Microbial community structure and metabolic characteristics in the five different hadal trenches

doi:10.21203/rs.3.rs-4618462/v1

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Microbial community structure and metabolic characteristics in the five different hadal trenches

https://doi.org/10.21203/rs.3.rs-4618462/v1

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Microbial community growth efficiency, the ratio of production to substrate assimilated, could provide insights into carbon flow among microbes and the regulation of marine biogeochemical cycles. However, by far microbial metabolic characters were largely undetermined in the deep hadal trench. Here, the structural and metabolism characteristics of microbial communities in five different trenches were investigated using Illumina high-throughput sequencing and quantitative PCR, as well as incubation with the ³H-leucine incorporation method and electron transport system. The community structure and diversity in the trenches located in different hemispheres were significantly different, with significantly higher of diversity and gene abundance appear in the northern and southern hemispheres, respectively. Depth, TOC and TP were identified as key factors. Cooperative relationship existed among different microbial groups as demonstrated by the co-occurrence network and Pearson correlation analysis. The respiration rates were significantly higher in the northern hemisphere than those in the southern hemisphere under atmospheric pressure. The prokaryotic growth efficiencies (PGE) were significantly higher under atmospheric pressure than under high hydrostatic pressure, this negative effect possibly because carbon flow was more inclined to maintain respiration under high hydrostatic pressure. This study represented the first comprehensive investigation of the microbial community structure and metabolic characteristics of sediments in different trenches, providing a preliminary insight into the processes and efficiency of microbial-driven carbon cycles in the deep biosphere.

Biological sciences/Ecology/Ecosystem ecology

Biological sciences/Microbiology/Environmental microbiology/Soil microbiology

Community structure

Production rate

Respiration rate

Growth efficiency

Trenches

The deep-sea environment is characterized by near-complete darkness, high hydrostatic pressure, low temperature and low organic matter availability (Jørgensen and Boetius, 2007). The deep-sea microbial food web was fundamentally dependent on the flux of particulate organic carbon from primary production in the euphotic zone (Nagata et al., 2010). Previous discoveries challenge the long-held view that the cycling of organic matter was slow in the deep sea and that microbial food webs in this environment were static in structure and function (Nagata et al., 2010). Data showing spatial variation in prokaryotic abundance and activity supported the hypothesis that deep-sea microorganisms respond dynamically to variations in organic matter input to the bathypelagic realm (Nagata et al., 2010). Although the deep ocean supported a diverse array of prokaryotes, their assimilation and transformation of natural carbon sources remain poorly understood. Therefore, a systematic investigation of the composition, distribution, and metabolic status of microbial groups is fundamental to the ecological functioning of the unique deep-ocean ecosystems.

A number of surveys using 16S rRNA gene amplicon sequencing had revealed the remarkable diversity of microbial communities present in deep-sea sediments (Jing et al., 2022; Zhang et al., 2024). These microbial communities display unique metabolic properties, playing essential roles in global biogeochemical cycles. However, there is still limited knowledge regarding the carbon metabolic rates of microbial communities present in deep-sea sediments. The prokaryotic growth efficiency (PGE) of microbes, based on the ratio of prokaryotic respiration (PR) and production (PP), is a proxy of prokaryotic carbon metabolism that evaluates the fate of organic inputs in aquatic systems (del Giorgio and Cole, 1998). The measurement of microbial metabolic characters allows for the determination of the amount of carbon required for living, thus contributing to quantitative biogeochemical studies. It was already known that enhanced supply of dissolved organic carbon (DOC) and nutrients could increase microbial growth and stimulate bacterial activity (Yuan et al., 2010); and increased organic matter deposition in the benthic sediments would enhance microbial activity and high microbial carbon conversion rates in the Mariana trench (Glud et al., 2013; Jing et al., 2022). Pressure had significant impact on the heterotrophic prokaryotic carbon demand (Amano et al., 2022). Nevertheless, research on deep-sea sediments, especially hadal trenches, remains comparatively limited (Røy et al., 2012).

