Insights into Ecological Features of Microbial Dark Matter Within the Symbiotic Community During Alexandrium pacificum Bloom: Co-Occurrence Interactions and Assembly Processes

Abstract

The symbiotic microbiome constitutes a consortium that has been persistently domesticated by a specific algal species, fostering a close and enduring association with the host. The majority of microbial taxa remain uncharacterized. These unknown microbes, often referred to as “microbial dark matter (MDM)”, have important ecological contributions. Given the challenges in discerning symbiotic microbes in natural environments, herein, ecological characteristics of MDM and known taxa within symbiotic communities were investigated in a simulated bloom process using Alexandrium pacificum without antibiotic treatment. Specifically, increased diversification was observed in MDM along the bloom process. Higher trophic interaction and less vulnerability of the molecular network were found in MDM taxa. The “bridge” role of MDM species was better than that of known taxa, as shown by higher betweenness centralization. Deterministic processes dominated in MDM taxa, which promote phylogenic diversity of such groups to some extent. The findings highlight that MDM taxa play an important role in sustaining community stability and functioning. This study broadens our understanding of the ecological contribution of MDM under disturbances from dinoflagellate blooms, providing essential theoretical insights and empirical data to inform the management of coastal toxic blooms.

1. Introduction

Microorganisms are nearly ubiquitous across the globe and play crucial roles within a variety of ecosystems [1]. Although microbes constitute the majority of the world’s biomass, a significant portion of species and their genomic features remain largely undiscovered [2]. These unknown types of microbes are commonly named as “microbial dark matter” (MDM) [3], posing a significant challenge to our understanding of microbial ecology. It is not hard to understand that incomprehensive investigation of microbial roles impede the characterization of microbe-dominated ecosystem processes. To date, much of our understanding of the microbial world is biased towards a limited number of taxa that are amenable to cultivation and genetic manipulation. Moreover, most of the cultivated species, accounting for about 88%, are assigned into four major phyla including Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes [4]. However, the uncultured and unsequenced microbes in our world likely embody significant evolutionary lineages within the phylogeny and are anticipated to play pivotal roles in ecosystem sustainability, functioning, and so on [5]. Hence, exploring the roles of unknown taxa in an ecosystem would enhance our understandings of how these organisms influence their neighbors, the surrounding environment, and the broader tapestry of life [5].

Harmful algal blooms (HABs) have surfaced as a formidable natural disaster, capturing significant attention in recent decades [6] and posing grave threats to public health and aquatic ecosystems [7]. Dinoflagellates, a phylum of unicellular microalgae, are vital primary producers within marine ecosystems, operating either as free-living plankton (autotrophic, heterotrophic, or mixotrophic) or as endosymbionts within coral reefs. They are also widely recognized as species responsible for causing harmful algal blooms [8]. Microorganisms have been found to be intricately associated with the processes underlying dinoflagellate harmful algal blooms [9,10,11]. Within them, symbiotic microbiomes refer to a distinct assemblage of microbes residing alongside HAB species [12,13]. These microbiomes demonstrate long-term coexistence and maintain a close association with their host populations, significantly influencing the host’s functions and overall fitness [12,14,15]. Investigating such microbes can enhance our understanding of the complex interactions between algae and microbes. However, it is difficult to accurately distinguish them from meta microbes in natural environments, due to unicellular traits of most HAB species and their living habitat with continuously shifting and dynamic conditions [16]. Long-term co-culture of HAB-forming species and microbes can perhaps help in domesticating and obtaining stable symbiotic communities [12,13]. Possibly, this is an important reason for why most investigations on them are conducted in laboratory-reared dinoflagellate [12,17,18], enhancing our understanding of algal–microbe interactions and establishing a vital connection between laboratory findings and field data. Considerable efforts have explored the composition, diversity and potential function of symbiotic microbiome [12,13,17,18]. For instance, there is a distinct symbiotic bacteria composition and related community function between toxic and non-toxic dinoflagellate hosts [13]. Dynamic changes in symbiotic bacteria are associated with the bloom process of Akashiwo sanguinea [17]. However, to our knowledge, the current literature mainly focuses on the whole symbiotic community [12,13,17,18], rather than known taxa or MDM. Due to the large number of species of MDM present in symbiotic microbiota living with dinoflagellate hosts [12,13,17,18], it is essential to conduct an investigation on MDM’s response to a dinoflagellate bloom process, particularly its ecological importance, and to perform the relevant comparisons between species of known and MDM taxa.

