Glucose priming effect on microbial intercellular metabolic flux diversity in a marine intertidal sediment

Tao Ji; Zhiao Xu; Lihong Wen; Yuxue Yang; Yue Wu; Xueqin Zhao; Ruilian Yao; Rulong Liu; Yunping Xu; Weichao Wu

doi:10.1371/journal.pone.0335053

Abstract

Microbial communities inhabiting intertidal sediment show diverse metabolisms to adapt to hydrodynamic conditions. However, the priming effect of exogenous organic matter on microbial metabolic fluxes remains poorly understood. Here we investigated microbial intercellular activities in surface sandy intertidal sediment (0–5 cm depth, from the Nanhui tidal flat, East China Sea) under anoxic conditions by incubating with position-specific ¹³C labelled glucose isotopologues. Elevated production of ¹³C-CO₂ and phospholipid-derived fatty acids (PLFAs) was observed after 96-hour incubation, suggesting a positive priming effect on both catabolic and anabolic activities. The different incorporations of ¹³C-label from glucose isotopologues into CO₂ and PLFAs reveal distinct intracellular carbon fluxes through the central metabolic network. Specifically, microbial communities preferentially utilize the Embden-Meyerhof-Parnas (EMP) pathway (62% flux), followed by the Pentose Phosphate (PP, 25%) and Entner-Doudoroff (ED, 13%) pathways. These flux distributions closely mirror their corresponding functional genome proportion (55% EMP, 26% PP, 20% ED), suggesting a metabolic balance between energy yield and enzyme cost across glycolytic routes. Furthermore, metabolic flux analysis based on ¹³C-PLFA profiles highlights diverse metabolic strategies among different microbial taxa, primarily involving the EMP, ED and tricarboxylic acid pathways. This underscores the dual importance of energy production and carbon allocation for biomass synthesis. Given the high protein cost for the EMP pathway, energy acquisition may be prioritized by anaerobes to withstand the fluctuating conditions. Our results suggest that microbial interactions with intercellular networks may play a critical role in facilitating the priming and subsequent degradation of sedimentary organic matter.

Citation: Ji T, Xu Z, Wen L, Yang Y, Wu Y, Zhao X, et al. (2025) Glucose priming effect on microbial intercellular metabolic flux diversity in a marine intertidal sediment. PLoS One 20(11): e0335053. https://doi.org/10.1371/journal.pone.0335053

Editor: Saki Raheem, University of Westminster - Regent Street Campus: University of Westminster, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND

Received: May 6, 2025; Accepted: October 6, 2025; Published: November 26, 2025

Copyright: © 2025 Ji et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files. The metagenome data was archived on NCBI repository with access number: PRJNA1245057.

Funding: This study was funded by the Youth Program of National Science Foundation of China (W. Wu; 42106046), and by the Shanghai Science and Technology Commission (Y. Xu; 23230760300).

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Flat intertidal sediments are characterized by hydrodynamic conditions, resulting in the formation of various types of cohesive and permeable sediments. Microbial communities inhabiting these sediments exhibit adaptability to such fluctuating conditions, demonstrating metabolic flexibility both as generalists and specialists [1–3]. In coastal tidal flat, sandy sediments cover a larger area than mud sediments. Microbes within sandy sediments are particularly susceptible to disturbance caused by tidal and wave actions. Despite the relatively lean organic matter content in sandy intertidal sediments, microbial communities exhibit elevated metabolic activities, particularly in terms of CO₂ production and biomass synthesis. This is attributed to the rapid turnover of seawater and the supply of labile organic matter [1,4–7]. Consequently, how the exogenous labile organic matter shapes the microbial metabolism and further alters indigenous sedimentary organic matter remains uncertain.

Investigating microbial metabolism is essential for understanding the early diagenesis of organic matter and its long-term burial in sedimentary systems. Microorganisms play a pivotal role in the transformation of sedimentary organic matter through a variety of metabolic pathways, which generally follow the redox cascade, involving oxygen, iron/manganese, nitrate/nitrite and sulfate reduction. However, when oxygen becomes depleted, fermentation is the primary process for cracking organic matter, subsequently providing volatile organic compounds (e.g., acetate, propionate) that support anaerobic processes [8]. Microbes in intertidal sediments have adapted to facultative and anaerobic strategies [8,9]. Sugars, primarily originating from phytoplankton, generally exhibit a more rapid turnover rate than other substrates, e.g., lipids and proteins [10]. Glucose is thus a commonly used substrate to stimulate microbial activity and metabolism, further gaining microbial processes of the labile organic matter [11,12]. In addition, glucose serves as an ideal component to study the priming effect of exogenous organic on the indigenous sedimentary organic matter [13–16].

Glucose metabolism is a well-defined process that involves upstream glycolytic pathways, such as the Embden-Meyerhof-Parnas (EMP), Pentose Phosphate (PP), and Entner-Doudoroff (ED) pathways, as well as the downstream tricarboxylic acid (TCA) cycle. These pathways are integral to central carbon metabolism pathways (CCMPs), which provide the foundation for carbon and energy metabolism for other biomolecules, such as proteins, lipids and carbohydrates. Moreover, the TCA cycle functions as a central energy engine for microbial growth. Although the three main glycolytic pathways share similarity in CCMPs, microbes tend to preferentially utilize specific pathways for catabolic processes. This preference can be characterized by microbial intercellular fluxes, which describe the distribution and direction of carbon flow through the CCMPs [17–20]. For instance, Escherichia coli and Bacillus subtilis metabolize glucose primarily through EMP and PP pathways with the flux ratios of 80% and 20%, respectively [21]. By contrast, Pseudomonas species (e.g., P. putida, P. aeruginosa, and P. fluorescens) predominantly utilize the ED pathway with over 90% flux directed through this pathway and only a minor flux into the PP pathway [22,23]. The large-scale studies of marine bacteria reveal a distinct metabolic flux pattern with 90% of strains using the ED pathway, compared to only 10% using the EMP pathway. However, the genome surveys suggest that higher ATP yield associated with the EMP pathway may be advantageous for anaerobes, whereas the ED pathway is more common among facultative anaerobes [24]. The apportionment of carbon flux through the CCMPs is hypothesized to be driven by energy supply and resistance to oxidative stress [24,25]. Therefore, further investigation into the flux patterns of microbial species, particularly anaerobes, is needed to deepen our understanding of microbial metabolic strategies.

Microbial intercellular flux can be estimated using position-specific ¹³C substrate incubation, a technique known as ¹³C-metabolic flux analysis techniques (¹³C-MFA), which has been widely used for growth optimization or specific substrate production in metabolic engineering [26,27]. The selection of position-specific ¹³C labelled glucose (e.g., glucose-1-¹³C or glucose-6-¹³C) is primarily driven by the carbon divergence on the CCMPs. Additionally, the concurrent application of different ¹³C labelled isotopologues enables the detailed tracing of intercellular fluxes in both individual microbes and microbial communities [8,18,20,28]. This approach has been employed to investigate intercellular flux patterns within environmental microbial communities, revealing diverse intercellular flux profiles under different conditions [18,19,29–31]. In soil systems, microbial communities predominantly use the PP pathway for glucose metabolism [19,32,33], which contrasts with the preference observed in pure strains. Detailed modelling analysis revealed equal importance for the ED and PP pathways [19,31]. In contrast, microbes in intertidal sediments favor the EMP as the dominant fermentation route to metabolize glucose [8]. Environmental stressors, e.g., water content, temperature, respiration inhibitor, oxygen level and even different growth times, can alter the flux pattern [8,30,31,34].

Metabolic flux patterns for environmental microorganisms are mainly estimated at the level of microbial communities, rather than the diversity at group or individual levels. Consequently, the behavior of specific microbial groups or individuals, as well as their interactions during glucose metabolism, remains poorly understood. To the best of our knowledge, Hutchinson et al. [8] first reported microbial flux patterns in intertidal sediments, identifying the dominance of the EMP pathway for glucose metabolism. These flux patterns exhibit significant variability depending on oxygen level and sampling time points. This raises the question of whether such variation is due to the rapid regulation of dominant microbial groups or the dynamic shifts in microbial groups or individuals with distinct flux patterns in response to environmental stressors. Studies of pure strains have demonstrated diverse flux patterns across different species, suggesting that exploring microbial groups or individuals could further elucidate the diversity of metabolic flux patterns within microbial communities. Additionally, functional genomic surveys could provide valuable insights into the regulatory mechanisms that govern these flux patterns.

To further probe the mechanism controlling the intercellular flux patterns of microbial communities in intertidal sediment, we conducted the incubation of intertidal sediment collected from the Nanhui tidal flat, East China Sea by using ¹³C-labelled glucose isotopologues. Combining the modelling ¹³C -MFA approach with the ¹³C-CO₂ and PLFA production, this study aims: 1) to quantify the flux patterns across the microbial community and group levels; to further explore the relationship between functional genome and intercellular fluxes as well as functional metagenome abundance. This study is expected to provide insight into the intercellular dynamics of microbial groups or individuals, and further address microbial metabolism in the utilization of sedimentary carbon.

2. Materials and methods

2.1. Material and incubation setup

In April 2024, surface sediment (0−5 cm) was collected from the intertidal zone of Nanhui East Shoal, East China Sea (31.0213° N,121.9365°E) during low tide. The sediment was placed into sterile sampling bags. Additionally, 2 L of in-situ seawater was collected for further incubation. The sediment was mainly consisted of silt and sand with TOC amount ranging from 0.01–0.34 at% (with an average of 0.18wt%) [35].

