甘肃农业大学：食品科学与工程学院（文献讲义）Bacterial community associated with rhizosphere of maize and cowpea in a subsequent cultivation.pdf_大学文库

Contents lists available at ScienceDirect Applied Soil Ecology journal homepage: www.elsevier.com/locate/apsoil Bacterial community associated with rhizosphere of maize and cowpea in a subsequent cultivation Ademir Sergio Ferreira de Araujoa,⁎ , Ana Roberta Lima Mirandaa , Ricardo Silva Sousaa , Lucas William Mendesb , Jadson Emanuel Lopes Antunesa , Louise Melo de Souza Oliveiraa , Fabio Fernando de Araujoc , Vania Maria Maciel Melod , Marcia do Vale Barreto Figueiredoe a Soil Quality Lab., Agricultural Science Center, Federal University of Piauí, Teresina, PI, Brazil b Laboratory of Molecular Ecology, CENA-USP, Piracicaba, SP, Brazil c University of Sao Paulo West, UNOESTE, Campus II, Presidente Prudente, SP, Brazil d Laboratório de Ecologia Microbiana e Biotecnologia, Federal University of Ceara, Fortaleza, CE, Brazil e Agronomic Institute of Pernambuco, Av. San Martin, Recife, PE, Brazil ARTICLE INFO Keywords: Bulk soil Metagenomics Zea mays Vigna unguiculata Soil microbiome ABSTRACT The rhizosphere is the soil zone influenced by the roots and its characteristics vary according to plant species and their stage of development. These different characteristics can influence the bacterial community inhabiting the rhizosphere niche that, in turn, influences plant growth and health. In this study, the bacterial community dynamics in the rhizosphere of maize and cowpea, in subsequent cultivation, were assessed using the 16S rRNA sequencing. Thus, during maize growth, soils were collected at 45 (flowering) and 75 (senescence) days. Afterward, during cowpea growth, the sampling occurred at 35 (flowering) and 65 (senescence) days. Our results showed differences between developmental stages within the same plant species. For maize, Acidobacteria decreased from flowering to senescence; while for cowpea, Proteobacteria, Armatimonadetes, WPS-2, and OP11 increased from flowering to senescence. Comparing the same developmental stage for both plant species, Proteobacteria, Elusimicrobia, WPS-2 decreased from maize flowering to cowpea flowering; while for the senescence stage, Acidobacteria, Armatimonadetes, and OP11 increased from maize to cowpea. Also, the rhizosphere community dynamic was more complex at the senescence stage compared to the flowering stage and bulk soil for both plants species. The results showed that the structure and diversity of bacterial community vary significantly according to plant species and, in a minor extent, their developmental stage. Also, we showed that the variation in rhizosphere activity during the plant growth could drive the responses of the bacterial community. 1. Introduction The rhizosphere is defined as the narrow region of soil that is directly influenced by plant roots (Chen et al., 2001). In this region, plant roots release exudates, i.e., organic compounds such as proteins and sugars, which stimulate the microbial community in comparison in with the bulk soil (Mougel et al., 2006). In the rhizosphere microbiome, bacterial community establishes positive interactions with roots and, thus, acts on essential functions in agriculture, such as nutrient acquisition, plant growth-promotion and protection against pathogens (Cavaglieri et al., 2009; Mendes et al., 2014, 2018a). The characteristics of rhizosphere vary in function of different plant species (Eisenhauer et al., 2017). Different plant groups, such as legumes and grass, present contrasting rhizosphere traits regarding root exudation and nutrient requirements (Rovira, 1969; Gransee and Wittenmayer, 2000). Legume roots can exude more amino acids and sugar than grassroots (Gransee and Wittenmayer, 2000), while grass requires more nutrients than legumes (Ghosh et al., 2009). Also, plants species within the same taxonomic group share similar characteristics of the rhizosphere, such as root biomass, and the amount and availability of the exudation (Zhou et al., 2017), while plant species belonging to different groups could present distinct rhizosphere traits. Therefore, it is expected that the bacterial community in the rhizosphere could be driven by differences in these traits, which may select different groups of bacteria. Indeed, a previous study has shown a distinct bacterial community in the rhizosphere of Lotus corniculatus https://doi.org/10.1016/j.apsoil.2019.05.019 Received 4 February 2019; Received in revised form 15 May 2019; Accepted 23 May 2019 ⁎ Corresponding author. E-mail address: ademir@ufpi.edu.br (A.S.F. de Araujo). Applied Soil Ecology 143 (2019) 26–34 Available online 06 June 2019 0929-1393/ © 2019 Elsevier B.V. All rights reserved. T