Hadal trenches, the deepest oceanic areas with extremely high hydrostatic pressure (e.g., > 60 MPa) and isolated hydrotopographical conditions (Jamieson et al, 2010), generally host a diversity of hadal life with a high degree of endemism and density (Jamieson et al, 2010), geological and physicochemical conditions were highly varied with inter/intra hadal trenches. The Kermadec Trench (KT) reaches a maximum depth of 10,047 m and is located approximately 120 km off the north-eastern coast of New Zealand (Angel, 1982). It is 1,500 km long with an average width of 60 km and has the characteristic V-shaped cross section common to hadal trenches. The Diamantina Trench, which is around 1,500 km west of Perth, Australia, is located in the Indian Ocean and has a maximum depth of approximately 8,047 m. It is approximately 520 km long and 70 km wide, running in a northeast-southwest direction (Stewart and Jamieson, 2019). The Wallaby-Zenith Trench (WT), which extends from the continental margin of Western Australia, runs northwest for approximately 2,000 km, representing a structurally complex area of rugged topography composed of numerous plains, troughs, and ridges (Gibbons et al., 2012; Bond et al., 2023). The Yap and Mariana trenches (YT and MT), formed by the collision of the plate, are both located in the western Pacific Ocean, and the southern end of the Mariana Trench is intersected by the north-south trending Yap Trench (Crawford et al., 1986). Among the five trenches, the KT, DT and WT are in the southern hemisphere, while the MT and YT are located in the northern hemisphere. Although variations in terms of microbial diversity and associated biogeochemical activities had been revealed in different trench sediments (Jing et al. 2022; Sun et al., 2024; Zhang et al., 2024), very few studies have characterized both southern and northern hemisphere together, and the microbial metabolic characteristics in the trench habitats were largely unknown (Wit et al., 1997y et al., 2012). Considering the highly varied geological structures and physicochemical conditions with different hadal trenches, thus it would be necessary to elucidate the composition and metabolic characteristics of microbial communities among multiple trenches. The present study investigated the diversity, community composition and metabolic characteristics of microbes in sediment collected from five trenches In order to elucidate the geographical distribution and metabolic features of microbial communities among trenches and the underlying mechanisms that may be responsible for the possible discrepancies.

2.1. Sample collection and chemical analysis

Pushcore sediment samples were collected from the Yap and Mariana trenches during cruise TS14 on R/V “Tan Suo Yi Hao” in 2019; and from the Kermadec, Diamantina and Wallaby-Zenith trenches during cruise TS29 on R/V “Tan Suo Yi Hao” in 2022 (Fig. 1). In situ hydrographic parameters (location and depth, etc.) were measured with the manned submersible “Shen hai yong shi” and “Fen dou zhe”, respectively. The top two layers, each of 10 cm, were sliced into two fractions. One fraction for subsequent cultivation were stored at 4 ℃ in the dark, and the other fraction for DNA extraction were stored at -80℃ as showed in Table 1.

