Co-occurrence of rhizobacteria with nitrogen fixation and/or 1-aminocyclopropane-1-carboxylate deamination abilities in the maize rhizosphere Sébastien Renoud, Marie-Lara Bouffaud, Audrey Dubost, Claire Prigent-Combaret, Laurent Legendre, Yvan Moenne-Loccoz, Daniel Muller
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Sébastien Renoud, Marie-Lara Bouffaud, Audrey Dubost, Claire Prigent-Combaret, Laurent Leg- endre, et al.. Co-occurrence of rhizobacteria with nitrogen fixation and/or 1-aminocyclopropane- 1-carboxylate deamination abilities in the maize rhizosphere. FEMS Microbiology Ecology, Wiley- Blackwell, 2020, 96 (5), 10.1093/femsec/fiaa062. hal-02547931
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Co-occurrence of rhizobacteria with nitrogen fixation and/or 1-aminocyclopropane-1-carboxylate deamination abilities in the maize rhizosphere
Journal: FEMS Microbiology Ecology ManuscriptFor ID FEMSEC-20-01-0023.R2 Peer Review Manuscript Type: Research article
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Complete List of Authors: Renoud, Sébastien; Université de Lyon, Université Claude Bernard Lyon 1 UMR5557 Bouffaud, Marie-Lara; Université de Lyon, UMR CNRS 5557 Ecologie Microbienne Dubost, Audrey; Universite Claude Bernard Lyon 1 Faculte des Sciences et Technologies, UMR 5557 Microbial Ecology Laboratory Prigent-Combaret, Claire; Université de Lyon, UMR CNRS 5557 Ecologie Microbienne Legendre, Laurent; Université de Lyon, UMR5557 Moënne-Loccoz, Yvan; Université de Lyon, UMR CNRS 5557 Ecologie Microbienne Muller, Daniel; Université de Lyon, UMR5557
microbiota, plant growth promotion, functional group, functional Keywords: microbiota, holobiont, ITSNTS theory
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1 2 3 1 Co-occurrence of rhizobacteria with nitrogen fixation and/or 1-aminocyclopropane-1- 4 5 6 2 carboxylate deamination abilities in the maize rhizosphere 7 8 3 9 10 4 11 12 5 13 14 1‡ 1‡ 1 1 15 6 Sébastien Renoud , Marie-Lara Bouffaud , Audrey Dubost , Claire Prigent-Combaret , 16 17 7 Laurent Legendre1,2, Yvan Moënne-Loccoz1 and Daniel Muller1* 18 19 8 20 21 For Peer Review 1 22 9 Univ Lyon, Université Claude Bernard Lyon 1, CNRS, INRAE, VetAgro Sup, UMR5557 23 24 10 Ecologie Microbienne, 43 bd du 11 novembre 1918, F-69622 Villeurbanne, France 25 26 11 2 Univ Lyon, Université de St Etienne, F-42000 St Etienne, France 27 28 29 12 30 31 13 ‡ Current addresses: 32 33 14 S.R. : BGene genetics Bâtiment B Biologie, F-38400 Saint Martin d'Hères, France 34 35 15 M.L.B. : Helmholtz Center for Environmental Research UFZ, Theodor-Lieser-Straβe 4, 36 37 38 16 06120 Halle, Germany 39 40 17 41 42 18 43 44 45 19 Running title: nifH and acdS bacteria on maize 46 47 20 48 49 21 50 51 52 22 *Corresponding author: UMR CNRS 5557 Ecologie Microbienne, Université Lyon 1, 43 bd 53 54 23 du 11 Novembre 1918, 69622 Villeurbanne cedex, France. Phone: +33 4 72 43 27 14. E-mail: 55 56 24 [email protected] 57 58 25 59 60
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1 2 3 26 ABSTRACT 4 5 6 27 7 8 28 The plant microbiota may differ depending on soil type, but these microbiota probably share 9 10 29 the same functions necessary for holobiont fitness. Thus, we tested the hypothesis that 11 12 30 phytostimulatory microbial functional groups are likely to co-occur in the rhizosphere, using 13 14 15 31 groups corresponding to nitrogen fixation (nifH) and 1-aminocyclopropane-1-carboxylate 16 17 32 deamination (acdS), i.e. two key modes of action in plant-beneficial rhizobacteria. The analysis 18 19 33 of three maize fields in two consecutive years showed that quantitative PCR numbers of nifH 20 21 For Peer Review 22 34 and of acdS alleles differed according to field site, but a positive correlation was found overall 23 24 35 when comparing nifH and acdS numbers. Metabarcoding analyses in the second year indicated 25 26 36 that the diversity level of acdS but not nifH rhizobacteria in the rhizosphere differed across 27 28 29 37 fields. Furthermore, between-class analysis showed that the three sites differed from one 30 31 38 another based on nifH or acdS sequence data (or rrs data), and the bacterial genera contributing 32 33 39 most to field differentiation were not the same for the three bacterial groups. However, co- 34 35 40 inertia analysis indicated that the genetic structures of both functional groups and of the whole 36 37 38 41 bacterial community were similar across the three fields. Therefore, results point to co-selection 39 40 42 of rhizobacteria harboring nitrogen fixation and/or 1-aminocyclopropane-1-carboxylate 41 42 43 deamination abilities. 43 44 45 44 46 47 45 Keywords: microbiota; phytostimulation; functional group; functional microbiota; holobiont; 48 49 46 ITSNTS theory 50 51 52 47 53 54 48 55 56 57 58 59 60
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1 2 3 49 INTRODUCTION 4 5 6 50 7 8 51 Plant Growth-Promoting Rhizobacteria (PGPR) colonize plant roots and implement a range of 9 10 52 plant-beneficial traits, which may result in enhanced plant development, nutrition, health and/or 11 12 53 stress tolerance (Almario et al. 2014; Cormier et al. 2016; Gamalero and Glick 2015; Hartman 13 14 15 54 et al. 2018; Vacheron et al. 2013). As a consequence, PGPR strains have received extensive 16 17 55 attention for use as microbial inoculants of crops (Bashan et al. 2014; Couillerot et al. 2013). 18 19 56 Plant-beneficial effects exhibited by PGPR are underpinned by a wide range of modes 20 21 For Peer Review 22 57 of actions, which include (i) enhanced nutrient availability via associative nitrogen fixation 23 24 58 (Puri et al. 2016, Deynze et al. 2018) or phosphate solubilization (Arruda et al. 2013), (ii) 25 26 59 stimulation of root system establishment through phytohormone synthesis (Cassán et al. 2014) 27 28 29 60 or consumption of the ethylene precursor 1-aminocyclopropane-1-carboxylate (ACC) via an 30 31 61 enzymatic deamination (Glick 2014), and (iii) the induction of systemic resistance responses in 32 33 62 plant (Pieterse and Van Wees 2015). In addition to phytostimulation, certain PGPR may also 34 35 63 achieve inhibition of phytoparasites using antimicrobial secondary metabolites (Agaras et al. 36 37 38 64 2015) or lytic enzymes (Pieterse and Van Wees 2015). Often, PGPR strains display more than 39 40 65 one phytostimulatory mode of action, which is considered important for effective plant- 41 42 66 beneficial effects (Bashan and de-Bashan 2010; Bruto et al. 2014; Rana et al. 2011; Vacheron 43 44 45 67 et al. 2017). Therefore, the co-occurrence of multiple phytostimulation traits is likely to have 46 47 68 been subjected to positive evolutionary selection in PGPR populations to maximize success of 48 49 69 the plant-PGPR cooperation. This hypothesis is substantiated by genome sequence analysis of 50 51 52 70 many prominent PGPR strains from contrasted taxa (Bertalan et al. 2009; Chen et al. 2007; 53 54 71 Redondo-Nieto et al. 2013; Wisniewski-Dyé et al. 2012). 55 56 72 Even though PGPR strains tend to accumulate several plant-beneficial traits (Bruto et 57 58 73 al. 2014), the co-occurrence patterns of these traits are not random. This takes place in part 59 60
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1 2 3 74 because many past horizontal gene transfers of the corresponding genes were ancient (Frapolli 4 5 6 75 et al. 2012), often leading to clade-specific profiles of plant-beneficial traits (Bruto et al. 2014). 7 8 76 However, the analysis of 304 proteobacterial genomes from contrasted taxa evidenced, overall, 9 10 77 the co-occurrence of nifHDK (nitrogen fixation) and acdS (ACC deamination) based on Exact- 11 12 78 Fisher pairwise tests (Bruto et al. 2014), raising the possibility that nitrogen fixation and ACC 13 14 15 79 deamination might be useful traits when combined in a bacterium. Indeed, nitrogen fixation and 16 17 80 ACC deamination occur together in various rhizobacteria (Blaha et al. 2006; Duan et al. 2009; 18 19 81 Jha et al. 2012; Ma, Guinel, and Glick, 2003; Nukui et al. 2006), but the relation between both 20 21 For Peer Review 22 82 traits can be complex. In Azospirillum lipoferum 4B for instance, the plasmid-borne gene acdS 23 24 83 is eliminated during phase variation while nif genes are maintained (Prigent-Combaret et al. 25 26 84 2008), and in Mesorhizobium loti transcription of acdS is controlled by the nitrogen fixation 27 28 29 85 regulator gene nifA2 (Nukui et al. 2006). Moreover, ACC deamination was described as 30 31 86 facilitator of the legume-rhizobia symbiosis (Ma et al. 2003; Nascimento et al. 2012). 32 33 87 At the scale of an individual plant, the rhizosphere is colonized by a diversified range 34 35 88 of bacteria, including nifH acdS bacteria as well as bacteria harboring only nifH or acdS (Blaha 36 37 38 89 et al. 2006; Bouffaud et al. 2018). There is additional level of complexity in that many of these 39 40 90 bacteria are PGPR, but some of them are not (Bruto et al. 2014). However, the overall impact 41 42 91 of nitrogen fixation and ACC deamination on the plant is likely to be the sum of the contribution 43 44 45 92 of individual root-colonizing bacteria displaying these traits. This raises the question whether 46 47 93 there is, for the plant, an optimal balance between the functional microbial groups of nifH 48 49 94 rhizobacteria and acdS rhizobacteria in the rhizosphere. On this basis, we tested here the 50 51 52 95 hypothesis that rhizobacteria with either nitrogen fixation ability or ACC deamination ability 53 54 96 (or with both) co-occur on roots. For that purpose, we used three maize fields under reduced 55 56 97 nitrogen fertilization practices, with samplings carried out at 6-leaf and flowering stages during 57 58 98 two consecutive years, and numbers of nifH and acdS rhizobacteria were monitored by 59 60
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1 2 3 99 quantitative PCR. In addition, nifH and acdS rhizobacteria were assessed by metabarcoding 4 5 6 100 (MiSeq Illumina sequencing) of nifH and acdS genes at one sampling, in parallel to sequencing 7 8 101 of 16S rRNA genes for the whole rhizobacterial community. 9 10 102 11 12 103 2. MATERIALS AND METHODS 13 14 15 104 16 17 105 2.1. Field experiment 18 19 106 The experiment was conducted in 2014 and 2015 at field sites located in Chatonnay (L), 20 21 For Peer Review 22 107 Sérézin-de-la-Tour (FC) and Saint Savin (C), near the town of Bourgoin-Jallieu (Isère, France). 23 24 108 According to the FAO soil reference base, L field corresponds to a luvisol, FC a fluvic cambisol 25 26 109 and C a calcisol (Table 1). The trial set-up has been described in Rozier et al. (2017). 27 28 29 110 For each of the fields, the crop rotation consists in one year wheat, six years maize and 30 31 111 one year rapeseed, and wheat was grown the year before the 2014 experiment. The maize 32 33 112 sowing season ranges from middle April to middle May in the area. Maize seeds (Zea mays 34 35 113 ‘Seiddi’; Dauphinoise Company, France) were sown on April 18 (FC) and 23 (C and L) in 2014 36 37 38 114 and April 30 (C) and May 11 (FC and L) in 2015. Five replicate plots, which were 12 (FC and 39 40 115 C) or 8 (L) maize rows wide and 12 m long, were defined in each field. The fields were 41 42 116 undergoing a reduction in chemical fertilization usage and did not receive any nitrogen 43 44 45 117 fertilizers in 2014 and 2015. Only non-inoculated plots from the overall trial (Rozier et al. 2017) 46 47 118 were used. 48 49 119 50 51 52 120 2.2. Plant sampling 53 54 121 In 2014 and 2015, plants were sampled at six leaves and at flowering. In 2014, the first sampling 55 56 122 was done on May 25 (FC) and 26 (C and L). On each replicate plot, six plants were chosen 57 58 123 randomly, the entire root system was dug up and shaken vigorously to dislodge soil loosely 59 60
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1 2 3 124 adhering to the roots. At sites FC and C, one pooled sample of six roots system was obtained 4 5 6 125 per plot, i.e. a total of five pooled samples per field site. At site L, each of the six roots system 7 8 126 was treated individually to obtain 30 samples. The second sampling was done on July 8 (FC 9 10 127 and C) and 9 (L), on all five plots. Six plants were sampled per plot and treated individually to 11 12 128 obtain 30 samples per field site. 13 14 15 129 In 2015, the first sampling was done on May 27 (C), June 5 (FC) and June 8 (L). In each 16 17 130 replicate plot, four root systems were sampled and treated individually to obtain 20 samples per 18 19 131 field site. The second sampling was done on July 15 (C), 16 (FC) and 17 (L), and four root 20 21 For Peer Review 22 132 systems were sampled and treated individually to obtain 20 samples per field site. 23 24 133 Each sample was immediately flash-frozen on site, in liquid nitrogen, and lyophilized 25 26 134 back at the laboratory (at -50°C for 24 h). Roots and their adhering soil were separated and the 27 28 29 135 latter stored at -80°C. 30 31 136 32 33 137 2.3. DNA extraction from root-adhering soil 34 35 138 DNA from root-adhering soil was extracted with the FastDNA SPIN kit (BIO 101 Inc., 36 37 38 139 Carlsbad, CA). To this end, 500 mg (for the pooled samples from FC and C in 2014) or 300 mg 39 40 140 samples (for all other samples) were transferred in Lysing Matrix E tubes from the kit, and 5 µl 41 42 141 of the internal standard APA9 (109 copies ml-1) was added to each Lysing Matrix E tube to 43 44 45 142 normalize DNA extraction efficiencies between rhizosphere samples, as described (Park and 46 47 143 Crowley, 2005; Couillerot et al. 2010). This internal standard APA9 (i.e. vector pUC19 with 48 49 144 cassava virus insert; GenBank accession number AJ427910) requires primers AV1f 50 51 52 145 (CACCATGTCGAAGCGACCAGGAGATATCATC) and AV1r 53 54 146 (TTTCGATTTGTGACGTGGACAGTGGGGGC). After 1 h incubation at 4°C, DNA was 55 56 147 extracted and eluted in 50 µl of sterile ultra-pure water, according to the manufacturer’s 57 58 148 instructions. DNA concentrations were assessed by Picogreen (ThermoFisher). 59 60
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1 2 3 149 4 5 6 150 2.4. Size of microbial functional groups 7 8 151 The amounts of nifH genes were estimated by quantitative PCR based on the primers polF/polR 9 10 152 (Poly, Jocteur Monrozier, and Bally, 2001), as described by Bouffaud et al. (2016). The reaction 11 12 153 was carried out in 20 µl containing 4 μl of PCR-grade water, 1 μl of each primer (final 13 14 15 154 concentration 0.50 μM), 10 μl of LightCycler-DNA Master SYBR Green I master mix (Roche 16 17 155 Applied Science, Meylan, France) and 2 µl of sample DNA (10 μg). The cycling program 18 19 156 included 10 min incubation at 95°C, followed by 50 cycles of 95°C for 15 s, 64°C for 15 s and 20 21 For Peer Review 22 157 72°C for 10 s. Melting curve calculation and Tm determination were performed using the Tm 23 24 158 Calling Analysis module of Light-Cycler Software v.1.5 (Roche Applied Science). 25 26 159 The amount of acdS genes was estimated by quantitative PCR based on the primers 27 28 29 160 acdSF5/acdSR8 (Bouffaud et al. 2018). The reaction was carried out in 20 µl containing 4 μl of 30 31 161 PCR grade water, 1 μl of each primer (final concentration 1 μM), 10 μl of LightCycler-DNA 32 33 162 Master SYBR Green I master mix (Roche Applied Science) and 2 µl of sample DNA (10 μg). 34 35 163 The cycling program included 10 min incubation at 95°C, followed by 50 cycles of 94°C for 36 37 38 164 15 s, 67°C for 15 s and 72°C for 10 s. The fusion program for melting curve analysis is described 39 40 165 above. 41 42 166 Real-time PCR quantification data were converted to gene copy number per gram of 43 44 45 167 lyophilized root-adhering soil, as described (Bouffaud et al. 2018; Bouffaud et al. 2016). 46 47 168 48 49 169 2.5. nifH, acdS and rrs sequencing from rhizosphere DNA 50 51 52 170 Sequencing was performed on 2015’ samples taken when maize reached 6 leaves. Each sample 53 54 171 was an equimolar composite sample of four DNA extracts obtained from root-adherent soil, 55 56 172 resulting in 5 samples per field site, i.e. a total of 15 samples. DNA extracts were sent to MR 57 58 173 DNA laboratory (www.mrdnalab.com; Shallowater, TX) for sequencing. 59 60
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1 2 3 174 For nifH and acdS sequencing, PCR primers were the same ones used for quantitative 4 5 6 175 PCR (i.e., polF/polR for nifH and acdSF5/acdSR8 for acdS). For rrs sequencing, PCR primers 7 8 176 515/806 were chosen for the V4 variable region of the 16S rRNA gene. For all three genes, the 9 10 177 forward primer carried a barcode. Primers were used in a 30-cycle PCR (5 cycles implemented 11 12 178 on PCR products), using the HotStarTaq Plus Master Mix Kit (Qiagen, Valencia, CA) under 13 14 15 179 the following conditions: 94°C for 3 min, followed by 28 cycles of 94°C for 30 s, 53°C for 40 16 17 180 s and 72°C for 1 min, with a final elongation step at 72°C for 5 min. PCR products were checked 18 19 181 in 2% agarose gel to determine amplification success and relative band intensity. Multiple 20 21 For Peer Review 22 182 samples were pooled together in equal proportions based on their molecular weight and DNA 23 24 183 concentrations. Pooled samples were purified using calibrated Ampure XP beads and used to 25 26 184 prepare a DNA library following Illumina TruSeq DNA library preparation protocol. 27 28 29 185 Sequencing was performed on a MiSeq following the manufacturer’s guidelines. 30 31 186 Sequence data were processed using the analysis pipeline of MR DNA. Briefly, 32 33 187 sequences were depleted of barcodes, sequences < 150 bp or with ambiguous base calls 34 35 188 removed, the remaining sequences denoised, operational taxonomic units (OTUs; defined at 36 37 38 189 3% divergence threshold for the three genes) generated, and chimeras removed. Final OTUs 39 40 190 were taxonomically classified using BLASTn against a curated database derived from 41 42 191 Greengenes (DeSantis et al. 2006), RDPII (http://rdp.cme.msu.edu) and NCBI 43 44 45 192 (www.ncbi.nlm.nih.gov). Final OTUs of the acdS sequencing were classified using an in-house 46 47 193 curated acdS database, obtained after curation of acdS homolog genes from the FunGene acdS 48 49 194 8.3 database, as described by Bouffaud et al. (2018). Diversity indices of Shannon (H) and 50 51 52 195 Simpson (1-D) were calculated using sequencing subsample data for which each sample had 53 54 196 the same number of sequences. 55 56 197 An acdS phylogenetic tree (based on maximum-likelihood method) was computed using 57 58 198 acdS sequences from ten arbitrarily-chosen OTUs per genus recovered in our sequencing data 59 60
