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. 2024 Apr 5;100(5):fiae048. doi: 10.1093/femsec/fiae048

Microbial community structures and bacteria-Cylindrospermopsis raciborskii interactions in Yilong Lake

Yuanpei Jin 1,#, Sanguo Ren 2,#, Yichi Wu 3, Xu Zhang 4, Zhengjun Chen 5, Bo Xie 6,✉
PMCID: PMC11057442  PMID: 38578661

Abstract

Cylindrospermopsis raciborskii-dominated harmful algae blooms have been reported globally in recent years. However, our understanding of the ecology of C. raciborskii in natural conditions is still poor. In this study, we collected the water samples from a C. raciborskii-blooming lake, Yilong Lake, in Yunnan province, China, and used both culture-dependent and culture-independent approaches to investigate their microbial communities and the interactions between C. raciborskii and the other bacteria. The composition and diversity of microbial communities were revealed with 16S rRNA gene high-throughput sequencing data analysis. Microbial co-occurrences analysis suggests C. raciborskii may have complex associations with other bacteria. Based on co-inoculation tests, we obtained 14 strains of bacterial strains from the water samples that exhibited either algicidal or promoting effects on a strain of C. raciborskii. Two bacterial isolates exhibited a consistent performance between co-occurrence analysis and experimental results. Effects of these bacteria-algae interspecies interactions on the bloom event are discussed. All these results may provide new insights into the C. raciborskii-dominated blooms and how its interspecies relationships with other bacteria may influence the bloom events in eutrophic waters throughout the world.

Keywords: 16S rRNA gene high-throughput sequencing, bacteria-algae interaction, bioinformatics, C. raciborskii, water bloom

This paper mainly focused on exploring the microbial communities and bacteria-Cylindrospermopsis raciborskii interactions by using both culture-dependent and culture-independent approaches in Yilong Lake, Yunnan province, China, a Cylindrospermopsis raciborskii-blooms lake.

Introduction

Cylindrospermopsis raciborskii (also Raphidiopsis raciborskii) is a filamentous cyanobacterium belonging to the Nostocales order. With its wide adaptability to environmental temperature and light conditions and diverse nutrient-uptake strategies, C. raciborskii has extensive distribution all over the world (Haande et al. 2008, Sinha et al. 2012, Antunes et al. 2015), and its dominated harmful algae blooms (HABs) have been reported frequently (Figueredo and Giani 2009). Because of its potential production of toxins, mainly cylindrospermopsin (CYN) and saxitoxin, C. raciborskii has attracted increasing public attention in recent years. However, our understanding of the growth of C. raciborskii in natural environments and its bloom-forming mechanism is still poor.

The outbreak of HABs is associated not only with various environmental abiotic factors, but with the biotic interactions with other organisms (Karasiewicz et al. 2020). Within aquatic microbial communities, phytoplankton and other bacteria can form complex interspecies interactions that can greatly impact the growths of both partners (Cole 1982, Kim et al. 2009, Amin et al. 2012, Xie et al. 2013, Cooper et al. 2019). Among these microorganisms, while the beneficial bacteria can promote the growth of algae or enhance their stress tolerance, the algicidal bacteria can inhibit the growth of algae or lyse algal cells (Meyer et al. 2017, Coyne et al. 2022). All these interspecies interactions are believed to play roles in shaping aquatic microbial communities and the development of HABs (Seymour et al. 2017). Certain algicidal bacteria are suggested to be useful in developing efficient and eco-friendly control approaches of HABs (Imai et al. 2021, Coyne et al. 2022, Ren et al. 2023).

However, our understanding of the interactions between C. raciborskii and bacteria is still limited. Only a few C. raciborskii-interacting bacteria are reported. For instance, Bacillus cereus L7 and Lysobacter sp. SG-3 can lyse C. raciborskii cells; also, Bacillus flexus SSZ01 can inhibit C. raciborskii growth and degrade CYN (Flaherty et al. 2007, Hu et al. 2023, Mohamed et al. 2023). However, compared with other bacteria-algae interaction studies, the diversity of C. raciborskii-interacting bacteria, as well as the mechanisms behind it and the ecological impacts, require further examination.

