T4-type viruses: Important impacts on shaping bacterial community along a chronosequence of 2000-year old paddy soils

Accepted Manuscript T4-type viruses: Important impacts on shaping bacterial community along a chronosequence of 2000-year old paddy soils Yong Li, Haiyang Liu, Hong Pan, Xinyu Zhu, Chen Liu, Qichun Zhang, Yu Luo, Hongjie Di, Jianming Xu PII:

S0038-0717(18)30355-9

DOI:

10.1016/j.soilbio.2018.10.007

Reference:

SBB 7311

To appear in:

Soil Biology and Biochemistry

Received Date: 21 July 2018 Revised Date:

26 September 2018

Accepted Date: 15 October 2018

Please cite this article as: Li, Y., Liu, H., Pan, H., Zhu, X., Liu, C., Zhang, Q., Luo, Y., Di, H., Xu, J., T4type viruses: Important impacts on shaping bacterial community along a chronosequence of 2000-year old paddy soils, Soil Biology and Biochemistry (2018), doi: https://doi.org/10.1016/j.soilbio.2018.10.007. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Title

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T4-type viruses: important impacts on shaping bacterial community along a

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chronosequence of 2000-year old paddy soils

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Authors

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Yong Li1, Haiyang Liu1, Hong Pan1, Xinyu Zhu2, Chen Liu3, Qichun Zhang1, Yu Luo1,

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Hongjie Di1, Jianming Xu1*

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Institute of Soil and Water Resources and Environmental Science, College of

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Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China;

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Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment,

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310007, China;

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Agricultural Sciences, Hangzhou 310021, China.

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Correspondence: Jianming Xu, College of Environmental and Resource Sciences,

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Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, Zhejiang, China. Tel.:

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+86 88982069; Fax: 86-571-88982069; E-mail: [email protected].

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Environmental Science Research & Design Institute of Zhejiang Province, Hangzhou

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Institute of Environment, Resource, Soil and Fertilizer, Zhejiang Academy of

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ACCEPTED MANUSCRIPT Abstract

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There is increasing evidence to suggest that viruses may influence the succession of

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individual populations of microorganisms, biogeochemical cycles and, ultimately,

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microbial community structure. However, it is still not well understood if T4-type

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viruses can affect the bacterial communities of terrestrial ecosystems. Here, we

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report an investigation of the impact of T4-type phage and bottom-up

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(environmental factors) controls on bacterial community structures along a

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2000-year paddy soil chronosequence. T4-type myoviral and bacterial communities

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were evaluated by clone sequencing and high-throughput sequencing of the gene

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encoding the major capsid protein (g23) and 16S ribosomal DNA, respectively.

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Long-term (centurial/millennial) anthropogenic managements of paddy soils resulted

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in an accumulation of nutrients and soil acidification. Significant shifts in soil

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bacterial and phage communities were detected during the development of paddy

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soils at millennial time scales. The Mantel test and variation partitioning analysis

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(VPA) suggested that the profile of bacterial community composition was strongly

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affected by both T4-type phage and environmental variables. Network analysis

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between phage and bacterial taxa indicated that six bacterial families were

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implicated as potential hosts of T4-type phages. These results suggest that phage

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lysis is important in shaping bacterial communities in the soil environment.

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Key Words: T4-type phage, Bottom-up controls, Bacteria, g23, Paddy soil

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chronosequence.

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ACCEPTED MANUSCRIPT 1. Introduction

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As the most abundant biological entities, viruses are increasingly recognized as a

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major driving force of global biogeochemical nutrient cycles and have received

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considerable attention over the past two decades (Fuhrman, 1999; Suttle, 2005;

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Weitz and Wilhelm, 2012). Through direct mortality of nearly all forms of cellular life,

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including bacteria, archaea and microeukaryotes, and the release of cellular

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nutrients, viruses influence the succession of

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microorganisms, biogeochemical cycles and, ultimately, microbial community

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structure (Suttle, 2007; Weinbauer, 2004; Wilhelm and Matteson, 2008; Zhong and

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Jacquet, 2014). In contrast to this top-down control (Viral lysis), bacterial activity is

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also mediated by bottom-up controls (e.g., resource availability and competition)

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(Chow et al., 2014). It is generally accepted that the dominant top-down controls, or

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sources of bacterial mortality, in the open ocean are thought to be viral lysis and

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protistan grazing (Chow et al., 2014). For example, viruses are important agents of

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bacterial removal and are believed to be responsible for 10–50% of the total

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bacterial mortality in surface waters (Fuhrman, 1999; Suttle, 2007). Many studies

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have sought to quantify grazing and viral lysis to determine the impact of top-down

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controls on structuring microbial communities in aquatic ecosystems (Baudoux et al.,

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2008; Fuhrman and Noble, 1995; Longnecker et al., 2010; Staniewski et al., 2012;

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Weinbauer et al., 2003; Weinbauer et al., 2007). Chow et al (2014) found that virus–

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bacteria relationships were more cross-linked than protist–bacteria relationships,

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and their association networks supported the paradigm that microbes were

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individual populations of

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regulated by both bottom-up and top-down controls. Despite the significant effects of viruses on shaping microbial communities, all

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those studies focused on virus–bacteria relationships either in marine water (Cram et

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al., 2016; Fuhrman and Noble, 1995; Suttle, 1994; Weinbauer et al., 2003; Weitz et

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al., 2015; Wilhelm and Matteson, 2008) or in sediments (Corinaldesi et al., 2010;

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Engelhardt et al., 2015; Middelboe and Glud, 2006). There is only limited information

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on the impact of viruses on microbial communities in terrestrial ecosystems (Allen et

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al., 2010; Helsley et al., 2014). Nevertheless, direct counts have shown that phages

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are extremely abundant in terrestrial ecosystems. Moreover, Srinivasiah et al (2015)

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recently reported that phage abundance responded quickly to changes in substrate

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availability and host growth in soils, implying that phages were active and dynamic

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members of the community. T4-type bacteriophages, an important component of

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the Myoviridae family, have been widely studied after Filée et al (2005) first

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investigated uncultured bacteriophage from diverse marine environments.