Table 1

The environmental variables and sequence information of sediment samples collected from different trenches. S and B represent the 0–10cm and 11–20cm sediment layers, respectively.
Regions	Stns.	Longitude	Latitude	Depth (m)	TOC (mg/kg)	NO₃⁻ (mg/kg)	NH₄⁺ (mg/kg)	TP (mg/kg)	Original Reads	Quality Reads	ASVs
Kermadec Trench (KT)	FDZ127	176.91°W	30.69°S	7,600	3,927.95	8.66	1.96	596.88	114,341	60,842	93
	FDZ130	178.04°W	30.69°S	5,861	3,594.19	1.97	1.42	539.16	102,078	42,973	143
	FDZ138	175.64°W	27.54°S	9,100	4,038.48	2.17	7.67	953.98	101,956	40,236	78
	FDZ142	179.00°W	35.57°S	6,471	4,286.11	3.79	2.98	544.33	103,817	41,234	99
	FDZ146S	176.76°W	29.79°S	3,983	1,507.91	2.04	10.08	934.09	103,335	41,325	110
	FDZ146B	176.76°W	29.79°S	3,983	1,137.24	1.69	8.48	863.35	101,300	50,090	87
	FDZ152S	178.15°W	34.23°S	6,969	2,798.29	2.03	6.04	672.71	101,208	49,870	105
	FDZ152B	178.15°W	34.23°S	6,969	2,206.03	2.48	3.90	849.80	101,534	44,818	117
Diamantina Trench (DT)	FDZ157	103.74°E	33.87°S	6,205	4,665.22	3.45	4.09	930.00	115,462	55,593	93
	FDZ164S	101.26°E	33.44°S	6,792	5,741.78	5.15	5.46	889.56	111,920	47,188	85
	FDZ164B	101.26°E	33.44°S	6,792	3,256.76	3.81	4.69	1176.05	105,961	49,990	66
	FDZ167	100.65°E	33.17°S	5,953	5,108.78	2.58	6.63	1149.78	105,961	49,990	96
	FDZ169	99.42°E	32.69°S	5,154	9,998.64	2.29	6.84	837.69	113,418	63,719	87
	FDZ173S	96.53°E	32.50°S	4,625	8,408.55	3.16	5.86	1029.55	101,986	72,593	95
	FDZ173B	96.53°E	32.50°S	4,625	8,020.80	1.59	5.41	908.14	99,056	80,526	54
	FDZ178	98.58°E	32.72°S	5,677	5,176.20	5.12	5.65	1546.62	100,900	61,683	84
Wallaby-Zenith Trench (WT)	FDZ188S	102.45°E	22.37°S	6,671	17,043.50	2.72	6.45	728.86	102,134	56,768	114
	FDZ188B	102.45°E	22.37°S	6,671	11,218.83	2.10	5.96	902.41	100,718	62,567	148
	FDZ189	102.08°E	22.17°S	6,491	5,331.73	5.65	5.12	1022.44	99,012	43,541	131
Mariana Trench (MT)	SY207	140.32°E	11.61°N	3,279	37,760.78	2.35	5.87	870.44	103,230	41,233	549
	SY212S	141.72°E	12.32°N	3,489	11,604.82	3.17	8.21	1009.70	99,304	41,895	561
	SY212B	141.72°E	12.32°N	3,489	11,076.31	3.47	9.36	1028.12	99,056	38,926	558
	SY220S	139.41°E	11.44°N	2,600	7,427.14	3.06	3.16	376.24	101,645	65,648	113
	SY220B	139.41°E	11.44°N	2,600	5,824.72	2.42	11.74	453.40	114,547	98,636	43
Yap Trench (YT)	SY221S	138.52°E	11.37°N	2,844	9,602.35	2.62	3.89	209.74	102,496	55,863	137
	SY221B	138.52°E	11.37°N	2,844	8,352.63	4.07	11.30	267.27	113,679	89,212	176
	SY223S	138.72°E	11.82°N	3,231	27,440.89	3.92	6.94	874.60	99,050	49,213	600
	SY223B	138.72°E	11.82°N	3,231	7,072.10	2.63	7.05	916.44	99,983	55,666	572

Approximate 5 g sediment was used for measurement of chemical parameters at the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (Chengdu, Sichuan, China). The sediment treated with 1M HCl was analysed by a colorimetric auto-analyzer (SEAL Analytical AutoAnalyzer 3, Germany) to detect nitrate(NO₃⁻)and ammonium (NH₄⁺) concentrations. The content of total organic carbon (TOC) was determined using an element analyzer (Elementar vario Macro cube, Germany) after over-drying the sediment at 105℃ (Wang et al., 2016). After treated with nitric-perchloric acid, the content of total phosphate (TP) was measured by using the molybdate colorimetric method with a UV2450 (Shimadzu, Japan) (Murphy and Riley, 1962).

2.2 Microbial community productivity and respiration

The incubation experiment was conducted on aboard in dark at the in-situ temperature (4°C), and the production and respiration samples after the incubation experiment were frozen at -80°C until analyzed in the laboratory. Total microbial community productivity was quantified using a modified version of the ³H-leucine incorporation method described by Knap et al, (1996) with the atmospheric pressure (AP). The high hydrostatic pressure (HHP) experimental group was prepared and processed identically as above except with the in situ pressure. The control replicates were prepared and processed identically as above except a final concentration of 5% trichloroacetic acid (TCA) was added before the addition of the isotope. The ³H activity was measured with a Beckman LS 6500 scintillation counter. Scintillation counts were converted into carbon units according to Simon and Azam (1989).

Total microbial community respiration was measured using electron transport system (ETS) activity method, which was a routine technique for estimation of respiration rate due to its high sensitivity. The sediment samples were re-suspended in the seawater, and then were pre-filtered through a 20 µm nylon mesh before measuring respiration rates. In vivo ETS activity was evaluated using the 2-(4-iodophenyl)-3-(4-nitrophenyl)-5-(phenyl) tetrazolium chloride (INT) as electron acceptor to obtain the microbial respiration rate (Martínez-García et al., 2009) with AP. HHP experimental group was prepared and processed identically as above except with the in suit pressure. The standard solution (0.02–42 µM) was made by adding the configured INT mother liquor (7.9 mM) to the n-propanol solution. The standard curve was obtained based on OD values measured at 485nm by spectrophotometer. According to the transformation parameter 12.8 of INT production rate and O₂ consumption rate in living cells (Martínez-García et al., 2009), the respiration rate for the total microbial community in each sample was obtained with the AP and HHP. The oxygen consumption was converted to carbon respiration by assuming that sediment oxygen consumption was a 1:1 ratio between O₂ consumption and organic matter oxidation to CO₂ (Nakamura, 2003).