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1 2 3 199 and from one reference taxa for each genus, and related D-cystein desulfhydrase genes D- 4 5 6 200 cystein desulfhydrase genes from strains Escherichia coli strains K-12, ER3413, 042 and 7 8 201 RM9387, Escherichia albertii KF1, Escherichia fergusonii ATCC 35469, Enterobacter 9 10 202 sacchari SP1, Enterobacter cloacae ECNIH2, Enterobacter asburiae L1, Enterobacter sp. 638 11 12 203 and Enterobacter lignolyticus SCF1 (used as out-group). 13 14 15 204 16 17 205 2.6. Statistical analysis 18 19 206 Statistical analysis of quantitative PCR data was carried out by ANOVA and Fishers’ LSD tests. 20 21 For Peer Review 22 207 For each gene sequenced, comparison of bacterial diversity between field sites was carried out 23 24 208 by Between-Class Analysis (BCA) using ADE4 (Chessel et al. 2004; Culhane et al. 2005; Dray, 25 26 209 Dufour, and Chessel, 2007) and ggplot2 packages for R, and the 12 genera contributing most 27 28 29 210 to field site differentiation were identified. To assess co-trends between nifH and acdS 30 31 211 variables, as well as between rrs and nifH or acdS variables, sequence data were also assessed 32 33 212 using co-inertia analysis (CIA) (Dray et al. 2003; Dray et al. 2007), which was computed with 34 35 213 the ADE4 package in the R statistical software environment (Culhane et al. 2005). CIA is a 36 37 38 214 dimensional reduction procedure designed to measure the similarity of two sets of variables, 39 40 215 here the proportions of nifH and acdS bacterial genera obtained during between-class analyses. 41 42 216 Its significance was assessed using Monte-Carlo tests with 10,000 permutations. Unless 43 44 45 217 otherwise stated, statistical analyses were performed using R v3.1.3 (Team, 2014), at P < 0.05 46 47 218 level. 48 49 219 50 51 52 220 2.7. Nucleotide sequence accession numbers 53 54 221 Illumina MiSeq paired-end reads have been deposited in the European Bioinformatics Institute 55 56 222 (EBI) database under accession numbers PRJEB14347 (ERP015984) for rrs; PRJEB14346 57 58 223 (ERP015983) for nifH, PRJEB14343 (ERP015981) for acdS. 59 60
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1 2 3 224 4 5 6 225 3. RESULTS 7 8 226 9 10 227 3.1. Relation between numbers of nifH and acdS alleles in the three field sites 11 12 228 The number of acdS bacteria in the rhizosphere of maize harvested at 6-leaf stage in 2014 (7.87 13 14 7 -1 7 15 229 to 17.4 × 10 acdS gene copies g of dry soil) and 2015 (1.76 to 2.81 × 10 acdS gene copies 16 17 230 g-1 of dry soil) did not differ significantly between field sites (Figure 1AB). At flowering stage, 18 19 231 however, the number of acdS bacteria differed from one site to the next, both in 2014 and in 20 21 For Peer Review 22 232 2015 (Figure 1EF). At that growth stage, the lowest rhizosphere abundance was observed in 23 24 233 site L (5.08 × 107 acdS gene copies g-1 of dry soil) and the highest in site C (1.76 × 108 acdS 25 26 234 gene copies g-1 of dry soil) in 2014, whereas site ranking was the opposite in 2015 (8.35 versus 27 28 6 -1 29 235 44.0 × 10 acdS gene copies g of dry soil for sites C and L, respectively). 30 31 236 The numbers of nifH rhizobacteria differed according to field site (Figure 1CDGH). In 32 33 237 2014, the lowest nifH abundance was observed in rhizospheres of site L (1.06 and 20.8 × 107 34 35 238 nifH gene copies g-1 of dry soil at respectively six leaves and flowering) and the highest in those 36 37 7 -1 38 239 of site C (6.43 and 147.0 × 10 nifH gene copies g of dry soil at respectively six leaves and 39 40 240 flowering) (Figure 1CG). In 2015, the numbers of nifH rhizobacteria was higher in site C (9.31 41 42 241 × 108 nifH gene copies g-1 of dry soil) than in FC (1.30 × 108 nifH gene copies g-1 of dry soil) 43 44 8 -1 45 242 and L (2.52 × 10 nifH gene copies g of dry soil) at six leaves, whereas the situation was 46 47 243 opposite at flowering, with higher abundance in site L (40.7 × 107 nifH gene copies g-1 of dry 48 49 244 soil) than C (9.81 × 107 nifH gene copies g-1 of dry soil) and FC (5.66 × 107 nifH gene copies 50 51 -1 52 245 g of dry soil) (Figure 1DH). 53 54 246 When comparing the log numbers of nifH rhizobacteria and acdS rhizobacteria across 55 56 247 the 12 site × sampling combinations, significant (3.8 × 10-5 < P < 0.01) positive correlations 57 58 248 (0.67 < r < 0.98, n = 20) were found in 9 of 12 cases, with only three correlations that were not 59 60
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1 2 3 249 significant, i.e. in site C at 6-leaf stage in 2014 (P = 0.10, n = 5) and FC at flowering in 2014 4 5 6 250 (P = 0.67, n = 5) and 2015 (P = 0.19, n = 20) (Figure 2). In summary, moderate but significant 7 8 251 differences in the numbers of nifH and/or acdS rhizobacteria could take place according to field 9 10 252 site, sampling year and/or maize phenology, and in most cases a positive correlation was found 11 12 253 between the log values of both numbers. 13 14 15 254 16 17 255 3.2. Relation between diversities of nifH and acdS alleles in the three field sites 18 19 256 Illumina MiSeq sequencing of nifH and acdS (as well as rrs) was carried out on 15 rhizosphere 20 21 For Peer Review 22 257 samples from 6-leaf maize grown in 2015. For nifH, 1,342,966 reads were obtained (10,775 to 23 24 258 62,752 sequences per sample), for a total of 36,241 OTUs. Rarefaction analysis showed that 25 26 259 curves reached a plateau (Figure S1A). Subsampling was done with 10,775 sequences per 27 28 29 260 sample, for a total of 34,459 OTUs. For acdS, 5,490,230 reads were obtained (68,376 to 139,245 30 31 261 sequences per sample), with a total of 32,468 OTUs. Rarefaction curves reached a plateau 32 33 262 (Figure S1B). Subsampling was done with 68,376 sequences per sample, for a total of 26,246 34 35 263 OTUs. After quality filtering, 6,082,255 reads were obtained for rrs (51,696 to 223,926 36 37 38 264 sequences per sample), giving a total of 39,600 OTUs (3% cut-off). Rarefaction analysis 39 40 265 showed that the sequencing effort captured most of the diversity with curves reaching a plateau 41 42 266 (Figure S1C). Subsampling was done with 51,696 sequences per sample, for a total of 25,437 43 44 45 267 OTUs. 46 47 268 The effect of field site on nifH diversity of diazotrophic bacteria was not significant 48 49 269 based on analysis of Shannon and Simpson indices. Conversely, the effect of field site on acdS 50 51 -4 52 270 diversity of ACC deaminase bacteria was significant based on the Shannon (P = 1.9. × 10 ) 53 54 271 and Simpson indices (P = 8.6 × 10-4). The Shannon index was lower in FC (6.32) than in L 55 56 272 (6.82) and C (6.92), whereas the Simpson index was higher in FC (6.42 × 10-3) than in L (2.88 57 58 273 × 10-3) and C (2.38 × 10-3). The effect of field site on rrs diversity of the total bacterial 59 60
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1 2 3 274 community was significant based on the Shannon (P = 1.8 × 10-5) and Simpson indices (P =1.6 4 5 -4 6 275 × 10 ). As in the case of acdS data, the Shannon index was lower in FC (7.20) than in L (7.41) 7 8 276 and C (7.71), whereas the Simpson index was higher in FC (3.42 × 10-3) than in L (2.28 × 10- 9 10 277 3) and C (1.40 × 10-3). 11 12 278 The correlation (n = 5) between nifH diversity and acdS diversity was positive and 13 14 15 279 significant at site L, when considering both the Shannon index (r = 0.98 ; P = 0.01; Figure 3) 16 17 280 and the Simpson index (r = 0.86 ; P = 0.06; Figure 3). However, the correlation was not 18 19 281 significant at the other two sites, regardless of the diversity index. When considering also rrs 20 21 For Peer Review 22 282 diversity, a significant correlation was found only with nifH diversity at site C (r = 0.91 ; P = 23 24 283 0.03; Figure 3). In summary, there was no relation between the diversities of nifH rhizobacteria 25 26 284 and acdS rhizobacteria, based on comparison of diversity indices in the three field sites and 27 28 29 285 correlation analyses at two of the three field sites. 30 31 286 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 287 3.3. Relation between prevalence of nifH and/or acdS rhizobacterial taxa in the three field 4 5 6 288 sites 7 8 289 Between-class analysis of nifH data showed that the composition of diazotrophic bacteria 9 10 290 differed according to field site (Figure 4A). The first axis (54% of between-class variability) 11 12 291 distinguished site C from FC and L, and the 12 genera contributing most to this differentiation 13 14 15 292 were Xanthobacter, Dechloromonas, Methyloferula, Ideonella, Nitrospirillum and Tolumonas 16 17 293 (more prevalent in C than in L and FC), as well as Desulfovibrio, Selenomonas, 18 19 294 Ruminiclostridium, Paludibacter, Gloeocapsopsis and Ruminococcus (less prevalent in C than 20 21 For Peer Review 22 295 in FC and L). The second axis (46% of between-class variability) distinguished site L from the 23 24 296 two other sites, and the 12 genera contributing most to this differentiation included Rhizobium, 25 26 297 Gluconacetobacter, Skermanella, Leptothrix, Streptomyces and Methylocapsa (more prevalent 27 28 29 298 in L than in FC and C), as well as Marichromatium, Pelobacter, Gordonibacter, Desulfobulbus, 30 31 299 Desulfovibrio and Sideroxydan (less prevalent in L than in C and FC). 32 33 300 Between-class analysis of acdS data showed that the composition of ACC deaminase 34 35 301 bacteria differed according to field site (Figure 4B). The first axis (66% of between-class 36 37 38 302 variability) distinguished site C from FC and L, and the 12 genera contributing most to this 39 40 303 differentiation were Achromobacter, Azospirillum, Pseudolabrys, Roseovarius, one unassigned 41 42 304 OTU and Polaromonas (more prevalent in C than in L and FC), as well as Cupriavidus, 43 44 45 305 Burkholderia, Bosea, Bradyrhizobium and Methylobacterium (less prevalent in C than in FC 46 47 306 and L). The second axis (34% of between-class variability) distinguished each of the three sites 48 49 307 from one another, and the 12 genera contributing most to this differentiation included 50 51 52 308 Azorhizobium, Pseudomonas, Gluconobacter, Collimonas, Herbaspirillum and Burkholderia 53 54 309 (more prevalent in FC than in C and L), as well as Ralstonia, Loktanella, Devosia, Variovorax, 55 56 310 Novosphingobium and Chelatococcus (more prevalent in L than in C and FC). 57 58 59 60
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1 2 3 311 Between-class analysis of rrs data showed that the composition of the total bacterial 4 5 6 312 community differed according to field site (Figure 4C). The first axis (71% of between-class 7 8 313 variability) distinguished C from the two other sites, and the 12 genera contributing most to this 9 10 314 differentiation were Algisphaera, Fibrobacter, Amaricoccus, Hirschia, Desulfacinum and 11 12 315 Saccharophagus (more prevalent in C than in L and FC), as well as Actinomadura, Lutispora, 13 14 15 316 Bacillus, Rhodopseudomonas, Kouleothrix and Roseiflexus (less prevalent in C than in FC and 16 17 317 L). The second axis (29% of between-class variability) distinguished site L from FC and C, and 18 19 318 the 12 genera contributing most to this differentiation included Flavobacterium, 20 21 For Peer Review 22 319 Gluconobacter, Maricaulis, Prolixibacter, ‘Candidatus Xiphinematobacter’, Chthoniobacter 23 24 320 (more prevalent in FC than L), as well as Conexibacter, Hyphomicrobium, Pseudonocardia, 25 26 321 Tumebacillus, Chelatococcus and Mycobacterium (less prevalent in FC than in L). 27 28 29 322 In summary, between-class analysis of nifH and acdS data indicated that the 30 31 323 composition of diazotrophic bacteria and of ACC deaminase bacteria differed according to field 32 33 324 site, but the main discriminant genera differed completely for both types of bacteria. In both 34 35 325 cases, the discriminant taxa were also different from the main range of bacterial taxa 36 37 38 326 distinguishing the three sites most when comparing the latter based on rrs data, at the scale of 39 40 327 the entire rhizobacterial community. 41 42 328 43 44 45 329 3.4. Relation between the genetic structures of nifH and acdS rhizobacteria in the three 46 47 330 field sites 48 49 331 Since there was a positive correlation between log numbers of nifH and/or acdS rhizobacteria 50 51 52 332 but the corresponding bacterial genera discriminating most between the three fields studied 53 54 333 were not the same, the co-structuration between nifH and acdS diversity was explored by co- 55 56 334 inertia analysis to compare more globally the genetic structures of these rhizobacterial groups 57 58 335 across the three field sites. Monte-Carlo permutation tests showed a significant co-structuration 59 60
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1 2 3 336 (P = 9 × 10-5) of nifH and acdS rhizobacteria, with a RV coefficient of 0.83. This accounted for 4 5 6 337 57% of data variability. The plot of the co-inertia matrix illustrates the strength of the 7 8 338 relationship between acdS and nifH diversities, as superposition of acdS and nifH groups 9 10 339 showed a strong co-trend in all three field sites (Figure 5). 11 12 340 Co-inertia analyses of nifH and acdS diversities were also performed with rrs diversity, 13 14 15 341 and permutations tests also showed co-structuration in both cases, with respectively RV 16 17 342 coefficients of 0.89 and 0.91, the two axes explaining 52% and 69% of variability. 18 19 343 Superposition of rrs community with acdS and with nifH groups indicated a strong co-trend 20 21 For Peer Review 22 344 across the three fields. 23 24 345 In summary, the genetic structures of nifH and acdS rhizobacterial groups across the 25 26 346 three field sites were very close. Co-inertia was strong also when comparing each with the 27 28 29 347 whole rhizobacterial community based on rrs data. 30 31 348 32 33 349 4. DISCUSSION 34 35 350 36 37 38 351 The current work made use of molecular tools available to characterize functional groups of 39 40 352 nifH and acdS bacteria. Quantification of nifH rhizobacteria was performed with primers 41 42 353 PolF/PolR (Poly et al. 2001) rather than other well-established primers such as Zf/Zr (Zehr and 43 44 45 354 McReynolds, 1989) since the latter are not effective for quantitative PCR (Boyd and Peters 46 47 355 2013; Gaby and Buckley 2017; Poly et al. 2001). The same primers have also been used for 48 49 356 sequencing, both for consistency and efficacy for diazotroph characterization (Mårtensson et 50 51 52 357 al. 2009; Wartiainen et al. 2008). Recently, acdS primers suitable for monitoring of ACC 53 54 358 deamination bacteria have been made available (Bouffaud et al. 2018). These primers are 55 56 359 effective to amplify true acdS genes while not amplifying related D-cystein desulfhydrase genes 57 58 360 coding for other PLP-dependent enzymes, which was verified again in the current work (Figure 59 60
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1 2 3 361 S2). Indeed, phylogenetic analysis of the acdS sequences showed that none clustered within the 4 5 6 362 out-group (built with strains harboring D-cystein desulfhydrase genes), confirming that the 7 8 363 sequences obtained were true acdS sequences, as highlighted in previous studies (Blaha et al. 9 10 364 2006; Bouffaud et al. 2018; Li et al. 2015; Nascimento et al. 2012). 11 12 365 The level of taxonomic information carried by nifH sequences has been described in the 13 14 15 366 literature, showing that nifH was sufficiently conserved to enable reliable taxonomic affiliations 16 17 367 including for the assessment of rhizobacteria (Vinuesa et al. 2005), and its phylogeny was 18 19 368 congruent with the one derived from rrs (Achouak et al. 1999; Zehr et al. 2003). As for acdS, 20 21 For Peer Review 22 369 phylogenetic analysis of the new sequences obtained (along with reference acdS sequences) 23 24 370 confirmed that the taxonomic affiliations made at the genus level were correct. However, the 25 26 371 130-bp acdS amplicons obtained with the current quantitative PCR primers do not enable any 27 28 29 372 taxonomic affiliation below the genus level, i.e. at the species level (Bouffaud et al. 2018). 30 31 373 In this work, the hypothesis that nifH and acdS rhizobacterial populations co-occur on 32 33 374 roots was assessed with maize taken from three fields, using quantitative PCR and MiSeq 34 35 375 sequencing. The results that were obtained did substantiate this hypothesis, based on (i) positive 36 37 38 376 correlations between the sizes of nifH and acdS rhizobacterial groups, and (ii) comparable 39 40 377 genetic structures indicated by inertia analysis for both functional groups across the three field 41 42 378 sites studied. Several studies have assessed the co-occurrence of particular microorganisms and 43 44 45 379 measured between-taxa correlations in soil systems (Barberán et al. 2011; Freilich et al. 2010), 46 47 380 but few have done so at the level of functional groups. For instance, co-occurrence analysis of 48 49 381 nitrite-dependent anaerobic ammonium oxidizers and methane oxidizers in paddy soil showed 50 51 52 382 that the structure of these communities changed with soil depth (Wang et al. 2012). The co- 53 54 383 occurrence of plant-beneficial functions in the rhizosphere has been investigated, but often the 55 56 384 assessment was restrained to narrow taxonomic levels, such as within the Pseudomonas genus 57 58 385 (Almario et al. 2014; Frapolli et al. 2012; Vacheron et al. 2016). It is interesting to note that not 59 60