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Subsequent studies revealed that T4-type bacteriophages in soil environments were

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different from those in aquatic environments (Fujii et al., 2008; Jia et al., 2007; Liu et

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al., 2011; Wang et al., 2009a; Wang et al., 2009b; Zheng et al., 2013). Furthermore,

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the T4-type phage communities in paddy fields were found to be more

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phylogenetically diverse than those in marine environments, and the soil-specific

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phages retrieved from Japanese and Chinese paddy field soils were phylogenetic

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clustered into nine paddy groups (Wang et al., 2009a; Wang et al., 2009c; Li et al.,

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2013). The extremely abundant and higher phylogenetic diversity of bacteriophages

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ACCEPTED MANUSCRIPT point to potential impacts of viruses on microbial communities in soil environments,

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even though the roles that viruses may play in soil are different from those in aquatic

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environments (Kimura et al., 2008). Allen et al (2010) investigated top-down controls

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by phages on soil microbes using both field and laboratory experiments and

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suggested that top-down controls, such as phage lysis, are critical to the regulation of

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microbial activities in Arctic soils. Nevertheless, Helsley et al (2014) found little

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evidence that phages exerted significant top-down controls on bacterial abundance,

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respiration, or growth in temperate soils and suggested that the role phages play in

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soil ecosystems varied dramatically with biome type. However, information on the

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effect of viral lysis on soil microbes is scarce (Ghosh et al., 2008; Nakayama et al.,

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2007; Williamson et al., 2003). Most studies on soil bacterial communities focus on

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bottom-up controls (e.g., pH, soil organic carbon, N availability), while not enough

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attention has been paid to the impact of viruses (Liu et al., 2014; Luo et al., 2017; Sul

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et al., 2013; Zeng et al., 2016).

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Here, we present evidence of T4-type phages, which has an important impact

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on shaping bacterial communities in a paddy soil chronosequence. We investigated

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the structure and diversity of both bacterial and bacteriophage communities on a soil

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chronosequence of up to 2000 years of rice cultivation after reclamation from a

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mudflat in the Yangtze River Delta by means of deep MiSeq sequencing of the 16S

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rRNA gene amplicons for bacterial communities, and sequencing major capsid gene

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(g23) amplicons for T4-type phages, respectively. We focused specifically on the

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T4-type myovirus family because T4-type viruses are diverse, abundant, and

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ACCEPTED MANUSCRIPT detectable through cultivation-independent methods and because their major capsid

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gene (g23) has been shown to serve as a reasonable proxy for variation in globally

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ubiquitous myovirus genomes (Chow et al., 2014; Comeau and Krisch, 2008; Filee et

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al., 2005; Needham et al., 2013). This investigation was designed to answer two

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questions: (i) How are the soil bacterial and phage communities associated with

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paddy soil chronosequence? (ii) Would T4-type phage affect the distribution of the

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bacterial community?

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2. Materials and Methods

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2.1 Soil sampling and analysis of basic soil properties

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The soil samples used in this study were collected from paddy fields located in Cixi,

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Zhejiang Province, China (121°12’-121°42’N, 30°21’-30°24’E). The sampling area is a

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marine deposit plain. The deposited materials originated from the nearby Yangtze

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River as evidenced by geochemical studies (Cheng et al., 2009). Chronosequences

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were established on dikes constructed in different periods. Rice cultivation occurring

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in the sampling area was identified according to the Cixi County Annals. A paddy

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chronosequence with 50-, 100-, 300-, 700-, 1000-, and 2,000-year (S50, S100, S300,

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S700, S1000 and S2000) durations of rice cultivation was sampled in March 2011,

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before rice was sown (Fig. S1). We sampled mudflats in sites near the Yangtze River

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as a reference for initial soil development characteristics, and this was defined as age

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0 (S0). Soil sampling was carried out in triplicate fields using soil auger, and 0- to

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10-cm surface soils were collected by mixing five random soil cores. The soil samples

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were transported to the laboratory at 4°C. The soil samples were passed through a

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2-mm mesh sieve and stored at -20°C until use. All analyses were conducted according to the protocols of the Handbook of Soil

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Analysis (Pansu and Gautheyrou, 2007). In brief, soil pH and electric conductivity (EC)

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were measured at a soil:water ratio of 1:2.5. Total C was determined by oxidation

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with dichromate, total N was measured by the Kjeldahl method, and total P was

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determined following a wet acid digestion procedure with perchloric and sulfuric acid

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and then measured by the molybdenum blue method. All measurements are the

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mean of three replicate analyses.

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2.2 Soil sample DNA extraction and PCR amplification

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DNA was extracted from 0.5 g of soil using a Fast DNA SPIN kit for soil (MP

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Biomedicals; Solon, OH, USA) according to the manufacturer’s instructions.

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The major capsid gene, g23 of T4-type phages was amplified with the

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degenerate g23 primers MZIA1bis (5′-GAT ATT TGI GGI GTT CAG CCI ATG A-3′) and

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MZIA6 (5′-CGC GGT TGA TTT CCA GCA TGA TTT C-3′) (Filee et al., 2005) according to

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the method of Fujii et al (2008). PCRs were performed in a total volume of 50 µL in

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200-µL microtubes; each reaction contained 0.4 µL of each primer (50 pmol each), 5

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µL of 2.5 mM dNTP mixture, 5 µL of 10× Ex Taq DNA buffer (20 mM Mg2+; TaKaRa,

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Tokyo, Japan), 0.1 µL of 0.1% BSA (TaKaRa Bio Inc.), 0.5 µL of Ex Taq DNA polymerase

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(TaKaRa, Bio), 1 µL of DNA template and 31.75 µL of milli-Q water. The cycling

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conditions for PCR amplification were as follows: initial denaturation at 94°C for 5

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min, 35 cycles of denaturation at 95°C for 1 min, annealing at 55°C for 1 min and

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extension at 72°C for 1 min, and a final extension at 72°C for 5 min using a TaKaRa

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ACCEPTED MANUSCRIPT PCR Thermal Cycler Dice (TaKaRa) (Fujii et al., 2008). The PCR products were

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visualized on 2% (w/v) agarose gels made with TAE buffer (40 mM Tris-acetate and 2

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mM EDTA) and ethidium bromide (10 mg mL-1) staining. Electrophoresis of agarose

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gels was performed with 1× TAE buffer in a Mini Gel Electrophoretic System (Advance,

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Tokyo, Japan) at 100 V for 30 min.

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2.3 16S rRNA gene and g23 sequencing

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The V4-V5 regions of the 16S rRNA gene were analyzed by the MiSeq sequencing

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platform to investigate changes in the bacterial community structure with a universal

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515F-907R primer assay (Stubner, 2002). MiSeq sequencing was performed by

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Majorbio Bio-pharm Technology Co., Ltd. (Shanghai, China), on an Illumina® MiSeq

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sequencer (Illumina, San Diego, CA, USA). The high-throughput sequencing data were

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processed using Quantitative Insights into Microbial Ecology (QIIME) software

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(Caporaso et al., 2010), and only sequences >200 bp in length, with an average

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quality score >20, and without ambiguous base reads were included for the

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downstream analyses.