Recent studies have employed next-generation sequencing (i.e., metabarcoding) to investigate the ecological roles of MDM taxa within microbial consortia [5]. To attain a deeper comprehension of microbial life, especially the roles played by MDM, it is imperative to elucidate the connectivity and structural dynamics of the microbial world. Co-occurrence networks have been widely used to analyze species’ roles and interactions [19,20], offering crucial insights into how microbial taxa may contribute to ecosystem functioning. In addition, ecological processes play a pivotal role in shaping the distribution patterns and diversity of microbial communities within ecosystems [21,22]. Two distinct processes have been suggested to elucidate variations in microbial communities: deterministic processes and stochastic processes [21,22]. Growing efforts are being devoted to discerning the relative significance of these processes in shaping community assembly, yielding profound insights into their distinct roles across various ecosystems and organismal types [21,22]. Although these methods have been used in other studies on MDM [5], none have employed them in the context of MDM within the symbiotic microbiome of the phycosphere.

Alexandrium pacificum (A. tamarense Group IV), a typical species of HABs, is extensively distributed across the world and reported to produce paralytic shellfish toxins [23]. No studies have explored the ecological features of MDM of the symbiotic biosphere during the A. pacificum bloom process. Hence, using this species as a case study, the present investigation established a laboratory-scale bloom utilizing A. pacificum through extended indoor cultivation with no antibiotic treatments. Then, via metabarcoding, we investigated and compared the ecological features of known taxa and MDM within symbiotic microbiomes, including the aspects of co-occurrence patterns and community assembly processes. The findings would enhance the current understanding of microbial ecology during harmful algal bloom (HAB) disturbances, particularly regarding the interactions and assembly mechanism of MDM. Additionally, this study would address the research gap regarding the ecological methodology for MDM within phycosphere microorganisms via metabarcoding.

2. Materials and Methods

2.1. Dinoflagellate Strain, Cultivation and Sampling

The pure A. pacificum strain NJD-1 employed in the present study was isolated from the seawater along the coast of Zhejiang, China. The culture was maintained for several years in sterile natural seawater with f/2-Si medium [24], without the use of antibiotic treatments, under the conditions of 21 ± 1 °C, approximately 100 μmol quanta·m⁻²·s⁻¹, and a photoperiod of 12 h light and dark by white fluorescent lights (Ningbo Jiangnan Instrument Factory, Ningbo, China). For construction of simulated HAB, 500 mL solutions containing 100 mL of A. pacificum and 400 mL of culture medium were aliquoted into eight 1 L of sterile flasks in triplicate. Then, based on the algal density determined by cell counting using Lugo’s reagent with 2% final concentration [17], three distinct bloom stages were confirmed: AP1 and AP2 (pre-bloom stage); AP3, AP4, and AP5 (during-bloom stage); and AP6, AP7, and AP8 (post-bloom stage). This method was followed in another study about the construction of simulated dinoflagellate bloom in the laboratory [17].

2.2. DNA Extraction and Data Processing

Each sample collected from the same time was subjected to filtration via 3 μm and subsequently 0.22 μm polycarbonate membranes (Millipore, Billerica, MA, USA), to obtain microbes. Total DNA was extracted utilizing the Fast DNA SPIN Kit (MP Biomedicals, Santa Ana, CA, USA). The concentration and quality of the isolated DNA were subsequently assessed utilizing the ND-2000 NanoDrop spectrophotometer (Thermo Fisher Scientific, Somerset, NJ, USA). The V3–V4 region of the 16S rRNA gene was amplified via PCR using the common primer set, i.e., 338F and 806R. The raw sequence reads (Accession Numbers: PRJNA1192050 in NCBI database) were generated by Illumina MiSeq PE300 platform at Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The clean data were obtained via quality control and chimerism in QIIME [25]. Operational taxonomic units (OTUs) were subsequently generated at a 97% classification threshold utilizing VSEARCH, which are frequently utilized as an analytical unit in the realm of microbial ecology research [25]. The taxonomy of OTUs was annotated using the SLIVA132 database. Unknown taxa were taxonomically classified as “uncultured”, “uncultured bacterium”, “unknown” and other unidentified types of any OTU [5].

2.3. Bioinformatics

The evaluation of α-diversity, quantified through the Shannon indices, was executed using the QIIME software (version 1.8.0) [25]. With regard to β-diversity, Principal Coordinates Analysis (PCoA) based on OTUs was conducted based on Bray–Curtis distances within the R (https://www.r-project.org/) programming environment utilizing the packages of “vegan”, “ggforce”, “ggrepel” and “ggord”, which are widely applied to analyze the similarity of OTU composition among different samples [10,17]. The ANOSIM method (Analysis of Similarities), incorporating 999 permutations, was utilized to assess the statistical significance of community clustering using the QIIME software (version 1.8.0).