(legume) when compared with Holcus lanatus (graminea) (Ladygina and Hedlund, 2010). Recently, Zhou et al. (2017) have found differences between the bacterial community in soil under ryegrass (grass) and alfalfa (legume). The plant developmental stage is another factor influencing the bacterial community in the rhizosphere (Mougel et al., 2006). During the plant growth, the roots exudation changes according to the stage of development, and the abundance and structure of the rhizospheric microbial community are shaped according to the pattern of root exudation that may vary from young seedling, flowering, to plant senescence (Dunfield and Germida, 2003). Indeed, Houlden et al. (2008) evaluated the response of the microbial community to the different growth stage of pea, wheat and sugar beet and observed changes in the bacterial community following the developmental stage for all the tested plants. Considering the crucial role of the rhizosphere microbiome for plant growth and health, it is essential to elucidate how different plant species at different growth stages select differently the microbial community inhabiting their rhizosphere. Maize (grass) and cowpea (legume) are two important plant species cultivated in the world being used for human and animal feeding and cover crop (Ramirez-Cabral et al., 2017; Boukar et al., 2016). Although these plants are distributed in several agroecosystems, studies about the bacterial community in the rhizosphere of these species are scarce. Mainly for cowpea, there is no information about the structure of the bacterial community in the rhizosphere. Also, it is unclear the effect of the different stage of development in these plants on the soil bacterial community. In order to determine the extent to which different plant species are able to select a distinct rhizospheric microbiome, in this study we hypothesized that (1) maize and cowpea, as different plant species, exert different rhizosphere effect and shape the diversity and structure of bacterial community differently; and (2) the soil bacterial community changes during the flowering and senescence of plants. Thus, this study aimed to evaluate the diversity and structure of the bacterial community in the rhizosphere of maize and cowpea during the flowering and senescence using the 16S rRNA sequencing. 2. Material and methods 2.1. Experimental site and soil sampling The experiment was conducted at the Department of Soil Science from the Federal University of Piauí, Brazil. The soil of the experimental site is classified as Fluvisol soil. The experimental design was completely randomized with three replicates. Each experimental plot presents 20 m2 (12 m2 of usable area). Maize (Zea mays L.) was sowed at a density of five plants m−1 and was grown for 75 days. Afterward, cowpea [Vigna unguiculata (L.) Walp.] was sowed at the density of six plants m−1 and was grown for 65 days. To measure the rhizospheric influence of each plant species on the bacterial community, soil samples adhered to the roots were collected and mixed to form a composite sample in each plot. During maize growth, soils were collected at 45 (flowering, named MF) and 75 (senescence, MS) days. During cowpea growth, the sampling occurred at 35 (flowering, CF) and 65 (senescence, CS) days. Soil samples without plants effects were collected as control (bulk soil). The soil samples were sieved (2-mm) and stored at −20 °C. Soil chemical and physical properties, estimated according to Embrapa (1997), are shown in Table 1. 2.2. DNA extraction and sequencing Total DNA was extracted from 0.5 g (total humid weight) of soil using the PowerLyzer PowerSoil DNA Isolation Kit (MoBIO Laboratories, Carlsbad, CA, USA), according to the manufacturer's instructions. The DNA extraction was performed in triplicate for each soil sample, totalizing 15 samples [(bulk soil + 2 rhizosphere + 2 developmental stage) × 3 replicates]. The V4 region of the 16S rRNA gene was amplified with regionspecific primers (515F/806R) (Caporaso et al., 2011). Each 25 μL Polymerase Chain Reaction (PCR) reaction volume contained the following: 12.25 μL of nuclease-free water (Certified Nuclease-free, Promega, Madison, WI, USA), 5.0 μL of buffer solution 5× (MgCl2 2 Mm), 0.3 mM dNTP's 0.3 μM of each primer (515 YF 40 μM e 806 R 10 μM), 1.0 unit of Platinum Taq polymerase High Fidelity in concentration of 0.5 μL (Invitrogen, Carlsbad, CA, USA), and 2.0 μL (10 ng μL−1 ) of template DNA. Moreover, a control reaction was performed by adding water in place of DNA. The conditions for PCR were as follows: 95 °C for 3 min to denature the DNA, with 35 cycles at 98 °C for 20 s, 55 °C for 20 s, and 72 °C for 30 s, with a final extension of 3 min at 72 °C to ensure complete elongation. After indexing, the PCR products were cleaned up using Agencourt AMPure XP – PCR purification beads (Beckman Coulter, Brea, CA, USA), according to the manufacturer's manual, and quantified using the dsDNA BR assay Kit (Invitrogen, Carlsbad, CA, USA) on a Qubit 2.0 fluorometer (Invitrogen, Carlsbad, CA, USA). Once quantified, an equimolar concentration of each library (50 ng) was pooled into a single tube. After quantification, the molarity of the pool was determined and diluted to 2 nM, denatured, and then diluted to a final concentration of 8.0 pM with a 20% PhiX (Illumina, San Diego, CA, USA) spike for loading into the Illumina MiSeq sequencing machine (Illumina, San Diego, CA, USA). Sequence data were processed using QIIME following the UPARSE standard pipeline according to Brazilian Microbiome Project (Pylro et al., 2014), to produce an OTU table and a set of representative sequences. Briefly, the reads were truncated at 240 bp and quality-filtered using a maximum expected error value of 0.5. Pre-filtered reads were dereplicated, and singletons were removed and filtered for additional chimeras using the RDP_gold database using USEARCH 7.0. These sequences were clustered into OTUs at a 97% similarity cut-off following the UPARSE pipeline. After clustering, the sequences were aligned and taxonomically classified against the Greengenes database (version 13.8). 2.3. Statistical analysis The community structure and its correlation with soil parameters were visualized using Redundancy analysis (RDA). All matrices were initially analyzed using Detrended Correspondent Analysis (DCA) to evaluate the distribution of the data, revealing that the best-fit mathematical model for the data was RDA. Forward selection (FS) and the Monte Carlo permutation test were applied with 1000 random permutations to verify the significance of environmental parameters upon the microbial community structure. RDA plots were generated using Table 1 Chemical and physical properties of soil. BS MF MS CF CS pH (water) 5.6 6.3 6.7 6.2 6.0 EC (dS m−1 ) 0.3 0.5 0.7 0.5 0.5 TOC (g kg−1 ) 5.9 5.9 6.7 5.4 5.6 P (mg kg−1 ) 5.5 5.8 6.0 4.8 4.3 K (mg kg−1 ) 70 89 85 81 78 Ca (mg kg−1 ) 213 317 328 320 296 Mg (mg kg−1 ) 38 61 64 61 58 Na (mg kg−1 ) 69 98 93 85 91 Sand (%) 60 60 60 60 60 Silt (%) 28 28 28 28 28 Clay (%) 12 12 12 12 12 BS – Bulk soil; MF – maize flowering; MS – maize senescence; CF – cowpea flowering; CS – cowpea senescence; TOC – total organic C; EC – electric conductivity. A.S.F. de Araujo, et al. Applied Soil Ecology 143 (2019) 26–34 27

Canoco 4.5 software (Biometrics, Wageningen, the Netherlands). Permutational multivariate analysis of variance (PERMANOVA) was used to test whether sample categories harbored significantly different microbial community structures. Alpha diversity was calculated from a matrix of richness at the genus level using Shannon's index. PERMANOVA and alpha diversity indexes were calculated with the software PAST 3 (Hammer et al., 2001). Venn diagrams were also constructed to verify the proportion of groups exclusive and shared between treatments using the web tool Venny 2.1.0 (Oliveros, 2007). To visualize the differential microbial community composition among treatments, we used the Statistical Analysis of Metagenome Profile software (STAMP) (Parks et al., 2014). The OTU table generated from the 16S profiling was used as input. The comparison was based on P-values calculated using the two-sided Welch's t-test and the correction was made using Benjamini-Hochberg false discovery rate (Benjamini and Hochberg, 1995). To further assess the microbial community dynamics among the samples we conducted co-occurrence network analysis using the Phyton module ‘SparCC’ (Friedman and Alm, 2012). For the calculations, a table of frequency of hits affiliated at genus level was used for the analysis. For each network, SparCC correlations were calculated between microbial taxa and filtered based on the significance at P 50% of the variation in the first two axes of the plot (Fig. 1A), with the samples clustering according to the niche, i.e. bulk soil and rhizosphere (PERMANOVA, F = 4.75, P = 0.034). The RDA analysis also showed that the bacterial community of the bulk soil correlated with higher values of Ca. For rhizosphere samples, the bacterial community associated to maize correlated with higher values of Mg, P, TOC, and CEC, while for cowpea the community was associated with higher values of pH, K, Na, and EC. Also, the results showed a correlation between maize and cowpea rhizosphere in senescence, showing that bacteria that remain during senescence may be similar in both plant species, maybe due to the same rhizospheric soil that could change in different soils. To compare the bacterial community structure considering only rhizosphere samples, we computed a Bray-Curtis similarity matrix and coordinated into two dimensions using NMDS (Fig. 1B). The samples were grouped according to the plant species (PERMANOVA, F = 2.46, P = 0.005), with differences related to the stage of development only for cowpea. 