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PCR products with target g23 genes were purified using a QIAquick Gel

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Extraction kit (Qiagen, Tokyo, Japan). The purified DNA was cloned into the pEASY-T1

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Simple Cloning Vector (TransGen Biotech). Approximately 50 clones from each

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transformation were chosen from white colonies and were then PCR-amplified with

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the primers MZIA1bis and MZIA6. The PCR program was the same as described

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above, except for the reduction of the cycle number to 26. Plasmid DNA was

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extracted

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from

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ACCEPTED MANUSCRIPT EZNA™ Plasmid Mini kit (Omega Bio-Tek, USA), and approximately 300 ng of DNA was

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subjected to cycle sequencing reactions. Nucleotide sequences were determined

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with an ABI PRISM® 3730 Genetic Analyzer (Applied Biosystems, Foster City, CA, USA)

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using a BigDye® Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems).

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2.4 Community analysis

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The nucleotide sequences of g23 were translated into deduced amino acid

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sequences using the EMBOSS Transeq program at the European Bioinformatics

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Institute website (https://www.ebi.ac.uk/). The identities of deduced amino acid

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sequences were analyzed using the ClustalW program through the DNA Data Bank of

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Japan (DDBJ) website. The closest relatives of all sequences at the amino acid level

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were identified using a Basic Local Alignment Search Tool (BLAST) search on the

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National

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(http://www.ncbi.nlm.nih.gov).

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To evaluate whether the distributions of the g23 clone were related to soil

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chronosequence and their assemblages in different environments on the global scale,

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the UNIFRAC statistical analysis tool available at http://unifrac.colorado.edu/ was

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utilized (Lozupone and Knight, 2005). Briefly, all g23 clones of amino acid sequences

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with soil chronosequences in this study and those obtained from different

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environments were analyzed together using a P-test and principal coordinate analysis

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(PCoA). The alignments were first compared with T-evens, PseudoT-evens,

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SchizoT-evens, ExoT-evens, marine clones (Filee et al., 2005) and lake clones (Butina

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et al., 2010; Lopez-Bueno et al., 2009) and then with the g23 sequences obtained

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alignment was conducted with Clustal X 1.81 (Thompson et al., 1997), and an

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unrooted phylogenetic tree was drawn using the interactive Tree of Life online

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program (Letunic and Bork, 2007) based on the distance matrix data calculated by

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Molecular Evolutionary Genetic Analysis software (MEGA 4.1), and a rooted

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neighbor-joining tree was constructed by MEGA 4.1 with 1000-fold bootstrap

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support (Kumar et al., 2004).

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To compare community diversities of bacteria and phages between samples

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with different cultivation time, principal coordinate analyses based on pairwise

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weighted UniFrac (Lozupone and Knight, 2005) distances were calculated in the Ape

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package of R v.3.2.1 (https://www.r-project.org/). BIO-ENV based on the Speraman's

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rank correlation coefficient (ρ) was used to show the association between the

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microbial

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correspondence analysis (CCA) to identify the dominate factors including T4-type

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phage and soil properties (i.e., pH, EC, Tot C, and Tot N) on the bacterial community

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composition along the 2000-year paddy soil chronosequence. The environmental

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variables and diversity indices of phages were then used to construct a factors matrix

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for variation partitioning analysis (VPA) in R within the vegan package. Contributions

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of the selected T4-type phage and environmental variables to the bacterial

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community variation were estimated by VPA.

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and

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communities

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Nonrandom cooccurrence analyses were performed using SparCC, a tool capable

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of estimating correlation values from compositional data (Friedman and Alm, 2012). 10

ACCEPTED MANUSCRIPT In brief, quality reads were clustered at 97% sequence identity (Edgar, 2010), and

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the 10 most abundant families of OTUs per samples were retained for analysis. For

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each network analysis, P-values were obtained by 99 permutations of random

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selections of the data table and were subjected to the same analytical pipeline.

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SparCC correlations with statistically significant (P<0.01) were incorporated into

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network analyses. The nodes in the reconstructed networks represent the OTU

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families at 97% identity, whereas the edges (that is, connections) correspond to a

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strong and significant (positive denoted by red, and negative denoted by black)

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correlation between the nodes. Networks were visualized using the interactive

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platform Cytoscape (v3.2.1) (Shannon et al., 2003).

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2.5 Nucleotide sequence accession numbers

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The MiSeq sequencing reads of the 16S rRNA genes of the soil chronosequences

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were deposited in the National Center for Biotechnology Information (NCBI)

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database under accession number SRP095970. The DNA sequences of the g23 clones

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were deposited in DNA Data Bank of Japan (DDBJ) under accession numbers

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LC210641 to LC210722.

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3. Results

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3.1 Soil physicochemical properties

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The general physicochemical characteristics for the soil samples are summarized in

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Table 1. The soil pH dropped significantly from 8.65 in the S0 soil down to 5.62 in the

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S2000 soil, and soil total C and total N in the cultivated soils were significantly higher

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ACCEPTED MANUSCRIPT than those of the reference soil, varying from 26.13 to 45.60 g C kg-1 and from 0.82 to

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2.74 g N kg-1, respectively. In contrast, the EC of the paddy soils was significantly

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lower than that of the S0 soil. It was notable that the concentration of total

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phosphorus (tot P) in the S0 soil was significantly lower than that in the S100, S300

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and S700 soils and was significantly higher than that in the S2000 soil.

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3.2 Taxonomic assemblages of the bacterial community

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In total, we obtained 944,069 quality sequences from seven samples (mean,

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134,867), and they were clustered into 46,748 OTUs after trimming and filtration. To

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compare the soil bacterial community diversity among all the soils, the same survey

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effort level of 104,000 sequences was randomly selected from each sample in the

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sequencing library. The dominant groups (relative abundance > 1%) across all soil

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samples were Proteobacteria, Acidobacteria, Actinobacteria, Planctomycetes,

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Verrucomicrobia, Chloroflexi, Bacteroidetes, Nitrospirae, Gemmatimonadetes and

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Firmicutes, and these groups accounted for more than 90% of the bacterial

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sequences (Fig. 1a). Soil exploitation history was closely correlated with the

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abundance of some dominant bacterial groups. Proteobacteria decreased in relative

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abundance in the soils along the chronosequence (r=-0.798, p<0.05), while

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Verrucomicrobia increased (r=0.755, p<0.05). Actinobacteria and Chloroflexi showed

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the lowest and highest relative abundances in the control and 50-year duration of

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cultivation soils, respectively, and then decreased along the chronosequence.