Growth efficiency was calculated based on respiration rate and productivity (Warkentin et al., 2011), according this, we put forward the following formula:

PGE = PP ÷ (PP + PR)

In equations, PP and PR stand for prokaryotic productivity and respiration rate, respectively; PGE means prokaryotic growth efficiency.

2.3. DNA extraction, PCR amplification and sequencing

Genomic DNA was extracted from the different layers of the sediments (surface layer: 0–10 cm, deeper layer: 11–20 cm) with the PowerSoil DNA Isolation Kit (MO BIO Laboratories, Inc., Carlsbad, USA), according to the manufacturer's protocol. The concentration of DNA was determined by Qubit® 2.0 (Life Technologies, USA), and the quality was checked via gel electrophoresis. The V3-V4 region of the 16S rRNA gene was amplified with universal prokaryotic primers of Uni341F (5’-CCTACGGGNBGCASCAG-3’) and Uni805R (5’-GACTACNVGGGTATCTAATCC-3’) (Takahashi et al., 2014). For each sample, a unique pair of barcoded was used. PCR amplification was carried out in triplicate using the BIO-RAD C1000 Touch™ Thermal Cycler PCR System in a 20 µl PCR reaction mix, containing 2.0 µl 10 × PCR-MgCl₂ buffer, 0.5 µl 2.5 mM dNTPs, 0.5 µl MgCl₂, 0.5 µl forward primer, 0.5 µl reverse primer, 0.2 µl Platinum® TaqDNA ploymerase, 2.2 µl template DNA and 13.8 µl dd H₂O. Thermal cycling was performed at 95°C for 3 min, followed by 40 cycles at 95°C for 0.5 min, 53°C for 45 s, 72°Cfor 30 s, and a final extension at 72°C for 8 min. A negative control of double-distilled water was also performed during amplification in order to avoid reagent contamination. The paired-end sequencing of the amplicons was then performed with an Illumina HiSeq PE250 sequencer (Novogene Co., Ltd., www.novogene.com).

2.4. Quantitative PCR

The abundance of 16S rRNA genes in each sample was quantified by real-time quantitative PCR (qPCR) via a StepOnePlusReal-Time PCR system (Applied Biosystems Inc. Carlsbad, CA, USA). Each qPCR reaction comprised 10 µL 2×SYBR® Premix Ex Taq™ II (Takara Bio Inc., Shiga, Japan), Uni340F (5′-CCTACGGGRBGCASCAG-3′)/Uni806R (5′-GGACTACNNGGGTATCTAAT-3′) primer (Takai and Horikoshi, 2000), 2 µL DNA as the template, 0.4 µL ROX reference dye, and water to a total of 20 µL. The qPCR reactions and calibrations were performed following a protocol described previously (Takai and Horikoshi, 2000). In brief, triplicate qPCR reactions were performed for each sample with efficiencies range of 90.20%, respectively, and the C_t values were calculated as gene copies against the standard curve. As a positive control, a linear plasmid was constructed using the amplified PCR products and a TOPO-TA vector cloning kit (Invitrogen). The gene abundance per sample was obtained by multiplying the elution volume and dividing the weight of sediment with DNA extraction.

