Depth-discrete metagenomics reveals the roles of microbes in biogeochemical cycling in the tropical freshwater Lake Tanganyika

Tran, Patricia Q.; Bachand, Samantha C; McIntyre, Peter B.; Kraemer, Benjamin M.; Vadeboncoeur, Yvonne; Kimirei, Ismael A.; Tamatamah, Rashid; McMahon, Katherine D.; Anantharaman, Karthik

doi:10.1101/834861

Cited by 5 publications

(9 citation statements)

References 111 publications

(177 reference statements)

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“…Trout Bog Lake is a small, dimictic humic lake in a rural area near Minocqua, WI, that is surrounded by sphagnum moss, which leaches large amounts of organic carbon. Lake Tanganyika is a meromictic lake with an anoxic monimolimnion in the East African Rift Valley and is the second largest lake in the world both by volume and depth, from which we recently reconstructed nearly 4,000 MAGs ( 34 ). Lake Mendota is a large, dimictic eutrophic lake in an urban setting in Madison, WI, with a sulfidic anoxic hypolimnion.…”

Section: Resultsmentioning

confidence: 99%

“…We collected depth-discrete samples along the northern basin of Lake Tanganyika at two stations (Kigoma and Mahale). DNA extraction, metagenomic sequencing of 24 samples from Lake Tanganyika, and subsequent genome assembly and refinement were performed as described by Tran et al ( 34 ). Briefly, 24 samples were passed through a 0.2-μm-pore-size fraction filter and used for shotgun metagenomic sequencing on the Illumina HiSeq 2500 platform at the Department of Energy Joint Genome Institute.…”

Section: Methodsmentioning

confidence: 99%

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Expanded Phylogenetic Diversity and Metabolic Flexibility of Mercury-Methylating Microorganisms

et al. 2020

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Methylmercury is a potent bioaccumulating neurotoxin that is produced by specific microorganisms that methylate inorganic mercury. Methylmercury production in diverse anaerobic bacteria and archaea was recently linked to the hgcAB genes. However, the full phylogenetic and metabolic diversity of mercury-methylating microorganisms has not been fully unraveled due to the limited number of cultured experimentally verified methylators and the limitations of primer-based molecular methods. Here, we describe the phylogenetic diversity and metabolic flexibility of putative mercury-methylating microorganisms by hgcAB identification in publicly available isolate genomes and metagenome-assembled genomes (MAGs) as well as novel freshwater MAGs. We demonstrate that putative mercury methylators are much more phylogenetically diverse than previously known and that hgcAB distribution among genomes is most likely due to several independent horizontal gene transfer events. The microorganisms we identified possess diverse metabolic capabilities spanning carbon fixation, sulfate reduction, nitrogen fixation, and metal resistance pathways. We identified 111 putative mercury methylators in a set of previously published permafrost metatranscriptomes and demonstrated that different methylating taxa may contribute to hgcA expression at different depths. Overall, we provide a framework for illuminating the microbial basis of mercury methylation using genome-resolved metagenomics and metatranscriptomics to identify putative methylators based upon hgcAB presence and describe their putative functions in the environment. IMPORTANCE Accurately assessing the production of bioaccumulative neurotoxic methylmercury by characterizing the phylogenetic diversity, metabolic functions, and activity of methylators in the environment is crucial for understanding constraints on the mercury cycle. Much of our understanding of methylmercury production is based on cultured anaerobic microorganisms within the Deltaproteobacteria, Firmicutes, and Euryarchaeota. Advances in next-generation sequencing technologies have enabled large-scale cultivation-independent surveys of diverse and poorly characterized microorganisms from numerous ecosystems. We used genome-resolved metagenomics and metatranscriptomics to highlight the vast phylogenetic and metabolic diversity of putative mercury methylators and their depth-discrete activities in thawing permafrost. This work underscores the importance of using genome-resolved metagenomics to survey specific putative methylating populations of a given mercury-impacted ecosystem.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Expanded Phylogenetic Diversity and Metabolic Flexibility of Mercury-Methylating Microorganisms

et al. 2020

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“…To benchmark and test the performance of METABOLIC in different environments, eight datasets of metagenomes and metagenomic reads from marine, terrestrial, and human environments were used. These included marine subsurface sediments [45] (Deep biosphere beneath Hydrate Ridge offshore Oregon), freshwater lake [46] (Lake Tanganyika, eastern Africa), colorectal cancer (CRC) patient gut [47], healthy human gut [47], deep-sea hydrothermal vent (Guaymas Basin, Gulf of California) [43], terrestrial subsurface sediments and water (Ri e, CO, USA) [2], meadow soils [48] (Angelo Coastal Range Reserve, CA, USA), and advanced water treatment facility [49] (Groundwater Replenishment System, Orange County, CA, USA). Default settings were used for running METABOLIC-C.…”