Yilong Lake, one of the nine plateau lakes in Yunnan province, China, provides great help to the living and economic development of the local areas. However, with the increase of human activities and climate changes, Yilong Lake is highly eutrophic with a great deterioration of water quality (Wu et al. 2021b). In recent years, water blooms with C. raciborskii as the dominant species have occurred frequently in Yilong Lake. To explore the microbiome associated with C. raciborskii is important for the understanding of the bloom events in Yilong Lake. In this study, we used both culture-dependent and culture-independent approaches to investigate the microbial communities in Yilong Lake, and the interactions between C. raciborskii and the other bacteria. These results may shed new insights into the ecology of C. raciborskii and the control approaches of C. raciborskii-dominated HABs in eutrophic waters.

Materials and methods

Lake sample collection and bacterial isolation

The sampling areas Y1, Y2, Y3 and Y4 are uniformly distributed from the west to the east of Yilong Lake, and each area contains three sampling sites (Fig. 1). Water samples from 0.5-meter depth were collected in June 2021 (Fig. 1) and stored in sterilized bottles in ice before the downstream laboratory experiments. To isolate bacterial strains, water samples were spread on R2A (Reasoner and Geldreich 1985), 1/2TY (Beringer 1974) or M9 (Miller 1972) agar and incubated at 28°C for 1 week. Representative bacterial colonies were then purified with plate streak as described previously (Ren et al. 2023). Bacterial isolates were then separately co-inoculated with the target algal or cyanobacterial strains for interspecies interaction tests.

Figure 1.

Figure 1.

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Sampling sites in Yilong Lake.

16S rRNA gene high-throughput sequencing and data processing

Water samples were filtered with a 5-mm net to remove large particles and centrifuged at 12 000 g for 10 min. The pellets were used for total DNA extraction with DNeasy PowerSoil Kit (QIAGEN) following the standard protocol. The V3-V4 region of 16S rRNA gene was amplified using primers 343F (5′-TACGGRAGGCAGCAG-3′) and 798R (5′-AGGGTATCTAATCCT-3′), and paired-end sequencing library of the 16S rRNA gene fragment amplicons was prepared and sequenced in OE biotech Co., Ltd. (Shanghai, China). Raw sequencing reads were demultiplexed, denoised and quality filtered using dada2 in QIIME2 (Callahan et al. 2016, Bolyen et al. 2019). Taxonomic assignment of the amplicon sequence variants (ASVs) was performed against the Silva database (version 138) at 99% identity (Robeson et al. 2020). Chloroplast and mitochondrial sequences were removed before the downstream analysis.

To evaluate the abundances of isolated bacterial strains in the microbial communities, the bacterial 16S rRNA gene was amplified using universal primers 27F and 1492R (Frank et al. 2008). The V3-V4 regions of 16S rRNA gene sequences were searched against the ASVs and the 99% identity threshold was used for screening the closest targeted sequences. The closest relative was identified using EzBioCloud databases (Yoon et al. 2017).

A rarefaction at 15 977 reads corresponding to the lowest read number in the samples was used for the alpha-diversity and beta-diversity comparisons. Comparisons of alpha diversities indices among different groups were performed using the non-parametric Kruskal–Wallis by Vegan package in R. Beta-diversity was analyzed using weighted unifrac distance (Lozupone and Knight 2005) and principal co-ordinates analysis (PCoA). Multivariate analysis of variance (PERMANOVA) was performed to examine the effect of location on β-diversities with 999 permutations.

Co-occurrence network

The QIIME2-generated ASVs were collapsed at the species level and those species with less than 10 reads were removed before co-occurrence analysis. We used the SparCC (Friedman and Alm 2012) method in the iNAP website (Feng et al. 2022) to calculate the pairwise correlations of species, with 20 as the number of inference iterations, 10 for exclusion iterations, 0.1 as the correlation strength exclusion threshold and 1000 as the number of times shuffled. P values were corrected (q scores) using the Benjamini–Hochberg method (Benjamini and Hochberg 1995) and the thresholds of |R| (SparCC coefficient value) ≥ 0.65 and q < 0.05 were used to screen the robust co-occurrences. The visualization of the co-occurrences network was completed using Gephi software (Bastian et al. 2009). In visualization, species are represented as nodes, and the interspecies correlations are represented as edges. Positive correlations and negative correlations are colored in red and green edges, respectively. Node size is proportional to its relative abundance and the layout of the network was distributed using the Furchterman Reingold plugin in Gephi.

