Carbon nanomaterials affect carbon cycle-related functions of the soil microbial community and the coupling of nutrient cycles

Journal Pre-proof Carbon nanomaterials affect carbon cycle-related functions of the soil microbial community and the coupling of nutrient cycles Fan Wu, Yaqi You, David Werner, Shuo Jiao, Jing Hu, Xinyu Zhang, Yi Wan, Junfeng Liu, Bin Wang, Xilong Wang

PII:

S0304-3894(20)30132-1

DOI:

https://doi.org/10.1016/j.jhazmat.2020.122144

Reference:

HAZMAT 122144

To appear in:

Journal of Hazardous Materials

Received Date:

6 December 2019

Revised Date:

13 January 2020

Accepted Date:

19 January 2020

Please cite this article as: Wu F, You Y, Werner D, Jiao S, Hu J, Zhang X, Wan Y, Liu J, Wang B, Wang X, Carbon nanomaterials affect carbon cycle-related functions of the soil microbial community and the coupling of nutrient cycles, Journal of Hazardous Materials (2020), doi: https://doi.org/10.1016/j.jhazmat.2020.122144

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Carbon nanomaterials affect carbon cycle-related functions of the soil microbial community and the coupling of nutrient cycles Fan Wua, Yaqi Youb, David Wernerc, Shuo Jiaoa, Jing Hua, Xinyu Zhanga, Yi Wana, Junfeng Liua, Bin Wangd, Xilong Wanga,*

Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking

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University, Beijing 100871, China

Department of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557, USA

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School of Engineering, Newcastle University, Newcastle upon Tyne, UK

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School of Public Health, Peking University, Beijing 100191, China

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Corresponding author. Email address: [email protected] (X. Wang)

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Graphical abstract

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Highlights

CNMs strongly impacted the functional genes and pathways of C and N cycles

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S and P cycles were less vulnerable to CNMs

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M50 had broader and severer impacts on microbially mediated nutrient cycles

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Network analysis revealed that CNMs decouples nutrient cycles in soil

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Abstract

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Many studies have examined changes in soil microbial community structure and composition by carbon nanomaterials (CNMs). Few, however, have investigated their impact on microbial community functions. This study explored how fullerene (C60) and multi-walled carbon nanotubes (M50) altered functionality of an agricultural soil microbial community (Archaea, Bacteria and Eukarya), using microcosm experiments combined with GeoChip microarray. M50 had a stronger effect than C60 on 2

alpha diversity of microbial functional genes; both CNMs increased beta diversity, resulting in functional profiles distinct from the control. M50 exerted a broader, severer impact on microbially mediated nutrient cycles. Together, these two CNMs affected CO2 fixation pathways, microbial degradation of diverse carbohydrates, secondary plant metabolites, lipids and phospholipids, proteins, as well as methanogenesis and methane oxidation. They also suppressed nitrogen fixation, nitrification,

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dissimilatory nitrogen reduction, eukaryotic assimilatory nitrogen reduction, and anaerobic ammonium oxidation (anammox). Phosphorous and sulfur cycles were less vulnerable; only phytic

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acid hydrolysis and sulfite reduction were inhibited by M50 but not C60. Network analysis suggested

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decoupling of nutrient cycles by CNMs, manifesting closer and more hierarchical gene networks. This work reinforces profound impact of CNMs on soil microbial community functions and ecosystem

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services, laying a path for future investigation in this direction.

Keywords: carbon nanomaterials, soil microbial community, nutrient cycle, coupling of nutrient

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cycles, metagenomics, gene networks.

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1. Introduction Carbon nanomaterials (CNMs) have been widely used in many fields due to their unique electrical properties, chemical stability, and thermal conductivity (De Volder et al., 2013). With increasing production and usage, a large amount of CNMs will inevitably enter the environment, which may affect ecosystem functions (Chen et al., 2017, Petersen et al., 2011, Sharifi et al., 2012). The impact of

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CNMs on elemental cycles that largely shape the Earth’s landscape and climate has been attracting increasing research interests (Bailey et al., 2018, Nelson et al., 2016, Reed et al., 2015). Specifically,

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terrestrial ecosystem plays an important role in elemental cycles, with soil being a large Carbon (C)

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sink (Heimann and Reichstein 2008). Whether and to which extent CNM exposure might alter soil C turnover remains largely unaddressed. Even less studied are effects of CNMs on cycle of other nutrient

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elements in soil (e.g., nitrogen (N), phosphorus (P), sulfur (S)), despite immense importance of these nutrients to soil ecosystem services (Gruber and Galloway 2008, Sattari et al., 2012). Remarkably,

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nutrient cycles are intricately linked to each other. Their coupling and dynamics are of great importance to ecosystems and climate feedbacks (Delgado-Baquerizo et al., 2013, Marklein and

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Houlton 2012, Thornton et al., 2007). A comprehensive understanding of how CNMs may impact the

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ecosystem functions requires elucidation of CNM’s effects on nutrient cycle and particularly, to which extent these engineered carbonaceous materials could impair connections between individual elemental cycles.

Microorganisms drive innumerable biogeochemical reactions on the Earth, playing an essential role in individual elemental cycles (Bardgett et al., 2008, Elshahed et al., 2003, Nelson et al., 2016).

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Microbial metabolism also couples elemental reactions, through both assimilation and dissimilation processes (Falkowski et al., 2008, Janssens et al., 2010, Klausmeier et al., 2004). Assimilatory coupling of C, N, and P cycles occurs when microbes incorporate these nutrient elements into biomass, and its decomposition leads to nutrient release. Dissimilatory coupling of C, N, and S elements occurs when microbes catalyze energy-yielding redox reactions, during which oxidized and/or reduced

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nutrient elements are released to the environment (Burgin et al., 2011). Given the importance of microbial communities in individual nutrient cycles and their coupling, it is urgent to elucidate the

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to the coupling of individual nutrient cycles.