2.5. Bioinformatics analysis

After sequencing, barcodes and low-quality sequences were removed using QIIME2 (Caporaso et al., 2010). Chimeras detected with UCHIME against the SILVA database release 138 (Quast et al., 2013) and reads presented as a single copy were removed manually. The remaining reads were then clustered into Amplicon Sequences Variant (ASV) by DADA2 (Divisive Amplicon Denoising Algorithm) (Callahan et al., 2016). Taxonomy assignment of ASVs that were not affiliated with prokaryotes, as determined from the SILVA database release 138, were further removed (Caporaso et al., 2010). The Shannon index, Simpson index and richness estimator (Chao1) was calculated based on the Bray-Curtis dissimilarity matrix. Network analysis was conducted to explore the co-occurrence patterns within/between the taxa of microbial communities. A similarity matrix was firstly generated by inputting a typical ASV matrix file, and then the correlation matrix, r value and p value were calculated using corr. test in the “psych” package (Revelle, 2015) of R version 3.5.3. ASVs, which are strongly and significantly correlated (Spearman's |r| > 0.6 and FDR-adjusted p < 0.05), were used to construct the networks using Gephi version 0.9.3 (Bastian et al., 2009). Predicted potential carbon metabolic pathways for microbial communities based on the 16S rRNA gene were conducted with the open-source R package Tax4Fun (Asshauer et al., 2015) with the short reads mode disabled along with SILVA 16S rRNA database (version 138), and were visualized by bubble plots using “ggplot” package. The 16S rRNA gene abundance, production rate, respiration rate and growth efficiency of the prokaryotic community were plotted with Sigmaplot software package 12.0 version (Systat Software, Inc. SigmaPlot for Windows).

2.6. Statistical analysis

Non-metric multidimensional scaling (NMDS), based on the Bray-Curtis similarity index, was calculated with PRIMER 5 (Plymouth Marine Laboratory, West Hoe, Plymouth, UK) (Clarke and Warwick, 2001) to show the distribution pattern of microbial communities. An analysis of similarities (ANOSIM), based on the ASVs relative abundance, was conducted with Paleontological Statistics (PAST) version 3 (Hammer, 2001) to test whether there was a significant difference in the microbial community among the sampling sites. The Pearson correlation coefficients between the environmental variables and the metabolic features of the microbial communities were calculated separately using the Graphpad Prism software package version 8.0 (GraphPad Software Inc., San Diego, CA, USA) after the data were square-rooted transformed. Values of p < 0.05 and p < 0.01 were considered to indicate different levels of statistical significance. Since the length of axis 1 of detrended correspondence analysis (DCA) > 3.0, canonical correspondence analysis (CCA) was performed to analyze the associations between prokaryotic communities and environmental factors using CANOCO v5.0 software ( Šmilauer and Lepš,2014). In addition. Welch’s t-test was used to identify the differences in relative abundance of microbial communities in sediments between the southern and northern hemispheres by the confidence interval method of the Statistical Analysis of Metagenomic Profiles (STAMP, Parks et al., 2014) software package (version 2.1.3).

3.1. Geochemical characterization of the samples

In total, nine sediment samples were collected from 2,600 m to 3,489 m in the northern hemisphere, and 19 sediment samples were collected from 3,938 m to 9,100 m in the southern hemisphere. Generally, the TOC content of sediments was significantly higher in the northern hemisphere than in the southern hemisphere (p < 0.01), might be due to the sampling depth being much lower in the former. Furthermore, the TOC content of surface sediments was significantly higher than that of deeper sediments (p < 0.01). The highest and lowest values of TOC in the northern hemisphere were observed at Stns. SY207 and SY220B, respectively, while those were found at Stns. FDZ188S and FDZ146B of the southern hemisphere, respectively. In the southern hemisphere, the TOC and NO₃⁻ of sediments in DT and MT were significantly higher than those of sediments in KT (p < 0.01). Lower concentration of NH₄⁺ was found in the surface layer of the northern hemisphere, while in the deeper layer of the southern hemisphere. TP content was significantly higher in the southern hemisphere than its counterpart (p < 0.05) (Table 1).

3.2. Community composition, diversity and gene abundances

A total of 1,551,838 sequences and 1,541 ASVs were generated by the microbial communities, with the highest and lowest numbers of ASVs found in the deeper sediment layer at Stns. FDZ188 and FDZ173 in the southern hemisphere (Table 1). With the highest and lowest numbers of ASVs found the deeper sediment layer at Stn. SY220 and the surface sediment layer at Stn. SY223 in the northern hemisphere, respectively. The numbers of ASVs in sediments were also higher in the deeper layers than those in the surface layers, except for Stns. FDZ152 and SY212.

