Dynamics of Sediment Microbial Functional Capacity and Community Interaction Networks in an Urbanized Coastal Estuary

Dai, Tianjiao; Yan, Zhang; Ning, Daliang; Su, Zhiguo; Tang, Yushi; Huang, Bei; Mu, Quanquan; Wen, Donghui

doi:10.3389/fmicb.2018.02731

Cited by 22 publications

(21 citation statements)

References 56 publications

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“…The stability difference between upper and lower Bay could be due to the different disturbance and interference from freshwater vs. oceanic water. Similar environmental variations were also found in many other estuaries, and spatial variation also could affect bacterial associations, including Ems estuary [92], Hangzhou Bay [43,53] and Pearl River Estuary [104]. River input, land runoff, suspended particles/turbidity, and accompanied nutrients availability are all pulsating with strong seasonal/inter-annual variations and uncertain patterns in the upper Bay.…”

Section: Seasonal and Spatial Variations Of Cb Microbial Networkmentioning

confidence: 55%

“…Co-occurrence networks can reveal information on associations within microbiomes and stability of whole communities [31,36,37]. It has been increasingly used to infer microbial interactions [30,38] in soils [39,40], oceans [41,42], coastal waters [43], lakes [44,45], rivers [46], and even in metabolic modeling [47] and genomic surveys [48]. These correlation-based networks show important details of community interaction rules re ecting ecological processes such as cooperation and habitat partitioning and could represent mathematical associations among different microbial groups [30,37].…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Variations Dictate Stable and Resilient Microbiome Interactions in the Chesapeake Bay

Wang¹,

Chen²,

Zhang³

et al. 2020

Preprint

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Background: Annually reoccurring microbial populations with strong spatial and temporal variations have been identified in estuarine environments, especially in those with long residence time such as the Chesapeake Bay (CB). However, it is unclear how microbial taxa interact with each other (e.g., mutualistic and competitive interactions) and how these interactions respond to their surrounding environments. Specifically, there is a lack of understanding of how these interactions influence microbiome population dynamics, and its adaptability and resilience to estuarine gradients. Results: Here, we constructed co-occurrence networks on prokaryotic microbial communities in the Bay, which included seasonal samples from seven spatial stations along the salinity gradients for three consecutive years. Our results showed that spatiotemporal variations of planktonic microbiomes promoted differentiations of the characteristics and stability of prokaryotic microbial networks in the CB estuary. Prokaryotic microbial networks are more stable seasonally than spatially, and microbes were more strongly connected during warm season compared to the associations during cold season. In addition, microbial interactions were more stable in the lower Bay (ocean side) than those in the upper Bay (freshwater side). Interestingly, compared to the abundant groups, the rare taxa such as SAR116 clade, SAR11 clade III, and OM182 clade contributed greatly to the stability and resilience of prokaryotic microbial interactions in the Bay. Modularity and cluster structures of microbial networks varied spatiotemporally, which provided valuable insights into the ‘small world’ (a group of more interconnected species), network stability, and habitat partitioning/preferences. Multivariate regression tree (MRT) analysis and Piecewise structural equation modeling (SEM) indicated that temperature, salinity and total suspended substances directly or indirectly (through nutrient availability, particulate carbon and Chl a) affected the distribution and associations of microbial groups, such as Actinobacteria, Bacteroidetes, Cyanobacteria, Planctomycetes, Proteobacteria, and Verrucomicrobia.Conclusion: Our results shed light on how spatiotemporal variations alter the nature and stability of prokaryotic microbial networks in the estuarine ecosystem, as well as the ability of planktonic microbiomes and their interactions to resist future disturbances.

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Section: Seasonal and Spatial Variations Of Cb Microbial Networkmentioning

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variations Dictate Stable and Resilient Microbiome Interactions in the Chesapeake Bay

Wang¹,

Chen²,

Zhang³

et al. 2020

Preprint

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“…Furthermore, we determined the linear relationship between copies of ARGs ( Supplementary Tables S7 and S8 ) and rrn copy number ( Supplementary Table S9 ) based on the metagenomics data of HB from previous studies ( Figures 7C , D ; Dai et al, 2016 , 2018 ). And the copies of ARGs normalized by prokaryotic cell showed higher correlation ( R 2 = 0.52, p < 0.05) with rrn copy number, indicating that rrn copy number was more likely to represent the intrinsic antibiotic resistance.…”

Section: Resultsmentioning

confidence: 99%

Evaluating the Potential Antibiotic Resistance Status in Environment Based on the Trait of Microbial Community

Huang

et al. 2020

Front. Microbiol.