Section: Test Of Software Performance For Different Environmentsmentioning

confidence: 99%

“…To compare the metabolic pro le of two environments at the community scale, MN-score was used as the benchmarker. Two sets of environment pairs were compared, including marine subsurface sediments [45] and terrestrial subsurface sediments and water [2], and freshwater lake [46] and deep-sea hydrothermal vent [43]. To demonstrate differences between these environments to speci c biogeochemical processes, we focused on the biogeochemical cycling of sulfur.…”

Section: Comparison Of Community-scale Metabolismmentioning

confidence: 99%

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METABOLIC: High-throughput Profiling of Microbial Genomes for Functional Traits, Biogeochemistry, and Community-scale Metabolic Networks

Zhou

Tran

Breister

et al. 2020

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Background: Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent, however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and contributions to biogeochemical cycling. Results: We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, identification of metabolism markers, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the community, potential microbial metabolic handoffs and metabolite exchange, and calculation of microbial community contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or from single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, and community-scale metabolic networks using a newly defined metric ‘MN-score’ (metabolic network score). METABOLIC takes ~3 hours with 40 CPU threads to process ~100 genomes and metagenomic reads within which the most compute-demanding part of hmmsearch takes ~45 mins, while it takes ~5 hours to complete hmmsearch for ~3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut.Conclusion: METABOLIC enables consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available at https://github.com/AnantharamanLab/METABOLIC under GPLv3.

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Spatio-temporal variations in physicochemical water quality parameters of Lake Bunyonyi, Southwestern Uganda

et al. 2021

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The current study was carried out to examine the spatial and temporal variations of physicochemical water quality parameters of Lake Bunyonyi. The observations were made on the surface water of Lake Bunyonyi for 1 year to determine the water quality. The basic 12 variables used to determine the quality of water were measured monthly at nine stations. Water temperature, dissolved oxygen (DO), turbidity, electric conductivity (EC), pH and Secchi depth (SD) were measured in the field, while parameters like total nitrogen (TN), total phosphorus (TP), nitrite-nitrogen (NO2-N), nitrate-nitrogen (NO3-N), soluble reactive phosphorus (SRP) were determined following APHA 2017 standard guidelines for physicochemical analysis. Taking into account standard guidelines for drinking water by the Uganda National Bureau of Standards (UNBS) and the World Health Organization (WHO), the water quality index (WQI) was used to determine the water quality. Temperature, DO, pH, turbidity and EC did not differ significantly among the study stations (p > 0.05) but showed significant temporal variations among the study months (p < 0.05). Likewise, TN, TP, NO2-N, NO3-N and SRP did not differ significantly among the study stations (p > 0.05) but showed significant temporal variations among the study months (p < 0.05). The WQI values ranged from 28.36 to 49 across and from 28.2 to 56.2 between study months with an overall mean value of 36.9. The measured water quality variables did not exceed the UNBS and WHO standards for drinking water in all months and at all stations. According to these values, the water quality of Lake Bunyonyi generally belongs to the ‘good’ class in terms of drinking water quality based on the WQI classification. The study findings are fundamentally important for policy makers in setting guidelines for effective lake management.

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Depth-discrete metagenomics reveals the roles of microbes in biogeochemical cycling in the tropical freshwater Lake Tanganyika

Cited by 5 publications

References 111 publications

Expanded Phylogenetic Diversity and Metabolic Flexibility of Mercury-Methylating Microorganisms

Expanded Phylogenetic Diversity and Metabolic Flexibility of Mercury-Methylating Microorganisms

METABOLIC: High-throughput Profiling of Microbial Genomes for Functional Traits, Biogeochemistry, and Community-scale Metabolic Networks

Spatio-temporal variations in physicochemical water quality parameters of Lake Bunyonyi, Southwestern Uganda

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