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effects of CNMs on microbially mediated nutrient cycles, especially the key processes that are central

Recent studies including ours have investigated how CNMs would impact soil microbial community

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(Ge et al., 2018, Wu et al., 2019, Zhang et al., 2018). CNMs have been shown to alter soil microbial community in microbial biomass, taxonomic diversity, relative abundance of specific microbial groups,

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community structure and composition, and enzyme activity (Chung et al., 2011, Jin et al., 2013, Khodakovskaya et al., 2013, Rodrigues et al., 2013, Shrestha et al., 2013). However, much less

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attention has been paid to the effects of CNMs on soil microbial community’s metabolic potential,

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especially those processes involved in nutrient cycles (Shrestha et al., 2013). Even less clear are the effects of CNMs on microbial pathways and regulatory networks that are essential to the coupling of nutrient cycles. Furthermore, when examined, soil microbial functions were mostly interrogated using bioassay-based phenotyping or inferred from changes in the relative abundance of microbial functional groups (Ge et al., 2018, Rodrigues et al., 2013), which overlooks the vast diversity of

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metabolic and functional traits of soil microbial community, as well as complex metabolic networks therein. To bridge this knowledge gap we investigated how two widely used CNMs, fullerene (C60) and multi-walled carbon nanotubes (outer diameter about 50 nm; M50) may impact the functions (e.g., nutrients cycle) of an agricultural soil microbial community, using microcosm experiments coupled

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with GeoChip 5.0 60 K microarray and network analysis. These two CNMs have distinct physical structure and surface composition. They could exert dissimilar effects on soil microbial community

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functions. In this work, we asked whether C60 and M50 would impact soil ecosystem functions by

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changing the diversity and composition of microbial functional genes, particularly those central to nutrient cycle, and more importantly, by changing the coupling of nutrient cycles. Informed by the

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previous studies, we conducted 30-day microcosm experiments, focusing on two dose levels representing the estimated CNM concentrations in soils treated with biosolids (Gottschalk et al., 2013).

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In addition, we measured actual activities of several extracellular enzymes, focusing on decomposition of polysaccharides that is essential to soil organic matter turnover. Finally, we compared the GeoChip

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results with bioassay-based enzyme activity results and discussed the implications of these methods

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for data interpretation. This work reveals profound effects of CNMs on soil health and global nutrient cycles, laying a path for future investigation in this direction.

2. Materials and methods 2.1 Characterization of CNMs Fullerene (C60) and multi-walled carbon nanotubes with an outer diameter of around 50 nm (M50) 6

(purity > 98% for both) were purchased from Chengdu Organic Chemicals Co. Ltd., Chinese Academy of Sciences. Their morphology was characterized using transmission electron microscopy (Tecnai G2 F30, FEI, USA). Their specific surface area and porosity were obtained from N2 sorption-desorption isotherms at 77 K using a surface area analyzer (Autosorb-1-MP, Quantachrome Instruments, USA). Details for sample measurement are described in the Supplementary Information (SI). The C, H, and

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N contents of C60 and M50 were measured by an elemental analyzer (Vario EL CHN, Elementar, Germany). Ash content was determined by heating at 900 °C for 4 h (Wang et al., 2010; Shen et al.,

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2015); O contents were calculated by mass balance. Surface oxygen and carbon contents of C60 and

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M50 were measured using X-ray Photoelectron Spectroscopy (XPS) (PHI Quantera Scanning XPS Microprobe, Physical Electronics, Inc., USA) with a monochromatic Al K radiation source operated

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at 225 W, 15 mA, and 15 kV. Zeta potential of CNMs at the soil pH was measured by Nano-ZS90 Zeta Sizer (Malvern Instruments Technical Ltd., UK) immediately after suspension of CNMs was prepared.

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Additional information regarding CNMs properties characterization is detailed in SI. The distribution and aggregation of CNMs in soil were observed by environmental scanning electron microscopy

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(ESEM, Quattro ESEM, Thermo Fisher). Methods and results for analyzing aggregation of CNMs in

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soil along with the basic soil properties are shown in SI. 2.2 Cytotoxicity of CNMs to bacterial cells Cytotoxicity of CNMs to bacterial cells was tested using Escherichia coli (E. coli, with a strain number of CICC 24085), a model bacterium widely used for stress testing (Kang et al., 2007), with the aid of TEM imaging (FEI Tecnai G2 T20). In brief, the E. coli strain was incubated in an appropriate

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culture medium at 37 °C and harvested at the exponential growth phase. E. coli cells were rinsed twice with 0.9% saline solution and were re-suspended in the same saline solution before exposure to CNMs. CNMs were dispersed in 0.9% saline solution with the assistance of ultra-sonication for 15 min at 70% amplitude (Branson 250 Digital Sonifier, USA) to make a CNM suspension; it was then added to the cell suspension with a final concentration of 30 or 300 mg/L, representing the dose levels applied later

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to soil microcosms. Cell suspensions with and without CNMs were gently shaken and incubated at 37 °C for 2 h (Kang et al., 2007). Afterwards, E. coli cells were fixed, dehydrated, and observed under

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TEM. Also, after E.coli was incubated with C60 or M50 for 2 h, the viability loss of E.coli cells was

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evaluated using colony counting method (Liu et al., 2011). Specifically, the cell suspension was 10-fold progressively diluted to 10-7 and 100 μL diluted suspension was uniformly plastered in