Co-culture of bacteria and C. raciborskii and other microalgae

Cylindrospermopsis raciborskii FACHB-1503 was obtained from Freshwater Algae Culture Collection at the Institute of Hydrobiology, Wuhan, China (FACHB; http://algae.ihb.ac.cn; Supplementary Note 1) and was used to screen the bacterial strains with algicidal or growth-promoting activity. Several other target microalgae, including Synechocystis sp. PCC6803, Microcystis aeruginosa FACHB-524, M. aeruginosa FACHB-927, Anabaena 7120, Chlorella sp. FACHB-5 and Chlamydomonas reinhardtii 21gr (CC1690), were also tested in the co-culture tests, as described previously (Ren et al. 2023). Cyanobacterial strains were grown in BG-11 medium, which consists of K2HPO4 0.04 g/L, MgSO4·7H2O 0.075 g/L, CaCl2·2H2O 0.036 g/L, citric acid 0.006 g/L, ferric ammonium citrate 0.006 g/L, EDTA (disodium magnesium salt) 0.001 g/L, Na2CO3 0.02 g/L and A5 trace mental solution 1 ml/L with pH 7.1 (Rippka et al. 1979). Algal strains were grown in Tris-acetone-phosphate (TAP) medium, which consists of 2.42 g/L Tris, 1 ml/L glacial acetic acid, 375 mg/L NH4Cl, 100 mg/L MgSO4·7H2O, 50 mg/L CaCl2·7H2O, 108 mg/L K2HPO4, 54 mg/L KH2PO4 and 1 ml/L Hutner's trace elements with pH 7.4 (Gorman and Levine 1965). All the microalgae were grown at 25°C under the condition of continuous light (120 μmol photons m−2 /s).

For the co-inoculations, algal or cyanobacterial suspension was adjusted to ~107 cells/mL in TAP or BG-11 medium (1.5 mL) based on direct microscopy counts. Cylindrospermopsis raciborskii cell density was determined by multiplying average cells per filament by the number of C. raciborskii filaments per mL, as described previously (Davis et al. 2014). Bacterial cells cultured in R2A or 1/2TY medium were washed twice and re-suspended in 0.5 ml TAP or BG-11 medium, before inoculating them into the algal or cyanobacteria cultures, respectively. Bacterial cell densities were adjusted from 1010 to 104 cells/mL based on colony-forming unit (CFU) counting on plate. Different inoculum ratios (1000:1 to 1:1000) of bacterial cells: microalgal cells were used in the co-inoculations. To examine whether a close cell-cell contact is required for the bacteria-microalgae interaction, the 12-well Tissue Culture Plate Insert (Labselect, China) was used for the co-inoculations, in which bacteria and the target microaglae were separated with a 0.1-μm polycarbonate filter (Ren et al. 2023). Bacterial algicidal or growth-promoting abilities were evaluated 7 days after inoculation using the formula: (1-Chlorophyll+bacteria/Chlorophyllcontrol) × 100%, where Chlorophyll+bacteria is the chlorophyll content of the co-culture, and Chlorophyllcontrol is the chlorophyll content of alga/cyanobacterium alone.

For chlorophyll measurement, briefly, 1–2 mL of microalgal cultures was centrifuged to remove the medium, and the cell pellet was resuspended in 95% ethanol to extract chlorophyll at 4°C overnight. Chlorophyll-a and Chlorophyll-b contents in the extracts were measured spectrophotometrically (Synergy 2, BioTek, USA) and calculated as described previously (Lichtenthaler 1987). All co-inoculations were performed with at least three independent replications.

Results

Microbial community structure of Yilong Lake

We collected water samples from Yilong Lake at June 2021, which was in the early-peak period of a C. raciborskii bloom. The total DNA of each sample was extracted and 16S rRNA gene amplicon sequencing was performed to investigate the microbial community structures and diversities. Rarefaction curves were presented to determine the rationality of sequencing data; the results indicated the read numbers are sufficient for the downstream diversity analysis (Supplementary  Fig. S1).