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1 2 3 386 all microorganisms harboring acdS and/or nifH expressed the corresponding functions in 4 5 6 387 rhizosphere based on assessment of qRT-PCR data, as previously described for nifH (Bouffaud 7 8 388 et al. 2016) or acdS (Bouffaud et al. 2018). 9 10 389 Specific taxa can be selected by environmental conditions prevailing on plant roots 11 12 390 (Bakker et al. 2014; Berg and Smalla, 2009; Raaijmakers et al. 2009; Vandenkoornhuyse et al. 13 14 15 391 2015). Thus, a first possibility to account for the co-occurrence of both functional groups could 16 17 392 be that both nifH bacteria and acdS bacteria do well in the maize rhizosphere. Indeed, both types 18 19 393 of bacteria are readily found on roots (Almario et al. 2014; Arruda et al. 2013; Blaha et al. 2006; 20 21 For Peer Review 22 394 Bruto et al. 2014; Bruto et al. 2014; Mårtensson et al. 2009). Such co-occurrence would make 23 24 395 sense in ecological terms, because associative nitrogen fixation and ACC deamination are 25 26 396 functions limiting plant nutrient deficiency by supplying nitrogen (Pii et al. 2015) and 27 28 29 397 enhancing root system development (thereby improving uptake of mineral nutriments including 30 31 398 nitrogen) (Glick, 2014), respectively. 32 33 399 A second possibility could be that bacteria that harbor both genes/functions are well 34 35 400 adapted to maize roots. Indeed, Bruto et al. (2014) showed that the nif operon co-occurred with 36 37 38 401 acdS in several bacterial clades, and for instance the genera Bradyrhizobium or Burkholderia 39 40 402 contain several species harboring both functions (Bruto et al. 2014). Furthermore, the co-inertia 41 42 403 between these two functional groups and the total community raises the possibility that 43 44 45 404 additional functions could also be present in addition to associative nitrogen fixation and ACC 46 47 405 deamination. Indeed, comparative genomics studies showed that bacterial taxa display multiple 48 49 406 specific functions, including plant interaction functions (Bruto et al. 2014; Lassalle et al. 2015; 50 51 52 407 Vacheron et al. 2017), and thus these functions would also be co-selected when selecting the 53 54 408 corresponding rrs-based taxa. In the current study, Bradyrhizobium represented 17 to 25% of 55 56 409 acdS+ bacteria and 20 to 42% of nifH+ bacteria in the maize rhizosphere, and the high proportion 57 58 410 of this bacterial clade may contribute to the co-occurence of diazotrophs and ACC deaminase 59 60
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1 2 3 411 producers that was found. However, when the 10,369 completely-sequenced bacterial genomes 4 5 6 412 available in the NCBI database were screened, it showed that 833 of them harbored acdS and 7 8 413 461 others nifH, but only 122 genomes had both genes. Therefore, it could be that this second 9 10 414 possibility is insufficient for a complete explanation of the current findings. 11 12 415 A third possibility to consider is the joint occurrence of both functions in the 13 14 15 416 rhizosphere, regardless of the taxa harboring them, thereby providing functional redundancy 16 17 417 (Shade and Handelsman, 2012). Several studies in soil or aquatic settings have suggested that 18 19 418 the metabolic/functional potential of microbial communities rather than their taxonomic 20 21 For Peer Review 22 419 variations are closely related to environmental conditions (Bouffaud et al. 2018; Burke et al. 23 24 420 2011; Louca et al. 2016; Louca et al. 2017 ). These observations were conceptualized as the 25 26 421 "It's the song, not the singer" theory (ITSNTS; Doolittle and Booth 2017), i.e. functional groups 27 28 29 422 within microbial communities (the songs) would be better conserved and more relevant 30 31 423 ecologically than the taxa themselves (the singers). Consistent with the ITSNTS theory, our 32 33 424 study suggests that the assembly of the rhizosphere microbial community would entail a balance 34 35 425 between phytostimulation-relevant genes, which may be needed to achieve an effective 36 37 38 426 holobiont (i.e., the plant host and its functional microbiota), and points to the preponderance of 39 40 427 functional interactions within the plant holobiont. This hypothesis, which has been put forward 41 42 428 recently for root-associated microorganisms (Lemanceau et al. 2017), remains speculative at 43 44 45 429 this stage and deserves further research attention. In particular, methodology development is 46 47 430 needed to enable direct assessment of key plant-beneficial groups when parallel monitoring of 48 49 431 several genes is required (e.g. for auxin production or P solubilization, which entail many 50 51 52 432 genetic pathways), in contrast to ACC deamination and N fixation for which analysis of a single 53 54 433 gene (acdS and nifH, respectively) may suffice. 55 56 434 To test whether the current findings could be also relevant under other environmental 57 58 435 conditions, we reassessed the data obtained for nifH (Bouffaud et al. 2016) and acdS (Bouffaud 59 60
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1 2 3 436 et al. 2018) from two maize lines grown in another soil (luvisol) with different management 4 5 6 437 histories (cropped soil vs meadow soil). A positive correlation (r = 0.45 ; P = 0.050 ; n = 20) 7 8 438 was found between the numbers of nifH and acdS bacteria in the monocropping soil but not in 9 10 439 meadow soil (P = 0.75 ; n = 10), suggesting that maize monocropping history could have been 11 12 440 an important factor. However, these findings were obtained with young plants only (21 days), 13 14 15 441 grown in sieved soil under greenhouse conditions. 16 17 442 In conclusion, the current findings indicate that rhizobacteria with nitrogen fixation 18 19 443 capacity and counterparts harboring ACC deamination ability co-occur in the maize 20 21 For Peer Review 22 444 rhizosphere, pointing to the possibility that plants may rely on multiple, complementary 23 24 445 phytostimulatory functions provided by their microbial partners. Additional method 25 26 446 development is needed to extend this type of assessment to additional phytostimulatory groups 27 28 29 447 and other microbial functional groups important for plant performance. 30 31 448 32 33 449 ACKNOWLEDGEMENTS 34 35 450 This work was supported in part by project Azodure (ANR Agrobiosphère ANR-12-AGRO- 36 37 38 451 0008). We are grateful to J. Haurat and H. Brunet for technical help, as well as D. Abrouk 39 40 452 (iBio platform, UMR CNRS 5557 Écologie Microbienne) and J. Thioulouse (UMR CNRS 41 42 453 LBBE) for helpful discussion. The authors declare no conflict of interest. 43 44 45 454 46 47 455 DATA ACCESSIBILITY 48 49 456 Illumina MiSeq paired-end reads have been deposited in the European Bioinformatics Institute 50 51 52 457 (EBI) database under accession numbers PRJEB14347 (ERP015984) for rrs; PRJEB14346 53 54 458 (ERP015983) for nifH, PRJEB14343 (ERP015981) for acdS. 55 56 459 57 58 460 AUTHOR CONTRIBUTIONS 59 60
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1 2 3 461 LL, YML and DM designed the project, SR, LL, CPC, YML and DM carried out field work 4 5 6 462 and samplings, SR conducted the molecular work, SR, MLB and AD implemented 7 8 463 bioinformatic analyses, SR, YML and DM analyzed data, SR, YML and DM prepared the first 9 10 464 draft of the manuscript, which was finalized by all authors. 11 12 465 13 14 15 16 17 18 19 20 21 For Peer Review 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 466 REFERENCES 4 5 6 467 7 8 468 Achouak W, Normand P, Heulin T. Comparative phylogeny of rrs and nifH genes in the Bacillaceae. 9 10 469 Int J Syst Evol Microbiol 1999;49(3):961-967. doi:10.1099/00207713-49-3-961 11 12 13 470 Agaras BC, Scandiani M, Luque A et al. Quantification of the potential biocontrol and direct plant 14 15 471 growth promotion abilities based on multiple biological traits distinguish different groups of 16 17 472 Pseudomonas spp. isolates. Biological Control 2015;90:173-186. 18 19 473 doi:https://doi.org/10.1016/j.biocontrol.2015.07.003 20 21 474 Almario J, Gobbin D, DéfagoFor G et al. Peer Prevalence ofReview type III secretion system in effective biocontrol 22 23 475 pseudomonads. Res Microbiol 2014;165(4):300-304. 24 25 476 doi:https://doi.org/10.1016/j.resmic.2014.03.008 26 27 477 Almario J, Muller D, Défago G et al. Rhizosphere ecology and phytoprotection in soils naturally 28 29 478 suppressive to Thielaviopsis black root rot of tobacco. Environ Microbiol 2014;16(7):1949- 30 31 32 479 1960. doi:10.1111/1462-2920.12459 33 34 480 Arruda L, Beneduzi A, Martins A et al. Screening of rhizobacteria isolated from maize (Zea mays L.) 35 36 481 in Rio Grande do Sul State (South Brazil) and analysis of their potential to improve plant 37 38 482 growth. Appl Soil Ecol 2013;63:15-22. doi:https://doi.org/10.1016/j.apsoil.2012.09.001 39 40 483 Bakker MG, Schlatter DC, Otto-Hanson L et al. Diffuse symbioses: roles of plant–plant, plant– 41 42 484 microbe and microbe–microbe interactions in structuring the soil microbiome. Mol Ecol 43 44 485 2014;23(6):1571-1583. doi:10.1111/mec.12571 45 46 486 Barberán A, Bates ST, Casamayor EO et al. Using network analysis to explore co-occurrence patterns 47 48 487 in soil microbial communities. ISME J 2011;6:343–351. doi:10.1038/ismej.2011.119 49 50 51 488 https://www.nature.com/articles/ismej2011119#supplementary-information 52 53 489 Bashan Y, de-Bashan LE. Chapter Two - How the plant growth-promoting bacterium Azospirillum 54 55 490 promotes plant growth—A critical assessment. In: Sparks DL (ed). Advances in Agronomy. 56 57 58 491 Academic Press, 2010, 108, 77-136. 59 60
21 ScholarOne Support 1-434/964-4100 Page 23 of 84 FEMS Microbiology Ecology
1 2 3 492 Bashan Y, de-Bashan LE, Prabhu SR et al. Advances in plant growth-promoting bacterial inoculant 4 5 493 technology: formulations and practical perspectives (1998–2013). Plant Soil 2014;378(1):1- 6 7 494 33. doi:10.1007/s11104-013-1956-x 8 9 495 Berg G, Smalla K. Plant species and soil type cooperatively shape the structure and function of 10 11 12 496 microbial communities in the rhizosphere. FEMS Microbiol Ecol 2009;68(1):1-13. 13 14 497 doi:10.1111/j.1574-6941.2009.00654.x 15 16 498 Bertalan M, Albano R, de Pádua V et al. Complete genome sequence of the sugarcane nitrogen-fixing 17 18 499 endophyte Gluconacetobacter diazotrophicus Pal5. BMC Genomics 2009;10(1):450. 19 20 500 doi:10.1186/1471-2164-10-450 21 For Peer Review 22 501 Blaha D, Prigent-Combaret C, Mirza MS et al. Phylogeny of the 1-aminocyclopropane-1-carboxylic 23 24 502 acid deaminase-encoding gene acdS in phytobeneficial and pathogenic Proteobacteria and 25 26 503 relation with strain biogeography. FEMS Microbiol Ecol 2006;56(3):455-470. 27 28 504 doi:10.1111/j.1574-6941.2006.00082.x 29 30 31 505 Bouffaud M-L, Renoud S, Dubost A et al. 1-Aminocyclopropane-1-carboxylate deaminase producers 32 33 506 associated to maize and other Poaceae species. Microbiome 2018;6(1):114. 34 35 507 doi:10.1186/s40168-018-0503-7 36 37 508 Bouffaud M-L, Renoud S, Moënne-Loccoz Y et al. Is plant evolutionary history impacting recruitment 38 39 509 of diazotrophs and nifH expression in the rhizosphere? Sci Rep 2016;6:21690. 40 41 510 doi:10.1038/srep21690 http://www.nature.com/articles/srep21690#supplementary-information 42 43 511 Boyd E, Peters J. New insights into the evolutionary history of biological nitrogen fixation. Front 44 45 512 Microbiol 2013;4:201. doi:10.3389/fmicb.2013.00201 46 47 513 Bruto M, Prigent-Combaret C, Luis P et al. Frequent, independent transfers of a catabolic gene from 48 49 bacteria to contrasted filamentous eukaryotes. Proc R Soc Lond B: Biol Sci 2014;281:1789. 50 514 51 52 515 doi:10.1098/rspb.2014.0848 53 54 516 Bruto M, Prigent-Combaret C, Muller D et al. Analysis of genes contributing to plant-beneficial 55 56 517 functions in plant growth-promoting rhizobacteria and related Proteobacteria. Sci Rep 57 58 518 2014;4:6261. doi:10.1038/srep06261 59 60
22 ScholarOne Support 1-434/964-4100 FEMS Microbiology Ecology Page 24 of 84
1 2 3 519 Burke C, Steinberg P, Rusch D et al. Bacterial community assembly based on functional genes rather 4 5 520 than species. Proc Natl Acad Sci USA 2011;108(34):14288-14293. 6 7 521 doi:10.1073/pnas.1101591108 8 9 10 522 Cassán F, Vanderleyden J, Spaepen S. Physiological and agronomical aspects of phytohormone 11 12 523 production by model Plant-Growth-Promoting Rhizobacteria (PGPR) belonging to the genus 13 14 524 Azospirillum. J. Plant Growth Regul 2014;33(2):440-459. doi:10.1007/s00344-013-9362-4 15 16 17 525 Chen XH, Koumoutsi A, Scholz R et al. Comparative analysis of the complete genome sequence of 18 19 526 the plant growth–promoting bacterium Bacillus amyloliquefaciens FZB42. Nature Biotechnol 20 21 527 2007;25:1007. doi:10.1038/nbt1325For Peer https://www.nature.com/articles/nbt1325#supplementary- Review 22 23 528 information 24 25 26 529 Chessel D, Dufour AB, Thioulouse J. The ade4 package-I-One-table methods. R News 2004;4(1):5-10. 27 28 530 Cormier F, Foulkes J, Hirel B et al. Breeding for increased nitrogen-use efficiency: a review for wheat 29 30 531 (T. aestivum L.). Plant Breeding 2016;135(3):255-278. doi:10.1111/pbr.12371 31 32 532 Couillerot O, Ramírez-Trujillo A, Walker V et al. Comparison of prominent Azospirillum strains in 33 34 533 Azospirillum–Pseudomonas–Glomus consortia for promotion of maize growth. Appl 35 36 534 Microbiol Biotechnol 2013;97(10):4639-4649. doi:10.1007/s00253-012-4249-z 37 38 39 535 Culhane AC, Thioulouse J, Perrière G et al. MADE4: an R package for multivariate analysis of gene 40 41 536 expression data. Bioinformatics 2005;21(11):2789-2790. doi:10.1093/bioinformatics/bti394 42 43 537 Deynze A, Zamora P, Delaux P-M et al. Nitrogen fixation in a landrace of maize is supported by a 44 45 538 mucilage-associated diazotrophic microbiota. PLoS Biol 2018;16(8):e2006352. https://doi- 46 47 539 org.inee.bib.cnrs.fr/10.1371/journal.pbio.2006352 48 49 540 DeSantis TZ, Hugenholtz P, Larsen N et al. Greengenes, a chimera-checked 16S rRNA gene database 50 51 and workbench compatible with ARB. Appl Environ Microbiol 2006;72(7):5069-5072. 52 541 53 54 542 doi:10.1128/aem.03006-05 55 56 543 Doolittle WF, Booth A. It’s the song, not the singer: an exploration of holobiosis and evolutionary 57 58 544 theory. Biol Philos 2017;32(1):5-24. doi:10.1007/s10539-016-9542-2 59 60
23 ScholarOne Support 1-434/964-4100 Page 25 of 84 FEMS Microbiology Ecology
1 2 3 545 Dray S, Chessel D, Thioulouse J. Co-inertia analysis and the linking of ecological data tables. Ecology 4 5 546 2003;84(11):3078-3089. doi:10.1890/03-0178 6 7 547 Dray S, Dufour AB, Chessel D. The ade4 Package—II: Two-table and K-table methods. R News 8 9 548 2007;7:47-52. 10 11 12 549 Duan J, Müller KM, Charles T et al. 1-aminocyclopropane-1-carboxylate (ACC) deaminase genes in 13 14 550 Rhizobia from southern Saskatchewan. Microb Ecol 2009;57(3):423-436. 15 16 551 doi:10.1007/s00248-008-9407-6 17 18 552 Frapolli M, Pothier JF, Défago G et al. Evolutionary history of synthesis pathway genes for 19 20 553 phloroglucinol and cyanide antimicrobials in plant-associated fluorescent pseudomonads. Mol 21 For Peer Review 22 554 Phylogenet Evol 2012;63(3):877-890. doi:https://doi.org/10.1016/j.ympev.2012.02.030 23 24 555 Freilich S, Kreimer A, Meilijson I et al. The large-scale organization of the bacterial network of 25 26 556 ecological co-occurrence interactions. Nucleic Acids Res 2010;38(12):3857-3868. 27 28 557 doi:10.1093/nar/gkq118 29 30 31 558 Gaby JC, Buckley DH. The use of degenerate primers in qPCR analysis of functional genes can cause 32 33 559 dramatic quantification bias as revealed by investigation of nifH primer performance. Microb 34 35 560 Ecol 2017;74(3):701-708. doi:10.1007/s00248-017-0968-0 36 37 561 Gamalero E, Glick BR. Bacterial modulation of plant ethylene levels. Plant Physiol 2015;169(1):13- 38 39 562 22. doi:10.1104/pp.15.00284 40 41 563 Glick BR. Bacteria with ACC deaminase can promote plant growth and help to feed the world. 42 43 564 Microbiol Res 2014;169(1):30-39. doi:https://doi.org/10.1016/j.micres.2013.09.009 44 45 565 Hartman K, van der Heijden MGA, Wittwe RA et al. Cropping practices manipulate abundance 46 47 566 patterns of root and soil microbiome members paving the way to smart farming. Microbiome 48 49 2018;6(1):14. doi:10.1186/s40168-017-0389-9 50 567 51 52 568 Jha B, Gontia I, Hartmann A. The roots of the halophyte Salicornia brachiata are a source of new 53 54 569 halotolerant diazotrophic bacteria with plant growth-promoting potential. Plant Soil 55 56 570 2012;356(1):265-277. doi:10.1007/s11104-011-0877-9 57 58 59 60
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1 2 3 571 Lassalle F, Muller D, Nesme X. Ecological speciation in bacteria: reverse ecology approaches reveal 4 5 572 the adaptive part of bacterial cladogenesis. Res Microbiol 2015;166(10):729-741. 6 7 573 doi:https://doi.org/10.1016/j.resmic.2015.06.008 8 9 574 Lemanceau P, Blouin M, Muller D, et al. Let the core microbiota be functional. Trends Plant Sci 10 11 12 575 2017;22(7):583-595. doi:https://doi.org/10.1016/j.tplants.2017.04.008 13 14 576 Li Z, Chang S, Ye S et al. Differentiation of 1-aminocyclopropane-1-carboxylate (ACC) deaminase 15 16 577 from its homologs is the key for identifying bacteria containing ACC deaminase. FEMS 17 18 578 Microbiol Ecol 2015;91(10):fiv112-fiv112. doi:10.1093/femsec/fiv112 19 20 579 Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. 21 For Peer Review 22 580 Science 2016;353(6305):1272-1277. doi:10.1126/science.aaf4507 23 24 581 Louca S, Jacques SMS, Pires APF et al. High taxonomic variability despite stable functional structure 25 26 582 across microbial communities. Nat Ecol Evol 2017;1:0015. doi:10.1038/s41559-016-0015 27 28 583 Ma W, Guinel FC, Glick, BR. Rhizobium leguminosarum biovar viciae 1-aminocyclopropane-1- 29 30 31 584 carboxylate deaminase promotes nodulation of pea plants. Appl Environ Microbiol 32 33 585 2003;69(8):4396-4402. doi:10.1128/aem.69.8.4396-4402.2003 34 35 586 Mårtensson L, Díez B, Wartiainen I et al. Diazotrophic diversity, nifH gene expression and 36 37 587 nitrogenase activity in a rice paddy field in Fujian, China. Plant Soil 2009;325(1):207-218. 38 39 588 doi:10.1007/s11104-009-9970-8 40 41 589 Nascimento FX, Brígido C, Glick BR et al. ACC deaminase genes are conserved among 42 43 590 Mesorhizobium species able to nodulate the same host plant. FEMS Microbiol Lett 44 45 591 2012;336(1):26-37. doi:10.1111/j.1574-6968.2012.02648.x 46 47 592 Nukui N, Minamisawa K, Ayabe S-I et al. Expression of the 1-aminocyclopropane-1-carboxylic acid 48 49 deaminase gene requires symbiotic nitrogen-fixing regulator gene nifA2 in Mesorhizobium loti 50 593 51 52 594 MAFF303099. Appl Environ Microbiol 2006;72(7):4964-4969. doi:10.1128/aem.02745-05 53 54 595 Pieterse CMJ, Van Wees SCM. Induced disease resistance. In: Lugtenberg B (ed). Principles of Plant- 55 56 596 Microbe Interactions: Microbes for Sustainable Agriculture. Springer International 57 58 597 Publishing, 2015, 123-33. 59 60