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triplicate on culture medium and incubated overnight in the dark at 37 °C. Plates without CNMs were used as control. The E.coli colonies were counted and compared with the control to evaluate the cell

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viability changes (Liu et al., 2011). 2.3 Soil microcosm experiments

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To set up microcosm experiments, the soil was pre-incubated at 25 °C for a week in the dark to revive

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soil microorganisms. The same method for reactivation of microbes has been used in other studies (Blagodatsky and Yevdokimov 1998, Li et al., 2015). After reactivating the soil microorganisms, 30 g of soil was filled into 100 mL sterilized glass vials. C60 and M50 powder was added to each vial to reach a final dose of 30 or 300 mg/kg. These dose levels reflect the estimated concentrations of these two CNMs in biosolid-treated soils, although actual environmental concentrations remain unknown

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(Gottschalk et al., 2013). CNMs were mixed with soil by vigorous stirring to make the microcosm systems homogeneous (Ge et al., 2014, Wu et al., 2019). Soil without CNM amendment was incubated as the control. Sterilized water was added to the microcosms every week to maintain the field soil moisture content of 12.29 ± 0.01% (Table S1). All treatments were performed with three replicates and microcosms were incubated in the dark at 25 °C for 30 days. This incubation duration was chosen

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based on a previous study also working with a similar agricultural soil which showed recovery of the soil microbial community within 56 days (Wu et al., 2019).

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2.4 Soil microbial metagenomics

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To determine microbial functions in soil treated by these two CNMs, genomic DNA was extracted (detailed in SI) from soil after 30-day incubation, and the GeoChip 5.0 60K microarray was performed

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in triplicate for each soil sample. This microarray includes approximately 60,000 distinct probes that covers 144,000 coding sequences from 393 functional gene families in Archaea, Bacteria, and

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Eukarya. It represents one of the most comprehensive functional gene arrays, allowing quantitative analysis of microbial genes involved in biogeochemical cycles of C, N, P, and S, and organic

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remediation, metal and antibiotic resistance, and secondary metabolism.

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Soil DNA was amplified and labeled with the fluorescent dye Cy-5 using a random priming method (He et al., 2010). The labeled DNA was hybridized onto the GeoChip 5.0 microarray; then the arrays were washed and scanned by the NimbleGen MS200 Microarray Scanner (Roche NimbleGen, Inc., USA). ImaGene 6.0 software was used to convert optical signals to digital ones, and only those spots automatically scored positively in the raw data were used for downstream analysis (Liang et al., 2011).

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Spots with a signal-to-noise ratio (SNR) of < 2.0 were removed (He et al., 2010). The raw data were normalized by log10 transformation and divided by mean values of the total signal intensity of the corresponding samples. The normalized total signal intensity was recorded as the sum of the normalized intensity of individual genes. All raw data generated in this study have been deposited into the Gene Expression Omnibus (GEO) database under accession number GSE135153.

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2.5 Soil enzyme assays

Activities of three hydrolytic enzymes catalyzing polysaccharide decomposition, β-D-cellubiosidase

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(EC 3.2.1.91), β-glucosidase (EC 3.2.1.21), and β-xylosidase (EC 3.2.1.37), were measured for soil

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under different treatments (Li et al., 2020). Polysaccharides from plant litter and cell wall debris play a key role in stabilization of soil aggregates, and microbial decomposition of polysaccharides influences

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soil organic matter turnover. Activities of the three extracellular enzymes analyzed here are proxies of soil processes in degradation of cellulose, cellobiose, and hemicellulose, respectively (Osburn et al.,

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2018). They were analyzed using the method by Bell et al. (2013). Briefly, after exposure to C60 or M50 for 30 d, 1 g fresh soil was taken from the microcosmic system and was mixed with 33 mL 0.05

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M sodium acetate in a sterile centrifuge tube. After shaking at 185 rpm for 20 min at room temperature,

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an aliquot of the slurry was transferred to a black 96-well plate containing 200 μL of the corresponding substrate for each enzyme. The mixture was incubated in the dark for 1.5 h at 35 °C, and then centrifuged at 1500 rpm for 15 min. Fluorescence of the supernatant was measured with an excitation wavelength at 365 nm and an emission wavelength at 450 nm using a multifunctional fluorescence microplate reader (Tecan Infinite M200, Tecan Group Ltd., Switzerland). For each soil

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sample, triplicate measurements were performed for each enzyme assay. 2.6 Data analysis All statistics were performed with SPSS 20.0 (IBM Co., USA) and R (v3.5.2). Differences in the relative abundance (i.e., normalized total signal intensity) of functional genes/pathways between soils were determined using one-way analysis of variance (ANOVA) or t-test after the Levene’s test for

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checking homogeneity of variance. Shannon index (within group diversity) was calculated based on the microbial functional genes identified in each soil sample. To compare beta diversity (among group

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diversity) of soil microbial functions, principal coordinates analysis (PCoA) based on Bray-Curtis

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distance of functional gene profiles was performed and significance of the differences among groups was tested by three statistical analysis: permutational multivariate analysis of variance

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(PERMANOVA) implemented in Adonis, analysis of similarities (ANOSIM), and multi-response permutation procedures (MRPP). Bray-Curtis distance of functional gene profiles was compared

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among treatments using ANOVA after the Levene’s test for checking homogeneity of variance. Difference in extracellular enzyme activity between treatments and the control was tested using

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ANOVA after the Levene’s test for homogeneity of variance.

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Network analysis was conducted to explore and compare the co-occurrence patterns of C/N/P/S cycle genes in soil under different treatments. Spearman correlation was tested for every two genes to assess significant co-occurrence; only genes showing robust correlations ( > 0.8 and p < 0.05) were included in the final networks, in which each node represents one microbial functional gene, and each edge represents a high and significant correlation between two nodes. Networks were visualized using

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Cytoscape (Shannon et al., 2003).