A topological network was utilized to analyze the co-occurrence patterns of known taxa and MDM along the bloom process, revealing intricate microbial interactions that are paramount for sustaining community diversity and ecosystem functionality [26,27]. The network construction was accomplished by using the “igraph”, “psych” and “WGCNA” packages. Robust correlations were identified using Spearman’s correlation coefficients (r > 0.6) with corresponding p-values < 0.05 for network construction. To mitigate the potential for false-positive findings, p-values were adjusted following the Benjamini and Hochberg procedure. For exploring the dynamic of interaction and co-occurrence, sub-networks were extracted, and the corresponding topological properties were calculated. The “igraph” package was employed to evaluate the network parameters, encompassing nodes, edges, betweenness centralization, and modularity, thereby elucidating the network topology. Network visualization was accomplished using the interactive platform Gephi (version 0.9.2). Furthermore, in order to test the stability of networks, robustness was employed as a formidable instrument to evaluate network stability, specifically by quantifying alterations in natural connectivity based on OTUs following the removal of nodes.

A null model analysis was utilized to assess the assembly processes across various HAB stages. This approach supports the fundamental drivers of these processes into deterministic mechanisms (such as homogeneous selection and heterogeneous selection) and stochastic processes (including dispersal limitation, homogeneous dispersal, and undominated processes), based on the Bray–Curtis-based Raup–Crick metric (RCbray) and the β-nearest taxon index (βNTI) [28]. βNTI values below 2 signify homogeneous selection, whereas values equal to or exceeding 2 indicate variable selection. When |βNTI| < 2 and RCbray < 0.95, the situation is recognized as homogenized dispersal; conversely, |βNTI| < 2 and RCbray ≥ 0.95 suggest dispersal limitation. Furthermore, |βNTI| < 2 and |RCbray| < 0.95 imply the influence of undominated processes. To mitigate the influence of species richness on the analysis of microbial phylogenetic diversity, the study utilized mean nearest taxon distance (MNTD), a metric that operates independently of species richness. MNTD was ascertained by computing the mean branch length between each OTU and its nearest phylogenetic relative within a given sample. Higher MNTD represents lower phylogenetic clustering within microbial community. In addition, the differences between MNTD of the MDM and known community were examined using one-way analysis of variance (ANOVA) followed by the least significant difference (LSD) test.

3. Results

3.1. Diversity and Composition of Known Taxa and MDM

The rarefaction curve, derived from the OTU data, displayed a tendency to plateau, while the Good’s coverage values for both bacterial and fungal sequencing data surpassed 95%. These findings suggest that the sequencing depth was adequate, successfully capturing the majority of bacterial species present within the samples. In both communities, based on Bray–Curtis pairwise distance PCoA analysis, there was a distinct community difference across the bloom process (p < 0.05) (Figure 1A,B). The Shannon index of known taxa ranged 3.94 to 4.65, while that of MDM fluctuated from 2.69 to 4.91 (Figure 1C). For the former, the Shannon index remained relatively stable in pre- and during-bloom stages, while a declining trend was observed in the post-bloom stage. Interestingly, the Shannon index of MDM continuously increased across bloom stages, even surpassing that of known taxa at the post-bloom stage (Figure 1C). Furthermore, the abundance of MDM increased from the pre- to post-bloom stages, while that of known taxa exhibited a contrary result (Figure 1D).

3.2. Ecological Networks of Known Taxa and MDM

To explore potential interactions among microbial species, co-occurrence networks were constructed for the two taxa, respectively (Figure 2 and Figure 3), on the basis of Spearman correlations among OTUs. There was a significantly higher percentage of positive correlations among microbial nodes within the two microbial taxa, specifically 71% for the known community and 86% for the MDM. The modularity index of the two communities, going beyond 0.4, indicated that the network possessed a modular structure and could be partitioned into several independent functional groups. Higher modularity was observed in the MDM community. Three primary modules were identified in both the consortia, accounting for 64% and 46% of total nodes, respectively (Figure 2A and Figure 3A). Via comparisons of topological features among different bloom stages (Figure 2B and Figure 3B), the numbers of node and edges of known taxa and MDM exhibited a rising trend along the bloom process, with a particularly significant increase in the latter (p < 0.05 or p < 0.01). Noticeably, betweenness centralization of known taxa and MDM showed a completely opposite changing trend (Figure 2B and Figure 3B). Furthermore, a robustness test grounded in natural connectivity was performed to evaluate the network’s invulnerability. The natural connectivity of all communities approached zero when 30% node was randomly deleted (Figure 2C and Figure 3C). On the other hand, compared to the MDM taxa, a steeper slope with a higher degree of fit (p < 0.001) of the trend was observed in the known community (Figure 2C and Figure 3C), indicating its weakened resistance.

