Image_1_Predicted Bacterial Interactions Affect in Vivo Microbial Colonization Dynamics in Nematostella.pdf

<p>The maintenance and resilience of host-associated microbiota during development is a fundamental process influencing the fitness of many organisms. Several host properties were identified as influencing factors on bacterial colonization, including the innate immune system, mucus composition, and diet. In contrast, the importance of bacteria–bacteria interactions on host colonization is less understood. Here, we use bacterial abundance data of the marine model organism Nematostella vectensis to reconstruct potential bacteria–bacteria interactions through co-occurrence networks. The analysis indicates that bacteria–bacteria interactions are dynamic during host colonization and change according to the host’s developmental stage. To assess the predictive power of inferred interactions, we tested bacterial isolates with predicted cooperative or competitive behavior for their ability to influence bacterial recolonization dynamics. Within 3 days of recolonization, all tested bacterial isolates affected bacterial community structure, while only competitive bacteria increased bacterial diversity. Only 1 week after recolonization, almost no differences in bacterial community structure could be observed between control and treatments. These results show that predicted competitive bacteria can influence community structure for a short period of time, verifying the in silico predictions. However, within 1 week, the effects of the bacterial isolates are neutralized, indicating a high degree of resilience of the bacterial community.</p

Table_1_Predicted Bacterial Interactions Affect in Vivo Microbial Colonization Dynamics in Nematostella.XLSX

<p>The maintenance and resilience of host-associated microbiota during development is a fundamental process influencing the fitness of many organisms. Several host properties were identified as influencing factors on bacterial colonization, including the innate immune system, mucus composition, and diet. In contrast, the importance of bacteria–bacteria interactions on host colonization is less understood. Here, we use bacterial abundance data of the marine model organism Nematostella vectensis to reconstruct potential bacteria–bacteria interactions through co-occurrence networks. The analysis indicates that bacteria–bacteria interactions are dynamic during host colonization and change according to the host’s developmental stage. To assess the predictive power of inferred interactions, we tested bacterial isolates with predicted cooperative or competitive behavior for their ability to influence bacterial recolonization dynamics. Within 3 days of recolonization, all tested bacterial isolates affected bacterial community structure, while only competitive bacteria increased bacterial diversity. Only 1 week after recolonization, almost no differences in bacterial community structure could be observed between control and treatments. These results show that predicted competitive bacteria can influence community structure for a short period of time, verifying the in silico predictions. However, within 1 week, the effects of the bacterial isolates are neutralized, indicating a high degree of resilience of the bacterial community.</p

Image_2_Predicted Bacterial Interactions Affect in Vivo Microbial Colonization Dynamics in Nematostella.PDF

<p>The maintenance and resilience of host-associated microbiota during development is a fundamental process influencing the fitness of many organisms. Several host properties were identified as influencing factors on bacterial colonization, including the innate immune system, mucus composition, and diet. In contrast, the importance of bacteria–bacteria interactions on host colonization is less understood. Here, we use bacterial abundance data of the marine model organism Nematostella vectensis to reconstruct potential bacteria–bacteria interactions through co-occurrence networks. The analysis indicates that bacteria–bacteria interactions are dynamic during host colonization and change according to the host’s developmental stage. To assess the predictive power of inferred interactions, we tested bacterial isolates with predicted cooperative or competitive behavior for their ability to influence bacterial recolonization dynamics. Within 3 days of recolonization, all tested bacterial isolates affected bacterial community structure, while only competitive bacteria increased bacterial diversity. Only 1 week after recolonization, almost no differences in bacterial community structure could be observed between control and treatments. These results show that predicted competitive bacteria can influence community structure for a short period of time, verifying the in silico predictions. However, within 1 week, the effects of the bacterial isolates are neutralized, indicating a high degree of resilience of the bacterial community.</p

Image_3_Predicted Bacterial Interactions Affect in Vivo Microbial Colonization Dynamics in Nematostella.PDF