Prior to incubation, the sediment was thoroughly homogenized. In-situ seawater was sterilized by filtration through a 0.22 µm membrane. A slurry mixture was then prepared by combining 12 g of wet sediment with 60 mL of seawater in a 100 mL wide-mouth bottle (measured volume of 140 mL). This operation was conducted in an anoxic glovebox. The headspace gas in each bottle was flushed with N₂ to keep anaerobic conditions following by the pre-incubation at 10°C for 3 days. Afterwards, seven types of ¹³C-labelled glucose isotopologues were added: D-glucose-1-¹³C, D-glucose-2-¹³C, D-glucose-3-¹³C, D-glucose-4-¹³C, D-glucose-5-¹³C, D-glucose-6-¹³C and D-glucose-U-¹³C₆ (99%, Cambridge Isotope Laboratories, USA). Additionally, controls including unlabelled glucose, autoclaved samples, and no-glucose addition (Table 1). The sterilization step was to autoclave sediment at 120 ºC for 20 min. Each bottle was added with 1 mL of 6 mM glucose stock solution, resulting in a final concentration of approximately 100 μM in medium. For each treatment, three replicates were processed.

Download:

Table 1. Overview of sediment incubation with ¹³C-labelled glucose.

https://doi.org/10.1371/journal.pone.0335053.t001

After glucose addition, the samples were incubated at 10 °C for 96 hours by monitoring the headspace CO₂ concentration with the sampling time points at 6, 12, 24, 36, 48, 72, and 96 hours. At each interval, 10 mL of headspace gas was collected in aluminium gas bags, and 0.6 mL was promptly used to determine CO₂ concentration via GC-FID. Remaining CO₂ gas at 72 hours was analysed for δ¹³C analysis. At the end of the incubation, slurries were centrifuged and the supernatants collected for further dissolved inorganic carbon (DIC) analysis. 0.5 g sediment of each replicate was collected and stored at −80°C for metagenomic analysis. DNA from different types of glucose were pooled for analysis. The remaining sediments were lyophilized for further analysis of phospholipid-derived fatty acids (PLFAs). The detailed analysis methods for CO₂ concentration, DIC and their carbon isotope composition are attached in the S1 File.

2.2. PLFA extraction and analysis

Five grams of lyophilized sediment were extracted in an ultrasonic bath with the modified Bligh-Dyer extraction method [4]. Prior to extraction, 5 µg of C₁₉-PC (1,2-dinonadecanoyl-sn-glycero-3-phosphocholine) was added as an internal standard. Twenty mL of methanol: dichloromethane (DCM): phosphate buffer (pH 7.4; 2:1:0.8) was used following by sonication for 15 min and centrifugation at 3,500 rpm for 10 min. This step was repeated three times. The supernatant was combined and transferred to a separatory funnel following by the addition of DCM and water (1:1) for separation. The lower organic phase was collected and dried under N₂. The total lipid extracts were purified on a silica column and eluted with 5 mL of DCM, acetone and methanol. The PLFA fraction in methanol was then saponified and esterified with sequential KOH (5%) and BBr₃ (14%) in methanol solution. The yield fatty acid methyl esters (FAMEs) were analysed on gas-chromatography (GC), GC-mass spectrometer (GC-MS), and GC-isotope ratio-MS (GC-IRMS) for concentration and ¹³C analysis. The detailed methodology is attached in the S1 File.

2.3. DNA extraction and metagenome analysis

Sediment samples of 0.5 g were used for DNA extraction using the FastDNA® SPIN Kit for Soil (MP Biomedicals, US) following the manufacturer’s instructions. The DNA concentration and purity were measured on a Quantus Fluorometer (Picogreen), and integrity was assessed using 1% agarose gel electrophoresis. DNA extracts were fragmented to an average size of about 350 bp using Covaris M220 (Gene Company Limited, China) for paired-end library construction. Metagenome sequencing was performed on Illumina NovaSeq ™XPlus (Illumina, USA) sequencing platform (Shanghai Majorbio Biomedical Technology Co., Ltd.), the raw metagenomic sequencing data associated with this project have been deposited in the NCBI Short Read Archive database with accession number: PRJNA1245057.

Metagenome sequencing data were subjected to quality control. Adapter sequences at the 3’ and 5’ ends of reads were trimmed using FASTP [36] (https://github.com/OpenGene/fastp, v0.23.0). Low quality reads of less than 50 bp and average scores below 20 were removed to retain high-quality sequences. Contigs longer than 300 bp were selected from the quality-filtered reads for further processing. De novo assembly was performed using MEGAHIT [37] (https://github.com/voutcn/megahit, version 1.1.2). Open reading frames (ORFs) were predicted from assembled contigs using Prodigal [38] (https://github.com/hyattpd/Prodigal, v2.6.3). Only genes with a nucleic acid length of ≥ 100 bp were retained and translated into amino acid sequences. The predicted gene sequences of all samples were clustered using CD-HIT with a 90% identity threshold and 90% coverage [39] (http://weizhongli-lab.org/cd-hit/, version 4.6.1). The longest gene in each cluster was selected as representative sequence to construct non-redundant gene set.

High-quality reads were aligned to the non-redundant gene using SOAPaligner with a 95% identity threshold [40] (https://github.com/ShujiaHuang/SOAPaligner,v2.21). Gene abundance in each sample was calculated based on the alignment results. For taxonomic and functional annotation, amino acid sequences from non-redundant gene sets were compared against NCBI-NR, COG and KEGG databases using Diamond [41] (https://github.com/bbuchfink/diamond,version 2.0.13) with BLASTP (e-value of 1e-5). Taxonomic assignments were determined using the NCBI-NR library, and species abundances were calculated by summing the gene abundances assigned to each species. Functional annotation was performed by aligning to the EggNOG database with COG abundance calculated as the sum of the abundances of associated genes. Similarly, gene functions were annotated to KO, Pathway, EC and Modules using the KEGG database, and the abundances of each functional category were derived from the cumulative abundance of the corresponding genes.

2.4. Calculation method for ¹³C enrichment

Atomic Percent Excess (APE) of ¹³C-CO₂ and PLFAs were calculated to compare ¹³C enrichment of different position-specific ¹³C labelled incubations.

(1)

(2)

(3)

Where ¹³F_t0 and ¹³F_tx represent the fractional abundance of ¹³C in the produced CO₂ or PLFAs at start (t₀) and at the time of sampling (t_x), respectively. The value of ¹³F_t0 was estimated using that from raw sediment at harvest by assuming no variation of ¹³C values of raw sediment during the incubation. ¹³R is the atom ratio of ¹³C/¹²C of CO₂ or PLFAs and ¹³R_std is the standard value 0.011180 for VPDB.

The measured ¹³C-APE values from the six singly ¹³C-labelled glucose treatments were further normalized by their cumulative sum (Eq. 4). This normalization reflects the relative contribution of individual carbon atoms in glucose to the total ¹³C observed in the produced CO₂ and PLFAs, compared to the uniformly ¹³C-labelled glucose. The normalized [¹³C-APE]_i values for each glucose carbon position, denoted as [¹³C-APE]_{i_nor}, were subsequently used in the modelling flux analysis in Eq. 5 (Section 2.6).

(4)

Where [¹³C -APE]_i refers to the ¹³C-APE of CO₂ or PLFA from incubations with six singly ¹³C labelled glucose isotopologues, where i denotes the position of the ¹³C label in glucose, e.g., Glc-1-¹³C, Glc-2-¹³C, Glc-3-¹³C etc.

2.5. Modelling ¹³C-metabolic flux analysis

The ¹³C-metabolic flux analysis was conducted on the modified model developed by Wu et al. [19,47]. The biochemical reactions are listed in S2 Table. In MFA analysis, the first reaction on the metabolic network (i.e., Glc → G6P) was assumed to 100 and other reactions were calculated relative to first reaction. The simulated ¹³C-CO₂ or acetyl-CoA enrichment under six ¹³C-labelled glucose (as [¹³C-APE]_{i _sim}) was used for the modelling optimization. The optimization objective is as in Eq. 5. In this study, the ¹³C-APE values of PLFAs were assumed to reflect those of the node acetyl-CoA in the CCMPs (S1 Table) given that acetyl-CoA is the precursor for the PLFA synthesis [42]. During modelling, the ¹³C-APE value vector of each PLFA was independently applied in the flux analysis. Additionally, microbially respirated CO₂ by microbes was also used for metabolic flux analysis proposed by literature [18,43].

(5)

Where [¹³C-APE]_{i_nor} is the ¹³C-APE values after normalization in Eq. 4. [¹³C-APE]_{i _sim} is the simulated values in the model. The optimization algorithm employed the non-linear optimization function fmincon built in MATLAB (2021, US). The model details were referred to work by Wu et al. [19,47] and the raw code in the Supplementary data.

3. Results

3.1. CO₂ production during incubation

Headspace CO₂ concentrations were monitored to assess microbial utilization of the amended glucose (Fig 1). During the initial 36 hours, no obvious CO₂ accumulation was observed. Subsequently, glucose-amended sediments exhibited markedly higher CO₂ production rates with an average increase rate of 31.9 ppm/h, equivalent to 0.103 μmol/h (Figs 1a and 1b). Raw sample also showed CO₂ production but at lower rates of 14.64 ppm/hour (eq. 0.0473 μmol/h). Autoclave control samples exhibited no detectable CO₂ production during the incubation period. After 96 hours, the CO₂ level in the glucose treatments ceased to increase, and the incubation was then stopped for the following genome and chemical analysis.

Download:

Fig 1. CO₂ gas production during incubation and ¹³C-CO₂ values at harvest.