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We analyzed twenty families with a relative abundance of >1.0% in at least one

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sample (Fig. 1b). Soil exploitation history had significant effects on the proportions of 12

ACCEPTED MANUSCRIPT Chthoniobacteraceae (r=0.839, p<0.05) and Chitinophagaceae (r=0.823, p<0.05) (Fig.

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S2). Soil pH was significantly corrected with the proportions of Chthoniobacteraceae

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(r=-0.994, p<0.01), Chitinophagaceae (r=-0.958, p<0.01) and Brucellaceae (r=-0.884,

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p<0.01). Total C were associated with the proportions of Syntrophobacteraceae

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(r=0.782, p<0.05), , Chthoniobacteraceae (r=-0.781, p<0.05) and Desulfobulbaceae

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(r=-0.895, p<0.01), wherever total K with that of Chthoniobacteraceae (r=-0.858,

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p<0.05) and Chitinophagaceae (r=-0.884, p<0.01) across the soil chronosequence (Fig.

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S2).

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3.3 Phylogeny of g23 clones

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The neighbor-joining tree showed that 202 of the g23 clones (92.6% of clones)

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obtained in this study belonged to paddy (68.8%), marine (11.9%) and lake groups

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(11.9%), with the remaining 15 clones ungrouped (7.4%) (Fig. 2; Table 2). Clones

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belonging to the paddy group were exclusively from the chronosequence soils with

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cultivation periods of more than 50 years, wherever clones from the control

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contributed most to the marine and lake groups, with 50% and 27.3% of the clones

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belonging to those two groups, respectively. Paddy groups VIII, IV and IX were three

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dominant groups with proportions of 54.7%, 12.9% and 12.9% among the paddy

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groups, respectively (Table 2).

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Figure S3 shows the phylogenetic comparison of amino acid sequences obtained

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from the paddy soil chronosequence in this study with amino acid sequences (i) from

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marine waters (Filee et al., 2005) and lake freshwaters (Butina et al., 2010;

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Lopez-Bueno et al., 2009) (Fig. S3a) and (ii) from paddy field soils from Japan (Fujii et 13

ACCEPTED MANUSCRIPT al., 2008; Jia et al., 2007; Wang et al., 2009a) and Northeast China (Wang et al.,

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2009b) (Fig. S3b). Most of the g23 clones obtained in this study formed several

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clusters separate from the clones of marine and lake origins, except for some clones

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from soils with 0 and 50 years of cultivation (Fig. S3a). Compared to the paddy field

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soils from Northeast China, these clones were closer to those from Japanese paddy

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field soils (Fig. S3b).

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The g23 clone assemblages from the chronosequence soils in this study were

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further compared with clone assemblages from other environments by UniFrac

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analysis (Fig. 3). PCoA plot based on PC1/PC2 showed that the g23 phages obtained

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from the reference soil (S0) were similar to those from Baikal lake, wherever the g23

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obtained from the soils with 50- and 300-year duration of rice cultivation were

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similar with those from the paddy soils in Japan and Northeast China. In contrast, the

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soils with longer cultivation periods were located in a cluster separated from those

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from marine waters, lake freshwaters and paddy field soils (Fig. 3).

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3.4 Bacterial and phage diversity

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We observed that both bacterial and T4-type phage community diversities were

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highly variable with respect to Faith’s phylogenetic diversity (PD) (ranging from 274

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to 386), phylotype richness (ranging from 3681 to 5985) for bacteria and phylotype

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richness (ranging from 5 to 21), shannon index (ranging from 2.19 to 3.45) for phage

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in the soil chronosequence, respectively (Table 1). Of the soil characteristics that

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were considered, we found that the soil EC was significantly negatively correlated

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with both the bacterial phylotype richness (r =-0.833, P <0.05) and the phylogenetic

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ACCEPTED MANUSCRIPT diversity (r=- 0.804, P <0.05), while TC was positively correlated with bacterial

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phylotype richness (r =0.785, P <0.05) and negatively correlated with T4-type phage

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phylotype richness(r =-0.784, P <0.05). All other soil parameters were not related to

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microbial diversity.

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Principal coordinate analyses (PCoAs) of weighted UniFrac distances were

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performed to evaluate the β-diversity of bacterial and T4-type phage communities in

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the soil chronosequences (Fig. 4). Bacterial microbiomes in the chronosequence soils

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and the control were separated along PCo1 (explaining 51.69% of the total variance),

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and this separation was mainly associated with the bacterial taxa of

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Planctomycetaceae, Piscirickettsiaceae, Desulfobulbaceae and Hyphomicrobiaceae

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(Fig. 4a). The PCo2 was indicative of Nocardioidaceae, Chitinophagaceae and

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Brucellaceae, separating the bacteria of S50 and S700 with other cultivated soils,

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which explained 18.17% of the total variance. T4-type phage communities also

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showed obvious separation among the control, short soil exploitation history (50 and

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300 y) and others. This separation was mainly associated with paddy groups II, VIII,

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and IX, the marine group, and the ungrouped phages along the first two axes

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(explaining 29.95% and 22.73% of total variance, respectively) (Fig. 4b).

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3.5 Impact of T4-type phage and bottom-up controls on shaping bacterial community

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The BIO-ENV analysis showed the best correlations of both bottom-up controls (pH

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and EC) and T4-type phage community (CCA index and Shannon index of T4-type

321

phage) with the bacterial population (Table 3), while only bottom-up controls (total C,

322

EC and Cultivation time) correlated with the phage community in soil

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ACCEPTED MANUSCRIPT chronosequence (Table S1). CCA analysis further showed that combination of these

324

factors explained the highest percentage of variance of bacterial communities (Fig.

325

5a), and 63.82% of the variance of bacteria could be explained by the selected

326

variables with the first two axes. The CCA index and the Shannon index of T4-type

327

phage and soil EC were significantly correlated with the first axis (explaining 45.26%

328

of the total variance). The soil pH was significantly correlated with the second axis

329

(explaining 18.56% of the total variance). VPA analysis was constructed by

330

designating the explanatory variables and covariates. A total of 80.5% of the variance

331

of bacteria could be explained by T4-type phage community and soil properties. It

332

showed that T4-type phage community explained 17.5% of the total variation and

333

that the soil properties explained 16.8% (Fig. 5b). These two parameters co-explained

334

46.2% of the total variation. A Mantel test showed that the important parameters

335

that significantly determined bacterial community structure were the CCA index of

336

phage (r=0.999, P<0.001), soil EC (r=-0.995, P<0.001) and pH (r=-0.776, P<0.05) (Fig.

337

5b).