<p>The maintenance and resilience of host-associated microbiota during development is a fundamental process influencing the fitness of many organisms. Several host properties were identified as influencing factors on bacterial colonization, including the innate immune system, mucus composition, and diet. In contrast, the importance of bacteria–bacteria interactions on host colonization is less understood. Here, we use bacterial abundance data of the marine model organism Nematostella vectensis to reconstruct potential bacteria–bacteria interactions through co-occurrence networks. The analysis indicates that bacteria–bacteria interactions are dynamic during host colonization and change according to the host’s developmental stage. To assess the predictive power of inferred interactions, we tested bacterial isolates with predicted cooperative or competitive behavior for their ability to influence bacterial recolonization dynamics. Within 3 days of recolonization, all tested bacterial isolates affected bacterial community structure, while only competitive bacteria increased bacterial diversity. Only 1 week after recolonization, almost no differences in bacterial community structure could be observed between control and treatments. These results show that predicted competitive bacteria can influence community structure for a short period of time, verifying the in silico predictions. However, within 1 week, the effects of the bacterial isolates are neutralized, indicating a high degree of resilience of the bacterial community.</p

Image_4_Predicted Bacterial Interactions Affect in Vivo Microbial Colonization Dynamics in Nematostella.PDF

<p>The maintenance and resilience of host-associated microbiota during development is a fundamental process influencing the fitness of many organisms. Several host properties were identified as influencing factors on bacterial colonization, including the innate immune system, mucus composition, and diet. In contrast, the importance of bacteria–bacteria interactions on host colonization is less understood. Here, we use bacterial abundance data of the marine model organism Nematostella vectensis to reconstruct potential bacteria–bacteria interactions through co-occurrence networks. The analysis indicates that bacteria–bacteria interactions are dynamic during host colonization and change according to the host’s developmental stage. To assess the predictive power of inferred interactions, we tested bacterial isolates with predicted cooperative or competitive behavior for their ability to influence bacterial recolonization dynamics. Within 3 days of recolonization, all tested bacterial isolates affected bacterial community structure, while only competitive bacteria increased bacterial diversity. Only 1 week after recolonization, almost no differences in bacterial community structure could be observed between control and treatments. These results show that predicted competitive bacteria can influence community structure for a short period of time, verifying the in silico predictions. However, within 1 week, the effects of the bacterial isolates are neutralized, indicating a high degree of resilience of the bacterial community.</p

Subcellular localization of RbdB GFP and co-localization with DrnB.

<p>AX2 cells were transformed with the integrating plasmid pDneo2a RbdB GFP and subcellular localization was analyzed by fluorescence microscopy. A: Living cells were analyzed in low fluorescence axenic medium showing a diffuse distribution of the fusion proteins in the nucleoplasm and distinct foci at the periphery of the nuclei. Scale bar represents 5 μm. B: To better localize the subnuclear foci, cells were fixed with methanol and analyzed by an OptiGrid microscope (Leica DM 5500). Genomic DNA was stained by DAPI (red). The nucleoli showed no or only a very weak staining. Merging GFP (green) and DAPI (red) signals indicated that RbdB-GFP foci were enriched adjacent to areas with weak or no DAPI staining. Scale bar represents 2.5 μm. C: Co-localization of GFP DrnB and RbdB mRFP in nucleoli associated foci was monitored by fluorescence microscopy using methanol fixed cells. Shown is a single nucleus. Fusion proteins were expressed from extrachromosomally replicating plasmids. Scale bar represents 2.5 μm.</p

Subcellular localization of the Serrate ortholog (SrtA) in <i>D</i>. <i>discoideum</i>.