(a) CO₂ concentrations (ppm) and (b) corresponding molar amount (μmol) in headspace were monitored over 96 hours of incubation. (c) δ¹³C and (d) atom percent excess of CO₂ were measured from gas samples collected at 72 hours. Calculation based on Eq. 1-3. Label C1 ~ C6 and U represent the different ¹³C-lablled glucose isotopologues: glucose-1-¹³C, glucose-2-¹³C, glucose-3-¹³C, glucose-4-¹³C, glucose-5-¹³C, glucose-6-¹³C, and glucose-U-¹³C₆, respectively.

https://doi.org/10.1371/journal.pone.0335053.g001

At the end of incubation, the DIC concentration in the raw samples was 2.55 ± 0.37 mM on average, while it was much lower (1.60 ± 0.40 mM) in the treatment with natural glucose (S1 Table). In contrast, the ¹³C-glucose treatment showed a notably higher DIC concentration with an average of 2.95 ± 0.35 mM (S1 Table). The applied amount of glucose was identical, but the yielded CO₂ varied, likely due to the sample heterogeneity. Excluding the natural glucose treatment, the excess DIC produced in the ¹³C-glucose treatment would be 0.40 ± 0.38 mM.

At harvest, headspace CO₂ and DIC were sampled for δ¹³C analysis. The raw sediments exhibited δ¹³C-CO₂ values of −10.2 ± 1.3 ‰, while samples amended with glucose showed slightly more negative values of −13.2 ± 1.2 ‰. In contrast, substantial ¹³C enrichment was observed in the samples with ¹³C-glucose amendments. A strong correlation between δ¹³C values of gas CO₂ and DIC was observed (S1 Fig). As expected, treatments with uniformly ¹³C-labeled glucose produced the highest δ¹³C-CO₂ values of up to 9730 ‰; whereas singly ¹³C -labeled glucose yielded δ¹³C-CO₂ values ranging from 800 to 2070 ‰ (Fig 1c). Correspondingly, ¹³C enrichment (expressed as ¹³C -APE) ranged from 0.88 to 2.23 at% with the sum of 9.53 at%, which was close to 9.62 at% observed for the uniformly ¹³C-glucose treatment (Fig 1d). One-way ANOVA revealed significant differences in δ¹³C and ¹³C-APE values (p = 0.011) among singly ¹³C-glucose treatments. The δ¹³C and ¹³C-APE values follow the order of ¹³C position in glucose: C3 ~ C4 > C1 ~ C2 > C5 > C6 (Fig 1).

3.2. ¹³C-PLFA concentrations

The most abundant straight and unsaturated PLFAs in the sediment were C_16:0, C_16:1ω7, C_18:1ω9, and C_18:1ω7, with individual concentrations ranging from 0.2 to 15.7 μg/g. After incubation with glucose addition, the total PLFA concentrations exhibited an obvious increase by up to two-fold (Fig 2a). To account for potential sample heterogeneity (also observed in CO₂ production; Fig 1), the relative abundance of PLFAs was also calculated. Notably, the relative proportion of C_16:1ω7 increased markedly from 6.2% to 15.2% (Fig 2b).

Download:

Fig 2. PLFA concentrations and carbon isotope enrichment after incubation with various ¹³C-glucose treatments at harvest.

(a) PLFA concentrations (μg/g dry sediment) and (b) relative abundances (%) across treatments. (c) ¹³C composition (expressed as δ¹³C) of individual PLFAs in incubations with singly ¹³C-labelled and (d) uniformly ¹³C -labelled glucose treatments. Labels C1 to C6 refer to the treatments with six singly ¹³C-labelled glucose isotopologues.

https://doi.org/10.1371/journal.pone.0335053.g002

For the treatment of ¹³C-labelled glucose, δ¹³C values of PLFA varied with the ¹³C position in glucose. As expected, the uniformly ¹³C-labelled glucose yielded the highest ¹³C-enrichment, particularly in C_16:1ω7 and C_18:1ω7, which reached δ¹³C values of up to 61,000 ‰ and 65,000 ‰, respectively (Fig 2d), corresponding to ¹³C-APE of 39.7 at% and 40.8 at%. Notably, even the singly labelled glucose treatments resulted in substantial ¹³C incorporation into these PLFAs. However, ¹³C-enrichment patterns were highly position-specific (Fig 2c). For iC₁₅, aC₁₅, C₁₅, and C₁₈, the ¹³C enrichment pattern followed the order of ¹³C position in glucose as: C1 > C5 ~ C6 > C2 > C3>> C4 (Fig 2c). In contrast, for C₁₆, C_16:1ω7 and C_18:1ω7, the order as: C2 > C5 > C6 ~ C1 > C3>> C4 (Fig 2c).

3.3. Modelling metabolic flux patterns

The ¹³C-CO₂ production was used to estimate the flux pattern for microbial communities in the sediment. At the community level, flux partitioning through CCMPs was estimated as 60.2% via EMP, 12.3% via PP, and 24.3% via ED pathways (Fig 3a). In contrast, ¹³C-MFA using individual ¹³C-PLFA profiles revealed the flux pattern of group- or species-level. The average fluxes across all PLFAs were 59.0% (EMP), 20.1% (PP), 15.5% (ED), consistently showing the EMP pathway as dominant but differing in PP and ED contributions. Notably, no net flux through the entire PP pathway was observed.

Download:

Fig 3. Intercellular metabolic flux through central carbon metabolism estimated by ¹³C-CO₂ (a) and individual ¹³C-PLFA (b).

The flux values for each biochemical reaction are proportional to the first step of glucose phosphorylation (Glucose -> Glucose-6-phosphate). The values are expressed as average and standard deviation.

https://doi.org/10.1371/journal.pone.0335053.g003

Carbon Use Efficiency (CUE), widely used in soil ecology, was also applied to estimate intercellular biochemical efficiency, following the assumption from Wu et al. [19,47] that excreted metabolites act as sink carbon in sediment. PLFA-based modelling indicated diverse CUE values across microbial groups, with the lowest CUE (i.e., 0.21; Table 2) found in producers producing aC₁₅ and C₁₅ PLFAs. These groups also exhibited extremely high fluxes through TCA with up to ca. 160% relative to glucose uptake, twice that observed in other microbial groups (Table 2).

Download:

Table 2. Microbial intercellular fluxes in the central carbon metabolism.

https://doi.org/10.1371/journal.pone.0335053.t002

3.4. Metagenome characterizations

Gene annotation based on the NCBI-NR database illustrated the composition of the top 10 most abundant species across all samples, with low-abundance taxa grouped as others (Fig 4a). After the addition of glucose, microbial community composition structure did not obviously change, but the percentage slightly changed. For example, the percentage of Pseudomonadota increased from 51.3% to 57.2%, whereas the percentage of Bacteroidota declined from 4.7% to 2.9% (Fig 4a). At the genus level, Woesela, Illumatobacter, and Pseudomonas were the dominant groups in both treatments, of which the percentage showed significant change: percentages of Woesela decreased vs Pseudomona increased. In addition, the percentage of rare genus Amphritea, Pseudomonas and Vibrio increased substantially (Fig 4b).

Download:

Fig 4. Microbial community composition and functional genomes after glucose amendment.

(a) Taxonomic profiles at the phylum level and (b) genus level showing relative abundance in raw sediment and glucose-amended samples. (c) Glucose addition stimulates diverse functional genes annotated by the COG database. X: mobilome, prophages, transposons; G: carbohydrate transport and metabolism; M: cell wall/membrane/envelope biogenesis; O: posttranslational modification, protein turnover, chaperones; R: general function prediction only; K: transcription; I: lipid transport and metabolism; L: replication, recombination and repair; J: translation, ribosomal structure and biogenesis; C: energy production and conversion, E: amino acid transport and metabolism. Statistical significance was determined using STAMP with a p-value threshold of 0.05. Raw = Light blue, Glucose = Orange.

https://doi.org/10.1371/journal.pone.0335053.g004

In addition to taxonomic composition, the functional genes of microbial communities showed further insights into microbial community responses to the glucose treatment. Based on COG database annotation, the addition of glucose stimulated the expression of mobilome (prophages, transposons; X), carbohydrate transport and metabolism, replication, recombination and repair (L), and amino acid transport and metabolism (E: Fig 4c). In contrast, KEGG-based functional annotation further showed that glucose enhanced activities in bacterial chemotaxis, ABC transporters, two-component system, and carbohydrate metabolism pathways (Fig 5). In particular, the pathway of biosynthesis of secondary metabolites was dominant, but the glucose addition did not appear to stimulate this process (Fig 5).

Download:

Fig 5. Microbial functional gene shifts in response to glucose addition based on KEGG annotation.

Proportions of functional genes and mean proportional difference were performed using STAMP with a p-value threshold set to 0.05.

https://doi.org/10.1371/journal.pone.0335053.g005

4. Discussion

4.1. Glucose priming effect for microbial communities

The artificial addition of glucose is intended to simulate the degradation of indigenous organic substrate on sedimentary organic matter. After the addition of glucose, substantial CO₂ production and significant incorporation of ¹³C label into both CO₂ and microbial biomass (PLFAs as an example) were observed. These findings indicate simultaneous activation of both anabolic and catabolic processes among sediment microbial communities, representing a critical component of secondary production in sediment [4,44]. However, the energy production rate, as inferred from the CO₂ production curve, appeared relatively slow during the initial 36-hour monitoring (Fig 1). This contrasts with prior results from a ¹⁴C-glucose experiment, which showed fast energy release within hours or even minutes [11]. The discrepancy may be attributed to the equilibrium of dissolved inorganic and headspace after flushing the headspace for three days of pre-incubation, as well as to limitations in analytical sensitivity.