3.2. Bacterial community composition and diversity After quality trimming, the bacterial community profiling using 16S rRNA gene sequencing generated 230,000 reads (an average of 15,000 sequences per sample) that were used for downstream analysis. The most abundant bacterial groups were affiliated to Actinobacteria (28.1% of the sequences), followed by Proteobacteria (21.5%), Firmicutes (11%), Acidobacteria (8.7%), Chloroflexi (8.7%) and Planctomycetes (8.6%) (Supplementary Fig. 1). When samples were compared using STAMP software, the abundance of specific bacterial phyla showed shifts from bulk soil to rhizospheric soils (Fig. 2). Bulk soil showed a higher abundance of Acidobacteria, Proteobacteria, Elusimicrobia, Armatimonadetes, and OP3; on the other hand, in the rhizosphere samples, there was an increase in abundance of the phyla Actinobacteria, Chlamydiae, Firmicutes and TM6 (P < 0.05). Comparing the rhizosphere sample within the same plant species, we found differences between developmental stages (Fig. 2). For maize, Acidobacteria decreased from flowering to senescence; while for cowpea, Proteobacteria, Armatimonadetes, WPS-2, and OP11 increased from flowering to senescence. Comparing the same developmental stage for both plant species, the results showed a decrease in Proteobacteria, Fig. 1. Structure of bacterial communities in bulk soil and rhizosphere of maize and cowpea plants at flowering and senescence developmental stages. (A) Redundancy analysis (RDA) of bacterial communities and soil characteristics. Arrows indicate correlation between environmental parameters and bacterial profile. (B) Non-metric multidimensional scaling ordination (NMDS) of Bray-Curtis similarity matrix of the bacterial profile from rhizosphere samples. The lines between dots represent the minimal spanning tree, which connects all points with minimal total length, based on the similarity index. Dashed lines in the graph indicate significant clusters (PERMANOVA, P < 0.05). MF = Maize flowering; MS = Maize senescence; CF = Cowpea flowering; CS = Cowpea senescence. A.S.F. de Araujo, et al. Applied Soil Ecology 143 (2019) 26–34 28

Elusimicrobia, WPS-2 from maize flowering to cowpea flowering; while for the senescence stage, Acidobacteria, Armatimonadetes, and OP11 increased from maize to cowpea. Interestingly, the phyla DeinococcusThermus and OP1 were exclusively abundant in maize and cowpea rhizosphere, respectively. In a lower taxonomic level, we compared bulk soil with the rhizosphere of maize and cowpea considering both developmental stages. In general, we observed an enrichment of specific families in the rhizosphere samples (Supplementary Fig. 2). Bulk soil presented a higher abundance of the families Isosphaeraceae, Chitinophagaceae, Nitrosomonadaceae among others (Supplementary Fig. 2A and B). In rhizosphere samples, there was an increased number of sequences af- filiated to the families Paenibacillaceae, Bacillaceae, Gaiellaceae, among others. Comparing the rhizosphere community between the plant species, we observed that 15 families were differently abundant. For example, the families Chthoniobacteraceae and 0319-6A21 (Nitrospirales) were more abundant in cowpea, while Oxalobacteraceae, Acetobacteraceae, Coxiellaceae, and Mycobacteriaceae were more abundant in maize rhizosphere (Supplementary Fig. 2C). We also compared the different developmental stages within each plant species and observed that nine and five bacterial families were differently distributed in the rhizosphere of maize and cowpea, respectively (Supplementary Fig. 3). The pattern of richness and diversity measurements revealed a decreased diversity in the rhizosphere of the cowpea plant in comparison with maize and bulk soil (Fig. 3). When the treatments were compared at the genus level using Venn diagrams, we found that bulk soil and rhizosphere samples shared > 50% of the detected genera (Fig. 4A, B). For maize, the proportion of genera exclusively present in the rhizosphere compared to the bulk soil increased from zero in the flowering, to 8.7% in the senescence (Fig. 4A). On the other hand, the proportion of genera exclusively present genera in cowpea decreased from the flowering (3.6%) to the senescence (2.4%) (Fig. 4B). This result reveals that there is a different rhizosphere effect depending on the plant species and the developmental time. When only the rhizosphere samples were compared, we observed that 51.9% of the detected genera are shared among all the samples, while the senescence of maize presented the highest proportion of exclusive genera (12.7%) (Fig. 4C). Fig. 2. Distribution of the most differential bacterial phyla based on 16S rRNA profile of samples from bulk soil and rhizosphere of maize and cowpea plants at flowering and senescence developmental stages. Boxes indicate IQR (75th to 25th of the data). The median value