Abstract
We propose a new strategy to identify and visualize bacterial consortia by conducting replicated culturing of environmental samples coupled with high-throughput sequencing and multidimensional scaling analysis, followed by identification of bacteria-bacteria correlations and interactions. We conducted a proof of concept assay with pine-tree resin-based media in ten replicates, which allowed detecting and visualizing dynamical bacterial associations in the form of statistically significant and yet biologically relevant bacterial consortia.
Highlights
We propose a new strategy to identify and visualize bacterial consortia by conducting replicated culturing of environmental samples coupled with high-throughput sequencing and multidimensional scaling analysis, followed by identification of bacteria-bacteria correlations and interactions
There is a growing interest on disentangling the complexity of microbial interactions in order to both optimize reactions performed by natural consortia and to pave the way towards the development of synthetic consortia with improved biotechnological properties[1,2]
The flexibility of the bacterial interactions, the lack of replicated assays and/or biases associated with different DNA isolation technologies and taxonomic bioinformatics tools hamper the clear identification of bacterial consortia
Summary
16S profiles-based monitoring of bacterial populations throughout the experiment. Ten replicated cultures of resin samples in resin-rich semisynthetic medium were set up and independently subcultured (nine times) during 24 days, as described in the Methods section. The taxonomic profiles displayed statistically significant variations within time steps (p-value = 0.01, see Methods). Richness index was virtually constant throughout the whole experiment; whereas Shannon and Simpson indexes fluctuated in time: they increased between subculturing steps 1 and 3, dropped, and increased again until subculturing step 7, showing a slightly decreasing trend at the end of the experiment (Supplementary Fig. 1). These fluctuations coincided with changes in the number of days between subculturing steps, suggesting, again, the influence of this factor in community composition. ID OTU_rc_1 OTU_rc_2 OTU_rc_3 OTU_rc_4 OTU_rc_5 OTU_rc_6 OTU_rc_7 OTU_rc_8 OTU_rc_9 OTU_rc_10 OTU_rc_11 OTU_rc_12 OTU_rc_13 OTU_rc_14 OTU_rc_15 OTU_rc_16 OTU_rc_17 OTU_rc_18 OTU_rc_19 OTU_rc_20 OTU_rc_21 OTU_rc_22 OTU_rc_23 OTU_rc_24 OTU_rc_25 OTU_rc_26
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