Moreover, the priming effect induced by glucose addition in this study appears to be transient, as CO₂ production plateaued after approximately 96 hours; whereas the untreated control samples exhibit a continued increase in CO₂ production beyond 72 hours, eventually reaching levels comparable to those observed in the glucose-amended treatments. The plateau of CO₂ production observed during glucose treatment likely reflects the limitation of nitrogen and phosphorus on cell growth and metabolism after the large addition of labile carbon [45,46]. In addition, this trend may imply an inhibitory effect of glucose-derived metabolites during incubation, such as the accumulation of volatile fatty acids, which are commonly detected in intertidal sediments [8]. Although volatile fatty acids were not directly measured in this study, metagenomic analysis reveals that the dominant functional pathway-biosynthesis of secondary metabolites- was downregulated by the addition of glucose (Fig 5). However, it requires further investigation into the effect of the potential glucose on the long-term incubation.

4.2. Anabolic activity in intertidal sediment

In addition to the respired CO₂, glucose was also incorporated into newly synthesized microbial biomass, as evidenced by the production of ¹³C-PLFA. Notably, specific unsaturated PLFAs such as C_16:1ω7, C_18ω9, and C_18:1ω7 exhibit a substantial increase in concentration. Despite inter-replicate variability, the relative abundance of PLFA C_16:1ω7 increased significantly by 10% (Fig 2). When combined with community composition, this pattern suggests that C_16:1ω7 is probably produced by Pseduomodota, which exhibits an increase in the gene abundance (Fig 3). This is further supported by the observation of C_16:1ω7 in pure strains of Pseduomodota [47]. Total PLFA concentration rose from 4.2 μg g_dw^-1 to 12.4 μg g_dw^-1 after the glucose addition, corresponding to an increase of 46.7 μg C, which is relative to 10% of the applied glucose amount (6 μmol glucose eqv. 432 μg C; Detailed calculation sees S1 File). When considering bacterial-specific PLFAs such as C₁₅, C_16:1ω7, and C_18:1ω7 in sediment [13], their concentration would be 2.53 ± 0.60 μg g_dw^-1. This translates to an estimated biomass carbon content of 838 ± 28 μg C (detailed calculation attached in S1 File), which is almost double the amount of glucose. This discrepancy suggests that exogenous glucose alone is insufficient to meet microbial C demands for biomass synthesis with extra contribution from indigenous sediment.

Together with the results from ¹³C-CO₂ and ¹³C-PLFAs, these findings indicate that the glucose addition stimulates both catabolic and anabolic processes, both of which are supported by indigenous sedimentary carbon. These elevated energy requirements associated with these processes are further supported by the significant upregulation of ATP-binding cassette (ABC) transporter genes after the glucose addition (Fig 5) given their known role in ATP-dependent glucose transport and hydrolysis [48,49]. Additionally, genes associated with biosynthetic pathways for lipids and amino acids are also upregulated (Fig 4), further highlighting the increased carbon and energy demand for microbial biomass synthesis. Considering a low TOC in sandy tidal flat (ca. 0.18 wt%) [50], high C demand for benthic microbes suggests a high turnover efficiency, which is likely facilitated by hydrodynamic nutrient supply [4,8,13,51,52].

4.3. Diverse metabolic flux activities within microbial communities

The availability of diverse organic matter foster a variety of metabolic strategies within communities [1,9], which can be insighted from the distinct production patterns of ¹³C-CO₂ and PLFAs under different ¹³C glucose isotopologues. This phenomenon is further associated with the intracellular fluxes observed on CCMPs observed in pure strains such as E. coli, B. subtilis, P. putida and S. cerevisiae, as well as in environmental communities [8,19].

In this study, we used ¹³C-CO₂ and ¹³C-PLFAs independently to estimate intercellular fluxes through CCMPs. The former represents an average result at microbial community levels, while the latter provides insights at group and even individual levels. Based on the results of ¹³C-CO₂, microbes in the sediment metabolize glucose through EMP, PP and ED with the flux ratios of 60.2%, 12.3%, and 24.3%, respectively. These findings are generally consistent with the average observations derived from ¹³C-PLFAs (Table 2). The dominance of EMP as the primary glycolytic route aligns with other studies at different sites [8]. However, Hutchinson et al. [8] found large variability in the EMP flux, ranging from 30% to 100%, across different intertidal sediments (muddy vs sandy), sampling points and incubation conditions (oxic vs anoxic). Such inconsistency might be attributed to different label strategies and applied models, which would result in mathematical differences in the flux estimation for specific microbes [20].

The overall pattern of microbial metabolic flux exhibits comparability across different sites when using identical models. When focusing solely on anoxic conditions as in this study, the estimated flux through EMP by Hutchinson et al. [8] remains relatively high at different time points. It remains debatable whether metabolic flux patterns are significantly altered by shifts in microbial community composition during incubation. However, our genomics data do not support this hypothesis, as microbial community structure showed no significant change after glucose treatment (Fig 3). Metabolic flux patterns are primarily governed by the functional genome [53]. In this study, we find that all three glycolytic pathways are complete (S2 Fig) with gene abundance of 56%, 25%, 19%, respectively. These ratios are remarkably consistent with metabolic flux estimates based on ¹³C-CO₂ production, which yield 60.2%, 12.3%, 24.3% for EMP, PP and ED pathways, respectively. However, it should be acknowledged that gene abundance and metabolic fluxes are inherently distinct processes. Beyond genomic potential, intercellular fluxes are shaped by a complex interplay between enzymic regulation and environmental conditions [53,54]. Due to biochemical variability, metabolic fluxes can deviate from genomic predictions. For example, 1 mole of glucose yields two molar ATP via the EMP pathway while 1 molar ATP via the ED pathway in specific microbes. While EMP offers a higher energy yield, it typically operates more slowly than ED or PP, which may be advantageous for microbes with high energy demands but limited respiration rates under anoxic conditions. The observed similarity between genome abundance and metabolic flux ratios in this study may reflect an adaptive trade-off microbial community with balancing energy production efficiency and enzyme cost [24]. Altogether, the equivalence between gene abundance and metabolic flux distribution suggests that genomic regulation plays a role in shaping intracellular metabolic processes at the community level, at least under the conditions examined in this study.

To further elucidate the diversity of metabolic flux across different microbial taxa, we performed the ¹³C-MFA using individual ¹³C-PLFA, which provides taxonomical resolution. Across all bacterial groups, the EMP pathway is the dominant route to catalyze glucose with the fluxes ranging from 37.7% to 78%. In contrast, fluxes through PP pathways remain relatively constrained, ranging from 15.5% to 20.5%, whereas the ED pathway varies largely between 1.5% and 34.3%. These results indicate that both EMP and ED pathways play significant roles in microbial glucose metabolism in intertidal sediments. This observation is consistent with the studies on marine bacterial isolates [25]. However, a large proportion of marine bacterial strains appear to preferentially use ED over EMP [25], which reflects the metabolic adaptations of anaerobic microbial communities prevalent in intertidal sediments.

Furthermore, clustering of metabolic flux patterns across individual PLFAs revealed distinct pathway utilization among bacterial groups, particularly in their engagement of the EMP and ED pathways and downstream TCA cycle activities (Fig 6). Specifically, producers of aC₁₅ and C₁₅ PLFAs allocate 76% glucose through EMP, with the remaining fluxes distributed between the PP and ED pathways. A similar pattern was observed for C_16:1ω7 producers, albeit with a notably higher flux directed into the TCA cycle. In marine sediments, branched and straight C₁₅ PLFAs are likely contributed by sulfate reducers [55,56], which predominantly use acetate as carbon and energy source. The conversion of glucose into acetate may enhance TCA cycle activity in sulfate reducing bacteria, reflecting their metabolic preference and capacity for acetate oxidation. In contrast, bacterial producers of C_18:1ω9 and C_18:1ω7, commonly found among Gram-negative bacteria, demonstrate more balanced usage of three glycolytic pathways with equal flux distribution.

Download:

Fig 6. NMDS analysis of metabolic flux patterns for different bacterial groups based on PLFAs.

Purple dots: unsaturated PLFAs; green dots: saturated PLFAs.

https://doi.org/10.1371/journal.pone.0335053.g006

PLFA C_16:1ω7 exhibits a prominent change in concentration after the addition of glucose. Together with microbial community data, this biomarker is likely derived from members of the phylum Pseudomonadota as previously demonstrated in pure strains [47,57]. In marine sediments, Pseudomonadota appear to channel ca. 60% of glucose through EMP. This contrasts with observation from pure strains of Pseudomonas, e.g., P. putida, P. fluorescens, which prefer to crack glucose via ED pathways accounting for over 90% of flux [22,23]. This apparent discrepancy may be attributed to metabolic interactions within the community. Notably, Pseudomonas species lack fructose-6-phosphatase, a key enzyme required for the forward operation of the EMP pathway. As a result, these organisms cannot efficiently utilize the EMP pathway unless fructose or other intermediates are externally supplied [22,23]. The elevated EMP flux observed in Pseudomonas in sediments could therefore result from cross-feeding, whereby metabolic metabolites are provided by co-occurring microbial taxa. Such cross-feeding interactions may play a crucial role in supporting microbial energy acquisition through a more ATP-efficient EMP pathway under anoxic conditions. This hypothesis is further suggested by genomic evidence, which shows upregulation of ABC transporter genes, which are energetically expensive systems responsible for the uptake of sugars and metabolites (Fig 4) [49]. Nevertheless, in the context of organic-lean intertidal sediment, anaerobic microbes appear to rely heavily on such cooperative metabolic networks to support both anabolic and catabolic processes. This interplay underscores the ecological importance of metabolic cross-feeding in optimizing energy yield and sustaining microbial activity via the EMP pathway, particularly under energy-limited conditions [24]. Considering the possibility of aerobic respiration, oxygen supply could stimulate energy production pathways and thereby regulate the biomass production including lipids [58]. A distinct pattern of ¹³C incorporation into fatty acids, similar to that observed in the soil system [19], would be expected under such conditions. However, this hypothesis requires further investigation in coastal sediment.