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1 2 3 598 Pii Y, Mimmo T, Tomasi N et al. Microbial interactions in the rhizosphere: beneficial influences of 4 5 599 plant growth-promoting rhizobacteria on nutrient acquisition process. A review. Biol Fertil 6 7 600 Soils 2015;51(4):403-415. doi:10.1007/s00374-015-0996-1 8 9 601 Poly F, Jocteur Monrozier L, Bally R. Improvement in the RFLP procedure for studying the diversity 10 11 12 602 of nifH genes in communities of nitrogen fixers in soil. Res Microbiol 2001;152(1):95-103. 13 14 603 doi:https://doi.org/10.1016/S0923-2508(00)01172-4 15 16 604 Prigent-Combaret C, Blaha D, Pothier J et al. Physical organization and phylogenetic analysis of acdR 17 18 605 as leucine-responsive regulator of the 1-aminocyclopropane-1-carboxylate deaminase gene 19 20 606 acdS in phytobeneficial Azospirillum lipoferum 4B and other Proteobacteria. FEMS 21 For Peer Review 22 607 Microbiol Ecol 2008;65(2):202-219. doi:10.1111/j.1574-6941.2008.00474.x 23 24 608 Puri A, Padda KP, Chanway CP. Evidence of nitrogen fixation and growth promotion in canola 25 26 609 (Brassica napus L.) by an endophytic diazotroph Paenibacillus polymyxa P2b-2R. Biol Fertil 27 28 610 Soils 2016;52(1):119-125. doi:10.1007/s00374-015-1051-y 29 30 31 611 Raaijmakers JM, Paulitz TC, Steinberg C et al. The rhizosphere: a playground and battlefield for 32 33 612 soilborne pathogens and beneficial microorganisms. Plant Soil 2009;321(1):341-361. 34 35 613 doi:10.1007/s11104-008-9568-6 36 37 614 Rana A, Saharan B, Joshi M et al. Identification of multi-trait PGPR isolates and evaluating their 38 39 615 potential as inoculants for wheat. Ann Microbiol 2011;61(4):893-900. doi:10.1007/s13213- 40 41 616 011-0211-z 42 43 617 Redondo-Nieto M, Barret M, Morrissey J et al. Genome sequence reveals that Pseudomonas 44 45 618 fluorescens F113 possesses a large and diverse array of systems for rhizosphere function and 46 47 619 host interaction. BMC Genomics 2013;14(1):54. doi:10.1186/1471-2164-14-54 48 49 Rozier C, Hamzaoui J, Lemoine D et al. Field-based assessment of the mechanism of maize yield 50 620 51 52 621 enhancement by Azospirillum lipoferum CRT1. Sci Rep 2017;7(1):7416. doi:10.1038/s41598- 53 54 622 017-07929-8 55 56 623 Shade A, Handelsman J. Beyond the Venn diagram: the hunt for a core microbiome. Environ 57 58 624 Microbiol 2012;14(1):4-12. doi:10.1111/j.1462-2920.2011.02585.x 59 60
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1 2 3 625 Team, R. R: A Language and Environment for Statistical Computing 2014. Vienna, Austria: R 4 5 626 Foundation for Statistical Computing. 6 7 627 Vacheron J, Desbrosses G, Bouffaud M-L et al. Plant growth-promoting rhizobacteria and root system 8 9 628 functioning. Front Plant Sci 2013;4:356. doi:10.3389/fpls.2013.00356 10 11 12 629 Vacheron J, Desbrosses G, Renoud S et al. Differential contribution of plant-beneficial functions from 13 14 630 Pseudomonas kilonensis F113 to root system architecture alterations in Arabidopsis thaliana 15 16 631 and Zea mays. Mol Plant-Microbe Interact 2017;31(2):212-223. doi:10.1094/MPMI-07-17- 17 18 632 0185-R 19 20 633 Vacheron J, Moënne-Loccoz Y, Dubost A et al. Fluorescent Pseudomonas strains with only few plant- 21 For Peer Review 22 634 beneficial properties are favored in the maize rhizosphere. Front Plant Sci 2016;7:1212. 23 24 635 doi:10.3389/fpls.2016.01212 25 26 636 Vandenkoornhuyse P, Quaiser A, Duhamel et al. The importance of the microbiome of the plant 27 28 637 holobiont. New Phytol 2015;206(4):1196-1206. doi:doi:10.1111/nph.13312 29 30 31 638 Vinuesa P, Silva C, Lorite MJ et al. Molecular systematics of rhizobia based on maximum likelihood 32 33 639 and Bayesian phylogenies inferred from rrs, atpD, recA and nifH sequences, and their use in 34 35 640 the classification of Sesbania microsymbionts from Venezuelan wetlands. Syst Appl Microbiol 36 37 641 2005;28(8):702-716. doi:https://doi.org/10.1016/j.syapm.2005.05.007 38 39 642 Wang Y, Zhu G, Harhangi HR et al. Co-occurrence and distribution of nitrite-dependent anaerobic 40 41 643 ammonium and methane-oxidizing bacteria in a paddy soil. FEMS Microbiol Lett 42 43 644 2012;336(2):79-88. doi:10.1111/j.1574-6968.2012.02654.x 44 45 645 Wartiainen I, Eriksson T, Zheng W et al. Variation in the active diazotrophic community in rice 46 47 646 paddy—nifH PCR-DGGE analysis of rhizosphere and bulk soil. Appl Soil Ecol 2008;39(1):65- 48 49 75. doi:https://doi.org/10.1016/j.apsoil.2007.11.008 50 647 51 52 648 Wisniewski-Dyé F, Lozano L, Acosta-Cruz E et al. Genome sequence of Azospirillum brasilense 53 54 649 CBG497 and comparative analyses of Azospirillum core and accessory genomes provide 55 56 650 insight into niche adaptation. Genes 2012;3(4):576. 57 58 59 60
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1 2 3 651 Zehr JP, Jenkins BD, Short SM et al. Nitrogenase gene diversity and microbial community structure: a 4 5 652 cross-system comparison. Envirol Microbiol 2003;5(7):539-554. doi:10.1046/j.1462- 6 7 653 2920.2003.00451.x 8 9 654 Zehr JP, McReynolds LA. Use of degenerate oligonucleotides for amplification of the nifH gene from 10 11 12 655 the marine cyanobacterium Trichodesmium thiebautii. Appl Environ Microbiol 13 14 656 1989;55(10):2522-2526. 15 16 657 17 18 19 20 21 For Peer Review 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 658 Legend 4 5 6 659 7 8 660 FIGURE 1. Size of the acdS and nifH functional groups compared in the three field sites L, FC 9 10 661 and C over four sampling times. Means and standard deviations are shown for the acdS group 11 12 662 at 6 leaves in 2014 (A) and 2015 (B) and at flowering in 2014 (E) and 2015 (F) and for the nifH 13 14 15 663 group at 6 leaves in 2014 (C) and 2015 (D) and at flowering in 2014 (G) and 2015 (H). The 16 17 664 analysis was done using pooled samples of six roots systems (n= 5) at FC and C and individual 18 19 665 root systems (n = 30) at L in 2014, and individual root systems (n = 20) at all three sites in 2015. 20 21 For Peer Review 22 666 Statistical differences between sites are indicated by letters a-c (ANOVA and Fischer’s LSD 23 24 667 tests, P < 0.05). 25 26 668 27 28 29 669 FIGURE 2. Correlation between log numbers of nifH (X axis) and acdS genes (Y axis). 30 31 670 Correlation was established using the Pearson coefficient. The analysis was done using pooled 32 33 671 samples of six roots systems (n= 5) at FC and C and individual root systems (n = 30) at L in 34 35 672 2014, and individual root systems (n = 20) at all three sites in 2015. 36 37 38 673 39 40 674 FIGURE 3. Correlation between Shannon diversity indices of nifH and acdS (A), Simpson 41 42 675 diversity indices of nifH and acdS (B), Shannon diversity indices of rrs and acdS or nifH (C), 43 44 45 676 and Simpson diversity indices of rrs and acdS or nifH (D). Correlation was established 46 47 677 separately at each of the three field sites L, FC and C, using the Pearson coefficient (n = 5). 48 49 678 50 51 52 679 FIGURE 4. Comparison of nifH (A), acdS (B) and rrs (C) diversity between sites L, FC and C 53 54 680 by between-class analysis. Red circles, green triangles and blue squares are used for samples 55 56 681 from sites FC, C and L, respectively. The curves at the top and the left of the panels show the 57 58 682 distribution of samples on respectively the X and Y axes. 59 60
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1 2 3 683 4 5 6 684 FIGURE 5. Co-inertia analysis between acdS and nifH diversities (A), rrs and nifH diversities 7 8 685 (B) and rrs and acdS diversities (C). Projection of the samples (n = 5) is based on both acdS 9 10 686 (Blue) and nifH (Green), rrs (Grey) and nifH (Green), or rrs (Grey) and acdS (Blue) diversity 11 12 687 variables (level = genus) into a same factorial plan. The vector in black shows the strength of 13 14 15 688 co-trends between the two barycenters of variables as related to each site (L, FC, C). Shorter 16 17 689 vectors indicate stronger convergent trends between the two variable groups. 18 19 690 20 21 For Peer Review 22 691 23 24 692 FIGURE S1: Rarefaction curves for nifH (A), acdS (B) and rrs (C) genes. 25 26 693 27 28 29 694 FIGURE S2. RAxML bipartition tree of 3322 sequenced acdS alleles from Poaceae 30 695 rhizosphere. The tree was visualized using iTOL software (Letunic I, Bork P. Interactive Tree 31 32 696 Of Life (iTOL) v4: recent updates and new developments (2019) Nucleic Acids Res doi: 33 34 697 10.1093/nar/gkz239). Branches colored in violet represent the out-group of D-cystein 35 698 desulfhydrase genes, whereas acdS alleles affiliated to Betaproteobacteria are shown in khaki, 36 37 699 to Gammaproteobacteria in blue, to Actinobacteria in green, to Alphaproteobacteria in red, 38 39 700 and to microeukaryotes in orange. The tree can be viewed online at the following link 40 41 701 http://itol.embl.de/shared/acdStree. 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 4 5 6 Table 1. Field characteristics of the top (5-30 cm) soil layer. 7 8 Field Soil type Texture (%) pH Organic C Total N C/N ratio Cation exchange (cmol/kg) 9 10 a 2+ 2+ + Sand Silt Clay H2O KCl (g/kg) (g/kg) CEC Ca Mg K 11 12 13 FC Fluvic cambisol 26.9 38.3 34.7 7.1 6.3 31.6 3.4 9.3 22.8 21.2 0.67 0.38 14 For Peer Review 15 L Luvisol 42.9 42.9 14.2 7.3 6.7 21.5 1.6 13.4 93.0 10.5 0.33 0.43 16 17 C Calcisol 15.6 74.1 10.3 8.2 7.7 25.9 3.1 8.4 97.0 36.1 0.24 0.29 18 19 aCEC, cation exchange capacity. 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 31 43 ScholarOne Support 1-434/964-4100 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 33 of 84 FEMS Microbiology Ecology
1 2 3 4 5 A B C D 6 6 leaves 2014 6 leaves 2015 6 leaves 2014 6 leaves 2015 / g / g
s 9 9 s 10 10 e e
i i a l l i p p
i b
7 o o o 9 a 9 o b s
c 8 c s 8 a
y b y S H r r
f 8 8 d i 8 d d c
7 7 n f f a o f o
f 7 7 o 9 o 6 g g 6 o 6 6 o l l 10 5 5 5 5 11 L FaCcd S C L FacCdS C L FniCfH C L nFifCH C E F G H Flowering 2014 Flowering 2015 Flowering 2014 Flowering 2015
12 / g / g s 9 a 9 s 10 10 e e a i i l
l b a
c b i p p i a o o o 13 o 9 c 9 b s c s 8 8 b c c y S y H r r f
d 8 8 c i d d 14 c 7 7 n f f a f o o f 7 7 o o g
15 g 6 6 o o l 6 6 l 16 5 5 5 5 L FaCcdS C L FacCdS C L FniCfH C L nFifHC C 17 18 19 20 21 For Peer Review 22 23
24 Fig. 1 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 4 Site L Site FC Site C 5 6
7 4 1
8 0 2
9 s e
10 v a
11 e l
12 6 Correlation 0.67 Correlation 0.98 Correlation 0.80 13 P = 7.4 × 10-5 P = 2.8 × 10-3 P = 0.10 14 15 16 17 4
18 1 0
19 2 g
20 n i r
21 e For Peer Review 22 o w l
23 F Correlation 0.90 Correlation 0.10 Correlation 0.86 24 P = 1.0 × 10-4 P = 0.67 P = 3.7 × 10-4 25 26 27 28
29 5 1
30 0 2
31 s e
32 v a e
33 l
34 6 Correlation 0.87 Correlation 0.67 Correlation 0.73 35 P = 3.8 × 10-5 P = 0.010 P = 4.0 × 10-3 36 37 38
39 5 1
40 0 2
41 g n i
42 r 43 e o w
44 l 45 F Correlation 0.86 Correlation 0.38 Correlation 0.69 P = 1.0 × 10-4 P = 0.19 P = 0.010 46 47 48 49 50 51 52 53 54 55 56 57 58 59 Fig. 2 60
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 For Peer Review 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 For Peer Review 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 Figure 5 4 5 A 6 B 7 d=1 d=0.5 8 9 10 FC C 11 FC 12 13 14 15 16 17 18 19 C L 20 L 21 acdS/nifH For Peer Reviewrrs/nifH 22 23 C 24 25 d=1 26 27 28 29 FC 30 31 32 33 34 C 35 36 37 38 L rrs/acdS 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 FIGURE 1. Size of the acdS and nifH functional groups compared in the three field sites L, FC 4 5 6 and C over four sampling times. Means and standard deviations are shown for the acdS group 7 8 at 6 leaves in 2014 (A) and 2015 (B) and at flowering in 2014 (E) and 2015 (F) and for the nifH 9 10 group at 6 leaves in 2014 (C) and 2015 (D) and at flowering in 2014 (G) and 2015 (H). The 11 12 analysis was done using pooled samples of six roots systems (n= 5) at FC and C and individual 13 14 15 root systems (n = 30) at L in 2014, and individual root systems (n = 20) at all three sites in 16 17 2015. Statistical differences between sites are indicated by letters a-c (ANOVA and Fischer’s 18 19 LSD tests, P < 0.05). 20 21 For Peer Review 22 23 24 FIGURE 2. Correlation between log numbers of nifH (X axis) and acdS genes (Y axis). 25 26 Correlation was established using the Pearson coefficient. The analysis was done using pooled 27 28 samples of six roots systems (n= 5) at FC and C and individual root systems (n = 30) at L in 29 30 31 2014, and individual root systems (n = 20) at all three sites in 2015. 32 33 34 35 FIGURE 3. Correlation between Shannon diversity indices of nifH and acdS (A), Simpson 36 37 38 diversity indices of nifH and acdS (B), Shannon diversity indices of rrs and acdS or nifH (C), 39 40 and Simpson diversity indices of rrs and acdS or nifH (D). Correlation was established 41 42 separately at each of the three field sites L, FC and C, using the Pearson coefficient (n = 5). 43 44 45 46 47 FIGURE 4. Comparison of nifH (A), acdS (B) and rrs (C) diversity between sites L, FC and C 48 49 by between-class analysis. Red circles, green triangles and blue squares are used for samples 50 51 from sites FC, C and L, respectively. The curves at the top and the left of the panels show the 52 53 54 distribution of samples on respectively the X and Y axes. 55 56 57 58 59 60
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1 2 3 FIGURE 5. Co-inertia analysis between acdS and nifH diversities (A), rrs and nifH diversities 4 5 6 (B) and rrs and acdS diversities (C). Projection of the samples (n = 5) is based on both acdS 7 8 (Blue) and nifH (Green), rrs (Grey) and nifH (Green), or rrs (Grey) and acdS (Blue) diversity 9 10 variables (level = genus) into a same factorial plan. The vector in black shows the strength of 11 12 co-trends between the two barycenters of variables as related to each site (L, FC, C). Shorter 13 14 15 vectors indicate stronger convergent trends between the two variable groups. 16 17 18 19 20 21 For Peer Review 22 FIGURE S1: Rarefaction curves for nifH (A), acdS (B) and rrs (C) genes. 23 24 25 26 FIGURE S2. RAxML bipartition tree of 3322 sequenced acdS alleles from Poaceae 27 rhizosphere. The tree was visualized using iTOL software. Branches colored in violet 28 represent the out-group of D-cystein desulfhydrase genes, whereas acdS alleles affiliated to 29 30 Betaproteobacteria are shown in khaki, to Gammaproteobacteria in blue, to Actinobacteria in 31 green, to Alphaproteobacteria in red, and to microeukaryotes in orange. 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
ScholarOne Support 1-434/964-4100 A B FEMS Microbiology Ecology C D Page 40 of 84 6 leaves 2014 6 leaves 2015 6 leaves 2014 6 leaves 2015 9 9 10 10 a 1 b 9 a 9 b soil
copies/g 8 copies/g 2 soil 8 b a 3 8 8
4 7 7 nifH acdS of dry 5 of dry 7 7 6 6 6 6 6 log of 7 log of 8 5 5 5 5 L FCacd S C L FCacdS C L FCnifH C L nifHFC C 9 10 E F G H 11 Flowering 2014 Flowering 2015 Flowering 2014 Flowering 2015 12 9 a 9 For Peer Review10 a 10 13 c b b a a 9 c 9 b soil 14 copies/g soil 8 8 b copies/g c 15 c 8 8
7 7 nifH
16 acdS of dry of dry 7 17 7 18 6 6 log of 6 6 19 log of 20 5 5 5 5 L FCacdS C L FCacdS C L FCnifH C L nifHFC C 21 22 23 24 25 26 27 28 29 30 31 32 33 34 ScholarOne Support 1-434/964-4100 35 Fig.36 1 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 41 of 84 Site L FEMS MicrobiologySite Ecology FC Site C
1 2 3 4 2014 5 6
7 leaves 8 6 Correlation 0.67 Correlation 0.98 Correlation 0.80 9 P = 7.4 × 10-5 P = 2.8 × 10-3 P = 0.10 10 11 12 13 14 15
16 2014 17 18 19 20 For Peer Review
21 Flowering Correlation 0.90 Correlation 0.10 Correlation 0.86 22 P = 1.0 × 10-4 P = 0.67 P = 3.7 × 10-4 23 24 25 26 27 28 29 30 2015 31 32
33 leaves 34 6 Correlation 0.87 Correlation 0.67 Correlation 0.73 35 P = 3.8 × 10-5 P = 0.010 P = 4.0 × 10-3 36 37 38 39 40 41 42 2015 43 44 45 46 47 Flowering Correlation 0.86 Correlation 0.38 Correlation 0.69 -4 48 P = 1.0 × 10 P = 0.19 P = 0.010 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 For Peer Review 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 FIGURE 3. Correlation between Shannon diversity indices of nifH and acdS (A), Simpson diversity indices of 46 nifH and acdS (B), Shannon diversity indices of rrs and acdS or nifH (C), and Simpson diversity indices of rrs 47 and acdS or nifH (D). Correlation was established separately at each of the three field sites L, FC and C, using the Pearson coefficient (n = 5). 48 49 238x359mm (72 x 72 DPI) 50 51 52 53 54 55 56 57 58 59 60 ScholarOne Support 1-434/964-4100 Page 43 of 84 FEMS Microbiology Ecology
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 For Peer Review 20 21 22 23 24 25 26 27 28 29 30 31 32 FIGURE 4. Comparison of nifH (A), acdS (B) and rrs (C) diversity between sites L, FC and C by between- 33 class analysis. Red circles, green triangles and blue squares are used for samples from sites FC, C and L, 34 respectively. The curves at the top and the left of the panels show the distribution of samples on 35 respectively the X and Y axes. 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 ScholarOne Support 1-434/964-4100 FEMS Microbiology Ecology Page 44 of 84