Taxonomic analysis indicated the major community members belong to Proteobacteria (58.91%), Bacteroidetes (21.21%), Firmicutes (8.2%), Actinobacteria (6.54%) and Cyanobacteria (2.8%) phyla (Fig. 2A). Within the whole community, Gammaproteobacteria (34%) and Alphaproteobacteria (20.9%) are the top two classes, and Burkholderia-Caballeronia-Paraburkholderia is the most dominant genus in all sampling sites (~13.5%; Supplementary Table S1). Our data confirmed C. raciborskii is the dominant cyanobacterial species: it accounted for ~44% of the total Cyanobacteria members (Fig. 2B). In particular, the relative abundances of C. raciborskii are greatly elevated in the Y2, Y3 (the middle of Yilong Lake) and Y4 (the east of Yilong Lake) areas. Low abundances of other cyanobacteria, such as Microcystis and Nodosilinea, were also detected in these samples (Fig. 2B).

Figure 2.

Figure 2.

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Microbial community structures of Yilong Lake samples. (A) Microbial relative abundances of the sampling sites in Yilong Lake at the phylum level. (B) Compositions of the cyanobacterial genus across sampling sites. Error bars are shown as mean ± SE of the relative abundances.

Alpha-diversity analysis indicated that Chao1 richness and Shannon richness were greatly altered among the sampling sites (Fig. 3A and B). The Y2 samples have the highest richness and Shannon richness index compared with the other area samples. PCoA results indicated that the Y1 and Y4 samples were closely clustered, with the exception of Y1-1 (Fig. 3C). PERMANOVA results showed that there are significant differences among these sites (R2 = 0.25, P = 0.013). This indicated that there are significant differences in microbial communities among different sampling areas of Yilong Lake, which may be due to the changes of geographical locations or micro-environmental conditions in these areas. These data indicated a dynamic of microbial community structure across the samples in Yilong Lake.

Figure 3.

Figure 3.

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α-diversities (A and B) and β-diversities (C) of microbial communities. (A) Chao1 richness. (B) Shannon evenness. Boxes marked with different lower-case letters indicate significant differences with other boxes according to non-parametric Kruskal–Wallis multiple comparisons (P < 0.05). (C) Principal coordinate analysis (PCoA) plot of microbial communities of the 12 sampling sites based on weighted unifrac distance.

Microbial co-occurrence network analysis

Three hundred and twenty-six species with an absolute abundance higher than 10 remained in the co-occurrence calculations using SparCC. We detected more than 7500 edges (4508 positive and 3152 negative) in the whole network (Supplementary Fig S2). To better understand the interspecies associations between microalgae and bacteria, we extracted all the cyanobacterial species and their linked nodes to generate a cyanobacteria-bacteria subnetwork (Fig. 4). Within this subnetwork, C. raciborskii is one of the cyanobacterial nodes with the highest degree number (73 positive and 29 negative edges) and betweenness centrality, suggesting this bloom-dominant species may have complex associations with the other microbial members and play a role in the shaping of microbial community structures. These various co-occurrences suggested the other bacterial members may have either promoting or inhibiting effects on C. raciborskii during the bloom events.

Figure 4.

Figure 4.

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The cyanobacteria-bacteria co-occurrence subnetwork. Nodes represent the species and the size is proportional to its relative abundance. The yellow nodes represent the cyanobacteria and the gray nodes represent the other bacteria that have significant correlations with cyanobacteria. Only C. raciborskii and the bacterial species showing an algicidal or growth-promoting effect on C. raciborskii in the co-inoculation tests are labeled in the network. The green edge represents a negative relationship and the red represents a positive relationship, and the edge thickness is proportional to the absolute SparCC correlation value.

Bacterial isolations from water samples and co-inoculation tests

To experimentally evaluate the interactions between bacteria and C. raciborskii, we isolated bacterial strains from Yilong Lake and screened them in the co-cultures with C. raciborskii FACHB-1503. Among ~200 isolated bacterial strains, 14 strains showing either an obviously algicidal or growth-promoting effect on C. raciborskii were obtained (Table 1). For example, MERYL5-20, MERYL1-35, MERYL1-1 and MERYL15 showed a strong algicidal ability against C. raciborskii (algicidal rate ≥ 60%), while MERYLM7 and MERYL1-54 could promote the growth of C. raciborskii. Interestingly, except for MERYL5, MERYL11-1, MERYLM7 and MERYL5-24, these strains were previously identified as algicidal bacteria against the other microalgae in our other study (Ren et al. 2023). These results confirmed that various types of interspecies interactions exist within the microbial community.