5. Conclusion

In this study, we investigated the microbial intercellular flux patterns in an intertidal sediment from the Nanhui tidal flat, East China Sea, by incubation with position-specific ¹³C labelled glucose. High production of ¹³C-CO₂ and PLFAs relative to the supply of limited glucose indicates a positive priming effect for microbes to use indigenous sedimentary organic matter. Diverse ¹³C-PLFAs production patterns by different ¹³C in glucose confirm the differential microbial groups with distinct intercellular flux patterns within communities. The metabolic flux patterns on three main glycolytic pathways are mainly controlled by their functional genome abundance, suggesting balanced energy and protein cost for each pathway across the microbial community level. Modelling metabolic flux analysis reveals the dominance of the classic EMP pathway for microbes to use glucose for high energy yield. Metabolite cross-feeding may be an important mechanism for anaerobes to select the thermodynamically slow but high energy yield pathway EMP in marine sediments. This study provides an insight into the energy and cost input for microbes inhabiting intertidal sediments with position-specific ¹³C-labelling glucose, further addressing the role of microbial intercellular flux pathways for the understanding of quantifying the carbon fluxes in intertidal sediments.

Supporting information

S1 File. Instrumental methods for CO₂ and PLFA analysis, and calculation method for priming effect.

https://doi.org/10.1371/journal.pone.0335053.s001

(DOCX)

S1 Table. Concentration and ¹³C composition of DIC and CO₂ in the end of incubation.

https://doi.org/10.1371/journal.pone.0335053.s002

(DOCX)

S2 Table. The modelled biochemical ratios for EMP, PP, ED, TCA, gluconeogenesis, and anaplerotic reactions.

https://doi.org/10.1371/journal.pone.0335053.s003

(DOCX)

S1 Fig. Correlation of carbon isotope between gas CO₂ and dissolved inorganic carbon (DIC).

https://doi.org/10.1371/journal.pone.0335053.s004

(DOCX)

S2 Fig. Reconstructed central carbon metabolism pathways based on the genome.

https://doi.org/10.1371/journal.pone.0335053.s005

(DOCX)

Acknowledgments

We thank Ke Xu, Dayu Duan, and Prof. Dr. Hongbo Pan for assisting with the sampling of intertidal sediments. We thank Yiwei Zhang and Prof. Dr. Dong Zhang for assisting the measurement of carbon isotope analysis.

