of microbial communities. Functional reconstruction and genome characterization of MAGs indicated that these shared MAGs possessed greater capabilities for carbon, nitrogen, sulfur, and arsenic metabolism compared to non-shared MAGs. We further identified a novel species of Zixibacteria in the leachate influent with discrete lineages from those in other environments that accounted for up to 17 % of the abundance of the shared microbial community and exhibited notable metabolic versatility. Meanwhile, we presented groundbreaking evidence of the involvement of Zixibacteria-encoded genes in the production of harmful gas emissions, such as N2O and H2S, at the transcriptional level, thus suggesting that influent microbes may pose safety risks to downstream treatment systems. In summary, this study revealed the complex impact of the influent microbiome on LLTP and emphasizes the need to consider these microbial characteristics when designing treatment technologies and strategies for landfill leachate management.

Large scale exploration reveals rare taxa crucially shape microbial assembly in alkaline lake sediments.

Alkaline lakes are extreme environments inhabited by diverse microbial extremophiles. However, large-scale distribution patterns, environmental adaptations, community assembly, and evolutionary dynamics of microbial communities remain largely underexplored. This study investigated the characteristics of microbial communities on rare and abundant taxa in alkaline lake sediments in west and northwest China. We observed that abundant taxa varied significantly with geographical distance, while rare taxa remained unaffected by regional differences. The assembly process of abundant taxa was influenced by dispersal limitation, whilst rare taxa were predominantly driven by heterogeneous selection. Network analysis indicated that rare taxa as core species for community interactions and community stability. Rare taxa exhibited higher speciation and transition rate than abundant taxa, serving as a genetic reservoir and potential candidates to become abundance taxa, highlighting their crucial role in maintaining microbial diversity. These insights underscore the significant influence of rare taxa on ecosystem biodiversity and stability in alkaline lakes.

An integrated meta-omics approach reveals the different response mechanisms of two anammox bacteria towards fluoroquinolone antibiotics.

The emerging fluoroquinolone antibiotics (FQs) are highly influential in nitrogen removal from livestock wastewater. However, beyond the capability of nitrogen removal, little is known about the molecular mechanisms (e.g., shift of core metabolism and energy allocation) of different anaerobic ammonium-oxidizing bacteria (AnAOB) under continuous FQ stress. This study investigated the effects of ciprofloxacin, ofloxacin and their mixture at concentrations detected in livestock wastewater on two key anammox species in membrane bioreactors. It was found 20 µg/L FQs promoted nitrogen removal efficiency and community stability, and42-51 % of FQs were removed simultaneously. Integrated meta-omics analysis revealed varied gene expression patterns between the two dominant AnAOB, Candidatus Brocadia sapporoensis (B AnAOB) and Candidatus Kuenenia stuttgartiensis (K AnAOB). The nitrogen metabolic processes were bolstered in B AnAOB, while those involved in anammox pathway of K AnAOB were inhibited. This difference was tentatively attributed to the up-regulation of reactive oxygen species scavenger genes (ccp and dxf) and FQ resistance gene (qnrB72) in B AnAOB. Importantly, most enhanced core biosynthesis/metabolism of AnAOB and close cross-feeding with accompanying bacteria were also likely to contribute to their higher levels of biomass yield and metabolism activity under FQ stress. This finding suggests that B AnAOB has the advantage of higher nitrogen metabolism capacity over K AnAOB in livestock wastewater containing FQs, which is helpful for efficient and stable nitrogen removal by the functional anammox species.

BASALT refines binning from metagenomic data and increases resolution of genome-resolved metagenomic analysis.

Metagenomic binning is an essential technique for genome-resolved characterization of uncultured microorganisms in various ecosystems but hampered by the low efficiency of binning tools in adequately recovering metagenome-assembled genomes (MAGs). Here, we introduce BASALT (Binning Across a Series of Assemblies Toolkit) for binning and refinement of short- and long-read sequencing data. BASALT employs multiple binners with multiple thresholds to produce initial bins, then utilizes neural networks to identify core sequences to remove redundant bins and refine non-redundant bins. Using the same assemblies generated from Critical Assessment of Metagenome Interpretation (CAMI) datasets, BASALT produces up to twice as many MAGs as VAMB, DASTool, or metaWRAP. Processing assemblies from a lake sediment dataset, BASALT produces ~30% more MAGs than metaWRAP, including 21 unique class-level prokaryotic lineages. Functional annotations reveal that BASALT can retrieve 47.6% more non-redundant opening-reading frames than metaWRAP. These results highlight the robust handling of metagenomic sequencing data of BASALT.