Table 1.

Properties of the screened bacteria.

Strains ID Closest relativea Relative abundance (%)b Correlationc q-value Algicidal or promoting rate (%)d on C. raciborskii FACHB-1503
MERYL5-20 Bacillus velezensis CR-502 (99.57%) 0.32 −0.77 0.010 78.11 ± 0.80
MERYL1-5 Hydrogenophaga flava NBRC 102514 (98.60%) 0.21 0.26 0.368 59.98 ± 4.04
MERYL5 Lysinibacillus fusiformis NBRC 15717 (99.17%) 0.053 −0.59 0.023 79.26 ± 1.28
MERYL1-35 Chromobacterium rhizoryzae LAM1188 (98.88%) 0.0047 −0.11 0.499 51.93 ± 1.78
MERYL1-1 Sphingomonas echinoides ATCC 14820 (98.41%) N/A N/A N/A 84.13 ± 0.74
MERYL15 Chitinimonas viridis HMD2169 (99.55%) N/A N/A N/A 84.73 ± 1.39
MERYL11-1 Chitinimonas viridis HMD2169 (99.93%) N/A N/A N/A 80.44 ± 0.90
MERYL5-16 Pseudomonas QJRX's MB-090714 (99.30%) N/A N/A N/A 17.46 ± 1.56
MERYLM7 Azospirillum LGQW's TSH64 (97.44%) 2.79 0.81 0.002 −135.02 ± 4.06
MERYLM22 Roseateles terrae CCUG 52222 (99.86%) 0.056 0.66 0.013 −19.75 ± 8.19
MERYL1-54 Acidovorax RCCC's 106 (99.09%) 0.14 −0.61 0.020 −44.00 ± 3.71
MERYL5-26 Vogesella urethralis YM-1 (99.09%) 0.0072 0.17 0.421 −35.35 ± 8.90
MERYL5-24 Sphingomonas echinoides ATCC 14820 (98.55%) N/A N/A N/A −16.74 ± 0.68
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a

The closest relative was determined with the comparison of 16S rRNA gene sequences in EZbiocloud databases (identity is shown in parentheses).

b

Relative abundance of the closest ASVs in the 16S rRNA gene high-throughput sequencing data. N/A: no detection of the corresponding closest ASVs.

c

SparCC values.

d

The positive and negative values reflect bacterial algicidal abilities and growth-promoting abilities on C. raciborskii, respectively, according to the calculation method (see Materials and Methods).

16S rRNA gene sequences of these isolates were also searched against the 16S rRNA gene amplicon sequencing data, and the abundances of their closest species were evaluated (Table 1). For example, MERYL5-20 exhibited strong algicidal activity (78.11%) against C. raciborskii, and it consistently showed a negative correlation with C. raciborskii in the microbial communities (SparCC R = -0.77). However, the other isolates (e.g. MERYL1-54) did not show the expected experimental co-inoculation results when compared with the above co-occurrence results. This may be due to the limitations in co-occurrence calculations or the fact that the laboratory cannot restore the real environment conditions.

MERYL5-20 and MERYLM7 exhibited a consistent performance between co-occurrence analysis and experimental tests, which provided a valuable opportunity for better understanding the interactions between bacteria and C. raciborskii. More bacteria-algae co-inoculation tests were thus performed with them in the downstream study. Their algicidal or promoting effects were detected when bacterial cells and C. raciborskii cells were separated by a filter (Fig. 5A), suggesting these interactions do not require direct cell-cell contact. Their activities were detected under high inoculum ratios (bacterium: C. raciborskii ≥ 100:1; Supplementary Table S2), suggesting these effects are bacterial inoculum dose-dependent. In addition, when co-inoculated with other microalgal strains, MERYLM7 could promote the growth of all the tested cyanobacterial strains; its promotion rates for Anabaena 7120, Synechocystis sp. PCC6803, M. aeruginosa FACHB-524 and M. aeruginosa FACHB-927 are ~65.75%, ~55%, ~15.97% and ~34.23%, respectively. However, MERYLM7 showed inhibition effects on C. reinhardtii and Chlorella; both of the algicide rates are close to 35%. As described in our previous study (Ren et al. 2023), MERYL5-20 can also strongly inhibit the growth of M. aeruginosa, while it has little effect on C. reinhardtii. These results indicate that these bacterial isolates from C. raciborskii-dominated bloom samples may have different algicidal or growth-promoting abilities on the other microalgal species.