3.3. Assembly Process of Known Taxa and MDM

The null model was employed to investigate the assembly processes of known taxa and MDM during the bloom process (Figure 4). Deterministic processes, consisting of homogeneous and heterogeneous selection, played an important role in the MDM community (82.1%), while stochastic processes mainly contained undominated factors compared to known taxa (67.9%). Furthermore, MNTD values of MDM taxa were significantly higher than that of the known community (p < 0.001; Figure 4B), indicating lower phylogenetic clustering in the former.

4. Discussion

Like microbes inhabiting the rhizosphere and human gut [29,30], bacteria chronically co-living with a microalga also reflect a symbiotic relationship that forms potentially intimate partnerships with the host and collectively regulates algal health [12,13,17,31]. However, most studies mainly focus on meta microbes rather than symbiotic taxa [9,10,11]. Given the challenges of discerning symbiotic microorganisms from the whole microbial community in the field, long-term co-cultivation between a microalgae and microbes may allow us to identify them [12,13,17,31]. Especially for a HAB-forming species, research efforts regarding the response of symbiotic microbes to the HAB process are helpful to enhance understanding of complex algal–microbe interactions. MDM representing unknown taxa have been reported to possess significant ecological roles in an ecosystem [5]. Taken together, the present study focused on the potential ecological roles of MDM within symbiotic bacteria of a typical HAB species during the simulated bloom process, compared to that of the known taxa.

Here, the proliferation of A. pacificum resulted in increased bacterial diversity, aligning with other studies which have found a rising trend along with dinoflagellate bloom process of A. sanguinea and Alexandrium affine [17,18]. However, the MDM community exhibited a continual increase throughout the various stages of the bloom, while the known taxa showed an opposite phenomenon (Figure 1C), suggesting the former plays an important role in sustaining microbial diversity. This may be attributed to the better adaptability in MDM taxa, which could be associated with more diversified functions in this taxon, as evidenced by higher functional modularization (Figure 2 and Figure 3). Hence, they could attain a robust competitive advantage over known taxa during the harmful algal bloom (HAB) process, enabling them to mitigate the adverse disturbances associated with HAB events [32]. A previous study also indicates that MDM plays important ecological roles in maintaining the functioning and diversity of various marine systems, such as deep sea, hot springs, and polar environments [5]. In addition, the bloom process markedly changed both the MDM and known community structures, revealing a distinct temporal succession of species composition throughout the HAB, in accordance with the previous studies [21,30]. Low-molecular-weight compounds, such as organic acids, amino acids, carbohydrates, and so on, can be released by algae in earlier stages of growth, while high-molecular-weight compounds, like proteins, lipids, and polysaccharides, are released during the later stage [33,34]. These changes may influence microbial compositions, since their growth responds variably to the range of organic compounds generated by the phytoplankton host. Moreover, similar to the present study, dynamic interactions among microbes also can lead to a shift in community structure in a system [35].

