A multi-Omic resource for exploring microbial eukaryotes in the meromictic freshwater Lake Pavin

Area of Science:

• Aquatic Microbiology
• Eukaryotic Genomics
• Environmental Metagenomics

Background:

• High-throughput sequencing advanced aquatic ecosystem studies, but freshwater microbial eukaryotes remain under-researched due to genomic complexity and diversity.
• Limited genomic and physiological data exists for freshwater microbial eukaryotes, hindering a full understanding of their ecological roles.

Purpose of the Study:

• To generate a comprehensive, eukaryote-centered multi-omic dataset from Lake Pavin's microbial eukaryotes.
• To provide a resource for exploring the functional diversity and spatio-temporal dynamics of these organisms.
• To include under-represented taxa in public databases.

Main Methods:

• Targeted-metagenomic sequencing (18S rDNA V4 and V9), whole-genome shotgun metagenomics, and metatranscriptomics were employed.
• Single amplified genomes (SAGs) were generated for deeper genomic analysis.
• Sampling targeted two microbial eukaryote size classes (0.65–10 µm and 10–50 µm) from oxic and anoxic lake layers over four distinct time points in 2018, including day and night.

Main Results:

• A dataset comprising 106 eukaryotic metagenome-assembled genomes (MAGs), over 9 million unigenes, and 11 SAGs was generated.
• The dataset includes several taxa previously under-represented in public databases, such as Perkinsea, Chytridiomycota, and Cryptista.
• The multi-omic data captures spatio-temporal dynamics and functional potential of microbial eukaryotes in a stratified freshwater lake.

Conclusions:

• The generated multi-omic dataset is a valuable resource for advancing the study of freshwater microbial eukaryotes.
• This work enhances our understanding of the diversity, function, and ecological dynamics of microbial eukaryotes in aquatic ecosystems.
• The inclusion of under-represented taxa provides a foundation for future research into their specific roles and evolution.