is shown as a line within the box and outliers are represented by dots. Different lower case letters refer to significant differences between treatments within each soil type based on Tukey's test (P < 0.05). MF = Maize flowering; MS = Maize senescence; CF = Cowpea flowering; CS = Cowpea senescence. Fig. 3. Taxonomic (A) richness and (B) diversity based on genus level at 97% similarity of the 16S rRNA gene sequencing. Error bars represent the standard deviation of three independent replicates. Different lower case letters refer to significant differences between treatments based on Tukey's HSD test (P < 0.05). MF = Maize flowering; MS = Maize senescence; CF = Cowpea flowering; CS = Cowpea senescence. A.S.F. de Araujo, et al. Applied Soil Ecology 143 (2019) 26–34 29

3.3. Correlation between the bacterial community and soil properties and community network The correlation of bacterial phyla with soil chemical properties was analyzed by Spearman's rank correlation (Table 2). The soil factors that presented correlation with more number of bacterial phyla were pH (9 phyla in total), followed by P (6), EC (4) and CEC (3). Interestingly, the phylum that presented more correlation with chemical parameters was Gemmatimonadetes (4 factors in total). The co-occurrence network analysis showed that, in general, rhizosphere samples at the senescence stage presented the highest complexity compared to the flowering stage and bulk soil for both plants species (Fig. 5). In all treatments, the number of positive correlations was higher than negatives; however, we observed an increase of negative correlation in the senescence stage for both plants. More speci- fically, CS was the most complex network, with 152 significant correlations (52% positives), high modularity (8.43) and average degree and coefficient of 4.28 and 0.369, respectively. The MS network was the second most complex, with 133 correlations (52% positives), modularity of 5.22 and average degree and coefficient of 4.83 and 0.516, respectively. Also, based on the number of connections and betweenness centrality we identified the key groups in each network. For bulk soil, the keynote was DA101; for MF were Bacillus and Mycobacterium; for MS were Bacillus, Mycobacterium, and Symbiobacterium; for CF was DA101, and for CS were Bacillus and Microlunatus. 4. Discussion Our study assessed the bacterial community in the rhizosphere of maize (grass) and cowpea (legume) as compared with bulk soil during the flowering and senescence period in subsequent cultivation. In a general view, our study found a dominance of Actinobacteria and Proteobacteria that represented > 20% of the sequences. There are several studies that have found a high abundance of Proteobacteria and Actinobacteria in soils (Liebner et al., 2008, Männistö et al., 2013; Araújo et al., 2017). However, we observed a lower abundance of Acidobacteria in comparison with Actinobacteria and Proteobacteria. In this study, the soil presented suitable pH (6.2) and content of nutrients, i.e. improved soil fertility, and these conditions contributed for a decreased abundance of Acidobacteria, which are better adapted to acid soils and oligotrophic environments (Kielak et al., 2016). In contrast, Actinobacteria and Proteobacteria were favored by these soil conditions since they predominate in neutral and nutrient-rich soils (Yang et al., 2017). The results showed changes in the bacterial community in the rhizosphere as compared with bulk soil, as confirmed by RDA. For the structure of the bacterial community in both rhizosphere soils, the NMDS pattern showed segregation between plant species and, in a minor extent, differences between plant developmental stages. This result was expected since the rhizospheric environment influences the bacterial community via root exudation and nutrient input. Thus, Actinobacteria, Chlamydiae, and Firmicutes increased their abundance Fig. 4. Venn diagrams showing the number and proportion of unique and shared genera at 97% similarity between (A) bulk soil and rhizosphere microbiome of maize; (B) bulk soil and rhizosphere microbiome of cowpea; and (C) between rhizosphere samples at the different developmental stage. MF = Maize flowering; MS = Maize senescence; CF = Cowpea flowering; CS = Cowpea senescence. A.S.F. de Araujo, et al. Applied Soil Ecology 143 (2019) 26–34 30