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To further explore the relationship between the bacterial community and

339

phages, we constructed a network between the phage clusters and the bacterial taxa

340

(at the family level). Previous study showed that both negative and time lagged

341

positive correlations may suggest the presence of viral lysis (Chow et al., 2014). We

342

hypothesized that the non-random, co-occurrence patterns of phage and bacterial

343

taxa could be used to provide new insights into phages and their potential hosts if

344

the phages and the co-existing bacterial taxa possessed a strong and significantly

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ACCEPTED MANUSCRIPT correlation. As shown, we found that six bacterial families could be possible T4-type

346

phage hosts of the cooccurrence phages. For instance, Brucellaceae and

347

Xanthomonadaceae were possible hosts of T4-type phages belonging to paddy

348

groups IV, while Hyphomicrobiaceae for paddy groups VIII and Planctomycetaceae

349

and Pseudomonadaceae for paddy groups IX, respectively. Interestingly, phage clones

350

of paddy group II, with the lowest proportion (1%) in paddy fields, showed a strong

351

positive correlation with Nocardioidaceae (Fig. 6).

352

4. Discussion

353

Literature analysis suggests that the contemporary environment has a prominent role

354

in shaping microbial biogeographic patterns (Hanson et al., 2012). There have been

355

many previous studies demonstrating that environmental factors such as pH (Ding et

356

al., 2017; Kaiser et al., 2016; Liu et al., 2014; Luo et al., 2018), soil organic carbon

357

(Luo et al., 2013; Li et al., 2011; Li et al., 2013; Sul et al., 2013; Li et al., 2017), N

358

availability (Ai et al., 2013; Wang et al., 2018) and phosphorus contents (Wei et al.,

359

2017; Wu et al., 2017; Ai et al., 2018) are prevailing environmental factors in shaping

360

the soil bacterial community compositions in various environments (Santini et al.,

361

2015; Zeng et al., 2016). However, phage lysis, which exerts top-down controls on

362

bacterial communities, cannot be reasonably assessed and still poorly elucidate,

363

since methods for directly assessing top-down control and trophic impacts of phages

364

in soils have not been developed yet. Here, we found that bacterial communities

365

were also regulated at least in part by T4-type bacteriophages in addition to the

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ACCEPTED MANUSCRIPT bottom-up controls of environmental factors along the 2000-year paddy soil

367

chronosequence.

368

4.1 Soil physicochemical properties along the soil chronosequence

369

The observed significant accumulation of total C and total N and the clear decrease

370

in pH in the tested soil chronosequence (Table 1) were consistent with the

371

observations of previous studies, which attributed the accumulation of total C and

372

total N in the soil to long-term rice cultivation (Ding et al., 2017; Liu et al., 2016). The

373

accumulation of P on the decadal time scale and a decrease in P on the millennial

374

time scale were unexpected (Table 1). Many agronomic field studies on decadal time

375

scales have shown that P addition by paddy cultivation results in expected increases

376

in P in surface soils (Lee et al., 2004; Zhang et al., 2006). However, in contrast to this

377

tendency, long-term paddy cultivation significantly degraded phosphorus sorption

378

capacity in surface paddy soils due to the loss of phosphorus sorbents (CaCO3, Fe and

379

Al oxides, and clays) (Huang et al., 2014), which is responsible for the rapid decline in

380

P in the later paddy soil chronosequence in this study (Table 1). P becomes gradually

381

depleted and less biologically available during natural soil development (Selmants

382

and Hart, 2010; Wardle et al., 2004). These results suggest that the anthropogenic

383

management of paddy soils not only resulted in the accumulation of various

384

nutrients (e.g., organic carbon, nitrogen) over a considerably long (centurial) time

385

period but also caused negative effects (e.g., a decline in the pH and total P) on

386

millennial time scales. These findings are important for understanding the soil

387

nutrient status at different stages of pedogenesis and for providing theoretical and

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ACCEPTED MANUSCRIPT scientific bases for the rational fertilization and sustainable management of paddy

389

soils during crop production.

390

4.2 Bacterial community composition during paddy soil development

391

Significant shifts in soil bacterial diversity and community composition were detected

392

during the development of paddy soils derived from mudflat (Table 1 and Fig. 4). The

393

greatly increased bacterial community diversity observed from phylogenetic diversity

394

and phylotype richness analyses suggested that the longer cultivation and primary

395

productivity inputs led to more diverse microbial communities under rice cropping

396

(Zhu et al., 2016). This finding could be partially ascribed to the improved nutrient

397

availability (e.g., C and N) resulting from large amounts of fertilization applications

398

and continuous inputs of nutrients by root exudates (Ding et al., 2017; Su et al., 2017)

399

following the conversion of mudflat into paddy soils.

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The bacterial community composition differed significantly between the mudflat

401

and the paddy soil with 50 years of ongoing rice cultivation (Fig. 1 and Fig. 4a). The

402

shift in the soil bacterial community structure was mainly attributed to remarkable

403

enrichments

404

(Gammaproteobacteria),

405

Desulfobulbaceae

406

(Actinobacteria) and Chitinophagaceae (Bacteroidetes) in the paddy soil from areas

407

with 50- and 700-year durations of rice cultivation. In contrast, pronounced declines

408

were observed in Hyphomicrobiaceae (Alphaproteobacteria) and Brucellaceae

409

(Alphaproteobacteria) in mudflats and paddy soils of S50 and S700. The families

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the

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relative

abundances

Planctomycetaceae

(Deltaproteobacteria)

in

of

Piscirickettsiaceae

(Planctomycetes)

mudflats

and

and

Nocardioidaceae

19

ACCEPTED MANUSCRIPT Piscirickettsiaceae, Planctomycetaceae and Desulfobulbaceae in the mudflats are

411

typical marine taxa and are widely distributed in marine environments, such as

412

marine sands, mud flats and shelf seafloors (Nguyen and Landfald, 2015; Staley and

413

Sadowsky, 2016). Most species from these families are halotolerant microbes and

414

even halophilic (Kuever, 2014). In contrast, the families of Hyphomicrobiaceae and

415

Brucellaceae (Fig. 1 and Fig.4a), affiliated with Alphaproteobacteria, can benefit from

416

plants and thrive in the presence of some suitable carbon sources in the soil

417

rhizosphere, thus they were enriched in the cultivated soils (Kyselkova et al., 2014;

418

Oren and Xu, 2014). The enrichment of Nocardioidaceae in the paddy soils of S50

419

and S700 could be attributed to the relatively low organic matter but significantly

420

higher total K in the S50 and S700 soils, as Nocardioidaceae was reported to

421

significantly correlate with soil available elements (K) and dominate in soils with

422

relatively low organic matter and/or in oligotrophic waters (Bell et al., 2013; Huang et

423

al., 2013; Stevens et al., 2007). The members of Chitinophagaceae seemed to be

424

cosmopolitan taxa for both native and agricultural plants and function as

425

carbohydrate degraders adapting to diverse environments (Joseph et al., 2007;

426

Shiratori-Takano et al., 2016). Considering that rice cultivation brought in straw and

427

residue with high contents of cellulose and/or lignin, the increase in the relative

428

abundance of Chitinophagaceae with the cultivation in paddy soils is reasonable.