References

1. Chen Y-J, Leung PM, Cook PLM, Wong WW, Hutchinson T, Eate V, et al. Hydrodynamic disturbance controls microbial community assembly and biogeochemical processes in coastal sediments. ISME J. 2022;16(3):750–63. pmid:34584214
- View Article
- PubMed/NCBI
- Google Scholar
2. Kessler AJ, Chen Y-J, Waite DW, Hutchinson T, Koh S, Popa ME, et al. Bacterial fermentation and respiration processes are uncoupled in anoxic permeable sediments. Nat Microbiol. 2019;4(6):1014–23. pmid:30858573
- View Article
- PubMed/NCBI
- Google Scholar
3. Chen Y-J, Leung PM, Wood JL, Bay SK, Hugenholtz P, Kessler AJ, et al. Metabolic flexibility allows bacterial habitat generalists to become dominant in a frequently disturbed ecosystem. ISME J. 2021;15(10):2986–3004. pmid:33941890
- View Article
- PubMed/NCBI
- Google Scholar
4. Wu W, Meador T, Hinrichs KU. Production and turnover of microbial organic matter in surface intertidal sediments. Org Geochem. 2018;121:104–13.
- View Article
- Google Scholar
5. Yu T, Wu W, Liang W, Wang Y, Hou J, Chen Y, et al. Anaerobic degradation of organic carbon supports uncultured microbial populations in estuarine sediments. Microbiome. 2023;11(1):81. pmid:37081504
- View Article
- PubMed/NCBI
- Google Scholar
6. Vasquez-Cardenas D, van de Vossenberg J, Polerecky L, Malkin SY, Schauer R, Hidalgo-Martinez S. Microbial carbon metabolism associated with electrogenic sulphur oxidation in coastal sediments. ISME J. 2015.
- View Article
- Google Scholar
7. Middelburg JJ, Herman PMJ. Organic matter processing in tidal estuaries. Mar Chem. 2007;106(1–2):127–47.
- View Article
- Google Scholar
8. Hutchinson TF, Kessler AJ, Wong WW, Hall P, Leung PM, Jirapanjawat T, et al. Microorganisms oxidize glucose through distinct pathways in permeable and cohesive sediments. ISME J. 2024;18(1):wrae001. pmid:38365261
- View Article
- PubMed/NCBI
- Google Scholar
9. Bourke MF, Marriott PJ, Glud RN, Hasler-Sheetal H, Kamalanathan M, Beardall J, et al. Metabolism in anoxic permeable sediments is dominated by eukaryotic dark fermentation. Nat Geosci. 2017;10(1):30–5. pmid:28070216
- View Article
- PubMed/NCBI
- Google Scholar
10. Rodger Harvey H, Tuttle JH, Tyler Bell J. Kinetics of phytoplankton decay during simulated sedimentation: Changes in biochemical composition and microbial activity under oxic and anoxic conditions. Geochimica et Cosmochimica Acta. 1995;59(16):3367–77.
- View Article
- Google Scholar
11. Sawyer TE, King GM. Glucose uptake and end product formation in an intertidal marine sediment. Appl Environ Microbiol. 1993;59(1):120–8. pmid:16348837
- View Article
- PubMed/NCBI
- Google Scholar
12. Meyer-Reil LA. Uptake of glucose by bacteria in the sediment. Mar Biol. 1978;44:293–8.
- View Article
- Google Scholar
13. Middelburg JJ, Barranguet C, Boschker HT, Herman PM, Moens T, Heip CH. The fate of intertidal microphytobenthos carbon: An in situ 13C-labeling study. Limnol Oceanogr. 2000;45(6):1224–34.
- View Article
- Google Scholar
14. Bianchi TS. The role of terrestrially derived organic carbon in the coastal ocean: a changing paradigm and the priming effect. Proc Natl Acad Sci U S A. 2011;108(49):19473–81. pmid:22106254
- View Article
- PubMed/NCBI
- Google Scholar
15. van Nugteren P, Moodley L, Brummer G-J, Heip CHR, Herman PMJ, Middelburg JJ. Seafloor ecosystem functioning: the importance of organic matter priming. Mar Biol. 2009;156(11):2277–87. pmid:24489405
- View Article
- PubMed/NCBI
- Google Scholar
16. López-Sánchez R, Rebollar EA, Gutiérrez-Ríos RM, Garciarrubio A, Juarez K, Segovia L. Metagenomic analysis of carbohydrate-active enzymes and their contribution to marine sediment biodiversity. World J Microbiol Biotechnol. 2024;40(3):95. pmid:38349445
- View Article
- PubMed/NCBI
- Google Scholar
17. Sauer U, Lasko DR, Fiaux J, Hochuli M, Glaser R, Szyperski T, et al. Metabolic flux ratio analysis of genetic and environmental modulations of Escherichia coli central carbon metabolism. J Bacteriol. 1999;181(21):6679–88. pmid:10542169
- View Article
- PubMed/NCBI
- Google Scholar
18. Dijkstra P, Dalder JJ, Selmants PC, Hart SC, Koch GW, Schwartz E, et al. Modeling soil metabolic processes using isotopologue pairs of position-specific 13C-labeled glucose and pyruvate. Soil Biology and Biochemistry. 2011;43(9):1848–57.
- View Article
- Google Scholar
19. Wu W, Dijkstra P, Hungate BA, Shi L, Dippold MA. In situ diversity of metabolism and carbon use efficiency among soil bacteria. Sci Adv. 2022;8(44):eabq3958. pmid:36332015
- View Article
- PubMed/NCBI
- Google Scholar
20. Crown SB, Long CP, Antoniewicz MR. Integrated 13C-metabolic flux analysis of 14 parallel labeling experiments in Escherichia coli. Metab Eng. 2015;28:151–8. pmid:25596508
- View Article
- PubMed/NCBI
- Google Scholar
21. Fuhrer T, Fischer E, Sauer U. Experimental identification and quantification of glucose metabolism in seven bacterial species. J Bacteriol. 2005;187(5):1581–90. pmid:15716428
- View Article
- PubMed/NCBI
- Google Scholar
22. Nikel PI, Chavarría M, Fuhrer T, Sauer U, de Lorenzo V. Pseudomonas putida KT2440 Strain Metabolizes Glucose through a Cycle Formed by Enzymes of the Entner-Doudoroff, Embden-Meyerhof-Parnas, and Pentose Phosphate Pathways. J Biol Chem. 2015;290(43):25920–32. pmid:26350459
- View Article
- PubMed/NCBI
- Google Scholar
23. Nikel PI, Fuhrer T, Chavarría M, Sánchez-Pascuala A, Sauer U, de Lorenzo V. Reconfiguration of metabolic fluxes in Pseudomonas putida as a response to sub-lethal oxidative stress. ISME J. 2021;15(6):1751–66. pmid:33432138
- View Article
- PubMed/NCBI
- Google Scholar
24. Flamholz A, Noor E, Bar-Even A, Liebermeister W, Milo R. Glycolytic strategy as a tradeoff between energy yield and protein cost. Proc Natl Acad Sci U S A. 2013;110(24):10039–44. pmid:23630264
- View Article
- PubMed/NCBI
- Google Scholar
25. Klingner A, Bartsch A, Dogs M, Wagner-Döbler I, Jahn D, Simon M, et al. Large-Scale 13C flux profiling reveals conservation of the Entner-Doudoroff pathway as a glycolytic strategy among marine bacteria that use glucose. Appl Environ Microbiol. 2015;81(7):2408–22. pmid:25616803
- View Article
- PubMed/NCBI
- Google Scholar
26. Long CP, Antoniewicz MR. High-resolution 13C metabolic flux analysis. Nat Protoc. 2019;14(10):2856–77. pmid:31471597
- View Article
- PubMed/NCBI
- Google Scholar
27. Zamboni N, Fendt S-M, Rühl M, Sauer U. (13)C-based metabolic flux analysis. Nat Protoc. 2009;4(6):878–92. pmid:19478804
- View Article
- PubMed/NCBI
- Google Scholar
28. Hoon Yang T, Wittmann C, Heinzle E. Respirometric 13C flux analysis--Part II: in vivo flux estimation of lysine-producing Corynebacterium glutamicum. Metab Eng. 2006;8(5):432–46. pmid:16750927
- View Article
- PubMed/NCBI
- Google Scholar
29. Bore EK, Apostel C, Halicki S, Kuzyakov Y, Dippold MA. Microbial Metabolism in Soil at Subzero Temperatures: Adaptation Mechanisms Revealed by Position-Specific 13C Labeling. Front Microbiol. 2017;8:946. pmid:28611748
- View Article
- PubMed/NCBI
- Google Scholar
30. Bore EK, Halicki S, Kuzyakov Y, Dippold MA. Structural and physiological adaptations of soil microorganisms to freezing revealed by position-specific labeling and compound-specific 13C analysis. Biogeochemistry. 2019;143(2):207–19.
- View Article
- Google Scholar
31. Martinez A, Dijkstra P, Megonigal P, Hungate BA. Microbial central carbon metabolism in a tidal freshwater marsh and an upland mixed conifer soil under oxic and anoxic conditions. Appl Environ Microbiol. 2024;90(6):e0072424. pmid:38771053
- View Article
- PubMed/NCBI
- Google Scholar
32. Apostel C, Dippold M, Kuzyakov Y. Biochemistry of hexose and pentose transformations in soil analyzed by position-specific labeling and 13C-PLFA. Soil Biology and Biochemistry. 2015;80:199–208.
- View Article
- Google Scholar
33. Bore EK, Kuzyakov Y, Dippold MA. Glucose and ribose stabilization in soil: Convergence and divergence of carbon pathways assessed by position-specific labeling. Soil Biology and Biochemistry. 2019;131:54–61.
- View Article
- Google Scholar
34. K Bore E, Apostel C, Halicki S, Kuzyakov Y, Dippold MA. Soil microorganisms can overcome respiration inhibition by coupling intra- and extracellular metabolism: 13C metabolic tracing reveals the mechanisms. ISME J. 2017;11(6):1423–33. pmid:28157187
- View Article
- PubMed/NCBI
- Google Scholar
35. Meng L, Tu J, Wu X, Lou S, Cheng J, Chalov S, et al. Wave, flow, and suspended sediment dynamics under strong winds on a tidal beach. Estuarine, Coastal and Shelf Science. 2024;303:108799.
- View Article
- Google Scholar
36. Chen S. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. Imeta. 2023;2(2):e107. pmid:38868435
- View Article
- PubMed/NCBI
- Google Scholar
37. Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31(10):1674–6. pmid:25609793
- View Article
- PubMed/NCBI
- Google Scholar
38. Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119. pmid:20211023
- View Article
- PubMed/NCBI
- Google Scholar
39. Li W, Jaroszewski L, Godzik A. Tolerating some redundancy significantly speeds up clustering of large protein databases. Bioinformatics. 2002;18(1):77–82. pmid:11836214
- View Article
- PubMed/NCBI
- Google Scholar
40. Gu S, Fang L, Xu X. Using SOAPaligner for Short Reads Alignment. Curr Protoc Bioinformatics. 2013;44:11.11.1-17. pmid:26270169
- View Article
- PubMed/NCBI
- Google Scholar
41. Buchfink B, Reuter K, Drost H-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods. 2021;18(4):366–8. pmid:33828273
- View Article
- PubMed/NCBI
- Google Scholar
42. Zhang X, Gillespie AL, Sessions AL. Large D/H variations in bacterial lipids reflect central metabolic pathways. Proc Natl Acad Sci U S A. 2009;106(31):12580–6. pmid:19617564
- View Article
- PubMed/NCBI
- Google Scholar
43. Yang TH, Heinzle E, Wittmann C. Theoretical aspects of 13C metabolic flux analysis with sole quantification of carbon dioxide labeling. Comput Biol Chem. 2005;29(2):121–33. pmid:15833440
- View Article
- PubMed/NCBI
- Google Scholar
44. Zhu Q-Z, Yin X, Taubner H, Wendt J, Friedrich MW, Elvert M, et al. Secondary production and priming reshape the organic matter composition in marine sediments. Sci Adv. 2024;10(20):eadm8096. pmid:38758798
- View Article
- PubMed/NCBI
- Google Scholar
45. Sundareshwar PV, Morris JT, Koepfler EK, Fornwalt B. Phosphorus limitation of coastal ecosystem processes. Science. 2003;299(5606):563–5. pmid:12543975
- View Article
- PubMed/NCBI
- Google Scholar
46. Yang J, Chen Y, She W, Xiao H, Wang Z, Wang H. Deciphering linkages between microbial communities and priming effects in lake sediments with different salinity. J Geophys Res G: Biogeosciences. 2020;125(11):e2019JG005611.
- View Article
- Google Scholar
47. Wu W, Dijkstra P, Dippold MA. 13C analysis of fatty acid fragments by gas chromatography mass spectrometry for metabolic flux analysis. Geochimica et Cosmochimica Acta. 2020;284:92–106.
- View Article
- Google Scholar
48. Jeckelmann J-M, Erni B. Transporters of glucose and other carbohydrates in bacteria. Pflugers Arch. 2020;472(9):1129–53. pmid:32372286
- View Article
- PubMed/NCBI
- Google Scholar
49. Zhang XC, Han L, Zhao Y. Thermodynamics of ABC transporters. Protein Cell. 2016;7(1):17–27. pmid:26408021
- View Article
- PubMed/NCBI
- Google Scholar
50. Xing Y, Chen L, Chen D. Geochemical characteristics of sediment pore water in different vegetation communities of Nanhui Dongtan wetland, Shanghai: Implications for carbon cycle processes in shallow sediments. Geochimica. 2022;51(6):667–83.
- View Article
- Google Scholar
51. Boschker HTS, Nold SC, Wellsbury P, Bos D, de Graaf W, Pel R, et al. Direct linking of microbial populations to specific biogeochemical processes by 13C-labelling of biomarkers. Nature. 1998;392(6678):801–5.
- View Article
- Google Scholar
52. Beck M, Reckhardt A, Amelsberg J, Bartholomä A, Brumsack H-J, Cypionka H, et al. The drivers of biogeochemistry in beach ecosystems: A cross-shore transect from the dunes to the low-water line. Mar Chem. 2017;190:35–50.
- View Article
- Google Scholar
53. Gerosa L, Sauer U. Regulation and control of metabolic fluxes in microbes. Curr Opin Biotechnol. 2011;22(4):566–75. pmid:21600757
- View Article
- PubMed/NCBI
- Google Scholar
54. Litsios A, Ortega ÁD, Wit EC, Heinemann M. Metabolic-flux dependent regulation of microbial physiology. Curr Opin Microbiol. 2018;42:71–8. pmid:29154077
- View Article
- PubMed/NCBI
- Google Scholar
55. Parkes RJ, Dowling NJE, White DC, Herbert RA, Gibson GR. Characterization of sulphate-reducing bacterial populations within marine and estuarine sediments with different rates of sulphate reduction. FEMS Microbiology Letters. 1993;102(3–4):235–50.
- View Article
- Google Scholar
56. Londry KL, Jahnke LL, Des Marais DJ. Stable carbon isotope ratios of lipid biomarkers of sulfate-reducing bacteria. Applied and Environmental Microbiology. 2004;70(2):745–51.
- View Article
- Google Scholar
57. Wijker RS, Sessions AL, Fuhrer T, Phan M. 2H/1H variation in microbial lipids is controlled by NADPH metabolism. Proc Natl Acad Sci U S A. 2019;116(25):12173–82. pmid:31152138
- View Article
- PubMed/NCBI
- Google Scholar
58. Gonzalez JE, Long CP, Antoniewicz MR. Comprehensive analysis of glucose and xylose metabolism in Escherichia coli under aerobic and anaerobic conditions by 13C metabolic flux analysis. Metab Eng. 2017;39:9–18. pmid:27840237
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Chen Y-J, Leung PM, Cook PLM, Wong WW, Hutchinson T, Eate V, et al. Hydrodynamic disturbance controls microbial community assembly and biogeochemical processes in coastal sediments. ISME J. 2022;16(3):750–63. pmid:34584214
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Kessler AJ, Chen Y-J, Waite DW, Hutchinson T, Koh S, Popa ME, et al. Bacterial fermentation and respiration processes are uncoupled in anoxic permeable sediments. Nat Microbiol. 2019;4(6):1014–23. pmid:30858573
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Chen Y-J, Leung PM, Wood JL, Bay SK, Hugenholtz P, Kessler AJ, et al. Metabolic flexibility allows bacterial habitat generalists to become dominant in a frequently disturbed ecosystem. ISME J. 2021;15(10):2986–3004. pmid:33941890
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Wu W, Meador T, Hinrichs KU. Production and turnover of microbial organic matter in surface intertidal sediments. Org Geochem. 2018;121:104–13.
View Article
Google Scholar

[14] View Article

[15] Google Scholar

[ref5] 5. Yu T, Wu W, Liang W, Wang Y, Hou J, Chen Y, et al. Anaerobic degradation of organic carbon supports uncultured microbial populations in estuarine sediments. Microbiome. 2023;11(1):81. pmid:37081504
View Article
PubMed/NCBI
Google Scholar