from bulk soil to rhizosphere due to the enrichment of soil with nutrient sources surrounding the roots. These results corroborate previous studies that found Actinobacteria, Chlamydiae and Firmicutes presenting high abundance in rhizospheric soils (Roesch et al., 2007; DeAngelis et al., 2009; Weinert et al., 2011; Mendes et al., 2011; Mendes et al., 2014). On the other hand, the decrease in Acidobacteria from bulk soil to rhizosphere may have occurred because the enrichment of the soil environment by root exudation since this group predominates in the oligotrophic environment as discussed above. Considering the bacterial community in the rhizosphere, the results support our hypotheses that different plant species and developmental stage affect the community assembly in the rhizosphere. The influence of plant species on the bacterial community is due to the physiological and biochemical differences between grass and legumes (Isobe et al., 2001). These differences are related with different quantity and quality of root exudation between plant species that vary over time (Miller et al., 1990), and alterations on soil chemical properties driven by these plants (Zhou et al., 2017). In this study, we did not characterize the root exudates, but it is well reported the differences between grass and legumes. These plants exudate compounds with different chemical pro- files into the rhizosphere, causing significant shifts in rhizosphere microbial communities (Alvey et al., 2003). According to Uroz et al., (2010), these changes in root exudation, i.e. the different amount of carbohydrates, carboxylic acids, and amino acids alter the rhizosphere environment and then discriminate the bacterial community. There is a distinct pattern of C source in the rhizosphere of grass and legume (Garland, 1996), where rhizosphere of grass species releases more carbohydrates (Merbach et al. 1999), while legume releases more amino acids (Isobe and Tsuboki, 1998). Previous studies have also shown that legume and grass differentially influence the structure and composition of the microbial community (Ladygina and Hedlund, 2010; Turner et al., 2013). Our data corroborate these previous observations, revealing that the rhizosphere community was distinct between the plant species (PERMANOVA P 0.7 (positive correlation – blue edges) or < −0.7 (negative correlation – red edges) and statistically significant (P < 0.01). Each node represents different bacterial genera. The size of the nodes is proportional to the number of connections (degree) and the color intensity indicates the betweeness centrality (darker color for higher values). The number inside the nodes refers to key genera as following: 1. DA101; 2. Mycobacterium; 3. Bacillus; 4. Symbiobacterium; 5. Microlunatus. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) A.S.F. de Araujo, et al. Applied Soil Ecology 143 (2019) 26–34 31

Proteobacteria presents high metabolic versatility and includes a large group of bacteria found in soils, mainly in the rhizosphere (Janssen, 2006). Proteobacteria was also found abundantly in the rhizosphere of maize in previous studies (Chauhan et al., 2011; Yang et al., 2017). On the other hand, cowpea presents the ability to change soil pH in the rhizosphere (Rao et al., 2002), which may have contributed to decrease the abundance of Proteobacteria. These results agree with Zhou et al. (2017) that reported root exudates and soil chemical properties as factors driving the soil microbial community, especially pH that is the primary chemical driver of the bacterial community in soil (Lauber et al., 2009). On the other hand, the increased abundance of Acidobacteria, Armatimonadetes, and OP11 in cowpea senescence suggests depletion of nutrients in the soil, since cowpea grew after maize and there was not nutrient reposition in the soil. Thus, these groups of bacteria were in- fluenced by the presence of oligotrophic soils as discussed previously. The increase of these groups in the senescence stage can also be linked to the decrease of rhizospheric effect. For example, both Acidobacteria and Armatimonadetes are described as oligotrophic groups that grow in environments with low input of nutrients (Tamaki et al., 2011; Kielak et al., 2016). The rhizospheric effect, i.e. the rhizodeposition of nutrients by plant roots, occurs up to the plant flowering, decreasing with the plant developmental stage. The differential abundance of some groups between flowering and senescence is also related to the type of exudate. Bacteria present in the rhizosphere of young plants have a preference for simple amino acids, while the groups present in mature plants prefer more complex carbohydrates (Houlden et al., 2008). This change in abundance of specific groups is an indicative of a change in the quality of plant root exudates. For example, Firmicutes plays a crucial role by initiating the degradation of complex substrates such as plant cells walls, starch particles and mucin (Flint et al., 2012). In this context, the increasing of these groups in the senescence of maize can be linked to the ability to use complex carbohydrates and respond under competition for nutrients. Interestingly, Deinococcus-Thermus was abundantly found in maize rhizosphere as compared with cowpea rhizosphere and it agrees with previous studies that found this phylum