For the community composition, Nitrososphaeria was the only archaeal class found in all the samples (Fig. 2A). Nitrososphaeria showed significant higher proportions in the northern hemisphere than that its counterpart (p < 0.05). As for bacteria, Alphaproteobacteria and Gammaproteobacteria were the dominant classes in the southern hemisphere, accounting more than 90% of the total retrieved sequences, while Alphaproteobacteria, Gammaproteobacteria, Actinobacteria and Bacteroidia dominant in the northern hemisphere (Fig. 2A). NMDS analysis demonstrated that the microbial community in trenches belonging the two hemispheres were significantly distinct from each other (p < 0.01, Fig. 2B). Significantly higher Chao1, Shannon and Simpson diversity index was found in the northern hemisphere (p < 0.01, Fig. 2C). Based on the STAMP analysis, the indicative groups belong to Oceanospirillales, Alteromonadales and Pseudomonadales in the southern hemisphere, while belong to Rhizobiales and Actinomarinales in the northern hemisphere (p < 0.05, Fig. S1). Comparatively, the 16S rRNA gene abundance was significantly higher in the southern hemisphere (p < 0.05, Fig. 2D) and in the surface layer, except for Stns. SY212 and SY223 (Fig. 2D).

3.3. Potential functions and co-occurrence network of microbial communities

Potential functions related to carbon metabolism were predicted based on the 16S rRNA genes by Tax4Fun (Fig. 3A). The processes involved in the hydrocarbon degradation, chemoheterotrophy, aliphatic non-methane hydrocarbon degradation and aerobic chemoheterotrophy were significantly enriched (p < 0.01), and the abundance of these potential functions in the southern hemisphere were significantly higher than its counterpart (p < 0.05).

To elucidate the interactions between different microbial groups in the sediments, network analyses were conducted based on the top 200 ASVs (Fig. 3B). The resulting network consisted of 200 nodes and 1,756 links for all samples, and 98.5% of the connections were positively correlated, and 1.5% were negative among these links in the network. In the southern hemisphere, the network consisted of 133 nodes and 458 links, and 96.3% of the connections were positively correlated. In the northern hemisphere, the network consisted of 171 nodes and 3,002 links, and 67.3% of the connections were positively correlated (Fig. 3B). Overall, positive connections dominated the interactions among microbial taxa in the sediments, although more negative connections revealed from the northern hemisphere. Negative correlations were found mainly between Rhodobacterales and Alteromonadales, Pseudomonadales and Bacteriovoracales, as well as Sphingomonadales and Alteromonadales in the southern hemisphere, while mainly between Rhizobiales and Burkholderiales, Pseudomonadales and Alteromonadales in the northern hemisphere (Fig. 3B).

3.4. Metabolic characteristics of microbial communities

The production rates had no significant difference between the two hemispheres under the same pressure. However, the production rates were significantly higher under AP than those under HHP (p < 0.01, Figs. 4A). On a vertical scale, the production and respiration rates were significantly higher in surface layers than those in deeper layers under the same pressure (p < 0.05, Fig. 4A). The respiration rates were significantly higher in the northern hemisphere than those in the southern hemisphere under AP ( p < 0.05, Fig. 4B), while no significant difference between the two hemispheres under HHP (Fig. 4B). However, significantly higher respiration rates under HHP were found in the northern hemisphere (p < 0.01, Fig. 4B) but significantly lower respiration rates under HHP were found in the southern hemisphere (p < 0.01, Fig. 4B), except Stns FDZ173 and FDZ188. The BGEs were significantly higher under AP than under HHP (p < 0.01, Fig. 4C). On a vertical scale, the PGEs were higher in surface layers than those in deeper layers under AP, while no significant difference under HHP (Fig. 4C).

3.5. Environmental effects and correlation analysis

Since microbial communities retrieved from the two hemispheres formed two distinct clades as demonstrated by the NMDS analysis, the environmental effects on microbial communities were further analyzed with CCA. After removing factors with VIF > 10, five environment parameters were used for CCA analysis. Among them, depth, TOC and TP had significant impacts on the microbial community (999 permutation testing, p < 0.01, Fig. 5A).

Pearson correlations among the gene abundance, metabolic rates and key environmental forces in sediments were further conducted (Figs. 5B-C). Only depth had significant correlations with microbial metabolic features in the sediments. Among metabolic features, respiration rate showed consistently negative correlation with growth efficiency and depth, but always had positive correlations with production rate; growth efficiency was always significantly correlated with depth and respiration rate. Production rate showed negative correlation with depth only under AP (Fig. 5B).