429

4.3 Phage community during paddy soil development

430

T4-type phage communities were reported to be different among different soil

431

environments (Zheng et al., 2013). The Bio-ENV analysis indicated association of

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ACCEPTED MANUSCRIPT phage communities with the soil properties, like total C and EC (Table S1). The

433

positive correction of total carbon with bacterial phylotype richness, and the

434

negative correlation between total carbon and phage phylotype richness was

435

unexpected. Forest soils with higher organic matter were reported to harbor more

436

diverse assemblages of viruses than agricultural soils in terms of morphological

437

distribution (Williamson et al., 2005).

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Phylogenetic analysis showed that most of the g23 clones obtained in this study

439

formed several clusters separate from the clones of marine and lake origins and

440

closer to those from Japanese paddy field soils relative to those from paddy soils in

441

Northeast China (Fig. S3). UniFrac analysis further showed that g23 clones from the

442

mudflat were similar to those from the aquatic environment, even though they were

443

separated from g23 by relatively short-term cultivation (50-300 years) of the Chinese

444

and Japanese paddy soils; they were also separated from the long-term cultivation

445

(700-2000 years) paddy soils (Fig. 3). It should be noted that the cultivation time of

446

these paddy soils was on the order of several decades in China and Japan (Fujii et al.,

447

2008; Wang et al., 2009b). The T4-type phage community composition was

448

speculated to be similar if the soil experienced the same ecological processes (Liu et

449

al., 2011). However, the phylogenetic and UniFrac analysis of g23 genes in the tested

450

soil chronosequence indicated that the T4-type phage community composition of

451

cultivated soil on centurial and/or millennial time scales was phylogenetically

452

separated from those on decadal time scales (Fig. 3 and Fig. 4b). Previous studies

453

have demonstrated that the distribution of g23 genes varied with the environment

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ACCEPTED MANUSCRIPT (Filee et al., 2005; Fujii et al., 2008; Jia et al., 2007; Liu et al., 2011; Nakayama et al.,

455

2009; Wang et al., 2011; Zheng et al., 2013). In this study, obvious shifts were

456

observed in the representation of nucleotide sequences of the g23 major capsid

457

protein, emblematic of the T4-type phages, across the chronosequence except for

458

S100 grouping with paddy soils with long term cultivation(S700, S1000, and S2000)

459

(Table 2; Fig. 4b). This shift in the soil phage community was mainly ascribed to the

460

phylogenetic distribution of the g23 clone in paddy groups VIII and IX and the marine

461

group (Fig. 4b). It is the g23 clone in paddy groups VIII and IX that resulted in the

462

phylogenetic separation of the T4-type phages in the paddy soils of S50 and S300

463

from those in the paddy field under longer cultivation times (Fig. 4b). These two g23

464

groups were widely distributed in paddy soils in China and Japan (Liu et al., 2012;

465

Wang et al., 2009c). It is interesting to note that relatively high contents of lake

466

group g23 were detected in the paddy soils of S700 and S2000 (Table 2; Fig. 2).

467

T4-type phages in wetland water and paddy field floodwater were phylogenetically

468

clustered close to those of lake waters but not to those of marine waters (Zheng et

469

al., 2013). Thus, we speculate that long-term (centurial and/or millennial) flooding

470

would result in shifts of T4-type phages towards the lake group.

471

4.4 Impact of T4-type phage and bottom-up controls on shaping bacterial community

472

Environmental factors (e.g. pH, soil organic carbon, N availability) were prevailing

473

factors in shaping soil bacterial community composition in various environments

474

(Kaiser et al., 2016; Liu et al., 2014; Santini et al., 2015; Sul et al., 2013; Zeng et al.,

475

2016). More recently, Ding et al (2017) reported that shifts in the bacterial

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ACCEPTED MANUSCRIPT community structure were mainly driven by changes in the soil physicochemical

477

properties during paddy soil development from the same site, as in this study, which

478

explained 62% of the variation in the bacterial community structure. However,

479

despite the increasing recognition that phages play a significant role in shaping

480

bacterial population dynamics and altering both intra- and inter-specific competition

481

among bacterial hosts, their effect on the bacterial community was ignored in these

482

previous studies. In this study, the results of the VPA analysis showed that over 80%

483

of the variation in the bacterial community structure could be explained by the

484

combined T4-type phage and soil parameters, which were closely associated with

485

continuous rice cultivation (Fig. 5b). This indicated that the shifts in bacterial

486

communities were driven not only by soil properties during the long-term paddy soil

487

development but also by T4-type phage lysis. The Mantel test further confirmed the

488

important role of T4-type phages in shaping the bacterial community (Fig. 5b). These

489

results implied that both the button-up (soil properties) and top-down controls

490

(T4-type phage) shaped the bacterial communities along the 2000-year paddy soil

491

chronosequence.

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Previous studies have often investigated the host range by describing a

493

bacteriophage that can infect multiple strains of the same species of bacteria

494

(Barrangou et al., 2002; Holmfeldt et al., 2007). Recently, some studies have

495

confirmed that network analysis can be used to provide new insights into

496

interactions between bacteriophages and their possible hosts in complex

497

environmental scenarios (Comeau et al., 2010; Flores et al., 2011; Koskella and 23

ACCEPTED MANUSCRIPT Brockhurst, 2014; Weitz et al., 2013). In the present study, we explored the

499

relationship between T4-type phages and bacterial taxa using network analysis, and

500

we were able to visualize the complicated associations between phage subtypes and

501

the potential hosts. The cooccurrence patterns between T4-type phages and

502

microbial taxa suggested that six bacterial families were implicated as being possible

503

T4-type phage hosts (Fig. 6). Nevertheless, we focused specifically on the T4-type

504

myovirus family in lieu of the entire viral community and ignored the function of

505

protist predation on shaping the bacterial communities. The investigated

506

protistan-bacterial associations were far fewer than virus-bacteria associations in

507

previous studies (Chow et al., 2014). Looking forward, there is a need to extend novel

508

culture-independent methods and tools to the study of currently underrepresented

509

viruses. There is also a need to use these methods to assess the functions of protists

510

in shaping microbial communities in diverse terrestrial environments.