<p>A: Protein structure of the <i>D</i>. <i>discoideum</i> Serrate ortholog SrtA. RRM: RNA recognition motif domain, Arsenite-R_2: Arsenite-resistance protein 2, C2H2: Zinc finger domain [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006057#pgen.1006057.ref043" target="_blank">43</a>]. B: AX2 cells expressing Srt GFP fusion proteins were fixed with methanol and analyzed by immunofluorescence. DNA was stained by DAPI (red). GFP (green) and DAPI signals were merged. C: ddi-miR-1176 miRNA processing was analyzed in AX2 and in srtA [RNAi 1] and srtA [RNAi 2] knockdown strains. 12 μg total RNA were loaded per lane. Mature ddi-miR-1176 was detected as described in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006057#pgen.1006057.g003" target="_blank">Fig 3A</a>. To show equal loading, the membrane was rehybridized with a probe directed against the snoRNA DdR6. D: The expression level of ddi-miR-1176 was quantified relative to DdR6 and normalized to the AX2 wt. Error bars: mean with SD, paired t-test: ddi-miR-1176: AX2/srtA [RNAi] p < 0, 0001 (***).</p

Schematic representation of dsRBD containing proteins in <i>D</i>. <i>discoideum</i>.

<p><b>Dhx9</b> (1472 aa: dsRBD (1) [365–440], dsRBD (2) [532–607], DEXDc [715–903], HELICc [963–1069], HA2 [1132–1243]), <b>HelF</b> (837 aa: dsRBD [2–76], DEXDc [228–431], HELICc [608–687]), <b>RbdA</b> (297 aa: dsRBD [4–70]), <b>RbdB</b> (733 aa: dsRBD [9–75]), P-rich [510–584]. Numbers in brackets indicate the position of protein domains in the amino acid (aa) sequence predicted by SMART [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006057#pgen.1006057.ref045" target="_blank">45</a>]. DsRBD (<i>double stranded</i> RNA <i>binding domain</i>), DEXDc (<i>Dead-like Helicases superfamily domain</i>), HA2 (<i>Helicase associated domain</i> 2), P-rich site (Proline rich site). Domains are drawn to scale.</p

Mature miRNAs in rbdA and rbdB- strains.

<p>Expression levels of ddi-miR-1176 (A, left) and ddi-miR-1177 (B, left) were determined by Northern Blot analysis in the indicated knockout mutants. 12 μg total RNA were loaded per lane. Mature miRNAs were detected by ³²P labeled oligonucleotides #2601 (α ddi-miR-1176) and #2602 (α ddi-miR-1177). To show equal loading, the membranes were rehybridized with a ³²P labeled probe (#2654) against the snoRNA DdR6. The expression levels of ddi-miR-1176 (A, right) and ddi-miR-1177 (B, right) were quantified based on independent Northern Blots. Quantification of miRNA expression is given relative to DdR6 and was normalized to the AX2 wt (= 1). Error bars: mean with SD, paired t-test: ddi-miR-1176: AX2/rbdB- p < 0,0001 (***), AX2/drnB- p < 0,0001 (***), AX2/agnA- p < 0,0001 (***). Ddi-miR-1177: AX2/rbdB- p < 0,0001 (***), AX2/drnB- p < 0,0001 (***), AX2/agnA- p = 0,0026 (**). Number of n is given in the graph. For each mutant strain, at least two biological replicates were analyzed.</p

Co-IP of RbdB Δ 504–612 GFP and DrnB 3xHA.

<p>RbdB (Δ 504–612) GFP and DrnB 3xHA were expressed in the AX2 wt background. Co-immunoprecipitation of DrnB 3xHA by GFP tagged RbdB (Δ 504–612) was performed. Different samples (IN = input, Pre = preclear, SN = supernatant, E = elution) were analyzed by Western Blots. The fusion proteins were detected by specific α-GFP and α-3xHA antibodies. Numbers indicate the percent of input that was loaded on the SDS-gel. Control IPs were performed with strains expressing the nuclear localized HcpA GFP + DrnB 3xHA or RbdB (Δ504–612) GFP + HcpA 3xHA (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006057#pgen.1006057.s003" target="_blank">S3 Fig</a>).</p