Microbial communities are both intricate and dynamic; however, given that the vast majority of worldwide microbes remain uncultured or uncharacterized, our comprehension of them is likely constrained and potentially biased by this significant knowledge gap. To gain a more comprehensive understanding of the influence of MDM on ecosystem structure and function, an approach rooted in network theory was used to evaluate the significance of uncultured and unknown taxa within their microbial communities. It is well known that co-occurrence networks provide profound insights into the complex interactions that may transpire among microbial communities, including predation, competition, mutualism, and commensalism [35]. In this study, both MDM and known taxa exhibited highly interspecific mutualism among their members, as shown by 86% and 71% positive edges, respectively. Trombetta et al. [32] also found a high proportion of positive correlation among species, reaching up to about 64%. All these findings may suggest that the relationship among bacteria tends to be cooperative when facing disturbance from algal bloom. In addition, the former obviously has less interspecific competition. This could be explained by a higher phylogenetic diversity and lower relative abundance found in the MDM community, reducing the race for nutrients and increasing ecological niche differentiation. This phenomenon is supported by another report, which documented that a kind of bacteria can reduce the stress of survival in a system by reducing richness and elevating genetic diversity [36]. Additionally, despite the numbers of nodes and edges associated with known taxa and MDM showing an upward trend throughout the bloom process, a particularly noteworthy increase was observed in the latter (p < 0.05 or p < 0.01). Not surprisingly, based on above-mentioned results, higher number of functional modules in such taxa may help them to obtain better stress resistance compared to known species. Betweenness centrality quantifies the degree to which a node resides along the pathways connecting other nodes, serving as a valuable metric for identifying which OTUs engage most frequently with other members of the community network. This analysis elucidates the taxa that are essential for facilitating co-occurrence with neighboring taxa. Betweenness centralization of MDM showed a significantly increasing trend, while that of known taxa did not, suggesting more communications among MDM members. All these findings indicate that the MDM taxa may be in the central position of the network, thereby executing more functions. Consistently higher modularity was observed in these taxa. Zamkovaya et al. [5] also indicated that MDM species act as key hubs within microbial networks. Furthermore, via robustness texts, the results proved that MDM taxa play an important role in maintaining stability of microbial community, as evidenced by a more gradual downward trend of natural connectivity. As mentioned above, potential niche differentiation together with low abundance in MDM taxa all result in reduced competition and thus increased stability [36].

As for community assembly, deterministic and stochastic processes are thought to collaboratively shape the assembly of microbial communities. Based on niche theory, determinism encompasses the effects of biotic and abiotic factors, primarily manifested through species interactions and environmental filtering, respectively [37]. In contrast, stochasticity stems from neutral theory, which posits that all species are ecologically equivalent, with community dynamics governed by processes such as birth, death, dispersal, and species formation [38]. These two processes exert markedly different influences on microbial communities. Herein, deterministic processes acted as a major community assembly mechanism in the MDM community, as opposed to stochastic factors in the known community. Previous studies have documented that different types of bacterial community can harbor two such processes during bloom cycle [32]. Within deterministic factors, homogenous selection was the main process, and heterogeneous selection came next. The community structure in a homogenous or heterogeneous state can lead to similar or dissimilar community composition. From the perspective of microbial ecology, the former is not conducive to maintaining community stability [39], which is in contrast to the aforementioned results. This phenomenon can be elucidated through the concept of “degeneracy,” which refers to the capacity of populations to exhibit both apparent homogeneity and diversification through augmented cryptic genetic variation [40]. Consequently, the enhanced stability of the community observed here is predominantly associated with increased intraspecies genetic diversity, encompassing putative cryptic species. Indeed, less phylogenetic clustering was observed in the MDM community. For known taxa, stochastic processes mainly dominated their community assembly. There, undominated factors, such as ecological drift and weak stress selection [21], acted as major mechanisms for assembly. This phenomenon may be associated with the competition from MDM taxa.

5. Conclusions

To date, the existing literature primarily concentrates on the whole symbiotic community. However, the response of different types of bacteria taxa within symbiotic microbiota to a dinoflagellate bloom remains largely unexplored. Considering the importance of the MDM group in ecological environments, the current study explored and compared the ecological characteristics of MDM and known taxa during a laboratory-scale dinoflagellate bloom process for the first time. Importantly, the biodiversity of MDM taxa gradually increased, ranging from 2.69 to 4.91, and finally surpassed that of known species along the bloom process. Topological properties of the co-occurrence network demonstrated that MDM taxa possess a central position within the meta microbial community, as evidenced by significantly increased nodes (increased by about 42%), edges (approximately 187%) and betweenness centralization (about for 217%), as well as higher modularity (i.e., 0.64). A robustness test indicated the MDM community had more stable natural connectivity, as shown by lower slope (k = −0.008) based on linear regression analysis. Moreover, deterministic processes promoted the diversification of MDM taxa, with about 18% heterogeneous selection. All these findings suggest that MDM plays a crucial role in maintaining system stability compared to known taxa. This study enhances our understanding of the ecological significance of MDM species in the context of disturbances caused by HABs.

Overall, the obtained results not only provide new insights for the ecological importance of MDM during bloom outbreaks, but also raise new directions for further investigation. These vital topics include (1) exploration targeting community function of MDM by genomic methods, which is helpful to clarify the comprehensive contribution of these microbes to HABs; (2) isolation and purification of MDM from meta community in a phycospheric environment, to enrich germplasm resource diversity of important unknown species; and (3) surveys on different niche microbes, for revealing the mechanisms underlying the maintenance of MDM communities.