511

5. Conclusions

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In conclusion, our study provides evidence for the important role of T4-type

513

phages in shaping the bacterial community with respect to soil development during

514

2000 years of rice cultivation after reclamation from mudflats. The conversion of

515

mudflats into paddy soils significantly increased soil nutrients (e.g., organic carbon

516

and nitrogen) and resulted in the acidification of the soil. Soil bacterial and T4-type

517

phage communities were significantly shifted in the development of the paddy soils

518

over millennial time scales from those at centurial time scales and the mudflat

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ACCEPTED MANUSCRIPT control. VPA analysis indicated that shifts in the bacterial communities were driven

520

not only by soil properties but also by T4-type phage lysis during the long-term

521

paddy soil development. The Mantel test provided further evidence for the

522

important role of T4-type phages in shaping the bacterial community. This study

523

provides insights into the impact of phage lysis on the profiles of bacterial

524

compositions in the soil environment.

525

Acknowledgements

526

This research was financially supported by the National Natural Science Foundation

527

of China (41671249 and 41721001). We gratefully acknowledge the support of

528

Analysis Center of Agrobiology and Environmental Sciences, Zhejiang University for

529

the network analysis. Yong Li extends his thanks to the Pao Yu-kong and Pao

530

Zhao-Long Scholarship for their financial support.

531

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39

ACCEPTED MANUSCRIPT Figure Legends

832

Fig. 1. Relative abundance of the soil bacterial community composition at the (a)

833

phylum and (b) family levels. The relative abundance is expressed as the

834

average percentage of the targeted sequences to the total high-quality bacterial

835

sequences of each soil (S0, S50, S100, S300, S700, S1000 and S2000).

836

RI PT

831

Fig. 2. Neighbor-joining phylogenetic tree of g23 sequences obtained in this study.

The black and gray circles indicate internal nodes with at least 90% and 50%

838

bootstrap support, respectively. The black and white squares and triangle denote

839

the reference g23 clones obtained from the paddy, upland soils and marine and

840

lake waters, respectively.

M AN U

841

SC

837

Fig. 3. Principal coordinate analysis of the g23 assemblages obtained in this study with those obtained from marine waters (Filee et al., 2005), lake waters (Butina

843

et al., 2010; Lopez-Bueno et al., 2009), paddy field soils in Japan (Fujii et al.,

844

2008; Jia et al., 2007; Wang et al., 2009a) and NE China (Wang et al., 2009b) by

845

the UniFrac method. Ellipses indicate g23 clusters obtained from different

846

habitats.

AC C

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842

847

Fig. 4. Ordination of soil (a) bacterial and (b) phage communities via principal

848

coordinate analysis (PCoA) based on weighted Unifrac distances. Arrows

849

represent bacteria at the family level and phage clusters whose relative

850

abundances were significantly (P < 0.05) correlated with the first two axes for

851

bacteria and phages, respectively.

40

ACCEPTED MANUSCRIPT Fig. 5. (a) Canonical correspondence analysis (CCA) compares soil bacterial

853

community structure and top-down parameters, including the CCA index and

854

Shannon index of the phages, and bottom-up parameters, including pH and

855

electrical conductivity (EC). (b) Variation partition analysis (VPA) and Mantel

856

test of the relationships between top-down controls (i.e., diversity index of

857

phage), bottom-up controls (i.e., pH and EC) and the microbial community.

858

Fig. 6. Network analysis revealing the cooccurrence patterns between phage

859

subtypes and bacterial taxa (at the family level). The nodes represent the top

860

ten OTUs of bacteria and clusters of phages, whereas the edges (that is,

861

connections) correspond to a strong and significant (positive denoted by red,

862

and negative denoted by black) correlation between nodes.

AC C

EP

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M AN U

SC

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852

41

ACCEPTED MANUSCRIPT

Soil type

pH

Total C

Total N

Total P

Total K

g kg-1

g kg-1

g kg-1

g kg-1 12.99c

EC

Number of

Phylogenetic

Number of

Shannon

dS m-1

phylotype*

diversity*

phylotype#

index#

3681

274

21

3.45

SC

Soils

RI PT

Table 1. Basic characteristics and number of sequences and OTUs for each soil chronosequence.

S0

Mudflat

8.65a

4.83e

0.56f

0.55c

S50

Paddy field

8.22b

26.13d

0.82e

0.57c

18.27a

0.34b

5407

366

19

3.40

S100

Paddy field

6.93d

30.11c

1.34d

0.72b

12.07c

0.41b

5985

386

9

2.70

S300

Paddy field

6.77d

37.94b

1.63c

0.76b

10.24d

0.15c

5337

346

7

2.49

S700

Paddy field

7.72c

37.58b

1.81b

1.07a

15.72b

0.34b

5397

358

11

2.94

S1000

Paddy field

6.44e

32.56c

1.21d

0.5c

7.28e

0.23bc

5356

353

5

2.19

S2000

Paddy field

5.62f

45.6a

0.39d

5.97e

0.13c

4796

326

10

2.84

AC C

2.74a

2.75a

M AN U

TE D

EP

863

864

Note: The symbol * and # indicates diversities of bacteria and phages, respectively. EC: electric conductivity. Significant differences between the

865

soil chronosequence were determined using one-way ANOVA followed by Duncan's multiple range test at P < 0.05, in which the conditions

866

of normality and homogeneity of variance were met.

42

ACCEPTED MANUSCRIPT Table 2. Phylogenetic groups of g23 amino acids for each soil chronosequence. Phylogenetic S0

S50

S100

S300

S700

S1000

S2000

g23 amino acids*

44

37

33

17

27

25

19

Marine Groups#

0.5

-

-

-

0.07

-

-

Lake Groups

0.27

0.05

-

-

0.15

0.04

0.26

Paddy groups

-

0.9

0.91

1

0.78

0.96

0.74

I

-

-

0.12

-

0.04

-

-

II

-

0.03

-

-

-

-

0.05

IV

-

0.22

0.15

0.18

-

-

0.11

V

-

-

-

0.29

-

-

0.11

VI

-

0.16

-

-

-

-

-

Paddy Un

M AN U

TE D 0.08

0.64

-

0.7

0.96

0.47

-

0.22

-

0.53

0.04

-

-

-

0.19

-

-

-

-

-

0.23

0.05

0.09

-

-

-

-

AC C

Ungrouped

-

EP

IX

RI PT

group

VIII

868

Soils

SC

867

Note: The symbol * and # indicates number and percent of g23, respectively.