3. Results and discussion 3.1 Characterization of CNMs TEM imaging showed that C60 and M50 were in spherical and tubular shapes, respectively. The actual outer diameter of M50 was 40  0.83 nm which was calculated based on the TEM images of 15

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nanotubes (Fig. S1). The specific surface area of M50 is 77.1 m2/g, which is 44 times that of C60 (1.75 m2/g) (Table S2). XPS results revealed that the surface oxygen content of M50 was almost 5 times that

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of C60. Zeta potential values of C60 and M50 at the soil pH were 6.12 and -23.7 mV, respectively.

3.2 Cytotoxicity of CNMs to E.coli cells

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These results indicated that C60 and M50 had distinct structures and surface properties.

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As a first step to evaluate effects of C60 and M50 on microbes, we estimated the cytotoxicity of these two CNMs to E. coli (CICC 24085) cells, a model bacterium commonly used in cytotoxicity tests

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(Kang et al., 2007). The TEM images of E. coli cells exposed to C60 and M50 showed that more cells were ruptured after exposure to M50 (Fig. S2). Furthermore, an analysis on cell growth inhibition

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indicated that M50 was more toxic to E.coli cells than C60 and the damage to E. coli cells was more

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pronounced at a higher dose of CNMs (Fig. S3). Specifically, the E.coli cell viability loss increased from 37.84 ± 2.70% to 49.55 ± 6.50% as the C60 dose was increased from 30 to 300 mg/L. Correspondingly, that exposed to M50 rose up from 60.36 ± 5.41% to 71.17 ± 3.60% with increasing dose level of M50. These results suggested that M50 posed stronger cytotoxicity to bacteria, consistent with many previous reports (Kang et al., 2009, Kang et al., 2007).

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3.3 Effects of CNMs on the soil microbial community’s functional diversity Among all the 57000 probes built on GeoChip 5.0, a total of 23016, 23204, 22839, 22361, and 22520 showed positive in the control and the soil exposed to 30 mg/kg C60, 300 mg/kg C60, 30 mg/kg M50, and 300 mg/kg M50, respectively. They represent a wide range of genes involved in C, N, P, and S cycle, as well as genes for metal homeostasis, organic remediation, virulence (e.g., antibiotic

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resistance), secondary metabolism, and others. In general, C60 had limited effects on alpha diversity of microbial functional genes in soil, while M50 significantly reduced alpha diversity of microbial

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functional genes (Fig. S4). Our previous study showed that M50 exposure for 28 days reduced soil

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microbial taxonomic diversity (Wu et al., 2019). The current data extended our and other previous

community’s functional diversity.

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observations, showing adverse effects of multi-walled carbon nanotubes on soil microbial

Effects of the two CNMs on beta diversity of the soil microbial community’s functional genes were

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illustrated by PCoA (Fig. 1A). Both CNMs resulted in distinct microbial functional gene profiles as compared to the control. The 30 mg/kg C60 treatment was quite different from the 300 mg/kg C60

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treatment and the two M50 treatments, as soil under 30 mg/kg C60 exposure clustered separately from

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those under other treatments (Fig. 1A). This was also reflected in Bray-Curtis distance of functional gene profiles, which showed that soil exposed to 30 mg/kg C60 was more similar to the control than that under other treatments (p < 0.05) (Fig. 1B). Statistical analysis further supported distinct microbial functional profiles after exposure to the two CNMs (C60 vs M50, PERMANOVA: F = 5.268, p = 0.002; ANOSIM: R = 0.516, p = 0.002; MRPP: A = 0.267, p = 0.001).

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The effect of C60 on soil microbial functional genes was also a function of its dose level (0 vs. 30 vs. 300 mg/kg, PERMANOVA: F = 8.542, p = 0.009; ANOSIM: R = 0.992, p = 0.005; MRPP: A = 0.410, p = 0.008). A higher C60 dose more strongly affected beta diversity of microbial functional genes, leading to larger dissimilarity from the control (Fig. 1B). Furthermore, impact of C60 on soil microbial community’s functional genes varied with its dose since its cytotoxicity to microbial cells followed a

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dose-dependent manner (Chen et al., 2007). A previous study consistently reported more pronounced effects of 500 than 50 mg/kg C60 on the soil bacterial community taxonomic structure (Hao et al.,

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2018). In contrast, M50 did not have such a dose-dependent effect fashion, as shown by Bray-Curtis

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distance to the control and by PCoA of functional gene profiles (Fig. 1). A similar effect of M50 at both doses on the soil microbial community’s functional genes suggested that they have been greatly

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influenced by this material at a relatively lower dose level; the difference between M50 and C60 can be ascribed to much higher cytotoxicity of M50 to bacterial cells relative to C60 (Jia et al., 2005).