4.1. Heterogeneity of microbial communities among trenches

Distinct microbial communities between the two hemispheres were observed, might be attributed to the differences in latitude, as demonstrated in previous studies (Jiao et al., 2020; Seppey et al., 2023). Microbial communities among trenches in the northern hemisphere had no significant difference, this is very likely due to the ocean circulation. Because a westward-flowing lower circumpolar deep water penetrate into the Philippine Sea through the Yap-Mariana Junction at depths below 4,000 m (Zhou et al., 2022), which might have facilitated the connectivity of microbial communities between the YT and MT. This is in agreement with previous studies (Peoples et al., 2019; Liu et al., 2020), which also indicated endemism did not cause community differences between trenches (Peoples et al., 2019). Higher diversity of bacterial community than that of archaea in each trench also was consisting with findings from different trenches (Liu et al., 2022; Zhang and Jing, 2024), where was predominated by Alphaproteobacteria and Gammaproteobacteria. Gammaproteobacteria could secrete a series of extracellular enzymes to degrade and utilize various organic substances, thereby playing a key role in the mineralization and biogeochemical transformation in the deep ocean (Boeuf et al., 2019). Alphaproteobacteria have been identified as the main degraders of transparent exopolymer particles (Taylor and Cunliffe, 2017) and chitins (Enke et al., 2018). Those microbial groups were capable of utilizing different carbon sources possibly occupied different niches (Teske et al., 2011) and cooperatively driven the carbon cycles in different trenches. Carbon metabolism as the major functional predictions further suggested that organic carbon utilization was a common process driven by microbes in the trench sediments.

Depth, TOC and TP were identified as the key ecological factors significantly affect the microbial communities, and their effects in trench ecosystems had been previously documented (Hu et al., 2021; Wang et al., 2022), although their relative contributions in each trench might be varied. Distinct community structures between the two hemispheres further suggested that spatial scale is an essential driving force for the biogeographic pattern of microbes (Martiny et al., 2006; Hanson et al., 2012). Therefore, differences in latitude on the global scale should be considered in future studies, especially due to the associated geographical locations and ocean circulation.

4.2. Microbial metabolic characteristics

Significantly higher production rates, respiration rates and growth efficiencies of microbial communities occurred in the surface layers than the deeper layers, this disparity could be partially attributed to the variances in dissolved oxygen and the accessibility of organic carbon (Paerl et al., 2002; Li et al., 2023). Predominantly positive connections revealed from the co-occurrence network analysis was consistent with the results of the Pearson analysis, collectively indicated a cooperative rather than competitive relationship between the microbial groups presented in the trench sediments.

The production rates were considerably lower than those observed in the Mediterranean (Amalfitano et al., 2008) and the Northeast Atlantic (Dixon and Turley, 2001), while the respiration rates were lower than those observed in the Omura Bay (Wada et al., 2012), the East China Sea (Song et al., 2016), the northern Gulf of Mexico (Rowe et al., 2002) and the Seto Inland Sea (Nakamura, 2003). It is noteworthy that the lower growth efficiency was observed in the northern hemisphere sediments, where microbial communities exhibited the higher respiration and production rates. Carbon oxidation required less energy (Røy et al., 2012), therefore, microbes maybe more inclined to respiration than production. While the TOC content of sediments was significantly higher in the northern hemisphere than in the southern hemisphere, might contribute to higher respiration and production rates in the former. Furthermore, in the northern hemisphere, a greater proportion of nitrite-oxidizing Nitrospira present, possibly driven the process of nitrification, thus might explain the higher respiration rates detected (Leung et al., 2022).

4.3. Pressure impact and ecology implications

Deep-sea microbial communities are exposed to high-pressure conditions, which has a variable impact on prokaryotes, depending on whether they are piezophilic, piezotolerant or piezosensitive. Although the effect of hydrostatic pressure on deep-sea prokaryotes were considered (Tamburini et al., 2013; Amano et al., 2022), the impacts on microbial metabolic processes remains poorly understood. This study revealed that the production rates were significantly higher under AP than under HHP, indicating the passive effect of pressure (Schedler et al., 2014; Amano et al., 2022). HHP could induce the accumulation of citrate and dihydroxyacetone (Scoma et al., 2019), indicating a reduction in the rates of aerobic oxidation of fatty acids in the TCA cycle. Dihydroxyacetone is connected to citrate through glycerol metabolism and glycolysis, both of which are upregulated with increased HHP. Consequently, the high degradation rates exhibited by obligate hydrocarbon degraders might become disadvantageous at elevated HHP (Scoma et al., 2019).