[17] View Article

[18] PubMed/NCBI

[19] Google Scholar

[ref6] 6. Vasquez-Cardenas D, van de Vossenberg J, Polerecky L, Malkin SY, Schauer R, Hidalgo-Martinez S. Microbial carbon metabolism associated with electrogenic sulphur oxidation in coastal sediments. ISME J. 2015.
View Article
Google Scholar

[21] View Article

[22] Google Scholar

[ref7] 7. Middelburg JJ, Herman PMJ. Organic matter processing in tidal estuaries. Mar Chem. 2007;106(1–2):127–47.
View Article
Google Scholar

[24] View Article

[25] Google Scholar

[ref8] 8. Hutchinson TF, Kessler AJ, Wong WW, Hall P, Leung PM, Jirapanjawat T, et al. Microorganisms oxidize glucose through distinct pathways in permeable and cohesive sediments. ISME J. 2024;18(1):wrae001. pmid:38365261
View Article
PubMed/NCBI
Google Scholar

[27] View Article

[28] PubMed/NCBI

[29] Google Scholar

[ref9] 9. Bourke MF, Marriott PJ, Glud RN, Hasler-Sheetal H, Kamalanathan M, Beardall J, et al. Metabolism in anoxic permeable sediments is dominated by eukaryotic dark fermentation. Nat Geosci. 2017;10(1):30–5. pmid:28070216
View Article
PubMed/NCBI
Google Scholar

[31] View Article

[32] PubMed/NCBI

[33] Google Scholar

[ref10] 10. Rodger Harvey H, Tuttle JH, Tyler Bell J. Kinetics of phytoplankton decay during simulated sedimentation: Changes in biochemical composition and microbial activity under oxic and anoxic conditions. Geochimica et Cosmochimica Acta. 1995;59(16):3367–77.
View Article
Google Scholar

[35] View Article

[36] Google Scholar

[ref11] 11. Sawyer TE, King GM. Glucose uptake and end product formation in an intertidal marine sediment. Appl Environ Microbiol. 1993;59(1):120–8. pmid:16348837
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref12] 12. Meyer-Reil LA. Uptake of glucose by bacteria in the sediment. Mar Biol. 1978;44:293–8.
View Article
Google Scholar

[42] View Article

[43] Google Scholar

[ref13] 13. Middelburg JJ, Barranguet C, Boschker HT, Herman PM, Moens T, Heip CH. The fate of intertidal microphytobenthos carbon: An in situ 13C-labeling study. Limnol Oceanogr. 2000;45(6):1224–34.
View Article
Google Scholar

[45] View Article

[46] Google Scholar

[ref14] 14. Bianchi TS. The role of terrestrially derived organic carbon in the coastal ocean: a changing paradigm and the priming effect. Proc Natl Acad Sci U S A. 2011;108(49):19473–81. pmid:22106254
View Article
PubMed/NCBI
Google Scholar

[48] View Article

[49] PubMed/NCBI

[50] Google Scholar

[ref15] 15. van Nugteren P, Moodley L, Brummer G-J, Heip CHR, Herman PMJ, Middelburg JJ. Seafloor ecosystem functioning: the importance of organic matter priming. Mar Biol. 2009;156(11):2277–87. pmid:24489405
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref16] 16. López-Sánchez R, Rebollar EA, Gutiérrez-Ríos RM, Garciarrubio A, Juarez K, Segovia L. Metagenomic analysis of carbohydrate-active enzymes and their contribution to marine sediment biodiversity. World J Microbiol Biotechnol. 2024;40(3):95. pmid:38349445
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref17] 17. Sauer U, Lasko DR, Fiaux J, Hochuli M, Glaser R, Szyperski T, et al. Metabolic flux ratio analysis of genetic and environmental modulations of Escherichia coli central carbon metabolism. J Bacteriol. 1999;181(21):6679–88. pmid:10542169
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref18] 18. Dijkstra P, Dalder JJ, Selmants PC, Hart SC, Koch GW, Schwartz E, et al. Modeling soil metabolic processes using isotopologue pairs of position-specific 13C-labeled glucose and pyruvate. Soil Biology and Biochemistry. 2011;43(9):1848–57.
View Article
Google Scholar

[64] View Article

[65] Google Scholar

[ref19] 19. Wu W, Dijkstra P, Hungate BA, Shi L, Dippold MA. In situ diversity of metabolism and carbon use efficiency among soil bacteria. Sci Adv. 2022;8(44):eabq3958. pmid:36332015
View Article
PubMed/NCBI
Google Scholar

[67] View Article

[68] PubMed/NCBI

[69] Google Scholar

[ref20] 20. Crown SB, Long CP, Antoniewicz MR. Integrated 13C-metabolic flux analysis of 14 parallel labeling experiments in Escherichia coli. Metab Eng. 2015;28:151–8. pmid:25596508
View Article
PubMed/NCBI
Google Scholar

[71] View Article

[72] PubMed/NCBI

[73] Google Scholar

[ref21] 21. Fuhrer T, Fischer E, Sauer U. Experimental identification and quantification of glucose metabolism in seven bacterial species. J Bacteriol. 2005;187(5):1581–90. pmid:15716428
View Article
PubMed/NCBI
Google Scholar

[75] View Article

[76] PubMed/NCBI

[77] Google Scholar

[ref22] 22. Nikel PI, Chavarría M, Fuhrer T, Sauer U, de Lorenzo V. Pseudomonas putida KT2440 Strain Metabolizes Glucose through a Cycle Formed by Enzymes of the Entner-Doudoroff, Embden-Meyerhof-Parnas, and Pentose Phosphate Pathways. J Biol Chem. 2015;290(43):25920–32. pmid:26350459
View Article
PubMed/NCBI
Google Scholar

[79] View Article

[80] PubMed/NCBI

[81] Google Scholar

[ref23] 23. Nikel PI, Fuhrer T, Chavarría M, Sánchez-Pascuala A, Sauer U, de Lorenzo V. Reconfiguration of metabolic fluxes in Pseudomonas putida as a response to sub-lethal oxidative stress. ISME J. 2021;15(6):1751–66. pmid:33432138
View Article
PubMed/NCBI
Google Scholar

[83] View Article

[84] PubMed/NCBI

[85] Google Scholar

[ref24] 24. Flamholz A, Noor E, Bar-Even A, Liebermeister W, Milo R. Glycolytic strategy as a tradeoff between energy yield and protein cost. Proc Natl Acad Sci U S A. 2013;110(24):10039–44. pmid:23630264
View Article
PubMed/NCBI
Google Scholar

[87] View Article

[88] PubMed/NCBI

[89] Google Scholar

[ref25] 25. Klingner A, Bartsch A, Dogs M, Wagner-Döbler I, Jahn D, Simon M, et al. Large-Scale 13C flux profiling reveals conservation of the Entner-Doudoroff pathway as a glycolytic strategy among marine bacteria that use glucose. Appl Environ Microbiol. 2015;81(7):2408–22. pmid:25616803
View Article
PubMed/NCBI
Google Scholar

[91] View Article

[92] PubMed/NCBI

[93] Google Scholar

[ref26] 26. Long CP, Antoniewicz MR. High-resolution 13C metabolic flux analysis. Nat Protoc. 2019;14(10):2856–77. pmid:31471597
View Article
PubMed/NCBI
Google Scholar

[95] View Article

[96] PubMed/NCBI

[97] Google Scholar

[ref27] 27. Zamboni N, Fendt S-M, Rühl M, Sauer U. (13)C-based metabolic flux analysis. Nat Protoc. 2009;4(6):878–92. pmid:19478804
View Article
PubMed/NCBI
Google Scholar

[99] View Article

[100] PubMed/NCBI

[101] Google Scholar

[ref28] 28. Hoon Yang T, Wittmann C, Heinzle E. Respirometric 13C flux analysis--Part II: in vivo flux estimation of lysine-producing Corynebacterium glutamicum. Metab Eng. 2006;8(5):432–46. pmid:16750927
View Article
PubMed/NCBI
Google Scholar

[103] View Article

[104] PubMed/NCBI

[105] Google Scholar

[ref29] 29. Bore EK, Apostel C, Halicki S, Kuzyakov Y, Dippold MA. Microbial Metabolism in Soil at Subzero Temperatures: Adaptation Mechanisms Revealed by Position-Specific 13C Labeling. Front Microbiol. 2017;8:946. pmid:28611748
View Article
PubMed/NCBI
Google Scholar

[107] View Article

[108] PubMed/NCBI

[109] Google Scholar

[ref30] 30. Bore EK, Halicki S, Kuzyakov Y, Dippold MA. Structural and physiological adaptations of soil microorganisms to freezing revealed by position-specific labeling and compound-specific 13C analysis. Biogeochemistry. 2019;143(2):207–19.
View Article
Google Scholar

[111] View Article

[112] Google Scholar

[ref31] 31. Martinez A, Dijkstra P, Megonigal P, Hungate BA. Microbial central carbon metabolism in a tidal freshwater marsh and an upland mixed conifer soil under oxic and anoxic conditions. Appl Environ Microbiol. 2024;90(6):e0072424. pmid:38771053
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref32] 32. Apostel C, Dippold M, Kuzyakov Y. Biochemistry of hexose and pentose transformations in soil analyzed by position-specific labeling and 13C-PLFA. Soil Biology and Biochemistry. 2015;80:199–208.
View Article
Google Scholar

[118] View Article

[119] Google Scholar

[ref33] 33. Bore EK, Kuzyakov Y, Dippold MA. Glucose and ribose stabilization in soil: Convergence and divergence of carbon pathways assessed by position-specific labeling. Soil Biology and Biochemistry. 2019;131:54–61.
View Article
Google Scholar

