Individual Physiological Adaptations Enable Selected Bacterial Taxa To Prevail during Long-Term Incubations

Abstract

Enclosure experiments are frequently used to investigate the impact of changing environmental conditions on microbial assemblages. Yet, how the incubation itself challenges complex bacterial communities is thus far unknown. In this study, metaproteomic profiling, 16S rRNA gene analyses, and cell counts were combined to evaluate bacterial communities derived from marine, mesohaline, and oligohaline conditions after long-term batch incubations. Early in the experiment, the three bacterial communities were highly diverse and differed significantly in their compositions. Manipulation of the enclosures with terrigenous dissolved organic carbon resulted in notable differences compared to the control enclosures at this early phase of the experiment. However, after 55 days, bacterial communities in the manipulated and the control enclosures under marine and mesohaline conditions were all dominated by gammaproteobacterium Spongiibacter In the oligohaline enclosures, actinobacterial cluster I of the hgc group (hgc-I) remained abundant in the late phase of the incubation. Metaproteome analyses suggested that the ability to use outer membrane-based internal energy stores, in addition to the previously described grazing resistance, may enable the gammaproteobacterium Spongiibacter to prevail in long-time incubations. Under oligohaline conditions, the utilization of external recalcitrant carbon appeared to be more important (hgc-I). Enclosure experiments with complex natural microbial communities are important tools to investigate the effects of manipulations. However, species-specific properties, such as individual carbon storage strategies, can cause manipulation-independent effects and need to be considered when interpreting results from enclosures.IMPORTANCE In microbial ecology, enclosure studies are often used to investigate the effect of single environmental factors on complex bacterial communities. However, in addition to the manipulation, unintended effects ("bottle effect") may occur due to the enclosure itself. In this study, we analyzed the bacterial communities that originated from three different salinities of the Baltic Sea, comparing their compositions and physiological activities both at the early stage and after 55 days of incubation. Our results suggested that internal carbon storage strategies impact the success of certain bacterial species, independent of the experimental manipulation. Thus, while enclosure experiments remain valid tools in environmental research, microbial community composition shifts must be critically followed. This investigation of the metaproteome during long-term batch enclosures expanded our current understanding of the so-called "bottle effect," which is well known to occur during enclosure experiments.

Keywords: Baltic Sea; Spongiibacter; bottle effect; enclosure; salinity.

FIG 1

Average cell counts (areas) and heterotrophic and autotrophic nanoflagellate (HNAN) counts of the control (A, C, and E) and terrigenous dissolved organic matter (tDOM) manipulation (B, D, and F) in the marine (salinity 32) (A, B), mesohaline (salinity 7) (C, D), and oligohaline (salinity 3) (E, F) enclosures. The sum of the high-nucleic acid (HNA; black) and low-nucleic acid (LNA; gray) cells is the total number of cells. HNAN counts for days 2, 7, and 12 are shown as bar graphs. The incubation phases (P1 to P3) are indicated at the top of each graph. Shown are the averages from n = 3.

FIG 2

Nonmetric multidimensional scaling (NMDS) plot (Bray-Curtis dissimilarity, stress 0.176) of the 16S rRNA gene-based changes in bacterial community composition (each setup has three parallels). The vectors indicate differences in salinity and the changes over time, and these parameters were added post hoc to the NMDS graph.

FIG 3

Changes in relative abundances of bacterial taxa (operational taxonomic units [OTUs]) in the enclosure experiments based on the 16S rRNA gene (only OTUs whose relative abundance in the sequences within a sample was >1% are included). Shown are the averages from n = 3.

FIG 4

Relative abundance of Clusters of Orthologous Groups (COGs) categories in all samples. (A) Total protein abundances, expressed as normalized spectral abundance factors (NSAF%), of all proteins assigned to the respective general COGs category. Missing from the 100% total are proteins of nonbacterial origin. (B) Distribution of functional COGs categories of bacterial proteins within the general category “metabolism” (set to 100%). Protein abundance values were obtained from the combined database search of two independent biological replicates (n = 2) per sample (see Materials and Methods for details).

FIG 5

Relative protein abundance in abundant genera/clades in the individual enclosure experiments. M1, marine; M2, mesohaline; M3, oligohaline. The total abundances of all proteins assigned to the respective genus, expressed as normalized spectral abundance factors (NSAF%), are displayed. Only the genera that belonged to the five most abundant genera in at least one of the samples are included. T2, middle phase; T6, late phase of the incubation; contr, control (untreated); tDOM, manipulation with terrigenous carbon; SCGC, single-amplification genomes. The cluster analysis is based on the UPGMA algorithm using the Bray-Curtis similarity index. Protein abundance values were obtained from the combined database search of two independent biological replicates (n = 2) per sample (see Materials and Methods for details).

FIG 6

Relative abundance of metabolic (COGs) functions within the general COGs category “metabolism.” The relative abundances of bacterial protein functions, expressed as normalized spectral abundance factors (NSAF%), are displayed for the most abundant genera. day 7, middle phase of the incubation; day 55, late phase of the incubation; DHG, dehydrogenase; tDOM, manipulation with terrigenous carbon; Can, Candidatus; uncl, uncultured. Protein abundance values were obtained from the combined database search of two independent biological replicates (n = 2) per sample (see Materials and Methods for details).