in the rhizosphere of maize (Chauhan et al., 2011; Correa-Galeote et al., 2016). Deinococcus comprises a group of bacteria found in several environments and, although this phylum is not well characterized, there is information that they can act as plant growth-promoting bacteria in the rhizosphere (Lai et al., 2006). Specifically, Deinococcus-Thermus was found acting as biological control of soil-borne pathogens in the rhizosphere of cotton (Zhang et al., 2011). It is also worthy to note that the group OP11 presented a significant enrichment only in cowpea senescence. This bacterial group was recently named Microgenomates and proposed to belong to the superphylum Patescibacteria (Rinke et al., 2013). This superphylum has small genomes and a presumed symbiotic or parasitic lifestyle (Sanchez-Osuna et al., 2016). Although Microgenomates is still poorly studied, this group was already found in the rhizosphere of maize, tomato, and soybean (Liang et al., 2014; Correa-Galeote et al., 2016; Sanchez-Osuna et al., 2016). The richness and diversity did not vary between bulk soil and maize rhizosphere, while decreased in cowpea rhizosphere. Usually, bulk soil can present similar, or higher richness and diversity than rhizosphere since plant rhizosphere selects a subset of bacterial groups from the soil, then decreasing the diversity (Weisskopf et al., 2005). Chaudhry et al. (2012) evaluated the microbial diversity in the rhizosphere of switchgrass and jatropha and did not find differences between bulk soil and rhizosphere soils. Comparing plant species, we found a difference in richness and diversity between legumes and grass, a similar result reported by Zhou et al. (2017), which indicated that bacterial community indices associated with legume were different from that associated with grass. When the rhizosphere samples were compared to the bulk soil, we found that 11% of the genera detected in the rhizosphere were not found, in a detectable ratio, in the bulk soil. On the other hand, most of the genera (67%) are shared between bulk soil and rhizosphere. In this sense, the rhizospheric community is a subset of the bulk soil, which is the main source of microbial species colonizing the rhizosphere (Mendes et al., 2014). When compared the rhizosphere groups between the two different plants, we detect a small proportion of genera exclusively present in each plant, reinforcing our observations of differential community assembly according to the plant species. Recent studies have shown that the host genotype, to a minor extent, shapes the root microbiota profiles (Bulgarelli et al., 2012; Pérez-Jaramillo et al., 2017; Mendes et al., 2018a); however, it is still unclear whether microbiome divergence is more significant in host species belonging to different plant families (Abbo et al., 2014). Finally, we used network analysis to understand the microbial community dynamics between bulk soil and rhizosphere and the different developmental stages of the plants. Interestingly, our data revealed that the rhizosphere community in the senescence was more complex than the flowering and bulk soil. Also, in the senescence period, there was an increase in negative correlations. These results suggest a probable competition between microorganisms since that, during the senescence, the rhizosphere activity and root exudation decrease (Miranda et al., 2018). Therefore, these characteristics of rhizosphere during the senescence may contribute to increase the complexity of the bacterial community. According to the network analysis, some keystone species of bacteria were identified in each treatment. In the bulk soil, we found that the genus DA101 was the most important in the network structure. This genus belongs to Verrucomicrobia phylum and was recently described as abundant and ubiquitous in soils, being characterized as an aerobic heterotroph with many putative amino acid and vitamin auxotrophies (Brewer et al., 2016). For rhizosphere of both maize and cowpea, the genus Bacillus was detected as keystone species in the network (specifically for MF, MS and CS treatment). The genus Bacillus has long been considered dominant in the rhizosphere of plants being recognized for their functions in several important biological processes related to plant growth (Mendes et al., 2013) and protection against pathogens (Mendes et al., 2018b). In maize, we also detected the genus Mycobacterium as a key group in the community network. This genus was abundantly found in the rhizosphere of maize and harbors diverse functional genes for nutrient transformation (Li et al., 2014). In cowpea, Microlunatus was detected as key species, and this group is related to phosphorus metabolism in soils (Kawakoshi et al., 2012). Taken together, the co-occurrence network analysis revealed that the dynamics of the communities in the rhizosphere change according to the plant developmental stage, and the key groups are involved in beneficial traits related to plant nutrition and health. 