Understanding the Mechanisms Behind the Response to Environmental Perturbation in Microbial Mats: A Metagenomic-Network Based Approach

Abstract

To date, it remains unclear how anthropogenic perturbations influence the dynamics of microbial communities, what general patterns arise in response to disturbance, and whether it is possible to predict them. Here, we suggest the use of microbial mats as a model of study to reveal patterns that can illuminate the ecological processes underlying microbial dynamics in response to stress. We traced the responses to anthropogenic perturbation caused by water depletion in microbial mats from Cuatro Cienegas Basin (CCB), Mexico, by using a time-series spatially resolved analysis in a novel combination of three computational approaches. First, we implemented MEBS (Multi-genomic Entropy-Based Score) to evaluate the dynamics of major biogeochemical cycles across spatio-temporal scales with a single informative value. Second, we used robust Time Series-Ecological Networks (TS-ENs) to evaluate the total percentage of interactions at different taxonomic levels. Lastly, we utilized network motifs to characterize specific interaction patterns. Our results indicate that microbial mats from CCB contain an enormous taxonomic diversity with at least 100 phyla, mainly represented by members of the rare biosphere (RB). Statistical ecological analyses point out a clear involvement of anaerobic guilds related to sulfur and methane cycles during wet versus dry conditions, where we find an increase in fungi, photosynthetic, and halotolerant taxa. TS-ENs indicate that in wet conditions, there was an equilibrium between cooperation and competition (positive and negative relationships, respectively), while under dry conditions there is an over-representation of negative relationships. Furthermore, most of the keystone taxa of the TS-ENs at family level are members of the RB and the microbial mat core highlighting their crucial role within the community. Our results indicate that microbial mats are more robust to perturbation due to redundant functions that are likely shared among community members in the highly connected TS-ENs with density values close to one (≈0.9). Finally, we provide evidence that suggests that a large taxonomic diversity where all community members interact with each other (low modularity), the presence of permanent of low-abundant taxa, and an increase in competition can be potential buffers against environmental disturbance in microbial mats.

Keywords: MEBS; environmental perturbation; microbial mats; network motifs; rare biosphere; time series ecological networks.

FIGURE 1

Sampling site and microbial mats samples description. (A) The Lagunita pond is located at Cuatro Cienegas Basin (CCB) in state of Coahuila, Mexico Map. Google Maps. Google, 15 Jun 2018. Web, 15 Jun 2018. Here is shown two contrasting conditions during the study period (dry and wet or “water” conditions). (B) Specific characteristics of the Lagunita pond are showed during the sampling period ranging from Autumn 2012 (time 1), to Spring 2014 (time 4). (C) Microbial mats were sampled seasonally from three geographically separated sites (A–C) during two-years resulting in a total of 12 samples (3 sites, 4 time points).

FIGURE 2

Taxonomic diversity within microbial mats from Lagunita pond at genera level. (A) Boxplot distribution showing the relative abundance of the total genera found within microbial mats from Churince where each boxplot represents the distribution of each genera across 12 samples. The horizontal red line indicates the low abundant taxa or RB <0.01 of relative abundance. The inner figure (A1) indicates the distribution of the six most abundant genera (>0.01 relative abundance). The second inner figure (A2) shows the rarefaction curves of the 12 microbial mats at genera level. (B) Stacked bar plots of 58 members of the microbial mat core whose abundance is >0.001 for comparative purposes, this plot shows how the actual composition of each site (A–C) change over time (four time points).

FIGURE 3

Venn diagram analysis showing the shared and unique taxa at genus level during contrasting water conditions (RB) rare biosphere, (U) unclassified.

FIGURE 4

Taxonomic statistical differences between dry and wet conditions. (A) Profile scatter plot of each site (A–C), x-axis (dry conditions) and y-axis (wet conditions) microbial mats with the difference in mean proportion among microbial mats within each site along with the associated confidence interval of this effect size (2th and 98th percentile). Points on each side of the gray dashed y = x line are enriched in one of the two samples, SD for proportion are shown as horizontal lines. A statistical hypothesis test is required to determine if the observed difference is large enough, to discount it being a sampling artifact safely, however, in dry conditions there is only one mat for each site therefore no p-values are indicated. (B) The error bar indicating all genera where Welch’s t-test with confidence interval method DP Welch’ inverted of 0.95 produces a p-value (<0.005). The difference in mean proportion between the microbial mats during dry and wet conditions are shown in blue and orange, respectively (RB, rare biosphere; C, microbial mat core).

FIGURE 5

Detrented correspondence analysis (DCA) according to taxonomic (A) and metabolic (B) composition of microbial mats explaining 80.2 and 78.2% of the variance among samples, respectively. These results are supported by PERMANOVA analysis.

FIGURE 6

Biogeochemical cycling within microbial mats across space and time using MEBS. (A) Dynamics of the main cycles within microbial mats samples during the two-year period of study by with a single value MEBS captured in bits. (B) Metabolic completeness in a color gradient, the more complete are red and the less shift to blue. Sulfur and methane cycle across 58 environments including those analyzed in this study and several environments such as hydrothermal vents, biofilms, microbial mats, stromatolites and soils. Samples from this study are named according to the site (A–C), and sampling season and year.

FIGURE 7

Network representation of the consensus networks displaying only to top 0.05% of total interactions. These interactions represent less than 50 from around 450 consensus families for three sites. In site (A) are displaying 61/122,841 interactions, from which 17/46,200 are positive and 44/76,641 negative. In site (B) 53/107548, interactions are shown, being 28/37, 170 positive and 25/70,378 negative. From microbial mats of site (C), the consensus network is composed from 60/121,056, from which 16/57,487 are positive and 40/63,569 negatives. The size of a circle (node) is proportional to the abundance of the family across the microbial mats from each site. The thickness of a connection (edge) is proportional to the strength of the interaction. Families are colored according to the Phylum. (D) Distribution of the percentage of positive and negative interactions in the consensus TS-ENs of each site.

FIGURE 8

Distribution of the 3-three-node subgraphs or network motifs across 48 microbial mat networks built across the taxonomic levels: Phylum (P), Class (C), Order (O), and Family (F). The top panel represents the motifs sorted by their ID identifier described in Mfinder. The motifs with specific ecological terminology are ID6, ID12, ID36, and ID38. The latter motif is also known as Feed Forward Loop (FFL) in regulatory networks. The ID98 represents Feedback Loop (FBL). To facilitate visual comparison, the abundance of each motif was normalized by its appearance across taxonomic levels. The relative abundance of each one indicates that a given motif is only found in that particular taxonomic lave (i.e., id 36 Site A in negative networks at class level). It is observed that particular motifs appear over-represented across the span of taxonomic levels when consensus networks are separated by type of interaction either positive or negative (i.e., Motif 108 in negative networks or motif 36 in positive ones). The color code in the bars indicate the scale from over representation (red) of a given motif in a given taxonomic level shifting to underrepresented (blue).