Decoding populations in the ocean microbiome

Abstract

Understanding the characteristics and structure of populations is fundamental to comprehending ecosystem processes and evolutionary adaptations. While the study of animal and plant populations has spanned a few centuries, microbial populations have been under scientific scrutiny for a considerably shorter period. In the ocean, analyzing the genetic composition of microbial populations and their adaptations to multiple niches can yield important insights into ecosystem function and the microbiome's response to global change. However, microbial populations have remained elusive to the scientific community due to the challenges associated with isolating microorganisms in the laboratory. Today, advancements in large-scale metagenomics and metatranscriptomics facilitate the investigation of populations from many uncultured microbial species directly from their habitats. The knowledge acquired thus far reveals substantial genetic diversity among various microbial species, showcasing distinct patterns of population differentiation and adaptations, and highlighting the significant role of selection in structuring populations. In the coming years, population genomics is expected to significantly increase our understanding of the architecture and functioning of the ocean microbiome, providing insights into its vulnerability or resilience in the face of ongoing global change. Video Abstract.

Keywords: Metagenomics; Metatranscriptomics; Microbes; Ocean; Populations.

Fig. 1

Microbial genomes that recombine (recombinogenic) and, therefore, belong to the same population or species would share longer identical regions than non-recombinogenic counterparts. Modified from Arevalo et al. [76]

Fig. 2

Single cell genomics [102]. In a nutshell, this approach starts with isolating single microbial cells, typically using fluorescence activated cell sorting (FACS) or microfluidics. Then, cells are lysed, and their genomic DNA is amplified, generating single amplified genomes (SAGs). SAGs are subsequently shotgun sequenced, and the produced reads (DNA sequences) are assembled and annotated. Those SAGs from the same species can then be used for population genomics analyses (as in Kashtan et al. [53, 98]). Furthermore, SAGs can be used as genomic templates in metagenome-based population genomics analyses [103] (Fig. 3)

Fig. 3

Metagenome-based population genomics [103]. Metagenome-assembled genomes (MAGs), Single Amplified Genomes (SAGs; Fig. 2), or genomes from isolates are generated after sampling or retrieved from collections. In parallel, marine metagenomes (MetaG) are produced from community DNA or retrieved from databases. Subsequently, unassembled metagenomes (reads) are mapped against MAGs, SAGs, or sequenced isolates. After mapping, the abundance and the horizontal and vertical coverages of each MAG, SAG, or isolate are calculated, and Single Nucleotide Variants (SNVs) are called. Based on the SNVs, population-level diversity, and structure (based on the Fst index) can be assessed. The trajectory of the TARA Oceans sampling campaign is shown as an example. See an application of this approach in Fig. 4

Fig. 4

Accessing the population-level dimension of diversity in marine microbes using metagenomics. The figure aims to provide a simple example of the additional information on population structure that the metagenome-based population genomics approach can produce compared to 16S rRNA surveys. Here, I use the MAG G4.480 (uncultured Flavobacteriales, ~ 95% completeness, and < 10% contamination) that we retrieved from the Mediterranean Sea (LTER Blanes Bay Microbial Observatory; http://bbmo.icm.csic.es/). From this MAG, a fragment of the 16S rRNA gene (770 base pairs) was extracted and then used to estimate the MAG abundance in the global ocean and the Mediterranean Sea using the Ocean Barcode Atlas (OBA) [144] (https://oba.mio.osupytheas.fr/ocean-atlas/); results are shown in A. Only two 16S mTag [145] references from the OBA with > 99% sequence similarity with MAG G4.480 were considered (references AACY020490277.719.2228 and EF572435.1.1502; both Flavobacteriales, Flavobacteriaceae, NS5 marine group). Furthermore, only surface samples originating from two size fractions (0.2–1.6 and 0.2–3.0 μm) from the TARA Oceans cruise were included. In sum, in A, we observe the distribution of the MAG G4.480 as one single taxonomic entity. In B, the diversity within this entity is explored using metagenome-based population genomics (Fig. 3), and we notice that additional patterns emerge. In the upper section of B, the Fst values (measuring population differentiation) among the investigated stations were clustered, and different clusters, which may correspond to populations, were colored (Fst ~ 0.2 was used to delineate clusters). Note that some clusters correspond to geographic regions (B, lower section). For example, the clusters in the Mediterranean Sea, Red Sea, and Indian Ocean suggest that they could represent geographically delineated populations. These patterns are missed by the 16S rRNA gene (A). The abundance of the Mediterranean MAG G4.480 across the global ocean and the Mediterranean Sea based on metagenomic read recruitment is shown in the lower section of B. MAG abundances are indicated in RPKG (Reads Per Kilobase of MAG and Gigabase of metagenomic data). To obtain the Fst values and the abundances of the MAG G4.480 (B), we followed the procedure indicated in Fig. 3, which is partially implemented in POGENOM [104]. Only surface metagenomes from TARA Oceans with enough coverage (horizontal and vertical) of MAG G4.480 were used in downstream analyses, which explains the different numbers of stations included in A and B