Figure 5.

Figure 5.

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Co-inoculation tests of bacteria and C. raciborskii FACHB-1503. (A) Schematic diagram of transwell plate. (B) Growth of C. raciborskii mono-culture, and bacteria-C. raciborskii co-cultures 7 days after inoculation. —: Bacterial and C. raciborskii cells were directly co-inoculated; +: Bacterial and C. raciborskii cells were separated by a 0.1-μm filter.

Discussion

The microbial community composition is critical for the ecological functions and bloom events in water environments. However, our understanding of the microbial community associated with C. raciborskii-dominated bloom is particularly poor compared with other cyanobacterial (e.g. M. aeruginosa) blooms. Proteobacteria, Bacteroidetes, Actinobacteria and Firmicutes are the main non-cyanobacterial phyla in Yilong Lake, which is similar to the microbial patterns in the other C. raciborskii bloom and M. aeruginosa bloom (Kim et al. 2020, Wu et al. 2021b, Ou-yang et al. 2022). However, the microbial abundances and compositions can be vastly different at the lower taxonomic levels. For example, Flavobacterium is the dominant genus (10%–35%) in the microbial community of a Microcystis bloom (Kim et al. 2020), while its relative abundance in our research is only ~4.4% (Supplementary Table S1). We postulate that these variations of microbial community composition are not only related to the spatial and temporal differences of water samples, but also to the dominant cyanobacterial species. More detailed comparisons of microbial communities in C. raciborskii blooms and the other cyanobacterial blooms should be performed in the future, and would improve our understanding of the ecology of Cyanobacteria blooms in natural condition.

Interspecies interactions between phytoplankton and bacteria play roles in the growths of both partners. Among the isolated bacterial strains, MERYL5-20 belongs to Bacillus velezensis, which has been widely reported in biological control studies. For example, B. velezensis FZB42 is recognized as a biocontrol strain for the foliar pathogen Phytophthora nicotianae (Fan et al. 2018); B. velezensis T149-19 is able to inhibit P. destruens, a pathogen of sweet potato foot rot disease (Mateus et al. 2021). A previous study also indicates that B. velezensis V4 can lyse Chlorella sp. and Scenedesmus sp. cells using bacilysin (Gao et al. 2022). Here, MERYL5-20 can strongly inhibit the growth of C. raciborskii FACHB-1503, clearly expanding the antimicrobial scope of B. velezensis. Its consistent performance in experimental co-inoculation tests and network analysis implies it may be useful for the control of C. raciborskii bloom in Yilong Lake.

Another isolated strain, MERYLM7, is close to Azospirillum bacteria based on the 16S rRNA gene sequence analysis. Azospirillum strains have been isolated from water samples, soil and plant rhizosphere. Some of them have been considered as a type of plant growth-promoting bacteria that not only can fix nitrogen (Reis et al. 2015), but also promote plant growth through gene expression regulation (Elías et al. 2018). For example, A. brasilense REC3 can produce indole-3-acetic acid to stimulate the production of ethylene in strawberry, thus promoting plant growth (Elías et al. 2018). Certain Azospirillum strains can activate the expressions of genes involved in photosynthesis and nutrient metabolism in rice buds, and increase the number of rice branches (Cook et al. 2022). An A. brasilense strain Cd has been proven to promote the growth or nitrogen uptake of alga C. vulgaris (de-Bashan et al. 2005). In this study, the growth promotion of C. raciborskii FACHB-1503 by MERYLM7 (Table 1; Fig. 5) suggests this bacterial species may have a positive role in the bloom formation in Yilong Lake. More studies should be performed to reveal the growth promotion mechanism of MERYLM7 in the future.