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 For Peer Review 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 FIGURE 5. Co-inertia analysis between acdS and nifH diversities (A), rrs and nifH diversities (B) and rrs and 37 acdS diversities (C). Projection of the samples (n = 5) is based on both acdS (Blue) and nifH (Green), rrs (Grey) and nifH (Green), or rrs (Grey) and acdS (Blue) diversity variables (level = genus) into a same 38 factorial plan. The vector in black shows the strength of co-trends between the two barycenters of variables 39 as related to each site (L, FC, C). Shorter vectors indicate stronger convergent trends between the two 40 variable groups. 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 ScholarOne Support 1-434/964-4100 Page 45 of 84 FEMS Microbiology Ecology
1 2 3 4 5 6 Table 1. Field characteristics of the top (5-30 cm) soil layer. 7 8 Field Soil type Texture (%) pH Organic C Total N C/N ratio Cation exchange (cmol/kg) 9 a 2+ 2+ + 10 Sand Silt Clay H2O KCl (g/kg) (g/kg) CEC Ca Mg K 11 12 FC Fluvic cambisol 26.9 38.3 34.7 7.1 6.3 31.6 3.4 9.3 22.8 21.2 0.67 0.38 13 14 L Luvisol 42.9 For42.9 14.2 Peer7.3 6.7 Review21.5 1.6 13.4 93.0 10.5 0.33 0.43 15 16 C Calcisol 15.6 74.1 10.3 8.2 7.7 25.9 3.1 8.4 97.0 36.1 0.24 0.29 17 18 a 19 CEC, cation exchange capacity 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 ScholarOne Support 1-434/964-4100 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 FEMS Microbiology Ecology Page 46 of 84
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 For Peer Review 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Fig S1 19 For Peer Review 20 904x322mm (72 x 72 DPI) 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 ScholarOne Support 1-434/964-4100 Thermomicrobium roseum DSM 515
Amycolatopsis vancoresmycina D
Amycolatopsis lurida NRRL 2430
Amycolatopsis orientalis HCCB1 Amycolatopsis mediterranei U32 FEMS Microbiology Ecology Page 48 of 84 Actinoplanes missouriensis 431
Amycolatopsis sp. MJM2582
Amycolatopsis rifamycinica
Amycolatopsis japonica 1
2 Meiothermus ruber DSM 1279 3 4 5 OTU 11649 OTU 10494 OTU 10266
6 OTU 10211 7 OTU 15408 8 OTU 36150
Amycolatopsis decaplanina DSM OTU 15978 Amycolatopsis azurea DSM 43854 OTU 11656 OTU 7136 9 OTU 10063 OTU 12732 Streptomyces violaceusniger Tu OTU 10001 Mycobacterium smegmatis str. M OTU 10027
10 OTU 10047 OTU 20818 Methylibium sp. T29-B 11 OTU 14234 Methylibium petroleiphilum PM1 Streptomyces iranensis OTU 10051 OTU 11223 12 Acidovorax sp. CF316
OTU 27042 13 Pseudonocardia dioxanivorans C OTU 11470 Acidovorax ebreus TPSY Mycobacterium goodii OTU 10353 OTU 31389 14 Diaphorobacter sp. J5-51
OTU 10089 15 OTU 1007 OTU 10133 16 Acidovorax citrulli AAC00-1 Acidovorax avenae subsp. avena Variovorax sp. CF313 17 OTU 10163 OTU 10176 18 OTU 11246 Pseudorhodoferax sp. Leaf274 OTU 11837 Pseudomonas cichorii JBC1 19 Agromyces sp. Soil535 OTU 10094 OTU 10235 20 OTU 11171 OTU 10606 OTU 10736 21 Phytophthora parasitica CJ01A1 OTU 10008 Tree scale: 1 Phytophthora nicotianae 22 Plasmopara halstedii OTU 11486 Macrophomina phaseolina MS6 23 OTU 10150 OTU 10000 24 OTU 10147 Variovorax paradoxus S110 25 Gammaproteobacteria bacterium 26 OTU 10403 Ralstonia syzygii R24 OTU 10095 Ralstonia solanacearum GMI1000 27 EnterobacterEscherichia aerogenes fergusonii EA1509E ATCC 35 Herbaspirillum hiltneri N3 Ralstonia sp. A12 28 Ralstonia pickettii OR214 Enterobacter lignolyticus SCF1 OTU 31537 29 OTU 12094 30 Enterobacter cloacae ECNIH2 31 Enterobacter asburiae L1 32 Enterobacter sacchari SP1 33 OTU 10325 Escherichia coli strain RM9387 OTU 11066 34 EscherichiaEnterobacter albertii sp KF1 638 Pseudomonas caricapapayae 35 Pseudomonas congelans Pseudomonas syringae BRIP34876 36 Escherichia coli K-12 strain E Escherichia coli 042 Pseudomonas ficuserectae 37 Escherichia coli Xuzhou21 38 OTU 10072 39 Pseudomonas oryzihabitans 40 Pseudomonas psychrotolerans L1 41 42 OTU 11455 43 Tistrella mobilis KA081020-065 Ralstonia mannitolilytica 44 45 46 Achromobacter xylosoxidans NH4 Achromobacter insuavis AXX-A 47 OTU 21161 48 49 50 OTU 11476 OTU 10594 Gluconobacter frateurii NBRC 1 51 OTU 30540 Gluconobacter thailandicus NBR 52 OTU 2376 53 Gluconobacter oxydans H24 OTU 11262 54 Pannonibacter phragmitetus 55 Fulvimarina pelagi HTCC2506 Pelagibaca bermudensis HTCC260 OTU 11377 56 Thielavia terrestris NRRL 8126 57 Salipiger mucosus DSM 16094 Myceliophthora thermophila ATC 58 Madurella mycetomatis Roseovarius tolerans 59alpha proteobacterium symbiont Colletotrichum gloeosporioides Colletotrichum fioriniae PJ7 60 Starkeya novella DSM 506 Colletotrichum sublineola Sinorhizobium sp. BL3 Fusarium langsethiae Labrenzia aggregata IAM 12614 Microlunatus phosphovorus NM-1 OTU 10069 Labrenzia sp. C1B70 OTU 10106 Sulfitobacter sp. H3 OTU 1004 Sagittula stellata E-37 For Peer Review Polaromonas sp. CF318 Sinorhizobium meliloti SM11 Sinorhizobium medicae WSM419 OTU 10203 OTU 10244 Rhizobium leguminosarum bv. tr OTU 10078 Acidiphilium multivorum AIU301 Burkholderia andropogonis Ensifer sp. Root954 OTU 10224 Ensifer adhaerens Prosthecomicrobium hirschii Tatumella ptyseos ATCC 33301 Azospirillum sp. B510 Pantoea sp. EcB2 Acetobacter syzygii 9H-2 Brenneria goodwinii Novosphingobium sp. AAP93 Serratia sp. ATCC 39006 Hoeflea sp. BAL378 Tatumella morbirosei Labrenzia alba Dickeya dadantii Ech703 OTU 18744 OTU 10278 OTU 28221 OTU 13179 OTU 12585 OTU 23716 OTU 12461 OTU 10363 Halomonas titanicae BH1 marine sediment metagenome Halomonas hydrothermalis OTU 14824 Halomonas sp. SUBG004 AzospirillumOTU thiophilum 25577 OTU 17940 OTU 102 Burkholderia fungorum Agrobacterium vitis S4 OTU 1009 Gynuella sunshinyii YC6258 OTU 10287 Burkholderia xenovorans LB400 PhyllobacteriumDevosia brassicacearum sp. Root413D1 Burkholderia terrae BS001 Burkholderia caribensis MBA4 Mesorhizobium alhagi CCNWXJ12- Burkholderia silvatlantica SRM Nitratireductor indicus C115 OTU 36084 Loktanella sp.Bosea 3ANDIMAR09 vaviloviae OTU 35466
Methylobacterium aquaticum OTU 21686 Pseudomonas brassicacearum sub RhizobiumMethylobacterium mesoamericanum tarhaniae STM36 Pseudomonas kilonensis Methylobacterium variabile Pseudomonas fluorescens F113
Burkholderia caryophylli Methylobacterium platani JCM 1 MethylobacteriumChelatococcusBosea nodulans sp.sp. ORSAAP35CO-6 Burkholderia phymatum STM815
OTU 1095 OTU 10847 OTU 21139 Burkholderia glathei PML1-12 Thalassospira xiamenensis M-5 Burkholderia graminis C4D1M OTU 10 Thalassospira permensis NBRC 1 Burkholderia grimmiae OTU 10044 OTU 4501 OTU 16057 OTU 10670 OTU 21314 OTU 40388 OTU 6668 OTU 10091 OTU 12899 OTU 10056 OTU 11021 OTU 13063 OTU 10733 OTU 1067 OTU 11413 OTU 4045 OTU 22625 Azospirillum lipoferum 4B OTU 12827 OTU 10315 OTU 10311 OTU 10902 OTU 11073 OTU 10716 OTU 10809 OTU 13409 OTU 1112 OTU 13731 OTU 12619 OTU 10489 Kribbella flavida DSM 17836
OTU 10826 OTU 11168 OTU 26460 OTU 13117 OTU 15461 OTU 2065 OTU 36028 OTU 10756 OTU 15139 OTU 20317 OTU 6184 Bradyrhizobium oligotrophicum OTU 13329 OTU 15901 OTU 21263 OTU 10615 Mesorhizobium ciceri OTU 31458 OTU 11381 OTU 1608 Phycicoccus sp. Soil803 OTU 1098 Mesorhizobium australicum WSM2 OTU 35558 Mesorhizobium opportunistum WS OTU 10601 OTU 10260 OTU 10770 OTU 11175 Mesorhizobium loti MAFF303099 OTU 11890 Mesorhizobium sp. L103C131B0 OTU 20430 OTU 16215 OTU 10778 OTU 29033 OTU 13434 Rhizobium grahamii CCGE 502 OTU 10557 Rhizobium gallicum bv. gallicu OTU 11189 OTU 44617 OTU 10745 OTU 11681 OTU 15349 OTU 11303 OTU 11841 OTU 1000 OTU 11501 Sinorhizobium americanum CCGM7Bradyrhizobium jicamae OTU 11185 OTU 20834 OTU 11387 OTU 10077 Bradyrhizobium retamae OTU 10617 OTU 11480 OTU 10134 OTU 26268 OTU 11207 OTU 11388 OTU 36571 OTU 10099 OTU 25847 OTU 28214 OTU 1063 OTU 12370 OTU 11099 OTU 9214 OTU 33871 OTU 12664 Plantibacter sp. Leaf314 OTU 1248 OTU 3106 OTU 10104 OTU 1571 OTU 10815 OTU 10336 OTU 40556 OTU 1056 Bradyrhizobium pachyrhizi OTU 10120 OTU 10645 OTU 15610 OTU 10614 Bradyrhizobium valentinum
OTU 10967 OTU 11631
OTU 22766 OTU 104
OTU 1040 OTU 11061
OTU 10309 OTU 14043
OTU 12380 OTU 10173
OTU 42187
Pseudomonas sp. GM18
Leifsonia rubra CMS 76R
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marine actinobacterium PHSC20C Page 49 of 84 FEMS Microbiology Ecology
1 2 3 1 Co-occurrence of rhizobacteria with nitrogen fixation and/or 1-aminocyclopropane-1- 4 5 6 2 carboxylate deamination abilities in the maize rhizosphere 7 8 3 9 10 4 11 12 5 13 14 1‡ 1‡ 1 1 15 6 Sébastien Renoud , Marie-Lara Bouffaud , Audrey Dubost , Claire Prigent-Combaret , 16 17 7 Laurent Legendre1,2, Yvan Moënne-Loccoz1 and Daniel Muller1* 18 19 8 20 21 For Peer Review 1 22 9 Univ Lyon, Université Claude Bernard Lyon 1, CNRS, INRAE, VetAgro Sup, UMR5557 23 24 10 Ecologie Microbienne, 43 bd du 11 novembre 1918, F-69622 Villeurbanne, France 25 26 11 2 Univ Lyon, Université de St Etienne, F-42000 St Etienne, France 27 28 29 12 30 31 13 ‡ Current addresses: 32 33 14 S.R. : BGene genetics Bâtiment B Biologie, F-38400 Saint Martin d'Hères, France 34 35 15 M.L.B. : Helmholtz Center for Environmental Research UFZ, Theodor-Lieser-Straβe 4, 36 37 38 16 06120 Halle, Germany 39 40 17 41 42 18 43 44 45 19 Running title: nifH and acdS bacteria on maize 46 47 20 48 49 21 50 51 52 22 *Corresponding author: UMR CNRS 5557 Ecologie Microbienne, Université Lyon 1, 43 bd 53 54 23 du 11 Novembre 1918, 69622 Villeurbanne cedex, France. Phone: +33 4 72 43 27 14. E-mail: 55 56 24 [email protected] 57 58 25 59 60
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1 2 3 26 ABSTRACT 4 5 6 27 7 8 28 The plant microbiota may differ depending on soil type, but these microbiota probably share 9 10 29 the same functions necessary for holobiont fitness. Thus, we tested the hypothesis that 11 12 30 phytostimulatory microbial functional groups are likely to co-occur in the rhizosphere, using 13 14 15 31 groups corresponding to nitrogen fixation (nifH) and 1-aminocyclopropane-1-carboxylate 16 17 32 deamination (acdS), i.e. two key modes of action in plant-beneficial rhizobacteria. The analysis 18 19 33 of three maize fields in two consecutive years showed that quantitative PCR numbers of nifH 20 21 For Peer Review 22 34 and of acdS alleles differed according to field site, but a positive correlation was found overall 23 24 35 when comparing nifH and acdS numbers. Metabarcoding analyses in the second year indicated 25 26 36 that the diversity level of acdS but not nifH rhizobacteria in the rhizosphere differed across 27 28 29 37 fields. Furthermore, between-class analysis showed that the three sites differed from one 30 31 38 another based on nifH or acdS sequence data (or rrs data), and the bacterial genera contributing 32 33 39 most to field differentiation were not the same for the three bacterial groups. However, co- 34 35 40 inertia analysis indicated that the genetic structures of both functional groups and of the whole 36 37 38 41 bacterial community were similar across the three fields. Therefore, results point to co-selection 39 40 42 of rhizobacteria harboring nitrogen fixation and/or 1-aminocyclopropane-1-carboxylate 41 42 43 deamination abilities. 43 44 45 44 46 47 45 Keywords: microbiota; phytostimulation; functional group; functional microbiota; holobiont; 48 49 46 ITSNTS theory 50 51 52 47 53 54 48 55 56 57 58 59 60
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1 2 3 49 INTRODUCTION 4 5 6 50 7 8 51 Plant Growth-Promoting Rhizobacteria (PGPR) colonize plant roots and implement a range of 9 10 52 plant-beneficial traits, which may result in enhanced plant development, nutrition, health and/or 11 12 53 stress tolerance (Almario et al. 2014; Cormier et al. 2016; Gamalero and Glick 2015; Hartman 13 14 15 54 et al. 2018; Vacheron et al. 2013). As a consequence, PGPR strains have received extensive 16 17 55 attention for use as microbial inoculants of crops (Bashan et al. 2014; Couillerot et al. 2013). 18 19 56 Plant-beneficial effects exhibited by PGPR are underpinned by a wide range of modes 20 21 For Peer Review 22 57 of actions, which include (i) enhanced nutrient availability via associative nitrogen fixation 23 24 58 (Puri et al. 2016, Deynze et al. 2018) or phosphate solubilization (Arruda et al. 2013), (ii) 25 26 59 stimulation of root system establishment through phytohormone synthesis (Cassán et al. 2014) 27 28 29 60 or consumption of the ethylene precursor 1-aminocyclopropane-1-carboxylate (ACC) via an 30 31 61 enzymatic deamination (Glick 2014), and (iii) the induction of systemic resistance responses in 32 33 62 plant (Pieterse and Van Wees 2015). In addition to phytostimulation, certain PGPR may also 34 35 63 achieve inhibition of phytoparasites using antimicrobial secondary metabolites (Agaras et al. 36 37 38 64 2015) or lytic enzymes (Pieterse and Van Wees 2015). Often, PGPR strains display more than 39 40 65 one phytostimulatory mode of action, which is considered important for effective plant- 41 42 66 beneficial effects (Bashan and de-Bashan 2010; Bruto et al. 2014; Rana et al. 2011; Vacheron 43 44 45 67 et al. 2017). Therefore, the co-occurrence of multiple phytostimulation traits is likely to have 46 47 68 been subjected to positive evolutionary selection in PGPR populations to maximize success of 48 49 69 the plant-PGPR cooperation. This hypothesis is substantiated by genome sequence analysis of 50 51 52 70 many prominent PGPR strains from contrasted taxa (Bertalan et al. 2009; Chen et al. 2007; 53 54 71 Redondo-Nieto et al. 2013; Wisniewski-Dyé et al. 2012). 55 56 72 Even though PGPR strains tend to accumulate several plant-beneficial traits (Bruto et 57 58 73 al. 2014), the co-occurrence patterns of these traits are not random. This takes place in part 59 60
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1 2 3 74 because many past horizontal gene transfers of the corresponding genes were ancient (Frapolli 4 5 6 75 et al. 2012), often leading to clade-specific profiles of plant-beneficial traits (Bruto et al. 2014). 7 8 76 However, the analysis of 304 proteobacterial genomes from contrasted taxa evidenced, overall, 9 10 77 the co-occurrence of nifHDK (nitrogen fixation) and acdS (ACC deamination) based on Exact- 11 12 78 Fisher pairwise tests (Bruto et al. 2014), raising the possibility that nitrogen fixation and ACC 13 14 15 79 deamination might be useful traits when combined in a bacterium. Indeed, nitrogen fixation and 16 17 80 ACC deamination occur together in various rhizobacteria (Blaha et al. 2006; Duan et al. 2009; 18 19 81 Jha et al. 2012; Ma, Guinel, and Glick, 2003; Nukui et al. 2006), but the relation between both 20 21 For Peer Review 22 82 traits can be complex. In Azospirillum lipoferum 4B for instance, the plasmid-borne gene acdS 23 24 83 is eliminated during phase variation while nif genes are maintained (Prigent-Combaret et al. 25 26 84 2008), and in Mesorhizobium loti transcription of acdS is controlled by the nitrogen fixation 27 28 29 85 regulator gene nifA2 (Nukui et al. 2006). Moreover, ACC deamination was described as 30 31 86 facilitator of the legume-rhizobia symbiosis (Ma et al. 2003; Nascimento et al. 2012). 32 33 87 At the scale of an individual plant, the rhizosphere is colonized by a diversified range 34 35 88 of bacteria, including nifH acdS bacteria as well as bacteria harboring only nifH or acdS (Blaha 36 37 38 89 et al. 2006; Bouffaud et al. 2018). There is additional level of complexity in that many of these 39 40 90 bacteria are PGPR, but some of them are not (Bruto et al. 2014). However, the overall impact 41 42 91 of nitrogen fixation and ACC deamination on the plant is likely to be the sum of the contribution 43 44 45 92 of individual root-colonizing bacteria displaying these traits. This raises the question whether 46 47 93 there is, for the plant, an optimal balance between the functional microbial groups of nifH 48 49 94 rhizobacteria and acdS rhizobacteria in the rhizosphere. On this basis, we tested here the 50 51 52 95 hypothesis that rhizobacteria with either nitrogen fixation ability or ACC deamination ability 53 54 96 (or with both) co-occur on roots. For that purpose, we used three maize fields under reduced 55 56 97 nitrogen fertilization practices, with samplings carried out at 6-leaf and flowering stages during 57 58 98 two consecutive years, and numbers of nifH and acdS rhizobacteria were monitored by 59 60
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1 2 3 99 quantitative PCR. In addition, nifH and acdS rhizobacteria were assessed by metabarcoding 4 5 6 100 (MiSeq Illumina sequencing) of nifH and acdS genes at one sampling, in parallel to sequencing 7 8 101 of 16S rRNA genes for the whole rhizobacterial community. 9 10 102 11 12 103 2. MATERIALS AND METHODS 13 14 15 104 16 17 105 2.1. Field experiment 18 19 106 The experiment was conducted in 2014 and 2015 at field sites located in Chatonnay (L), 20 21 For Peer Review 22 107 Sérézin-de-la-Tour (FC) and Saint Savin (C), near the town of Bourgoin-Jallieu (Isère, France). 23 24 108 According to the FAO soil reference base, L field corresponds to a luvisol, FC a fluvic cambisol 25 26 109 and C a calcisol (Table 1). The trial set-up has been described in Rozier et al. (2017). 27 28 29 110 For each of the fields, the crop rotation consists in one year wheat, six years maize and 30 31 111 one year rapeseed, and wheat was grown the year before the 2014 experiment. The maize 32 33 112 sowing season ranges from middle April to middle May in the area. Maize seeds (Zea mays 34 35 113 ‘Seiddi’; Dauphinoise Company, France) were sown on April 18 (FC) and 23 (C and L) in 2014 36 37 38 114 and April 30 (C) and May 11 (FC and L) in 2015. Five replicate plots, which were 12 (FC and 39 40 115 C) or 8 (L) maize rows wide and 12 m long, were defined in each field. The fields were 41 42 116 undergoing a reduction in chemical fertilization usage and did not receive any nitrogen 43 44 45 117 fertilizers in 2014 and 2015. Only non-inoculated plots from the overall trial (Rozier et al. 2017) 46 47 118 were used. 48 49 119 50 51 52 120 2.2. Plant sampling 53 54 121 In 2014 and 2015, plants were sampled at six leaves and at flowering. In 2014, the first sampling 55 56 122 was done on May 25 (FC) and 26 (C and L). On each replicate plot, six plants were chosen 57 58 123 randomly, the entire root system was dug up and shaken vigorously to dislodge soil loosely 59 60