43

ACCEPTED MANUSCRIPT 869

Table 3. BIO-ENV analysis based on the Speraman's rank correlation coefficient (ρ), showing the

870

association between bacterial composition, based on the relative abundance of the 16S rRNA

871

gene detected, and environmental variables. Spearman's coefficient (ρ)

RI PT

Combined variables

0.7454 0.9013 0.9247 0.9351 0.9143 0.8909 Note: P (Phage), Shannon, Phylotype and CCA1 indicate diversity indexes of phage.

M AN U TE D EP AC C

872

SC

EC pH + EC pH + EC + Shannon P pH + EC + CCA1 P + Shannon P pH + Tot C + EC + CCA1 P + Shannon P pH + Tot N + EC + CCA1 P + Phylotype P

44

ACCEPTED MANUSCRIPT

Fig. 2

S0-7 S0-10 S0-18 S0-19 S0-25 S0-33 S0-40

Lake Groups S0-34

S50-35 S50-10 S50-15 S300-3 S300-6 S300-7 S300-15 S300-17 S2000-12 S2000-4 S50-19 S50-20 S0-6

Paddy clones Paddy Group V

RI PT

Paddy Group IX Paddy clones

S50-9 S50-26 S50-28 S50-33 S0-4 S2000-15

Paddy Group II

S50-30

S700-12

SC

S0-13

Lake Groups

S1000-16 S2000-2 S2000-16 S0-14

M AN U

S100-19 S100-31 S100-32 S100-6 S100-23 S100-24 S100-28 S700-2

TE D

S700-21 S0-3 S0-15 S0-24 S0-29 S0-37 S0-43 S0-8 S0-17 S0-26 S0-30 S0-39 S0-44 S0-11 S0-20 S0-27 S0-32 S0-41 S0-12 S0-21 S0-28 S0-35 S0-42 S50-8, -21,-34 S100-1,-3~-5,-7,-9~-11 S100-13~-18,-20,22 S100-25~-27,-29,-30 S700-1,-3~-4,-6~-11 S700-13, -15~-18 S700-22,-24,-26~-27

S1000-1~-15,-17~-25 S2000-3,-5~-7,-10 S2000-13~-14,-17~-18 S50-1 S50-24 S100-8 S300-4 S2000-19 S50-7 S50-29 S100-12 S300-5 S50-11 S50-37 S100-21 S300-10 S50-23 S100-2 S100-33 S2000-11 S50-25 S0-23 S50-5 S50-17 S300-8 S300-13 S50-6 S50-18 S300-9 S300-14 S50-13 S300-1 S300-11 S300-16 S50-16 S300-2 S300-12 S700-5 S0-1 S0-2 S700-19 S700-23 S2000-8 S700-20 S2000-1

Marine Groups

Paddy Group VIII

Paddy Group IV

EP AC C

Paddy Group I

Lake Groups Paddy Group IX

Lake Groups

S50-31

Paddy Group VI

S50-12 S50-27 S50-3 S50-32 S50-36 S50-14

S0-22 S50-22

Lake Groups

S0-31 S50-2 S2000-9 S0-5,-9,-16,-36,-38 S50-4 S700-25

0.05 Upland

Paddy

Marine

Lake

T-+PseudoT-evens

Fig. 1b

100

others Flavobacteriaceae Xanthomonadaceae Planctomycetaceae Desulfobulbaceae Brucellaceae Ellin515 Solibacteraceae Pseudomonadaceae Piscirickettsiaceae Comamonadaceae Koribacteraceae Hyphomicrobiaceae Gaiellaceae Pirellulaceae Chitinophagaceae Chthoniobacteraceae Rhodospirillaceae Thermodesulfovibrionaceae Syntrophobacteraceae Nocardioidaceae

M AN U

99 96

60

TE D

40

30

Relative abundance(%)

others Euryarchaeota Spirochaetes Chlamydiae Cyanobacteria Chlorobi Firmicutes Gemmatimonadetes Nitrospirae Bacteroidetes Chloroflexi Verrucomicrobia Planctomycetes Actinobacteria Acidobacteria Proteobacteria

80

0 S0

S50

S100

S300

S700

S1000

EP

20

S2000

AC C

Relative abundance (%)

SC

Fig. 1a

RI PT

ACCEPTED MANUSCRIPT

20

10

0 S0

S50

S100

S300

S700

S1000

S2000

RI PT

ACCEPTED MANUSCRIPT

Fig. 3

SC

0.4 0.3

S100

S0

0.1 0 -0.1

S700

chronosequence soil Paddy soil of China Paddy soil of Japan Marine water Lake freshwater

TE D

S50

-0.2 -0.3 -0.4

-0.4

AC C

-0.6

-0.2

EP

PCo2: 14.93%

0.2

M AN U

S1000S2000

0

S300

0.2

PCo1: 21.98%

0.4

0.6

Fig. 4b

2

0.15 S2000 Chitinophagaceae Brucellaceae Hyphomicrobiaceae

0

Planctomycetaceae

S300

Piscirickettsiaceae Desulfobulbaceae

TE D

S100

M AN U

S0

-1

S0

S700 Nocardioidaceae

0.1

PCo2: 22.73%

S1000

-3

-2

-1

EP

-2

0

PCo1: 51.69%

1

Ungrouped Marine Groups

0.05

S2000 S1000

Paddy group II

0

S100 S700

-0.05

Paddy group IX S50

S50

AC C

PCo2: 18.17%

1

SC

Fig. 4a

RI PT

ACCEPTED MANUSCRIPT

-0.1 -0.15

-0.1

Paddy group VIII

S300

-0.05

0

PCo1: 29.95%

0.05

0.1

Fig. 5b

Fig. 5a

SC

2 S2000

T-like phage Environmental 46.2% 17.5% Variables 16.8%

M AN U

1

S1000 S300

S0

S100

0 EC

Phage CCA1

Phage Shannon index pH

-1

TE D

CCA2: 18.56%

RI PT

ACCEPTED MANUSCRIPT

S700

Mantel test： ： CCA: (r=0.999, P<0.001) EC: (r=-0.995, P<0.001) pH: (r=-0.776, P<0.05)

S50

-2 -0.8

0.2

EP

-1.8

CCA1: 45.26%

AC C

-2.8

Bacterial community

1.2

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

Fig. 6

-0.5

0

0.5

ACCEPTED MANUSCRIPT Highlights

AC C

EP

TE D

M AN U

SC

RI PT

Long-term rice cultivation resulted in an accumulation of paddy soil nutrients. Bacterial and T4-type phage communities distinctly shifted along the soil chronosequence. Bacterial community was strongly affected by both T4-type phage and soil properties. Network analysis indicated six bacterial taxa were potential hosts of T4-type phage.