Besides the above bacterial strains, we also isolated other strains with inhibiting or promoting effects on C. raciborskii (Table 1). However, some of them were not detected in the 16S rRNA gene high-throughput sequencing data, or their interactions in co-culture tests were not supported by network analysis (Table 1). A similar problem has been widely reported in other microbial network studies (Carr et al. 2019). Many factors, such as the limitations of the data-processing method and experimental conditions, may contribute to this inconsistency. Additionally, because we found that there are some bacterial contaminations in C. raciborskii FACHB-1503 culture, the interactions between our isolates and C. raciborskii may be influenced by these species. However, importantly, these results revealed that there are various interspecies interactions between C. raciborskii and the other microbial members. This may lead to a question of how C. raciborskii-bloom can outbreak in Yilong Lake under such a comprehensive interspecies interaction condition. This may have resulted from the actual low abundances of algicidal bacteria in the lake that are insufficient for their inhibiting activities against C. raciborskii (Table S2, and the data in Ren et al. 2023). Another possible reason could be that these interactions are highly environment-dependent and may shift greatly with the changes in the dynamic lake environmental conditions, as indicated previously (Wu et al. 2021a). More studies should be carried out to verify the actual direct or indirect relationships between these bacteria and C. raciborskii in the natural condition.

In summary, in the current study we investigated the microbial communities in a C. raciborskii-dominated water bloom in Yilong Lake, and the relationships between a strain of C. raciborskii and the other microbial members, using both culture-dependent and culture-independent approaches. All these data may provide new insights into the ecology of C. raciborskii and how its interspecies relationships with other bacteria may influence the bloom events.

Supplementary Material

fiae048_Supplemental_File
fiae048_supplemental_file.docx (5.7MB, docx)

Acknowledgements

We thank Zhengjun Chen's team for the help in sampling at Yilong Lake.

Contributor Information

Yuanpei Jin, School of Life Sciences, Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University, Wuhan 430079, China.

Sanguo Ren, School of Life Sciences, Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University, Wuhan 430079, China.

Yichi Wu, School of Life Sciences, Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University, Wuhan 430079, China.

Xu Zhang, School of Life Sciences, Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University, Wuhan 430079, China.

Zhengjun Chen, State Key Laboratory of Agricultural Microbiology, College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.

Bo Xie, School of Life Sciences, Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University, Wuhan 430079, China.

Author contributions

All authors contributed to the study conception and design. Yuanpei Jin (Data curation, Formal analysis, Investigation, Software, Visualization, Methodology, Writing – original draft, Writing – review & editing), Sanguo Ren (Data curation, Formal analysis, Investigation, Methodology, Validation, Resources), Yichi Wu (Data curation, Formal analysis, Investigation, Methodology, Validation, Resources), Xu Zhang (Formal analysis, Methodology, Investigation, Validation), Zhengjun Chen (Resources, Data curation, Investigation, Methodology) and Bo Xie (Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Validation, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing)

Conflict of interest

We declare no conflicts of interest.

Funding

This work was supported by National Natural Science Foundation of China (32370141, 31970109), Key Research and Development Program of Hubei Province (2023BCB103) and the Fundamental Research Funds for the Central Universities (CCNU22JC015, KJ02502022-0450).

Data Availability

The accession numbers (GenBank) of bacterial 16S rRNA gene sequences are reported previously (Ren et al. 2023), except for those of MERYL5 (OP828565.1), MERYL11-1 (OP828571.1), MERYLM7 (OP828570.1) and MERYL5-24 (OP828568.1). The 16S rRNA gene high-throughput sequencing data have been submitted to the NCBI SRA databases under accession number PRJNA1003624 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1003624).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

fiae048_Supplemental_File
fiae048_supplemental_file.docx (5.7MB, docx)

Data Availability Statement

The accession numbers (GenBank) of bacterial 16S rRNA gene sequences are reported previously (Ren et al. 2023), except for those of MERYL5 (OP828565.1), MERYL11-1 (OP828571.1), MERYLM7 (OP828570.1) and MERYL5-24 (OP828568.1). The 16S rRNA gene high-throughput sequencing data have been submitted to the NCBI SRA databases under accession number PRJNA1003624 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1003624).

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