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1 2 3 124 adhering to the roots. At sites FC and C, one pooled sample of six roots system was obtained 4 5 6 125 per plot, i.e. a total of five pooled samples per field site. At site L, each of the six roots system 7 8 126 was treated individually to obtain 30 samples. The second sampling was done on July 8 (FC 9 10 127 and C) and 9 (L), on all five plots. Six plants were sampled per plot and treated individually to 11 12 128 obtain 30 samples per field site. 13 14 15 129 In 2015, the first sampling was done on May 27 (C), June 5 (FC) and June 8 (L). In each 16 17 130 replicate plot, four root systems were sampled and treated individually to obtain 20 samples per 18 19 131 field site. The second sampling was done on July 15 (C), 16 (FC) and 17 (L), and four root 20 21 For Peer Review 22 132 systems were sampled and treated individually to obtain 20 samples per field site. 23 24 133 Each sample was immediately flash-frozen on site, in liquid nitrogen, and lyophilized 25 26 134 back at the laboratory (at -50°C for 24 h). Roots and their adhering soil were separated and the 27 28 29 135 latter stored at -80°C. 30 31 136 32 33 137 2.3. DNA extraction from root-adhering soil 34 35 138 DNA from root-adhering soil was extracted with the FastDNA SPIN kit (BIO 101 Inc., 36 37 38 139 Carlsbad, CA). To this end, 500 mg (for the pooled samples from FC and C in 2014) or 300 mg 39 40 140 samples (for all other samples) were transferred in Lysing Matrix E tubes from the kit, and 5 µl 41 42 141 of the internal standard APA9 (109 copies ml-1) was added to each Lysing Matrix E tube to 43 44 45 142 normalize DNA extraction efficiencies between rhizosphere samples, as described (Park and 46 47 143 Crowley, 2005; Couillerot et al. 2010). This internal standard APA9 (i.e. vector pUC19 with 48 49 144 cassava virus insert; GenBank accession number AJ427910) requires primers AV1f 50 51 52 145 (CACCATGTCGAAGCGACCAGGAGATATCATC) and AV1r 53 54 146 (TTTCGATTTGTGACGTGGACAGTGGGGGC). After 1 h incubation at 4°C, DNA was 55 56 147 extracted and eluted in 50 µl of sterile ultra-pure water, according to the manufacturer’s 57 58 148 instructions. DNA concentrations were assessed by Picogreen (ThermoFisher). 59 60
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1 2 3 149 4 5 6 150 2.4. Size of microbial functional groups 7 8 151 The amounts of nifH genes were estimated by quantitative PCR based on the primers polF/polR 9 10 152 (Poly, Jocteur Monrozier, and Bally, 2001), as described by Bouffaud et al. (2016). The reaction 11 12 153 was carried out in 20 µl containing 4 μl of PCR-grade water, 1 μl of each primer (final 13 14 15 154 concentration 0.50 μM), 10 μl of LightCycler-DNA Master SYBR Green I master mix (Roche 16 17 155 Applied Science, Meylan, France) and 2 µl of sample DNA (10 μg). The cycling program 18 19 156 included 10 min incubation at 95°C, followed by 50 cycles of 95°C for 15 s, 64°C for 15 s and 20 21 For Peer Review 22 157 72°C for 10 s. Melting curve calculation and Tm determination were performed using the Tm 23 24 158 Calling Analysis module of Light-Cycler Software v.1.5 (Roche Applied Science). 25 26 159 The amount of acdS genes was estimated by quantitative PCR based on the primers 27 28 29 160 acdSF5/acdSR8 (Bouffaud et al. 2018). The reaction was carried out in 20 µl containing 4 μl of 30 31 161 PCR grade water, 1 μl of each primer (final concentration 1 μM), 10 μl of LightCycler-DNA 32 33 162 Master SYBR Green I master mix (Roche Applied Science) and 2 µl of sample DNA (10 μg). 34 35 163 The cycling program included 10 min incubation at 95°C, followed by 50 cycles of 94°C for 36 37 38 164 15 s, 67°C for 15 s and 72°C for 10 s. The fusion program for melting curve analysis is described 39 40 165 above. 41 42 166 Real-time PCR quantification data were converted to gene copy number per gram of 43 44 45 167 lyophilized root-adhering soil, as described (Bouffaud et al. 2018; Bouffaud et al. 2016). 46 47 168 48 49 169 2.5. nifH, acdS and rrs sequencing from rhizosphere DNA 50 51 52 170 Sequencing was performed on 2015’ samples taken when maize reached 6 leaves. Each sample 53 54 171 was an equimolar composite sample of four DNA extracts obtained from root-adherent soil, 55 56 172 resulting in 5 samples per field site, i.e. a total of 15 samples. DNA extracts were sent to MR 57 58 173 DNA laboratory (www.mrdnalab.com; Shallowater, TX) for sequencing. 59 60
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1 2 3 174 For nifH and acdS sequencing, PCR primers were the same ones used for quantitative 4 5 6 175 PCR (i.e., polF/polR for nifH and acdSF5/acdSR8 for acdS). For rrs sequencing, PCR primers 7 8 176 515/806 were chosen for the V4 variable region of the 16S rRNA gene. For all three genes, the 9 10 177 forward primer carried a barcode. Primers were used in a 30-cycle PCR (5 cycles implemented 11 12 178 on PCR products), using the HotStarTaq Plus Master Mix Kit (Qiagen, Valencia, CA) under 13 14 15 179 the following conditions: 94°C for 3 min, followed by 28 cycles of 94°C for 30 s, 53°C for 40 16 17 180 s and 72°C for 1 min, with a final elongation step at 72°C for 5 min. PCR products were checked 18 19 181 in 2% agarose gel to determine amplification success and relative band intensity. Multiple 20 21 For Peer Review 22 182 samples were pooled together in equal proportions based on their molecular weight and DNA 23 24 183 concentrations. Pooled samples were purified using calibrated Ampure XP beads and used to 25 26 184 prepare a DNA library following Illumina TruSeq DNA library preparation protocol. 27 28 29 185 Sequencing was performed on a MiSeq following the manufacturer’s guidelines. 30 31 186 Sequence data were processed using the analysis pipeline of MR DNA. Briefly, 32 33 187 sequences were depleted of barcodes, sequences < 150 bp or with ambiguous base calls 34 35 188 removed, the remaining sequences denoised, operational taxonomic units (OTUs; defined at 36 37 38 189 3% divergence threshold for the three genes) generated, and chimeras removed. Final OTUs 39 40 190 were taxonomically classified using BLASTn against a curated database derived from 41 42 191 Greengenes (DeSantis et al. 2006), RDPII (http://rdp.cme.msu.edu) and NCBI 43 44 45 192 (www.ncbi.nlm.nih.gov). Final OTUs of the acdS sequencing were classified using an in-house 46 47 193 curated acdS database, obtained after curation of acdS homolog genes from the FunGene acdS 48 49 194 8.3 database, as described by Bouffaud et al. (2018). Diversity indices of Shannon (H) and 50 51 52 195 Simpson (1-D) were calculated using sequencing subsample data for which each sample had 53 54 196 the same number of sequences. 55 56 197 An acdS phylogenetic tree (based on maximum-likelihood method) was computed using 57 58 198 acdS sequences from ten arbitrarily-chosen OTUs per genus recovered in our sequencing data 59 60
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1 2 3 199 and from one reference taxa for each genus, and related D-cystein desulfhydrase genes D- 4 5 6 200 cystein desulfhydrase genes from strains Escherichia coli strains K-12, ER3413, 042 and 7 8 201 RM9387, Escherichia albertii KF1, Escherichia fergusonii ATCC 35469, Enterobacter 9 10 202 sacchari SP1, Enterobacter cloacae ECNIH2, Enterobacter asburiae L1, Enterobacter sp. 638 11 12 203 and Enterobacter lignolyticus SCF1 (used as out-group). 13 14 15 204 16 17 205 2.6. Statistical analysis 18 19 206 Statistical analysis of quantitative PCR data was carried out by ANOVA and Fishers’ LSD tests. 20 21 For Peer Review 22 207 For each gene sequenced, comparison of bacterial diversity between field sites was carried out 23 24 208 by Between-Class Analysis (BCA) using ADE4 (Chessel et al. 2004; Culhane et al. 2005; Dray, 25 26 209 Dufour, and Chessel, 2007) and ggplot2 packages for R, and the 12 genera contributing most 27 28 29 210 to field site differentiation were identified. To assess co-trends between nifH and acdS 30 31 211 variables, as well as between rrs and nifH or acdS variables, sequence data were also assessed 32 33 212 using co-inertia analysis (CIA) (Dray et al. 2003; Dray et al. 2007), which was computed with 34 35 213 the ADE4 package in the R statistical software environment (Culhane et al. 2005). CIA is a 36 37 38 214 dimensional reduction procedure designed to measure the similarity of two sets of variables, 39 40 215 here the proportions of nifH and acdS bacterial genera obtained during between-class analyses. 41 42 216 Its significance was assessed using Monte-Carlo tests with 10,000 permutations. Unless 43 44 45 217 otherwise stated, statistical analyses were performed using R v3.1.3 (Team, 2014), at P < 0.05 46 47 218 level. 48 49 219 50 51 52 220 2.7. Nucleotide sequence accession numbers 53 54 221 Illumina MiSeq paired-end reads have been deposited in the European Bioinformatics Institute 55 56 222 (EBI) database under accession numbers PRJEB14347 (ERP015984) for rrs; PRJEB14346 57 58 223 (ERP015983) for nifH, PRJEB14343 (ERP015981) for acdS. 59 60
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1 2 3 224 4 5 6 225 3. RESULTS 7 8 226 9 10 227 3.1. Relation between numbers of nifH and acdS alleles in the three field sites 11 12 228 The number of acdS bacteria in the rhizosphere of maize harvested at 6-leaf stage in 2014 (7.87 13 14 7 -1 7 15 229 to 17.4 × 10 acdS gene copies g of dry soil) and 2015 (1.76 to 2.81 × 10 acdS gene copies 16 17 230 g-1 of dry soil) did not differ significantly between field sites (Figure 1AB). At flowering stage, 18 19 231 however, the number of acdS bacteria differed from one site to the next, both in 2014 and in 20 21 For Peer Review 22 232 2015 (Figure 1EF). At that growth stage, the lowest rhizosphere abundance was observed in 23 24 233 site L (5.08 × 107 acdS gene copies g-1 of dry soil) and the highest in site C (1.76 × 108 acdS 25 26 234 gene copies g-1 of dry soil) in 2014, whereas site ranking was the opposite in 2015 (8.35 versus 27 28 6 -1 29 235 44.0 × 10 acdS gene copies g of dry soil for sites C and L, respectively). 30 31 236 The numbers of nifH rhizobacteria differed according to field site (Figure 1CDGH). In 32 33 237 2014, the lowest nifH abundance was observed in rhizospheres of site L (1.06 and 20.8 × 107 34 35 238 nifH gene copies g-1 of dry soil at respectively six leaves and flowering) and the highest in those 36 37 7 -1 38 239 of site C (6.43 and 147.0 × 10 nifH gene copies g of dry soil at respectively six leaves and 39 40 240 flowering) (Figure 1CG). In 2015, the numbers of nifH rhizobacteria was higher in site C (9.31 41 42 241 × 108 nifH gene copies g-1 of dry soil) than in FC (1.30 × 108 nifH gene copies g-1 of dry soil) 43 44 8 -1 45 242 and L (2.52 × 10 nifH gene copies g of dry soil) at six leaves, whereas the situation was 46 47 243 opposite at flowering, with higher abundance in site L (40.7 × 107 nifH gene copies g-1 of dry 48 49 244 soil) than C (9.81 × 107 nifH gene copies g-1 of dry soil) and FC (5.66 × 107 nifH gene copies 50 51 -1 52 245 g of dry soil) (Figure 1DH). 53 54 246 When comparing the log numbers of nifH rhizobacteria and acdS rhizobacteria across 55 56 247 the 12 site × sampling combinations, significant (3.8 × 10-5 < P < 0.01) positive correlations 57 58 248 (0.67 < r < 0.98, n = 20) were found in 9 of 12 cases, with only three correlations that were not 59 60
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1 2 3 249 significant, i.e. in site C at 6-leaf stage in 2014 (P = 0.10, n = 5) and FC at flowering in 2014 4 5 6 250 (P = 0.67, n = 5) and 2015 (P = 0.19, n = 20) (Figure 2). In summary, moderate but significant 7 8 251 differences in the numbers of nifH and/or acdS rhizobacteria could take place according to field 9 10 252 site, sampling year and/or maize phenology, and in most cases a positive correlation was found 11 12 253 between the log values of both numbers. 13 14 15 254 16 17 255 3.2. Relation between diversities of nifH and acdS alleles in the three field sites 18 19 256 Illumina MiSeq sequencing of nifH and acdS (as well as rrs) was carried out on 15 rhizosphere 20 21 For Peer Review 22 257 samples from 6-leaf maize grown in 2015. For nifH, 1,342,966 reads were obtained (10,775 to 23 24 258 62,752 sequences per sample), for a total of 36,241 OTUs. Rarefaction analysis showed that 25 26 259 curves reached a plateau (Figure S1A). Subsampling was done with 10,775 sequences per 27 28 29 260 sample, for a total of 34,459 OTUs. For acdS, 5,490,230 reads were obtained (68,376 to 139,245 30 31 261 sequences per sample), with a total of 32,468 OTUs. Rarefaction curves reached a plateau 32 33 262 (Figure S1B). Subsampling was done with 68,376 sequences per sample, for a total of 26,246 34 35 263 OTUs. After quality filtering, 6,082,255 reads were obtained for rrs (51,696 to 223,926 36 37 38 264 sequences per sample), giving a total of 39,600 OTUs (3% cut-off). Rarefaction analysis 39 40 265 showed that the sequencing effort captured most of the diversity with curves reaching a plateau 41 42 266 (Figure S1C). Subsampling was done with 51,696 sequences per sample, for a total of 25,437 43 44 45 267 OTUs. 46 47 268 The effect of field site on nifH diversity of diazotrophic bacteria was not significant 48 49 269 based on analysis of Shannon and Simpson indices. Conversely, the effect of field site on acdS 50 51 -4 52 270 diversity of ACC deaminase bacteria was significant based on the Shannon (P = 1.9. × 10 ) 53 54 271 and Simpson indices (P = 8.6 × 10-4). The Shannon index was lower in FC (6.32) than in L 55 56 272 (6.82) and C (6.92), whereas the Simpson index was higher in FC (6.42 × 10-3) than in L (2.88 57 58 273 × 10-3) and C (2.38 × 10-3). The effect of field site on rrs diversity of the total bacterial 59 60
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1 2 3 274 community was significant based on the Shannon (P = 1.8 × 10-5) and Simpson indices (P =1.6 4 5 -4 6 275 × 10 ). As in the case of acdS data, the Shannon index was lower in FC (7.20) than in L (7.41) 7 8 276 and C (7.71), whereas the Simpson index was higher in FC (3.42 × 10-3) than in L (2.28 × 10- 9 10 277 3) and C (1.40 × 10-3). 11 12 278 The correlation (n = 5) between nifH diversity and acdS diversity was positive and 13 14 15 279 significant at site L, when considering both the Shannon index (r = 0.98 ; P = 0.01; Figure 3) 16 17 280 and the Simpson index (r = 0.86 ; P = 0.06; Figure 3). However, the correlation was not 18 19 281 significant at the other two sites, regardless of the diversity index. When considering also rrs 20 21 For Peer Review 22 282 diversity, a significant correlation was found only with nifH diversity at site C (r = 0.91 ; P = 23 24 283 0.03; Figure 3). In summary, there was no relation between the diversities of nifH rhizobacteria 25 26 284 and acdS rhizobacteria, based on comparison of diversity indices in the three field sites and 27 28 29 285 correlation analyses at two of the three field sites. 30 31 286 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 287 3.3. Relation between prevalence of nifH and/or acdS rhizobacterial taxa in the three field 4 5 6 288 sites 7 8 289 Between-class analysis of nifH data showed that the composition of diazotrophic bacteria 9 10 290 differed according to field site (Figure 4A). The first axis (54% of between-class variability) 11 12 291 distinguished site C from FC and L, and the 12 genera contributing most to this differentiation 13 14 15 292 were Xanthobacter, Dechloromonas, Methyloferula, Ideonella, Nitrospirillum and Tolumonas 16 17 293 (more prevalent in C than in L and FC), as well as Desulfovibrio, Selenomonas, 18 19 294 Ruminiclostridium, Paludibacter, Gloeocapsopsis and Ruminococcus (less prevalent in C than 20 21 For Peer Review 22 295 in FC and L). The second axis (46% of between-class variability) distinguished site L from the 23 24 296 two other sites, and the 12 genera contributing most to this differentiation included Rhizobium, 25 26 297 Gluconacetobacter, Skermanella, Leptothrix, Streptomyces and Methylocapsa (more prevalent 27 28 29 298 in L than in FC and C), as well as Marichromatium, Pelobacter, Gordonibacter, Desulfobulbus, 30 31 299 Desulfovibrio and Sideroxydan (less prevalent in L than in C and FC). 32 33 300 Between-class analysis of acdS data showed that the composition of ACC deaminase 34 35 301 bacteria differed according to field site (Figure 4B). The first axis (66% of between-class 36 37 38 302 variability) distinguished site C from FC and L, and the 12 genera contributing most to this 39 40 303 differentiation were Achromobacter, Azospirillum, Pseudolabrys, Roseovarius, one unassigned 41 42 304 OTU and Polaromonas (more prevalent in C than in L and FC), as well as Cupriavidus, 43 44 45 305 Burkholderia, Bosea, Bradyrhizobium and Methylobacterium (less prevalent in C than in FC 46 47 306 and L). The second axis (34% of between-class variability) distinguished each of the three sites 48 49 307 from one another, and the 12 genera contributing most to this differentiation included 50 51 52 308 Azorhizobium, Pseudomonas, Gluconobacter, Collimonas, Herbaspirillum and Burkholderia 53 54 309 (more prevalent in FC than in C and L), as well as Ralstonia, Loktanella, Devosia, Variovorax, 55 56 310 Novosphingobium and Chelatococcus (more prevalent in L than in C and FC). 57 58 59 60
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1 2 3 311 Between-class analysis of rrs data showed that the composition of the total bacterial 4 5 6 312 community differed according to field site (Figure 4C). The first axis (71% of between-class 7 8 313 variability) distinguished C from the two other sites, and the 12 genera contributing most to this 9 10 314 differentiation were Algisphaera, Fibrobacter, Amaricoccus, Hirschia, Desulfacinum and 11 12 315 Saccharophagus (more prevalent in C than in L and FC), as well as Actinomadura, Lutispora, 13 14 15 316 Bacillus, Rhodopseudomonas, Kouleothrix and Roseiflexus (less prevalent in C than in FC and 16 17 317 L). The second axis (29% of between-class variability) distinguished site L from FC and C, and 18 19 318 the 12 genera contributing most to this differentiation included Flavobacterium, 20 21 For Peer Review 22 319 Gluconobacter, Maricaulis, Prolixibacter, ‘Candidatus Xiphinematobacter’, Chthoniobacter 23 24 320 (more prevalent in FC than L), as well as Conexibacter, Hyphomicrobium, Pseudonocardia, 25 26 321 Tumebacillus, Chelatococcus and Mycobacterium (less prevalent in FC than in L). 27 28 29 322 In summary, between-class analysis of nifH and acdS data indicated that the 30 31 323 composition of diazotrophic bacteria and of ACC deaminase bacteria differed according to field 32 33 324 site, but the main discriminant genera differed completely for both types of bacteria. In both 34 35 325 cases, the discriminant taxa were also different from the main range of bacterial taxa 36 37 38 326 distinguishing the three sites most when comparing the latter based on rrs data, at the scale of 39 40 327 the entire rhizobacterial community. 41 42 328 43 44 45 329 3.4. Relation between the genetic structures of nifH and acdS rhizobacteria in the three 46 47 330 field sites 48 49 331 Since there was a positive correlation between log numbers of nifH and/or acdS rhizobacteria 50 51 52 332 but the corresponding bacterial genera discriminating most between the three fields studied 53 54 333 were not the same, the co-structuration between nifH and acdS diversity was explored by co- 55 56 334 inertia analysis to compare more globally the genetic structures of these rhizobacterial groups 57 58 335 across the three field sites. Monte-Carlo permutation tests showed a significant co-structuration 59 60