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Effects of C60 and carbon nanotubes on soil microbial communities have been well documented (Ge et al., 2016, Jin et al., 2014, Tong et al., 2007). However, previous studies mainly focused on microbial

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community structure and composition, and potential functional changes were inferred from

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community compositional shifts. Here, results from the GeoChip microarray directly showed a profound impact of C60 and M50 on soil microbial community’s functions. The observed differences between C60 and M50 exposure can be a result of their distinct physical and chemical properties, such as shape, size, specific surface area, and surface oxygen and charge (Sharifi et al., 2012, Utembe et al., 2015). First, C60 is spherical while M50 is tubular (Fig. S1); this may lead to different contact modes 14

between individual CNM particles and their interactions with microbial cell wall/membrane (Zhang et al., 2018). For example, the tubular M50 could have a larger contact area with cell wall/membrane than the spherical C60 (Du et al., 2013). Moreover, Park et al. (2003) demonstrated that spherical nanoparticles such as C60 were less efficient in blocking cell membrane potassium channels than single-walled carbon nanotubes with a tubular shape. Second, the diameter of M50 used in this study

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was much smaller than that of C60 and the specific surface area of M50 was 44 times that of C60 (Fig. S1; Table S2). Consistently, a sum of the meso- and macropore volume of M50 was 139 times that of

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C60 (Table S2). Hence, M50 can more readily contact the microbial cells in soil, thereby affecting the

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whole community’s taxonomic and functional diversity and structure (Petersen et al., 2011). Third, M50 had 4.6 times surface oxygen content that of C60 suggesting its more hydrophilic surfaces. M50

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with a more hydrophilic surface would better contact and further interact with the hydrophilic surface of cell membrane which is composed of lipopolysaccharides and proteins relative to C60 (Botos et al.,

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2017). Fourth, M50 had a much more negative Zeta potential (-23.7 mV under pH 8.41) than C60 (6.12 mV under pH 8.41) (Table S2), indicating higher affinity for positively-charged molecules such as

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amino acids. A more negative Zeta potential may also lead to higher stability of M50 in soil; a greater

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number of M50 could thus interact with soil microorganisms. Additionally, interactions of these two CNMs with soil particles could also contribute to the observed differences. M50 has a larger contact area with soil particles as compared to C60 at the same dose (Fig. S5). Therefore, M50 has higher sorption affinity for organic matter and stronger hetero-aggregation with soil colloids which in turn would more strongly influence the mobility and bioavailability of M50 in soil (Petersen et al., 2011).

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As many soil microorganisms form biofilms or microcolonies on surfaces of soil particles and CNMs, this may increase the chance of CNMs to interact with microorganisms in soil. More microorganisms would be attached to M50 which has a larger specific surface area than C60, leading to stronger microorganism-CNM interactions. Overall, M50’s physical and chemical properties could have resulted in greater particle-cell interactions and thus severer effects on microbial cells and the soil

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microbial community’s functional diversity. This hypothesis was supported by greater cytotoxicity of

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M50 to E. coli cells.

Fig. 1 Principle coordinates analysis (PCoA) based on Bray-Curtis matrix of functional gene profiles (A), and Bray-Curtis distance of functional genes between different treatments and the control (B). C60 and M50 represent the two CNMs added to soil, while 30 and 300 represent the dose (mg/kg) of the

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added CNM. Three experimental replicates were performed for each treatment. Data are presented as average  standard deviation of the replicates. Treatments with different letters were significantly

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different (p < 0.05).

3.4 Effects of CNMs on microbially mediated C cycle in soil Compared to the control, both CNMs had a significant impact on C cycle pathways and genes; the influence on relative abundance of C cycle pathways and genes differed with C60 and M50 exposures (p < 0.05); more pathways and genes were negatively affected by M50 than by C60 (Figs. 2, S6 and 16

S7). Specifically, C60 at 30 mg/kg enhanced 2 carbon degradation processes (lactose, lipids) while inhibited 1 carbon fixation pathway (3-hydroxypropionate/4-hydroxybutyrate cycle); C60 at 300 mg/kg enhanced all the 3 aforementioned pathways. In comparison, M50 at 30 mg/kg inhibited 4 carbon degradation processes (alginate, cutin, sucrose, terminal -galactosyl moieties of oligosaccharides and polysaccharides), 2 carbon fixation pathways (reductive acetyl CoA pathway, reductive tricarboxylic

degradation

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(heparin,

sucrose)

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acid cycle), and 1 methane cycle process (methane oxidation); M50 at 300 mg/kg enhanced 2 carbon carbon

fixation

pathway

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(3-hydroxypropionate/4-hydroxybutyrate cycle) but inhibited 7 carbon degradation processes (agar,

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alginate, glucose, inulin, phospholipids, tannins, vanillin), 2 carbon fixation pathways (reductive acetyl CoA pathway, reductive tricarboxylic acid cycle), and 2 methane cycle processes (methane

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oxidation, methanogenesis). Furthermore, when the same pathway was affected by both M50 and C60, M50 at a higher dose generally exerted a stronger effect. Variation of the related genes and microbial

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groups associated with C, N, P, and S cycles that were induced by both CNMs are detailed in SI. These observations suggested that M50 had a broader, more adverse impact on microbially mediated C cycle

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in soil. This is most likely due to its physical and chemical properties as well as particle-cell

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interactions and cytotoxicity.

Our metagenomic results provided direct evidence that C60 and M50 could alter microbially mediated C cycle in soil; the effects on it were consistent with beta diversity trends that M50 had stronger effects on the soil microbial community’s functional traits. Notably, C60 occasionally promoted C degradation/fixation pathways and M50 inhibited a great many C degradation/fixation pathways, as

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well as methanogenesis and methane oxidation. This could have important implications in soil health

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and global C cycle.

