Landfills contribute substantially to global methane emissions, a significant concern given the projected increase in global solid waste production. In high-income countries, landfills can account for up to 20% of net methane emissions. Predicting methane cycling in landfills is challenging due to the spatially and geochemically heterogeneous nature of landfilled waste, with compositions changing over decades. A five-phase conceptual model describes the major biogeochemical transitions in sanitary landfills: aerobic phase, anaerobic acid phase, rapid methanogenesis, slow methanogenesis, and a final, poorly understood stabilization phase. Microbial metabolisms drive these transitions. Previous studies using 16S rRNA amplicon sequencing have shown that age, nutrient concentrations, physicochemical parameters, and contaminant concentrations influence microbial community structure. While these studies provided insights, they were limited by their focus on 16S rRNA gene sequencing and did not capture the full metabolic diversity involved in methane cycling. This includes anaerobic methane oxidation, a process rarely considered despite its prevalence in anoxic landfill environments. The application of metagenomics offers a more comprehensive approach to understand methane cycling in landfills.

Several studies have investigated the microbial communities in landfills using 16S rRNA amplicon sequencing. These studies revealed that the age of the waste and environmental conditions significantly shape the microbial communities in landfill leachate. Smaller-scale studies indicated that various factors, including age, nutrient concentrations, physicochemical parameters (temperature and pH), and contaminant concentrations, influence microbial community structure. The succession of microbial taxa during waste degradation has been characterized in controlled settings. Studies using 16S rRNA primers specific to methanogens and methanotrophs, as well as sequencing of the *mcrA* gene (for methanogenesis) and *pmoA* and *mmox* genes (for methanotrophy), have expanded understanding of methane-cycling guilds. However, these approaches are limited when applied to novel taxa and often overlook diverse metabolisms, including anaerobic methane oxidation. The recent application of metagenomics to landfills has provided valuable insights into physiological pathways, including cellulose metabolism and plastic biodegradation, as well as addressing human health concerns related to landfills. Genome-resolved metagenomics can identify factors constraining the distribution of methanogens and the range of methanotrophic lifestyles. Metagenomic surveys examining major guilds and physiological pathways across landfill lifecycles are still limited.

This study used metagenomic sequencing to analyze leachate samples from a sanitary landfill spanning its five lifecycle phases. The landfill comprised six cells (A-F), with cells A-C representing older waste (39, 37, and 31 years old respectively) and cells D-F representing newer waste (26, 20, and 5 years old). Leachate samples were collected from each cell, filtered, and DNA was extracted using the PowerSoil DNA Isolation Kit. Shotgun metagenome sequencing was performed using an Illumina HiSeq platform, generating 22.9 to 35.1 Gbp of total sequencing data. Reads were quality trimmed, assembled using SPAdes, and scaffolds were binned using CONCOCT, MaxBin2, and MetaBAT2. A total of 1881 metagenome-assembled genomes (MAGs) with >70% completion and <10% contamination were retained for analysis. Taxonomy was assigned using GTDB-tk and MAGs were annotated using DRAM. Relative abundance was calculated based on mean coverage. Beta diversity analysis (NMDS) was used to assess community composition. Putative methanogens were identified based on the presence of *mcrA* gene or a complete hydrogenotrophic methanogenesis pathway, while putative methanotrophs were identified based on the presence of *pmoA* and/or *mmox* genes. Phylogenetic analyses using GToTree were conducted to assess the distribution of methane oxidation marker genes and to investigate families with no known methanotrophy capacity.

The study revealed that newer landfill cells supported more diverse microbial communities with similar compositions compared to older cells. Older cells (A, B, C) were dominated by Proteobacteria (34-46%), while newer cells (D2, E, F1, F2) had <5% Proteobacteria and higher abundance of Bacteroidota (15-39%). Older cells (A, B, C) contained primarily autotrophic organisms (Gallionellaceae, Sulfurimonadaceae) with versatile redox metabolisms, indicating oxygen intrusion. Cell C was uniquely dominated by Arcobacteraceae (44%), potentially due to their adaptability and capacity for nitrate reduction. Newer cells (D2, E, F1, F2) contained a higher abundance of anaerobic fermenters (Dysgonomonadaceae, Cloacimonadaceae). 74 MAGs were identified as putative methanogens, classified into three phyla and ten families. Methanogen abundance and diversity were higher in newer landfill cells (D2, F1, F2), aligning with higher methane production. Older cells (A, B, C) contained mostly acetoclastic methanogens. Cell D showed contrasting trends between D1 (six methanogen MAGs, similar to A, B, C) and D2 (17 methanogen MAGs, broader metabolic capabilities), suggesting heterogeneous geochemistry. Cell E displayed low methanogen abundance and diversity despite high methane production, potentially due to bicarbonate fluctuations. Cell F, classified as transitioning from phase 2 to phase 3, showed consistent methanogen abundance and diversity between locations F1 and F2, with the presence of varied substrates supporting methanogenesis. The study detected 11 methanogen families, among the highest diversity reported. Methanosarcinaceae were the most frequently detected family across studies. 31 MAGs were identified as putative aerobic methanotrophs, and 2 ANMEs were identified as putative anaerobic methanotrophs. Methanotrophs displayed low abundance except in cell B. The presence of Methylomirabilaceae and Methanoperedenaceae suggests anaerobic methane oxidation, although further research is needed to confirm the methane oxidation capacity in the three families containing novel putative methanotrophs. The widespread adaptations in central redox metabolisms suggest that methanotrophy, even via oxygen-requiring pathways, is important to consider in anoxic landfill habitats.

This study highlights the importance of integrating microbial community analysis with geochemical monitoring to better understand and predict methane cycling in landfills. The findings challenge the current understanding of methane oxidation in landfills, revealing greater diversity in anaerobic methane oxidation pathways and potential methanotrophic lineages than previously assumed. The observed metabolic versatility in methane cycling guilds in response to changing geochemical conditions and oxygen availability emphasizes the need to consider a wider range of metabolisms in predictive models. The study's findings have implications for improving biogeochemical models to manage methane emissions from landfills, potentially enhancing the effectiveness of waste management practices such as waste diversion programs and substrate amendments for optimized methane production and recovery.

This study provides a detailed, time-resolved view of microbial methane cycling in a landfill. It reveals unexpectedly high diversity in anaerobic methane oxidation pathways and the presence of potential methanotrophic lineages in families not previously associated with methanotrophy. This underscores the need for more comprehensive models that incorporate the full diversity and metabolic flexibility of landfill microbial communities to accurately predict and mitigate methane emissions. Future research should focus on experimentally validating the methane oxidation capacity of novel lineages, investigating the role of syntrophic interactions, and developing targeted biostimulation strategies to enhance anaerobic methane oxidation in landfills.

The study focused on a single landfill, limiting the generalizability of the findings. The analysis relied on metagenomic data, which may not fully reflect the actual metabolic activity of the microbial community. Temporal resolution was limited by the availability of historical data and sampling frequency. The study didn't explicitly investigate syntrophic interactions, although evidence for potential syntrophic relationships is discussed.