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1 2 3 336 (P = 9 × 10-5) of nifH and acdS rhizobacteria, with a RV coefficient of 0.83. This accounted for 4 5 6 337 57% of data variability. The plot of the co-inertia matrix illustrates the strength of the 7 8 338 relationship between acdS and nifH diversities, as superposition of acdS and nifH groups 9 10 339 showed a strong co-trend in all three field sites (Figure 5). 11 12 340 Co-inertia analyses of nifH and acdS diversities were also performed with rrs diversity, 13 14 15 341 and permutations tests also showed co-structuration in both cases, with respectively RV 16 17 342 coefficients of 0.89 and 0.91, the two axes explaining 52% and 69% of variability. 18 19 343 Superposition of rrs community with acdS and with nifH groups indicated a strong co-trend 20 21 For Peer Review 22 344 across the three fields. 23 24 345 In summary, the genetic structures of nifH and acdS rhizobacterial groups across the 25 26 346 three field sites were very close. Co-inertia was strong also when comparing each with the 27 28 29 347 whole rhizobacterial community based on rrs data. 30 31 348 32 33 349 4. DISCUSSION 34 35 350 36 37 38 351 The current work made use of molecular tools available to characterize functional groups of 39 40 352 nifH and acdS bacteria. Quantification of nifH rhizobacteria was performed with primers 41 42 353 PolF/PolR (Poly et al. 2001) rather than other well-established primers such as Zf/Zr (Zehr and 43 44 45 354 McReynolds, 1989) since the latter are not effective for quantitative PCR (Boyd and Peters 46 47 355 2013; Gaby and Buckley 2017; Poly et al. 2001). The same primers have also been used for 48 49 356 sequencing, both for consistency and efficacy for diazotroph characterization (Mårtensson et 50 51 52 357 al. 2009; Wartiainen et al. 2008). Recently, acdS primers suitable for monitoring of ACC 53 54 358 deamination bacteria have been made available (Bouffaud et al. 2018). These primers are 55 56 359 effective to amplify true acdS genes while not amplifying related D-cystein desulfhydrase genes 57 58 360 coding for other PLP-dependent enzymes, which was verified again in the current work (Figure 59 60
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1 2 3 361 S2). Indeed, phylogenetic analysis of the acdS sequences showed that none clustered within the 4 5 6 362 out-group (built with strains harboring D-cystein desulfhydrase genes), confirming that the 7 8 363 sequences obtained were true acdS sequences, as highlighted in previous studies (Blaha et al. 9 10 364 2006; Bouffaud et al. 2018; Li et al. 2015; Nascimento et al. 2012). 11 12 365 The level of taxonomic information carried by nifH sequences has been described in the 13 14 15 366 literature, showing that nifH was sufficiently conserved to enable reliable taxonomic affiliations 16 17 367 including for the assessment of rhizobacteria (Vinuesa et al. 2005), and its phylogeny was 18 19 368 congruent with the one derived from rrs (Achouak et al. 1999; Zehr et al. 2003). As for acdS, 20 21 For Peer Review 22 369 phylogenetic analysis of the new sequences obtained (along with reference acdS sequences) 23 24 370 confirmed that the taxonomic affiliations made at the genus level were correct. However, the 25 26 371 130-bp acdS amplicons obtained with the current quantitative PCR primers do not enable any 27 28 29 372 taxonomic affiliation below the genus level, i.e. at the species level (Bouffaud et al. 2018). 30 31 373 In this work, the hypothesis that nifH and acdS rhizobacterial populations co-occur on 32 33 374 roots was assessed with maize taken from three fields, using quantitative PCR and MiSeq 34 35 375 sequencing. The results that were obtained did substantiate this hypothesis, based on (i) positive 36 37 38 376 correlations between the sizes of nifH and acdS rhizobacterial groups, and (ii) comparable 39 40 377 genetic structures indicated by inertia analysis for both functional groups across the three field 41 42 378 sites studied. Several studies have assessed the co-occurrence of particular microorganisms and 43 44 45 379 measured between-taxa correlations in soil systems (Barberán et al. 2011; Freilich et al. 2010), 46 47 380 but few have done so at the level of functional groups. For instance, co-occurrence analysis of 48 49 381 nitrite-dependent anaerobic ammonium oxidizers and methane oxidizers in paddy soil showed 50 51 52 382 that the structure of these communities changed with soil depth (Wang et al. 2012). The co- 53 54 383 occurrence of plant-beneficial functions in the rhizosphere has been investigated, but often the 55 56 384 assessment was restrained to narrow taxonomic levels, such as within the Pseudomonas genus 57 58 385 (Almario et al. 2014; Frapolli et al. 2012; Vacheron et al. 2016). It is interesting to note that not 59 60
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1 2 3 386 all microorganisms harboring acdS and/or nifH expressed the corresponding functions in 4 5 6 387 rhizosphere based on assessment of qRT-PCR data, as previously described for nifH (Bouffaud 7 8 388 et al. 2016) or acdS (Bouffaud et al. 2018). 9 10 389 Specific taxa can be selected by environmental conditions prevailing on plant roots 11 12 390 (Bakker et al. 2014; Berg and Smalla, 2009; Raaijmakers et al. 2009; Vandenkoornhuyse et al. 13 14 15 391 2015). Thus, a first possibility to account for the co-occurrence of both functional groups could 16 17 392 be that both nifH bacteria and acdS bacteria do well in the maize rhizosphere. Indeed, both types 18 19 393 of bacteria are readily found on roots (Almario et al. 2014; Arruda et al. 2013; Blaha et al. 2006; 20 21 For Peer Review 22 394 Bruto et al. 2014; Bruto et al. 2014; Mårtensson et al. 2009). Such co-occurrence would make 23 24 395 sense in ecological terms, because associative nitrogen fixation and ACC deamination are 25 26 396 functions limiting plant nutrient deficiency by supplying nitrogen (Pii et al. 2015) and 27 28 29 397 enhancing root system development (thereby improving uptake of mineral nutriments including 30 31 398 nitrogen) (Glick, 2014), respectively. 32 33 399 A second possibility could be that bacteria that harbor both genes/functions are well 34 35 400 adapted to maize roots. Indeed, Bruto et al. (2014) showed that the nif operon co-occurred with 36 37 38 401 acdS in several bacterial clades, and for instance the genera Bradyrhizobium or Burkholderia 39 40 402 contain several species harboring both functions (Bruto et al. 2014). Furthermore, the co-inertia 41 42 403 between these two functional groups and the total community raises the possibility that 43 44 45 404 additional functions could also be present in addition to associative nitrogen fixation and ACC 46 47 405 deamination. Indeed, comparative genomics studies showed that bacterial taxa display multiple 48 49 406 specific functions, including plant interaction functions (Bruto et al. 2014; Lassalle et al. 2015; 50 51 52 407 Vacheron et al. 2017), and thus these functions would also be co-selected when selecting the 53 54 408 corresponding rrs-based taxa. In the current study, Bradyrhizobium represented 17 to 25% of 55 56 409 acdS+ bacteria and 20 to 42% of nifH+ bacteria in the maize rhizosphere, and the high proportion 57 58 410 of this bacterial clade may contribute to the co-occurence of diazotrophs and ACC deaminase 59 60
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1 2 3 411 producers that was found. However, when the 10,369 completely-sequenced bacterial genomes 4 5 6 412 available in the NCBI database were screened, it showed that 833 of them harbored acdS and 7 8 413 461 others nifH, but only 122 genomes had both genes. Therefore, it could be that this second 9 10 414 possibility is insufficient for a complete explanation of the current findings. 11 12 415 A third possibility to consider is the joint occurrence of both functions in the 13 14 15 416 rhizosphere, regardless of the taxa harboring them, thereby providing functional redundancy 16 17 417 (Shade and Handelsman, 2012). Several studies in soil or aquatic settings have suggested that 18 19 418 the metabolic/functional potential of microbial communities rather than their taxonomic 20 21 For Peer Review 22 419 variations are closely related to environmental conditions (Bouffaud et al. 2018; Burke et al. 23 24 420 2011; Louca et al. 2016; Louca et al. 2017 ). These observations were conceptualized as the 25 26 421 "It's the song, not the singer" theory (ITSNTS; Doolittle and Booth 2017), i.e. functional groups 27 28 29 422 within microbial communities (the songs) would be better conserved and more relevant 30 31 423 ecologically than the taxa themselves (the singers). Consistent with the ITSNTS theory, our 32 33 424 study suggests that the assembly of the rhizosphere microbial community would entail a balance 34 35 425 between phytostimulation-relevant genes, which may be needed to achieve an effective 36 37 38 426 holobiont (i.e., the plant host and its functional microbiota), and points to the preponderance of 39 40 427 functional interactions within the plant holobiont. This hypothesis, which has been put forward 41 42 428 recently for root-associated microorganisms (Lemanceau et al. 2017), remains speculative at 43 44 45 429 this stage and deserves further research attention. In particular, methodology development is 46 47 430 needed to enable direct assessment of key plant-beneficial groups when parallel monitoring of 48 49 431 several genes is required (e.g. for auxin production or P solubilization, which entail many 50 51 52 432 genetic pathways), in contrast to ACC deamination and N fixation for which analysis of a single 53 54 433 gene (acdS and nifH, respectively) may suffice. 55 56 434 To test whether the current findings could be also relevant under other environmental 57 58 435 conditions, we reassessed the data obtained for nifH (Bouffaud et al. 2016) and acdS (Bouffaud 59 60
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1 2 3 436 et al. 2018) from two maize lines grown in another soil (luvisol) with different management 4 5 6 437 histories (cropped soil vs meadow soil). A positive correlation (r = 0.45 ; P = 0.050 ; n = 20) 7 8 438 was found between the numbers of nifH and acdS bacteria in the monocropping soil but not in 9 10 439 meadow soil (P = 0.75 ; n = 10), suggesting that maize monocropping history could have been 11 12 440 an important factor. However, these findings were obtained with young plants only (21 days), 13 14 15 441 grown in sieved soil under greenhouse conditions. 16 17 442 In conclusion, the current findings indicate that rhizobacteria with nitrogen fixation 18 19 443 capacity and counterparts harboring ACC deamination ability co-occur in the maize 20 21 For Peer Review 22 444 rhizosphere, pointing to the possibility that plants may rely on multiple, complementary 23 24 445 phytostimulatory functions provided by their microbial partners. Additional method 25 26 446 development is needed to extend this type of assessment to additional phytostimulatory groups 27 28 29 447 and other microbial functional groups important for plant performance. 30 31 448 32 33 449 ACKNOWLEDGEMENTS 34 35 450 This work was supported in part by project Azodure (ANR Agrobiosphère ANR-12-AGRO- 36 37 38 451 0008). We are grateful to J. Haurat and H. Brunet for technical help, as well as D. Abrouk 39 40 452 (iBio platform, UMR CNRS 5557 Écologie Microbienne) and J. Thioulouse (UMR CNRS 41 42 453 LBBE) for helpful discussion. The authors declare no conflict of interest. 43 44 45 454 46 47 455 DATA ACCESSIBILITY 48 49 456 Illumina MiSeq paired-end reads have been deposited in the European Bioinformatics Institute 50 51 52 457 (EBI) database under accession numbers PRJEB14347 (ERP015984) for rrs; PRJEB14346 53 54 458 (ERP015983) for nifH, PRJEB14343 (ERP015981) for acdS. 55 56 459 57 58 460 AUTHOR CONTRIBUTIONS 59 60
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1 2 3 461 LL, YML and DM designed the project, SR, LL, CPC, YML and DM carried out field work 4 5 6 462 and samplings, SR conducted the molecular work, SR, MLB and AD implemented 7 8 463 bioinformatic analyses, SR, YML and DM analyzed data, SR, YML and DM prepared the first 9 10 464 draft of the manuscript, which was finalized by all authors. 11 12 465 13 14 15 16 17 18 19 20 21 For Peer Review 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 466 REFERENCES 4 5 6 467 7 8 468 Achouak W, Normand P, Heulin T. Comparative phylogeny of rrs and nifH genes in the Bacillaceae. 9 10 469 Int J Syst Evol Microbiol 1999;49(3):961-967. doi:10.1099/00207713-49-3-961 11 12 13 470 Agaras BC, Scandiani M, Luque A et al. Quantification of the potential biocontrol and direct plant 14 15 471 growth promotion abilities based on multiple biological traits distinguish different groups of 16 17 472 Pseudomonas spp. isolates. Biological Control 2015;90:173-186. 18 19 473 doi:https://doi.org/10.1016/j.biocontrol.2015.07.003 20 21 474 Almario J, Gobbin D, DéfagoFor G et al. Peer Prevalence ofReview type III secretion system in effective biocontrol 22 23 475 pseudomonads. Res Microbiol 2014;165(4):300-304. 24 25 476 doi:https://doi.org/10.1016/j.resmic.2014.03.008 26 27 477 Almario J, Muller D, Défago G et al. Rhizosphere ecology and phytoprotection in soils naturally 28 29 478 suppressive to Thielaviopsis black root rot of tobacco. Environ Microbiol 2014;16(7):1949- 30 31 32 479 1960. doi:10.1111/1462-2920.12459 33 34 480 Arruda L, Beneduzi A, Martins A et al. Screening of rhizobacteria isolated from maize (Zea mays L.) 35 36 481 in Rio Grande do Sul State (South Brazil) and analysis of their potential to improve plant 37 38 482 growth. Appl Soil Ecol 2013;63:15-22. doi:https://doi.org/10.1016/j.apsoil.2012.09.001 39 40 483 Bakker MG, Schlatter DC, Otto-Hanson L et al. Diffuse symbioses: roles of plant–plant, plant– 41 42 484 microbe and microbe–microbe interactions in structuring the soil microbiome. Mol Ecol 43 44 485 2014;23(6):1571-1583. doi:10.1111/mec.12571 45 46 486 Barberán A, Bates ST, Casamayor EO et al. Using network analysis to explore co-occurrence patterns 47 48 487 in soil microbial communities. ISME J 2011;6:343–351. doi:10.1038/ismej.2011.119 49 50 51 488 https://www.nature.com/articles/ismej2011119#supplementary-information 52 53 489 Bashan Y, de-Bashan LE. Chapter Two - How the plant growth-promoting bacterium Azospirillum 54 55 490 promotes plant growth—A critical assessment. In: Sparks DL (ed). Advances in Agronomy. 56 57 58 491 Academic Press, 2010, 108, 77-136. 59 60
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1 2 3 492 Bashan Y, de-Bashan LE, Prabhu SR et al. Advances in plant growth-promoting bacterial inoculant 4 5 493 technology: formulations and practical perspectives (1998–2013). Plant Soil 2014;378(1):1- 6 7 494 33. doi:10.1007/s11104-013-1956-x 8 9 495 Berg G, Smalla K. Plant species and soil type cooperatively shape the structure and function of 10 11 12 496 microbial communities in the rhizosphere. FEMS Microbiol Ecol 2009;68(1):1-13. 13 14 497 doi:10.1111/j.1574-6941.2009.00654.x 15 16 498 Bertalan M, Albano R, de Pádua V et al. Complete genome sequence of the sugarcane nitrogen-fixing 17 18 499 endophyte Gluconacetobacter diazotrophicus Pal5. BMC Genomics 2009;10(1):450. 19 20 500 doi:10.1186/1471-2164-10-450 21 For Peer Review 22 501 Blaha D, Prigent-Combaret C, Mirza MS et al. Phylogeny of the 1-aminocyclopropane-1-carboxylic 23 24 502 acid deaminase-encoding gene acdS in phytobeneficial and pathogenic Proteobacteria and 25 26 503 relation with strain biogeography. FEMS Microbiol Ecol 2006;56(3):455-470. 27 28 504 doi:10.1111/j.1574-6941.2006.00082.x 29 30 31 505 Bouffaud M-L, Renoud S, Dubost A et al. 1-Aminocyclopropane-1-carboxylate deaminase producers 32 33 506 associated to maize and other Poaceae species. Microbiome 2018;6(1):114. 34 35 507 doi:10.1186/s40168-018-0503-7 36 37 508 Bouffaud M-L, Renoud S, Moënne-Loccoz Y et al. Is plant evolutionary history impacting recruitment 38 39 509 of diazotrophs and nifH expression in the rhizosphere? Sci Rep 2016;6:21690. 40 41 510 doi:10.1038/srep21690 http://www.nature.com/articles/srep21690#supplementary-information 42 43 511 Boyd E, Peters J. New insights into the evolutionary history of biological nitrogen fixation. Front 44 45 512 Microbiol 2013;4:201. doi:10.3389/fmicb.2013.00201 46 47 513 Bruto M, Prigent-Combaret C, Luis P et al. Frequent, independent transfers of a catabolic gene from 48 49 bacteria to contrasted filamentous eukaryotes. Proc R Soc Lond B: Biol Sci 2014;281:1789. 50 514 51 52 515 doi:10.1098/rspb.2014.0848 53 54 516 Bruto M, Prigent-Combaret C, Muller D et al. Analysis of genes contributing to plant-beneficial 55 56 517 functions in plant growth-promoting rhizobacteria and related Proteobacteria. Sci Rep 57 58 518 2014;4:6261. doi:10.1038/srep06261 59 60