Fig. 2 C60 and M50 significantly affected a wide range of C cycle processes, including carbon fixation,

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methane cycling, and carbon degradation (carbohydrates and derivatives, secondary plant metabolites, lipids and phospholipids). Each bar represents the sum of normalized total signal intensity of all genes, present in all microbial lineages, within the individual pathway. Significance between CNM

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treatments and the control: *, 0.01 ≤ p < 0.05; **, 0.001 ≤ p < 0.01; ***, p < 0.001 (ANOVA). Many previous studies have reported that CNM-introduced changes in relative abundance of soil

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microbes and potential functional shifts inferred from those data, but few have directly analyzed functional genes (Ge et al., 2018, Rodrigues et al., 2013, Shrestha et al., 2013). For example, Ren et al. (2015) reported significant decrease of Crenarchaeota in soil by 10-1000 mg/kg graphene. Wu et al. (2019) reported that significant increase or decrease of many bacterial phyla induced by single- and

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multi-walled carbon nanotubes occurred, however the community functions predicted by bacterial phylogenetic marker genes were relatively stable. Since the current database for reference genome lacks the majority of soil microbes, profiling marker genes alone may not accurately reconstruct a community’s function. For example, we noted that CNMs exposure could stimulate certain species but inhibit others within the same microbial group. Archaeal phylum Crenarchaeota, an extremely diverse

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group widely present in soils, was extremely sensitive to C60 and M50. Exposure to the CNMs increased CO2 fixation by certain crenarchaeotal species (Metallosphaera sedula and Metallosphaera

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cuprina) but decreased CO2 fixation by another crenarchaeotal species (Metallosphaera

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yellowstonensis).

In parallel with metagenomic analysis, we measured actual activities of three extracellular enzymes

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(β-D-cellubiosidase, β-glucosidase, and β-xylosidase) responsible for decomposition of cellulose and hemicellulose, the most abundant components of plant litter (Bayer et al., 2004, Bhatia et al., 2002,

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Sato et al., 2017,). Polysaccharides from plant litter and cell wall debris play a key role in stabilization of soil aggregates, and microbial decomposition of polysaccharides influences soil organic matter

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turnover. The three enzymes measured here were produced by archaea, bacteria, and fungi and were

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secreted outside from the cell to hydrolyze polysaccharides (Eichlerová et al., 2015, López-Mondéjar et al., 2016). M50 treatments significantly enhanced activities of all these three enzymes as compared to the control and C60 treatments (p < 0.05 for both comparisons). Specifically, compared to the control, 30 mg/kg M50 increased activity of β-D-cellubiosidase, β-glucosidase, and β-xylosidase by 61.90, 49.46, and 109.41%, respectively; 300 mg/kg M50 increased their activity by 46.03, 37.97, and

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108.97% respectively (Fig. 3). In contrast, C60 treatments had marginal impacts on activity of these enzymes as compared to the control, except that 300 mg/kg C60 significantly reduced β-xylosidase activity by 29.27% (p < 0.05). Previous studies reported that activity of these three extracellular enzymes was unaffected, marginally stimulated, or significantly repressed by 30-5000 mg/kg singleor multi-walled carbon nanotubes (Chung et al., 2011, Jin et al., 2013, Shrestha et al., 2013), whereas

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C60 at either 10 or 1000 mg/kg exerted limited influence on β-glucosidase activity in soil (Tong et al.,

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2007).

Fig. 3 Activities of extracellular enzymes responsible for the decomposition of cellulose and hemicellulose in soils treated by C60 and M50 at 30 mg/kg or 300 mg/kg. Columns with different

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letters are significantly different (p < 0.05). C60.30: 30 mg/kg C60; C60.300: 300 mg/kg C60; M50.30:

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30 mg/kg M50; M50.300: 300 mg/kg M50. Consistent with the actual enzyme activity measured here, our metagenomic data showed that C60 did not affect relative abundance of the genes encoding these three enzymes (Fig. S8). In contrast, M50 did not affect relative abundance of the genes encoding β-glucosidase or β-xylosidase; it significantly reduced relative abundance of β-D-cellubiosidase genes (p < 0.01 and p < 0.001 for 30 and 300 mg/kg,

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respectively). It could be that this discrepancy between the measured β-D-cellubiosidase activity and the gene’s relative abundance was largely due to CNM-extracellular enzyme interactions and/or CNM-substrate interactions (Datta et al., 2017, Foster et al., 2018, Tietjen and Wetzel, 2003), although regulation of gene expression cannot be excluded. CNMs are known to sorb enzymes to their surfaces, a process governed by CNM surface composition; this process in turn affects enzyme activity (Foster

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et al., 2018). Here M50 was negatively-charged at the soil pH of 8.41, while C60 was almost neutral; the absolute value of the surface charge of M50 was around 3.9 times that of C60 (Table S2). At the

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soil pH of 8.41, the three enzymes were all negatively-charged (Datta et al., 2017). Electrostatic

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interaction between these extracellular enzymes and C60 particles was relatively weak thus their activity in C60-treated soil was generally comparable to the control. In contrast, M50 with negative

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charges would repel the negatively-charged enzymes. Hence the enzyme activity in the M50-treated soil was significantly higher than that amended with C60. Also a much smaller specific surface area of

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C60 (1.75 m2/g) relative to that of M50 (77.1 m2/g) would more strongly limit enzyme loading and counteract adsorption effect (Pavlidis et al., 2012). Regardless of this discrepancy, C60 and M50

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reduced relative abundance of β-D-cellubiosidase genes in certain fungi, β-glucosidase and

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β-xylosidase genes in many archaea, bacteria, and fungi. Previously, Shrestha et al. (2013) found that two cellulose degrading bacterial genera, Cellulomonas and Pseudomonas, increased after exposure to 10000 mg/kg multi-walled carbon nanotubes for 90 days. Our functional gene data, however, suggested marginal or adverse effects of M50 on cellulose degradation. Changes in C cycle-related microbial functional traits could alter soil carbon balance and have critical implications in the global C

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cycle. Additional work involving direct function profiling is necessary in this area.

Fig. 4 C60 and M50 significantly affected N cycle processes, including nitrogen fixation, nitrification,

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dissimilatory N reduction, assimilatory N reduction, and anaerobic ammonium oxidation (anammox). P and S cycles were much less affected. Each bar represents the sum of normalized total signal

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intensity of all genes, present in all microbial lineages, within the individual pathway. Significance between CNM treatments and the control: *, 0.01 ≤ p < 0.05; **, 0.001 ≤ p < 0.01; ***, p < 0.001 (ANOVA).