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The overuse of antibiotics has promoted the propagation and dissemination of antibiotic resistance genes (ARGs) in environment. Due to the dense human population and intensive activities in coastal areas, the health risk of ARGs in coastal environment is becoming a severe problem. To date, there still lacks of a quantitative method to assess properly the gross antibiotic resistance at microbial community level. Here, we collected sediment samples from Hangzhou Bay (HB), Taizhou Bay (TB), and Xiangshan Bay (XB) of the East China Sea for community-level ARGs analysis. Based on the 16S rRNA genes and predictive metagenomics, we predicted the composition of intrinsic ARGs (piARGs) and some related functional groups. Firstly, a total of 40 piARG subtypes, belonging to nine drug classes and five resistance mechanisms, were obtained, among which the piARGs encoding multidrug efflux pumps were the most dominant in the three bays. Secondly, XB had higher relative abundances of piARGs and pathogens than the other two bays, which posed higher potential health risk and implied the heavier impact of long-term maricultural activities in this bay. Thirdly, the co-occurrence network analysis identified that there were more connections between piARGs and some potential pathogenic bacteria. Several piARG subtypes (e.g., tetA , aacA , aacC , and aadK ) distributed widely in the microbial communities. And finally, the microbial diversity correlated negatively with the relative abundance of piARGs. Oil, salinity, and arsenic had significant effects on the variations of piARGs and potential pathogenic bacteria. The abundance-weighted average ribosomal RNA operon ( rrn ) copy number of microbial communities could be regarded as an indicator to evaluate the antibiotic resistance status. In conclusion, this study provides a new insight on how to evaluate antibiotic resistance status and their potential risk in environment based on a quantitative analysis of microbial communities.

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“…On a global scale Zinger et al (2011) observed Bacilli and Clostridia in higher abundances in coastal sediments vs. open water or deep-sea sediments. Bacilli and Clostridia can be indicators of urban fecal contamination in coastal watersheds, lagoons, and nearshore seawater ( Wu et al, 2010 ; Dubinsky et al, 2012 ; Dai et al, 2018 ). As the Beaufort Sea is extremely remote from urban life, it is not surprising that these taxa were less prevalent.…”

Section: Discussionmentioning

confidence: 99%

Patterns in Benthic Microbial Community Structure Across Environmental Gradients in the Beaufort Sea Shelf and Slope

2021

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The paradigm of tight pelagic-benthic coupling in the Arctic suggests that current and future fluctuations in sea ice, primary production, and riverine input resulting from global climate change will have major impacts on benthic ecosystems. To understand how these changes will affect benthic ecosystem function, we must characterize diversity, spatial distribution, and community composition for all faunal components. Bacteria and archaea link the biotic and abiotic realms, playing important roles in organic matter (OM) decomposition, biogeochemical cycling, and contaminant degradation, yet sediment microbial communities have rarely been examined in the North American Arctic. Shifts in microbial community structure and composition occur with shifts in OM inputs and contaminant exposure, with implications for shifts in ecological function. Furthermore, the characterization of benthic microbial communities provides a foundation from which to build focused experimental research. We assessed diversity and community structure of benthic prokaryotes in the upper 1 cm of sediments in the southern Beaufort Sea (United States and Canada), and investigated environmental correlates of prokaryotic community structure over a broad spatial scale (spanning 1,229 km) at depths ranging from 17 to 1,200 m. Based on hierarchical clustering, we identified four prokaryotic assemblages from the 85 samples analyzed. Two were largely delineated by the markedly different environmental conditions in shallow shelf vs. upper continental slope sediments. A third assemblage was mainly comprised of operational taxonomic units (OTUs) shared between the shallow shelf and upper slope assemblages. The fourth assemblage corresponded to sediments receiving heavier OM loading, likely resulting in a shallower anoxic layer. These sites may also harbor microbial mats and/or methane seeps. Substructure within these assemblages generally reflected turnover along a longitudinal gradient, which may be related to the quantity and composition of OM deposited to the seafloor; bathymetry and the Mackenzie River were the two major factors influencing prokaryote distribution on this scale. In a broader geographical context, differences in prokaryotic community structure between the Beaufort Sea and Norwegian Arctic suggest that benthic microbes may reflect regional differences in the hydrography, biogeochemistry, and bathymetry of Arctic shelf systems.

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Dynamics of Sediment Microbial Functional Capacity and Community Interaction Networks in an Urbanized Coastal Estuary

Cited by 22 publications

References 56 publications

Spatiotemporal Variations Dictate Stable and Resilient Microbiome Interactions in the Chesapeake Bay

Spatiotemporal Variations Dictate Stable and Resilient Microbiome Interactions in the Chesapeake Bay

Evaluating the Potential Antibiotic Resistance Status in Environment Based on the Trait of Microbial Community

Patterns in Benthic Microbial Community Structure Across Environmental Gradients in the Beaufort Sea Shelf and Slope

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