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1 2 3 519 Burke C, Steinberg P, Rusch D et al. Bacterial community assembly based on functional genes rather 4 5 520 than species. Proc Natl Acad Sci USA 2011;108(34):14288-14293. 6 7 521 doi:10.1073/pnas.1101591108 8 9 10 522 Cassán F, Vanderleyden J, Spaepen S. Physiological and agronomical aspects of phytohormone 11 12 523 production by model Plant-Growth-Promoting Rhizobacteria (PGPR) belonging to the genus 13 14 524 Azospirillum. J. Plant Growth Regul 2014;33(2):440-459. doi:10.1007/s00344-013-9362-4 15 16 17 525 Chen XH, Koumoutsi A, Scholz R et al. Comparative analysis of the complete genome sequence of 18 19 526 the plant growth–promoting bacterium Bacillus amyloliquefaciens FZB42. Nature Biotechnol 20 21 527 2007;25:1007. doi:10.1038/nbt1325For Peer https://www.nature.com/articles/nbt1325#supplementary- Review 22 23 528 information 24 25 26 529 Chessel D, Dufour AB, Thioulouse J. The ade4 package-I-One-table methods. R News 2004;4(1):5-10. 27 28 530 Cormier F, Foulkes J, Hirel B et al. Breeding for increased nitrogen-use efficiency: a review for wheat 29 30 531 (T. aestivum L.). Plant Breeding 2016;135(3):255-278. doi:10.1111/pbr.12371 31 32 532 Couillerot O, Ramírez-Trujillo A, Walker V et al. Comparison of prominent Azospirillum strains in 33 34 533 Azospirillum–Pseudomonas–Glomus consortia for promotion of maize growth. Appl 35 36 534 Microbiol Biotechnol 2013;97(10):4639-4649. doi:10.1007/s00253-012-4249-z 37 38 39 535 Culhane AC, Thioulouse J, Perrière G et al. MADE4: an R package for multivariate analysis of gene 40 41 536 expression data. Bioinformatics 2005;21(11):2789-2790. doi:10.1093/bioinformatics/bti394 42 43 537 Deynze A, Zamora P, Delaux P-M et al. Nitrogen fixation in a landrace of maize is supported by a 44 45 538 mucilage-associated diazotrophic microbiota. PLoS Biol 2018;16(8):e2006352. https://doi- 46 47 539 org.inee.bib.cnrs.fr/10.1371/journal.pbio.2006352 48 49 540 DeSantis TZ, Hugenholtz P, Larsen N et al. Greengenes, a chimera-checked 16S rRNA gene database 50 51 and workbench compatible with ARB. Appl Environ Microbiol 2006;72(7):5069-5072. 52 541 53 54 542 doi:10.1128/aem.03006-05 55 56 543 Doolittle WF, Booth A. It’s the song, not the singer: an exploration of holobiosis and evolutionary 57 58 544 theory. Biol Philos 2017;32(1):5-24. doi:10.1007/s10539-016-9542-2 59 60
23 ScholarOne Support 1-434/964-4100 FEMS Microbiology Ecology Page 72 of 84
1 2 3 545 Dray S, Chessel D, Thioulouse J. Co-inertia analysis and the linking of ecological data tables. Ecology 4 5 546 2003;84(11):3078-3089. doi:10.1890/03-0178 6 7 547 Dray S, Dufour AB, Chessel D. The ade4 Package—II: Two-table and K-table methods. R News 8 9 548 2007;7:47-52. 10 11 12 549 Duan J, Müller KM, Charles T et al. 1-aminocyclopropane-1-carboxylate (ACC) deaminase genes in 13 14 550 Rhizobia from southern Saskatchewan. Microb Ecol 2009;57(3):423-436. 15 16 551 doi:10.1007/s00248-008-9407-6 17 18 552 Frapolli M, Pothier JF, Défago G et al. Evolutionary history of synthesis pathway genes for 19 20 553 phloroglucinol and cyanide antimicrobials in plant-associated fluorescent pseudomonads. Mol 21 For Peer Review 22 554 Phylogenet Evol 2012;63(3):877-890. doi:https://doi.org/10.1016/j.ympev.2012.02.030 23 24 555 Freilich S, Kreimer A, Meilijson I et al. The large-scale organization of the bacterial network of 25 26 556 ecological co-occurrence interactions. Nucleic Acids Res 2010;38(12):3857-3868. 27 28 557 doi:10.1093/nar/gkq118 29 30 31 558 Gaby JC, Buckley DH. The use of degenerate primers in qPCR analysis of functional genes can cause 32 33 559 dramatic quantification bias as revealed by investigation of nifH primer performance. Microb 34 35 560 Ecol 2017;74(3):701-708. doi:10.1007/s00248-017-0968-0 36 37 561 Gamalero E, Glick BR. Bacterial modulation of plant ethylene levels. Plant Physiol 2015;169(1):13- 38 39 562 22. doi:10.1104/pp.15.00284 40 41 563 Glick BR. Bacteria with ACC deaminase can promote plant growth and help to feed the world. 42 43 564 Microbiol Res 2014;169(1):30-39. doi:https://doi.org/10.1016/j.micres.2013.09.009 44 45 565 Hartman K, van der Heijden MGA, Wittwe RA et al. Cropping practices manipulate abundance 46 47 566 patterns of root and soil microbiome members paving the way to smart farming. Microbiome 48 49 2018;6(1):14. doi:10.1186/s40168-017-0389-9 50 567 51 52 568 Jha B, Gontia I, Hartmann A. The roots of the halophyte Salicornia brachiata are a source of new 53 54 569 halotolerant diazotrophic bacteria with plant growth-promoting potential. Plant Soil 55 56 570 2012;356(1):265-277. doi:10.1007/s11104-011-0877-9 57 58 59 60
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1 2 3 571 Lassalle F, Muller D, Nesme X. Ecological speciation in bacteria: reverse ecology approaches reveal 4 5 572 the adaptive part of bacterial cladogenesis. Res Microbiol 2015;166(10):729-741. 6 7 573 doi:https://doi.org/10.1016/j.resmic.2015.06.008 8 9 574 Lemanceau P, Blouin M, Muller D, et al. Let the core microbiota be functional. Trends Plant Sci 10 11 12 575 2017;22(7):583-595. doi:https://doi.org/10.1016/j.tplants.2017.04.008 13 14 576 Li Z, Chang S, Ye S et al. Differentiation of 1-aminocyclopropane-1-carboxylate (ACC) deaminase 15 16 577 from its homologs is the key for identifying bacteria containing ACC deaminase. FEMS 17 18 578 Microbiol Ecol 2015;91(10):fiv112-fiv112. doi:10.1093/femsec/fiv112 19 20 579 Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. 21 For Peer Review 22 580 Science 2016;353(6305):1272-1277. doi:10.1126/science.aaf4507 23 24 581 Louca S, Jacques SMS, Pires APF et al. High taxonomic variability despite stable functional structure 25 26 582 across microbial communities. Nat Ecol Evol 2017;1:0015. doi:10.1038/s41559-016-0015 27 28 583 Ma W, Guinel FC, Glick, BR. Rhizobium leguminosarum biovar viciae 1-aminocyclopropane-1- 29 30 31 584 carboxylate deaminase promotes nodulation of pea plants. Appl Environ Microbiol 32 33 585 2003;69(8):4396-4402. doi:10.1128/aem.69.8.4396-4402.2003 34 35 586 Mårtensson L, Díez B, Wartiainen I et al. Diazotrophic diversity, nifH gene expression and 36 37 587 nitrogenase activity in a rice paddy field in Fujian, China. Plant Soil 2009;325(1):207-218. 38 39 588 doi:10.1007/s11104-009-9970-8 40 41 589 Nascimento FX, Brígido C, Glick BR et al. ACC deaminase genes are conserved among 42 43 590 Mesorhizobium species able to nodulate the same host plant. FEMS Microbiol Lett 44 45 591 2012;336(1):26-37. doi:10.1111/j.1574-6968.2012.02648.x 46 47 592 Nukui N, Minamisawa K, Ayabe S-I et al. Expression of the 1-aminocyclopropane-1-carboxylic acid 48 49 deaminase gene requires symbiotic nitrogen-fixing regulator gene nifA2 in Mesorhizobium loti 50 593 51 52 594 MAFF303099. Appl Environ Microbiol 2006;72(7):4964-4969. doi:10.1128/aem.02745-05 53 54 595 Pieterse CMJ, Van Wees SCM. Induced disease resistance. In: Lugtenberg B (ed). Principles of Plant- 55 56 596 Microbe Interactions: Microbes for Sustainable Agriculture. Springer International 57 58 597 Publishing, 2015, 123-33. 59 60
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1 2 3 598 Pii Y, Mimmo T, Tomasi N et al. Microbial interactions in the rhizosphere: beneficial influences of 4 5 599 plant growth-promoting rhizobacteria on nutrient acquisition process. A review. Biol Fertil 6 7 600 Soils 2015;51(4):403-415. doi:10.1007/s00374-015-0996-1 8 9 601 Poly F, Jocteur Monrozier L, Bally R. Improvement in the RFLP procedure for studying the diversity 10 11 12 602 of nifH genes in communities of nitrogen fixers in soil. Res Microbiol 2001;152(1):95-103. 13 14 603 doi:https://doi.org/10.1016/S0923-2508(00)01172-4 15 16 604 Prigent-Combaret C, Blaha D, Pothier J et al. Physical organization and phylogenetic analysis of acdR 17 18 605 as leucine-responsive regulator of the 1-aminocyclopropane-1-carboxylate deaminase gene 19 20 606 acdS in phytobeneficial Azospirillum lipoferum 4B and other Proteobacteria. FEMS 21 For Peer Review 22 607 Microbiol Ecol 2008;65(2):202-219. doi:10.1111/j.1574-6941.2008.00474.x 23 24 608 Puri A, Padda KP, Chanway CP. Evidence of nitrogen fixation and growth promotion in canola 25 26 609 (Brassica napus L.) by an endophytic diazotroph Paenibacillus polymyxa P2b-2R. Biol Fertil 27 28 610 Soils 2016;52(1):119-125. doi:10.1007/s00374-015-1051-y 29 30 31 611 Raaijmakers JM, Paulitz TC, Steinberg C et al. The rhizosphere: a playground and battlefield for 32 33 612 soilborne pathogens and beneficial microorganisms. Plant Soil 2009;321(1):341-361. 34 35 613 doi:10.1007/s11104-008-9568-6 36 37 614 Rana A, Saharan B, Joshi M et al. Identification of multi-trait PGPR isolates and evaluating their 38 39 615 potential as inoculants for wheat. Ann Microbiol 2011;61(4):893-900. doi:10.1007/s13213- 40 41 616 011-0211-z 42 43 617 Redondo-Nieto M, Barret M, Morrissey J et al. Genome sequence reveals that Pseudomonas 44 45 618 fluorescens F113 possesses a large and diverse array of systems for rhizosphere function and 46 47 619 host interaction. BMC Genomics 2013;14(1):54. doi:10.1186/1471-2164-14-54 48 49 Rozier C, Hamzaoui J, Lemoine D et al. Field-based assessment of the mechanism of maize yield 50 620 51 52 621 enhancement by Azospirillum lipoferum CRT1. Sci Rep 2017;7(1):7416. doi:10.1038/s41598- 53 54 622 017-07929-8 55 56 623 Shade A, Handelsman J. Beyond the Venn diagram: the hunt for a core microbiome. Environ 57 58 624 Microbiol 2012;14(1):4-12. doi:10.1111/j.1462-2920.2011.02585.x 59 60
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1 2 3 625 Team, R. R: A Language and Environment for Statistical Computing 2014. Vienna, Austria: R 4 5 626 Foundation for Statistical Computing. 6 7 627 Vacheron J, Desbrosses G, Bouffaud M-L et al. Plant growth-promoting rhizobacteria and root system 8 9 628 functioning. Front Plant Sci 2013;4:356. doi:10.3389/fpls.2013.00356 10 11 12 629 Vacheron J, Desbrosses G, Renoud S et al. Differential contribution of plant-beneficial functions from 13 14 630 Pseudomonas kilonensis F113 to root system architecture alterations in Arabidopsis thaliana 15 16 631 and Zea mays. Mol Plant-Microbe Interact 2017;31(2):212-223. doi:10.1094/MPMI-07-17- 17 18 632 0185-R 19 20 633 Vacheron J, Moënne-Loccoz Y, Dubost A et al. Fluorescent Pseudomonas strains with only few plant- 21 For Peer Review 22 634 beneficial properties are favored in the maize rhizosphere. Front Plant Sci 2016;7:1212. 23 24 635 doi:10.3389/fpls.2016.01212 25 26 636 Vandenkoornhuyse P, Quaiser A, Duhamel et al. The importance of the microbiome of the plant 27 28 637 holobiont. New Phytol 2015;206(4):1196-1206. doi:doi:10.1111/nph.13312 29 30 31 638 Vinuesa P, Silva C, Lorite MJ et al. Molecular systematics of rhizobia based on maximum likelihood 32 33 639 and Bayesian phylogenies inferred from rrs, atpD, recA and nifH sequences, and their use in 34 35 640 the classification of Sesbania microsymbionts from Venezuelan wetlands. Syst Appl Microbiol 36 37 641 2005;28(8):702-716. doi:https://doi.org/10.1016/j.syapm.2005.05.007 38 39 642 Wang Y, Zhu G, Harhangi HR et al. Co-occurrence and distribution of nitrite-dependent anaerobic 40 41 643 ammonium and methane-oxidizing bacteria in a paddy soil. FEMS Microbiol Lett 42 43 644 2012;336(2):79-88. doi:10.1111/j.1574-6968.2012.02654.x 44 45 645 Wartiainen I, Eriksson T, Zheng W et al. Variation in the active diazotrophic community in rice 46 47 646 paddy—nifH PCR-DGGE analysis of rhizosphere and bulk soil. Appl Soil Ecol 2008;39(1):65- 48 49 75. doi:https://doi.org/10.1016/j.apsoil.2007.11.008 50 647 51 52 648 Wisniewski-Dyé F, Lozano L, Acosta-Cruz E et al. Genome sequence of Azospirillum brasilense 53 54 649 CBG497 and comparative analyses of Azospirillum core and accessory genomes provide 55 56 650 insight into niche adaptation. Genes 2012;3(4):576. 57 58 59 60
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1 2 3 651 Zehr JP, Jenkins BD, Short SM et al. Nitrogenase gene diversity and microbial community structure: a 4 5 652 cross-system comparison. Envirol Microbiol 2003;5(7):539-554. doi:10.1046/j.1462- 6 7 653 2920.2003.00451.x 8 9 654 Zehr JP, McReynolds LA. Use of degenerate oligonucleotides for amplification of the nifH gene from 10 11 12 655 the marine cyanobacterium Trichodesmium thiebautii. Appl Environ Microbiol 13 14 656 1989;55(10):2522-2526. 15 16 657 17 18 19 20 21 For Peer Review 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 658 Legend 4 5 6 659 7 8 660 FIGURE 1. Size of the acdS and nifH functional groups compared in the three field sites L, FC 9 10 661 and C over four sampling times. Means and standard deviations are shown for the acdS group 11 12 662 at 6 leaves in 2014 (A) and 2015 (B) and at flowering in 2014 (E) and 2015 (F) and for the nifH 13 14 15 663 group at 6 leaves in 2014 (C) and 2015 (D) and at flowering in 2014 (G) and 2015 (H). The 16 17 664 analysis was done using pooled samples of six roots systems (n= 5) at FC and C and individual 18 19 665 root systems (n = 30) at L in 2014, and individual root systems (n = 20) at all three sites in 2015. 20 21 For Peer Review 22 666 Statistical differences between sites are indicated by letters a-c (ANOVA and Fischer’s LSD 23 24 667 tests, P < 0.05). 25 26 668 27 28 29 669 FIGURE 2. Correlation between log numbers of nifH (X axis) and acdS genes (Y axis). 30 31 670 Correlation was established using the Pearson coefficient. The analysis was done using pooled 32 33 671 samples of six roots systems (n= 5) at FC and C and individual root systems (n = 30) at L in 34 35 672 2014, and individual root systems (n = 20) at all three sites in 2015. 36 37 38 673 39 40 674 FIGURE 3. Correlation between Shannon diversity indices of nifH and acdS (A), Simpson 41 42 675 diversity indices of nifH and acdS (B), Shannon diversity indices of rrs and acdS or nifH (C), 43 44 45 676 and Simpson diversity indices of rrs and acdS or nifH (D). Correlation was established 46 47 677 separately at each of the three field sites L, FC and C, using the Pearson coefficient (n = 5). 48 49 678 50 51 52 679 FIGURE 4. Comparison of nifH (A), acdS (B) and rrs (C) diversity between sites L, FC and C 53 54 680 by between-class analysis. Red circles, green triangles and blue squares are used for samples 55 56 681 from sites FC, C and L, respectively. The curves at the top and the left of the panels show the 57 58 682 distribution of samples on respectively the X and Y axes. 59 60
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1 2 3 683 4 5 6 684 FIGURE 5. Co-inertia analysis between acdS and nifH diversities (A), rrs and nifH diversities 7 8 685 (B) and rrs and acdS diversities (C). Projection of the samples (n = 5) is based on both acdS 9 10 686 (Blue) and nifH (Green), rrs (Grey) and nifH (Green), or rrs (Grey) and acdS (Blue) diversity 11 12 687 variables (level = genus) into a same factorial plan. The vector in black shows the strength of 13 14 15 688 co-trends between the two barycenters of variables as related to each site (L, FC, C). Shorter 16 17 689 vectors indicate stronger convergent trends between the two variable groups. 18 19 690 20 21 For Peer Review 22 691 23 24 692 FIGURE S1: Rarefaction curves for nifH (A), acdS (B) and rrs (C) genes. 25 26 693 27 28 29 694 FIGURE S2. RAxML bipartition tree of 3322 sequenced acdS alleles from Poaceae 30 695 rhizosphere. The tree was visualized using iTOL software (Letunic I, Bork P. Interactive Tree 31 32 696 Of Life (iTOL) v4: recent updates and new developments (2019) Nucleic Acids Res doi: 33 34 697 10.1093/nar/gkz239). Branches colored in violet represent the out-group of D-cystein 35 698 desulfhydrase genes, whereas acdS alleles affiliated to Betaproteobacteria are shown in khaki, 36 37 699 to Gammaproteobacteria in blue, to Actinobacteria in green, to Alphaproteobacteria in red, 38 39 700 and to microeukaryotes in orange. The tree can be viewed online at the following link 40 41 701 http://itol.embl.de/shared/acdStree. 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 4 5 6 Table 1. Field characteristics of the top (5-30 cm) soil layer. 7 8 Field Soil type Texture (%) pH Organic C Total N C/N ratio Cation exchange (cmol/kg) 9 10 a 2+ 2+ + Sand Silt Clay H2O KCl (g/kg) (g/kg) CEC Ca Mg K 11 12 13 FC Fluvic cambisol 26.9 38.3 34.7 7.1 6.3 31.6 3.4 9.3 22.8 21.2 0.67 0.38 14 For Peer Review 15 L Luvisol 42.9 42.9 14.2 7.3 6.7 21.5 1.6 13.4 93.0 10.5 0.33 0.43 16 17 C Calcisol 15.6 74.1 10.3 8.2 7.7 25.9 3.1 8.4 97.0 36.1 0.24 0.29 18 19 aCEC, cation exchange capacity. 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 31 43 ScholarOne Support 1-434/964-4100 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 FEMS Microbiology Ecology Page 80 of 84
1 2 3 4 5 A B C D 6 6 leaves 2014 6 leaves 2015 6 leaves 2014 6 leaves 2015 / g / g
s 9 9 s 10 10 e e
i i a l l i p p
i b
7 o o o 9 a 9 o b s
c 8 c s 8 a
y b y S H r r
f 8 8 d i 8 d d c
7 7 n f f a o f o
f 7 7 o 9 o 6 g g 6 o 6 6 o l l 10 5 5 5 5 11 L FaCcd S C L FacCdS C L FniCfH C L nFifCH C E F G H Flowering 2014 Flowering 2015 Flowering 2014 Flowering 2015
12 / g / g s 9 a 9 s 10 10 e e a i i l
l b a
c b i p p i a o o o 13 o 9 c 9 b s c s 8 8 b c c y S y H r r f
d 8 8 c i d d 14 c 7 7 n f f a f o o f 7 7 o o g
15 g 6 6 o o l 6 6 l 16 5 5 5 5 L FaCcdS C L FacCdS C L FniCfH C L nFifHC C 17 18 19 20 21 For Peer Review 22 23
24 Fig. 1 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 4 Site L Site FC Site C 5 6
7 4 1
8 0 2
9 s e
10 v a
11 e l
12 6 Correlation 0.67 Correlation 0.98 Correlation 0.80 13 P = 7.4 × 10-5 P = 2.8 × 10-3 P = 0.10 14 15 16 17 4
18 1 0
19 2 g
20 n i r
21 e For Peer Review 22 o w l
23 F Correlation 0.90 Correlation 0.10 Correlation 0.86 24 P = 1.0 × 10-4 P = 0.67 P = 3.7 × 10-4 25 26 27 28
29 5 1
30 0 2
31 s e
32 v a e
33 l
34 6 Correlation 0.87 Correlation 0.67 Correlation 0.73 35 P = 3.8 × 10-5 P = 0.010 P = 4.0 × 10-3 36 37 38
39 5 1
40 0 2
41 g n i
42 r 43 e o w
44 l 45 F Correlation 0.86 Correlation 0.38 Correlation 0.69 P = 1.0 × 10-4 P = 0.19 P = 0.010 46 47 48 49 50 51 52 53 54 55 56 57 58 59 Fig. 2 60
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 For Peer Review 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 For Peer Review 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 Figure 5 4 5 A 6 B 7 d=1 d=0.5 8 9 10 FC C 11 FC 12 13 14 15 16 17 18 19 C L 20 L 21 acdS/nifH For Peer Reviewrrs/nifH 22 23 C 24 25 d=1 26 27 28 29 FC 30 31 32 33 34 C 35 36 37 38 L rrs/acdS 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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