[121] View Article

[122] Google Scholar

[ref34] 34. K Bore E, Apostel C, Halicki S, Kuzyakov Y, Dippold MA. Soil microorganisms can overcome respiration inhibition by coupling intra- and extracellular metabolism: 13C metabolic tracing reveals the mechanisms. ISME J. 2017;11(6):1423–33. pmid:28157187
View Article
PubMed/NCBI
Google Scholar

[124] View Article

[125] PubMed/NCBI

[126] Google Scholar

[ref35] 35. Meng L, Tu J, Wu X, Lou S, Cheng J, Chalov S, et al. Wave, flow, and suspended sediment dynamics under strong winds on a tidal beach. Estuarine, Coastal and Shelf Science. 2024;303:108799.
View Article
Google Scholar

[128] View Article

[129] Google Scholar

[ref36] 36. Chen S. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. Imeta. 2023;2(2):e107. pmid:38868435
View Article
PubMed/NCBI
Google Scholar

[131] View Article

[132] PubMed/NCBI

[133] Google Scholar

[ref37] 37. Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31(10):1674–6. pmid:25609793
View Article
PubMed/NCBI
Google Scholar

[135] View Article

[136] PubMed/NCBI

[137] Google Scholar

[ref38] 38. Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119. pmid:20211023
View Article
PubMed/NCBI
Google Scholar

[139] View Article

[140] PubMed/NCBI

[141] Google Scholar

[ref39] 39. Li W, Jaroszewski L, Godzik A. Tolerating some redundancy significantly speeds up clustering of large protein databases. Bioinformatics. 2002;18(1):77–82. pmid:11836214
View Article
PubMed/NCBI
Google Scholar

[143] View Article

[144] PubMed/NCBI

[145] Google Scholar

[ref40] 40. Gu S, Fang L, Xu X. Using SOAPaligner for Short Reads Alignment. Curr Protoc Bioinformatics. 2013;44:11.11.1-17. pmid:26270169
View Article
PubMed/NCBI
Google Scholar

[147] View Article

[148] PubMed/NCBI

[149] Google Scholar

[ref41] 41. Buchfink B, Reuter K, Drost H-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods. 2021;18(4):366–8. pmid:33828273
View Article
PubMed/NCBI
Google Scholar

[151] View Article

[152] PubMed/NCBI

[153] Google Scholar

[ref42] 42. Zhang X, Gillespie AL, Sessions AL. Large D/H variations in bacterial lipids reflect central metabolic pathways. Proc Natl Acad Sci U S A. 2009;106(31):12580–6. pmid:19617564
View Article
PubMed/NCBI
Google Scholar

[155] View Article

[156] PubMed/NCBI

[157] Google Scholar

[ref43] 43. Yang TH, Heinzle E, Wittmann C. Theoretical aspects of 13C metabolic flux analysis with sole quantification of carbon dioxide labeling. Comput Biol Chem. 2005;29(2):121–33. pmid:15833440
View Article
PubMed/NCBI
Google Scholar

[159] View Article

[160] PubMed/NCBI

[161] Google Scholar

[ref44] 44. Zhu Q-Z, Yin X, Taubner H, Wendt J, Friedrich MW, Elvert M, et al. Secondary production and priming reshape the organic matter composition in marine sediments. Sci Adv. 2024;10(20):eadm8096. pmid:38758798
View Article
PubMed/NCBI
Google Scholar

[163] View Article

[164] PubMed/NCBI

[165] Google Scholar

[ref45] 45. Sundareshwar PV, Morris JT, Koepfler EK, Fornwalt B. Phosphorus limitation of coastal ecosystem processes. Science. 2003;299(5606):563–5. pmid:12543975
View Article
PubMed/NCBI
Google Scholar

[167] View Article

[168] PubMed/NCBI

[169] Google Scholar

[ref46] 46. Yang J, Chen Y, She W, Xiao H, Wang Z, Wang H. Deciphering linkages between microbial communities and priming effects in lake sediments with different salinity. J Geophys Res G: Biogeosciences. 2020;125(11):e2019JG005611.
View Article
Google Scholar

[171] View Article

[172] Google Scholar

[ref47] 47. Wu W, Dijkstra P, Dippold MA. 13C analysis of fatty acid fragments by gas chromatography mass spectrometry for metabolic flux analysis. Geochimica et Cosmochimica Acta. 2020;284:92–106.
View Article
Google Scholar

[174] View Article

[175] Google Scholar

[ref48] 48. Jeckelmann J-M, Erni B. Transporters of glucose and other carbohydrates in bacteria. Pflugers Arch. 2020;472(9):1129–53. pmid:32372286
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

[ref49] 49. Zhang XC, Han L, Zhao Y. Thermodynamics of ABC transporters. Protein Cell. 2016;7(1):17–27. pmid:26408021
View Article
PubMed/NCBI
Google Scholar

[181] View Article

[182] PubMed/NCBI

[183] Google Scholar

[ref50] 50. Xing Y, Chen L, Chen D. Geochemical characteristics of sediment pore water in different vegetation communities of Nanhui Dongtan wetland, Shanghai: Implications for carbon cycle processes in shallow sediments. Geochimica. 2022;51(6):667–83.
View Article
Google Scholar

[185] View Article

[186] Google Scholar

[ref51] 51. Boschker HTS, Nold SC, Wellsbury P, Bos D, de Graaf W, Pel R, et al. Direct linking of microbial populations to specific biogeochemical processes by 13C-labelling of biomarkers. Nature. 1998;392(6678):801–5.
View Article
Google Scholar

[188] View Article

[189] Google Scholar

[ref52] 52. Beck M, Reckhardt A, Amelsberg J, Bartholomä A, Brumsack H-J, Cypionka H, et al. The drivers of biogeochemistry in beach ecosystems: A cross-shore transect from the dunes to the low-water line. Mar Chem. 2017;190:35–50.
View Article
Google Scholar

[191] View Article

[192] Google Scholar

[ref53] 53. Gerosa L, Sauer U. Regulation and control of metabolic fluxes in microbes. Curr Opin Biotechnol. 2011;22(4):566–75. pmid:21600757
View Article
PubMed/NCBI
Google Scholar

[194] View Article

[195] PubMed/NCBI

[196] Google Scholar

[ref54] 54. Litsios A, Ortega ÁD, Wit EC, Heinemann M. Metabolic-flux dependent regulation of microbial physiology. Curr Opin Microbiol. 2018;42:71–8. pmid:29154077
View Article
PubMed/NCBI
Google Scholar

[198] View Article

[199] PubMed/NCBI

[200] Google Scholar

[ref55] 55. Parkes RJ, Dowling NJE, White DC, Herbert RA, Gibson GR. Characterization of sulphate-reducing bacterial populations within marine and estuarine sediments with different rates of sulphate reduction. FEMS Microbiology Letters. 1993;102(3–4):235–50.
View Article
Google Scholar

[202] View Article

[203] Google Scholar

[ref56] 56. Londry KL, Jahnke LL, Des Marais DJ. Stable carbon isotope ratios of lipid biomarkers of sulfate-reducing bacteria. Applied and Environmental Microbiology. 2004;70(2):745–51.
View Article
Google Scholar

[205] View Article

[206] Google Scholar

[ref57] 57. Wijker RS, Sessions AL, Fuhrer T, Phan M. 2H/1H variation in microbial lipids is controlled by NADPH metabolism. Proc Natl Acad Sci U S A. 2019;116(25):12173–82. pmid:31152138
View Article
PubMed/NCBI
Google Scholar

[208] View Article

[209] PubMed/NCBI

[210] Google Scholar

[ref58] 58. Gonzalez JE, Long CP, Antoniewicz MR. Comprehensive analysis of glucose and xylose metabolism in Escherichia coli under aerobic and anaerobic conditions by 13C metabolic flux analysis. Metab Eng. 2017;39:9–18. pmid:27840237
View Article
PubMed/NCBI
Google Scholar

[212] View Article

[213] PubMed/NCBI

[214] Google Scholar

Figures

Abstract

1. Introduction

2. Materials and methods

2.1. Material and incubation setup

2.2. PLFA extraction and analysis

2.3. DNA extraction and metagenome analysis

2.4. Calculation method for 13C enrichment

2.5. Modelling 13C-metabolic flux analysis

3. Results

3.1. CO2 production during incubation

3.2. 13C-PLFA concentrations

3.3. Modelling metabolic flux patterns

3.4. Metagenome characterizations

4. Discussion

4.1. Glucose priming effect for microbial communities

4.2. Anabolic activity in intertidal sediment

4.3. Diverse metabolic flux activities within microbial communities

5. Conclusion

Supporting information

S1 File. Instrumental methods for CO2 and PLFA analysis, and calculation method for priming effect.

S1 Table. Concentration and 13C composition of DIC and CO2 in the end of incubation.

S2 Table. The modelled biochemical ratios for EMP, PP, ED, TCA, gluconeogenesis, and anaplerotic reactions.

S1 Fig. Correlation of carbon isotope between gas CO2 and dissolved inorganic carbon (DIC).

S2 Fig. Reconstructed central carbon metabolism pathways based on the genome.

Acknowledgments

References

2.4. Calculation method for ¹³C enrichment

2.5. Modelling ¹³C-metabolic flux analysis

3.1. CO₂ production during incubation

3.2. ¹³C-PLFA concentrations

S1 File. Instrumental methods for CO₂ and PLFA analysis, and calculation method for priming effect.

S1 Table. Concentration and ¹³C composition of DIC and CO₂ in the end of incubation.

S1 Fig. Correlation of carbon isotope between gas CO₂ and dissolved inorganic carbon (DIC).