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3.5 Effects of CNMs on microbially mediated N, P and S cycles in soil CNMs altered the relative abundance of N-, P-, S-cycle pathways and genes. Among these, N cycle

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process was more vulnerable under exposure to CNMs (Figs. 4, S6 and S7). A comparison of these

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two CNMs suggested that M50 had broader and severer impacts and 300 mg/kg M50 induced the strongest effects among all treatments. Specifically, 300 mg/kg C60 significantly inhibited 3 nitrogen cycle pathways (anammox, eukaryotic assimilatory nitrate reduction, eukaryotic assimilatory nitrite reduction); 30 mg/kg M50 significantly suppressed 4 nitrogen cycle pathways (nitrogen fixation, nitrification, eukaryotic assimilatory nitrate reduction, anammox); 300 mg/kg M50 significantly

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repressed 6 nitrogen cycle pathways (nitrogen fixation, nitrification, dissimilatory nitrogen reduction, eukaryotic assimilatory nitrate reduction, eukaryotic assimilatory nitrite reduction, anammox), 1 phosphorus cycle pathway (phytic acid hydrolysis), and 1 sulfur cycle pathway (sulfite reduction). Similar results were also observed at the gene level (Figs. S6 and S7). These observations were consistent with higher cytotoxicity of M50 and suggested increasing stress by CNMs at higher doses.

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3.6 Effects of CNMs on C, N, P, and S cycles coupled by soil microorganisms

Co-occurrence network analysis was used to estimate impact of C60 and M50 on microbially mediated

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nutrient cycle coupling in soil (Fig. 5; Table S3). C60 decreased network average connectivity by

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7.76-11.3% with higher dose having stronger effect. In comparison, 30 mg/kg M50 decreased network average connectivity by 8.9%, while 300 mg/kg M50 increased network average connectivity by 4.3%.

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This was because 300 mg/kg M50 largely reduced network nodes (individual genes) by 10.5% while other treatments did not. C60 reduced network average path length more strongly than M50: 41.4-57.4%

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vs. 23.9-28.6%. Both CNMs increased network average clustering coefficient (7.3-10.3%). Except for 300 mg/kg M50, all treatments increased network modularity. In general, C60 at both doses and M50 at

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30 mg/kg resulted in a less complex, closer, and more hierarchical network with higher modularity,

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whereas 300 mg/kg M50 resulted in more complex, closer, and more hierarchical network with lower modularity (Deng et al., 2012). This means that C60 at both doses and M50 at 30 mg/kg led to more within-cluster gene interactions while 300 mg/kg M50 caused more between-cluster gene interactions (Faust and Raes, 2012). Reduction in average path length was observed for all treatments, especially for 300 mg/kg C60; a small average path length could increase speed of the network’s response to 23

perturbations (Faust and Raes, 2012). Furthermore, the number of weakly connected genes was increased (2.5-5.5 fold) by both CNMs, especially at 300 mg/kg; network diameter was reduced, especially by 300 mg/kg C60. These two observations reflected a general effect of CNMs on

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decoupling of nutrient cycles in soil.

Fig. 5 The network analysis depicting the co-occurrence pattern between the detected C and other

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nutrient cycle genes under exposure to CNMs at different doses and the control. The nodes with different colors represent genes within different nutrient cycles, and the edges represent strong and significant correlations between nodes (ρ > 0.8, p < 0.05 in the Spearman correlation test). The size of

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each node shows the degree of the node (the number of adjacent edges). The functional genes analyzed above were labeled in the networks of the control and that under exposure to C60 and M50 at different doses with numbers of 1-23. They are as follows: 1. fumarase-3HP4HB; 2. nasA; 3. ppx; 4. soxA; 5. C_CoA_hydratase_DiC4HB; 6. nirS; 7. phytase; 8. sqr; 9. acaB_arch; 10. hzo; 11. phytase; 12. aps_apra; 13. Rubisco_Haptophyceae; 14. nirA; 15. phytase; 16. soxY; 17. TIM; 18. xylose_isomerase_Oomycetes; 19. pectin_lyase_Oomycetes; 20. vdh; 21. narG; 22. ppx; 23. fccAB.

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We then examined the most connected genes (hub nodes) and the associated microbial groups in each network (Fig. 5). In the control soil, gene fumarase-3HP4HB in bacterial phylum Chloroflexi encoding fumarase (3-hydroxypropionate/4-hydroxybutyrate cycle of CO2 fixation) had the highest degree of 16. The most connected N-, P-, and S-cycle genes were nasA for assimilatory N reduction in various bacteria and archaeal phylum Euryarchaeota (degree 12), polyphosphate synthesis gene ppx in various

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bacteria, archaeal phylum Euryarchaeota, and fungal phylum Ascomycota (degree 8), and bacterial sulfur oxidation gene soxA (degree 10). In 30 mg/kg C60-treated soil, the gene with the highest degree 20

was

C_CoA_hydratase_DiC4HB

in

archaeal

phylum

Crenarchaeota

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of

encoding

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3-hydroxyacyl-CoA dehydrogenase, which fixes CO2 through dicarboxylate/4-hydroxybutyrate cycle. The most connected N-, P-, and S-cycle genes were bacterial denitrification gene nirS (degree 13),

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gene phytase for phytic acid hydrolysis in various bacteria and fungi (degree 14), and sqr for sulfide oxidation in various bacteria and archaeal phylum Crenarchaeota (degree 10). In 300 mg/kg