Similarly, the PGEs were significantly higher under AP than those under HHP, and the PGE value varied from 1–19% under HHP in this study. Because most of previous studies neglect the pressure effect and solely conducted under atmospheric pressure conditions (Herndl and Reinthaler, 2013; Amano et al., 2022). Therefore, it appeared that heterotrophic biomass production of deep-sea prokaryotes has been overestimated in the past. According to the method proposed by Amano et al. (2022), the estimated carbon demand obtained from in situ pressure activity measurements and the particulate organic carbon supply was largely balanced. It was very likely that the biomass production and respiration of the bulk prokaryotic community are reduced in proportion to the in situ pressure conditions. In our study, the pressure impacts on the respiration rates were not consistent, this might reflect the influence of hydrostatic pressure on the physiological, metabolic, and enzymatic activities of microorganisms in hadal trenches (Xiao et al., 2021). In the presence of HHP, microorganisms prefer anaerobic metabolisms over aerobic respiration as “common adaptation strategy”, since the former could cause less intracellular oxidative stress (Xiao and Zhang, 2014). Furthermore, the application of HHP resulted in a shift of the redox gradient, leading to a greater reaction of Gibbs free energy being generated from denitrification compared to that under AP (Zhang et al., 2015). The present study provides further proof that the heterotrophic activity of prokaryotes would be significantly suppressed by hydrostatic pressure in the bathypelagic and trench regions of the ocean.

This study represented the first comprehensive investigation of the microbial community structure and metabolic characteristics of sediments from five global trenches. Distinct community structure and diversity in the trenches located in the two hemispheres were found. Co-occurrence network demonstrated that the interaction between microbial communities may have been more cooperative than competitive. The activities of radioactive isotopes and the electron transport system were applied to a deeper insight into the processes and efficiency of microbial-driven carbon cycles. The growth efficiency of microbial communities under HHP was significantly lower than that under AP, indicating that carbon flow was more inclined to be used to maintain respiration under HHP. Future investigation on bacteria and archaea separately would help to gain a more comprehensive understanding of the carbon utilization and assimilation by microbes in the deep-sea biosphere.

Conflict of Interest

The authors (Hao Liu, Yao Xiao, Ziying Wu, Xuegong Li and Hongmei Jing) confirm that the study (Microbial community structure and metabolic characteristics in the five different hadal trenches) was carried out without any commercial or financial affiliations that might be perceived as a potential conflict of interest.

Acknowledgments

This work was supported by Innovational Fund for Scientific and Technological Personnel of Hainan Province (KJRC2023C37), the Hainan Provincial Natural Science Foundation of China (424MS115) and the International Partnership Program of Chinese Academy of Sciences for Big Science (183446KYSB20210002). We thank the pilots of the deep-sea HOV “Shen Hai Yong Shi” and “Fen Dou Zhe”, and the crew of the R/V“Tan Suo Yi Hao for their professional service. We would like to thank Shuai Tang and Jie Chen from the Institutional Center for Shared Technologies and Facilities of IDSSE, CAS for assistance with nutrient measurement.

Data availability

All of the 16S rRNA gene sequences obtained from this study have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under the accession number PRJNA1124523.

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FigureS1.png
Figure S1. Extended error plot for microbial communities of the sediments between the southern and northern hemispheres visualized through STAMP software. Mean proportions in different categories were displayed in the left bar graph. The colored circles (brown and blue) showed the 95% confidence intervals calculated using the Welch’s t-test.

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Microbial community structure and metabolic characteristics in the five different hadal trenches

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Abstract

Figures

1. Introduction

2. Materials and methods

2.1. Sample collection and chemical analysis

2.2 Microbial community productivity and respiration

2.3. DNA extraction, PCR amplification and sequencing

2.4. Quantitative PCR

2.5. Bioinformatics analysis

2.6. Statistical analysis

3. Results

3.1. Geochemical characterization of the samples

3.2. Community composition, diversity and gene abundances

3.3. Potential functions and co-occurrence network of microbial communities

3.4. Metabolic characteristics of microbial communities

3.5. Environmental effects and correlation analysis

4. Discussions

4.1. Heterogeneity of microbial communities among trenches

4.2. Microbial metabolic characteristics

4.3. Pressure impact and ecology implications

5. Conclusion

Declarations

Conflict of Interest

Acknowledgments

Data availability

References

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