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C60-treated soil, the gene with the highest degree of 25 was acaB_arch in archaeal phylum Crenarchaeota, which fixes CO2 through 3-hydroxypropionate/4-hydroxybutyrate cycle. The most

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connected N-, P-, and S-cycle genes were hzo for anammox in bacterial phyla Proteobacteria

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(Nitrosomonas genus) and Planctomycetes (degree 17), gene phytase for phytic acid hydrolysis in various bacteria and fungi (degree 11), and gene aps_apra for adenylylsulfate reductase in various bacteria and archaea (degree 13). In 30 mg/kg M50-treated soil, the most connected C-, N-, P-, and S-cycle genes were Rubisco_Haptophyceae in algal order Isochrysidales for CO2 fixation by the Calvin cycle (degree 21), gene nirA for assimilatory N reduction in various bacteria and archaea

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(degree 11), gene phytase for phytic acid hydrolysis in various bacteria and fungi (degree 5), and bacterial sulfur oxidation gene soxY (degree 10). In 300 mg/kg M50-treated soil, the most connected C-cycle genes (degree 16) were the bacterial triosephosphate isomerase gene TIM for CO2 fixation through the Calvin cycle, the xylose isomerase gene xylose_isomerase_Oomycetes and the pectin lyase gene pectin_lyase_Oomycetes in eukaryotic order Peronosporales for hemicellulose and pectin

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degradation, and vdh for vanillin/lignin degradation in various bacteria, archaeal phylum Crenarchaeota, and fungal phylum Ascomycota. The most connected N-, P-, and S-cycle genes were

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denitrification gene narG in various bacteria and archaeal phylum Euryarchaeota (degree 15),

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polyphosphate synthesis gene ppx in various bacteria, archaeal phylum Euryarchaeota, and fungal phylum Ascomycota (degree 12), and bacterial sulfide oxidation gene fccAB (degree 15). In general,

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CNMs led to highly connected hub nodes, which may mitigate the increase in weakly connected genes. Moreover, different gene networks, including hub nodes, evolved after CNMs exposure, illustrate a

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general strategy of the soil microbial community in response to perturbations. 3.7 Effects of CNMs on other microbially mediated functions

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Other functions and genes significantly inhibited by C60 and M50 included remediation of chlorinated

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solvents, halogenated compounds, polycyclic aromatics, and other hydrocarbons (e.g., alkane, aldehyde, alcohol), detoxification of chromium and tellurium, silicon transport and silaffin biosynthesis, bacteriorhodopsin genes, fungal phospholipase B genes, and eukaryotic genes encoding cytochrome c oxidase (phylum Chlorophyta), elongation factor 1, and glyceraldehyde-3-phosphate dehydrogenase. Specifically, the genes related to polycyclic aromatics degradation were significantly

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inhibited by C60 and M50 exposure at 300 mg/kg; those related to chromium detoxification were significantly suppressed by C60 exposure at 300 mg/kg and M50 exposure at both dose levels (Fig. S9; Tables S4 and S5). Interestingly, some functions and genes were stimulated, including detoxification of copper (by 30 mg/kg C60), and eukaryotic genes encoding microtubule constituent tubulin alpha chain (order Oxymonadida; by 30 mg/kg C60 and M50) and the chaperone protein, heat shock protein

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90 (by all treatments).

4. Conclusions

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This study highlights profound effects of C60 and M50 on soil microbial community’s functionality, especially coupling of nutrient cycles. M50 had a stronger effect than C60 on alpha diversity of microbial functional genes in soil and exerted a broader, severer impact on microbially mediated carbon, nitrogen, phosphorous, and sulfur cycles, likely due to greater cell-M50 particle interactions resulting from the material’s physicochemical properties. Both CNMs resulted in distinct microbial functional gene profiles as compared to the control (beta diversity); the 30 mg/kg C60 treatment was distinguishable from other treatments. Together, these two CNMs affected three CO2 fixation pathways, microbial degradation of carbohydrates and derivatives (agar, alginate, cellulose, chitin, glucose, hemicellulose, heparin, inulin, lactose, pectin, starch, sucrose), secondary plant metabolites (lignin, vanillin, tannin, terpene), lipids and phospholipids, and proteins, and both methanogenesis and methane oxidation. Their exposure also suppressed nitrogen fixation, nitrification, dissimilatory nitrogen reduction, assimilatory nitrogen reduction by eukaryotic microorganisms, and anammox. Phosphorous and sulfur cycles were less vulnerable; only phytic acid hydrolysis and sulfite reduction were inhibited by M50. Network analysis revealed an effect of the two CNMs on decoupling of nutrient cycles in soil, manifesting closer and more hierarchical networks of nutrient cycle genes, either less complex and more modular (by C60 at both doses and 30 mg/kg M50) or more complex and less modular (by 300 mg/kg M50). In addition, the two CNMs also affected soil microbial functions other than nutrient cycle, such as inhibiting remediation of chlorinated solvents, halogenated compounds, polycyclic aromatics, and other hydrocarbons. Our observations, based on direct functional gene results, provide baseline data that are critical for evaluating how CNMs may alter soil microbial community’s functionality and ecosystem services that are mediated by microbial synergy. More studies are needed to fully understand the impact of CNMs on ecosystem functions.

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Credit author statement

All authors contributed to the manuscript.

Conflicts of interest

Declaration of interest

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There are no conflicts of interest to declare.

The authors declare that they have no known competing financial interests or personal relationships

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that could have appeared to influence the work reported in this paper.

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Electronic supplementary information (ESI) available

Acknowledgments

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Supplementary data associated with this article can be found in the Supplementary Information.

This study was supported by the National Science Fund for Distinguished Young Scientist (41525005),

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