5. Conclusion In this study, the response of the bacterial community was different between bulk and rhizospheric soils, confirming the power of the rhizosphere environment to shape the microbial community. Also, this study showed that the structure and diversity of bacterial community vary significantly according to plant species and, in a minor extent, their developmental stage. Interestingly, the complexity of bacterial community increases during the senescence as compared with the flowering. It confirms that the variation in rhizosphere activity during the plant growth can drive the responses of the bacterial community, which in turn, can influence plant growth and health. Acknowledgments This study was funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq/Brazil (grant 305069/2018-1). The authors thank to the Centro de Genetica e Bioinformatica (CeGenBio) from the Unit of Research (NPDM/UFC). Ademir Sergio Ferreira de Araujo, Vania Maria Maciel Melo, and Marcia do Vale Barreto A.S.F. de Araujo, et al. Applied Soil Ecology 143 (2019) 26–34 32

Figueiredo thank CNPq for their fellowship of research. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Declaration of competing interest The authors declare that they have no conflict of interest. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.apsoil.2019.05.019. References Abbo, S., Pinhasi van-Oss, R., Gopher, A., Saranga, Y., Ofner, I., Peleg, Z., 2014. Plant domestication versus crop evolution: a conceptual framework for cereals and grain legumes. Trends Plant Sci. 19, 351–360. Alvey, S., Yang, C., Buerkert, A., Crowley, D.E., 2003. Cereal/legume rotation effects on rhizosphere bacterial community structure in West African soils. Biol Fertil. Soils 37, 73–82. Araújo, A.S.F., Bezerra, W.M., Santos, V.M., Rocha, S.M., Carvalho, N.D., Lyra, M.D., Figueiredo, M.D., Lopes, A.C.A., Melo, V.M., 2017. istinct bacterial communities across a gradient of vegetation from a preserved Brazilian Cerrado. Antonie Van Leeuwenhoek 110, 457–469. Bastian, M., Heymann, S., Jacomy, M., 2009. Gephi: An open source software for exploring and manipulating networks. In: Proceedings of the Third International ICWSM Conference. AAAI Publications. Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Stat. Soc. Series B 57, 289–300. Boukar, O., Fatokun, C.A., Huynh, B.L., Roberts, P.A., Close, T.J., 2016. Genomic tools in cowpea breeding programs: status and perspectives. Front. Plant Sci. 7, 757. Brewer, T.S., Handley, K.M., Carini, P., Gilbert, J.A., Fierer, N., 2016. Genome reduction in an abundant and ubiquitous soil bacterium ‘Candidatus Udaeobacter copiosus’. Nat. Microbiol. 2, 16198. Bulgarelli, D., Rott, M., Schlaeppi, K., Ver Loren van Themaat, E., Ahmadinejad, N., Assenza, F., Rauf, P., Huettel, B., Reinhardt, R., Schmelzer, E., 2012. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488, 91–95. Caporaso, J.G., Lauber, C.L., Walters, W.A., Berg-Lyons, D., Lozupone, C.A., Turnbaugh, P.J., 2011. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. USA 108 (Suppl 1), 4516–4522. Cavaglieri, L., Orlando, J., Etcheverry, M., 2009. Rhizosphere microbial community structure at different maize plant growth stages and root locations. Microbiol. Res. 164, 391–399. Chaudhry, V., Rehman, A., Mishra, A., Chauhan, P.S., Nautiyal, C.S., 2012. Changes in bacterial community structure of agricultural Land due to long-term organic and chemical amendments. Microb. Ecol 64, 450–460. Chauhan, P.S., Chaudhry, V., Mishra, S., Nautiyal, C.S., 2011. Uncultured bacterial diversity in tropical maize (Zea mays L.) rhizosphere. J. Basic Microbiol. 51, 15–32. Chen, C.C., Wang, M.K., Chiu, C.Y., Huang, P.M., King, H.B., 2001. Determination of low molecular weight dicarboxylic acids and organic functional groups in rhizosphere and bulk soils of Tsuga and Yushania in a temperate rain forest. Pl. Soil. 231, 37–44. Correa-Galeote, D., Bedmar, E.J., Fernández-González, A.J., Fernandéz-Lopez, M., Arone, G.J., 2016. Bacterial communities in the rhizosphere of amilaceous maize (Zea mays L.) as assessed by pyrosequencing. Frontiers in Plant Sci 7, 1016. DeAngelis, K.M., Brodie, E.L., DeSantis, T.Z., Andersen, G.L., Lindow, S.E., Firestone, M.K., 2009. Selective progressive response of soil microbial community to wild oat roots. ISME J 3, 168–178. Dunfield, K.E., Germida, J.J., 2003. Seasonal changes in the rhizosphere microbial communities associated with field-grown genetically modified canola (Brassica napus). Appl. Environ. Microbiol. 69, 7310–7318. Eisenhauer, N., Lanoue, A., Strecker, T., Scheu, S., Steinauer, K., Thakur, M.P., Mommer, L., 2017. Root biomass and exudates link plant diversity with soil bacterial and fungal biomass. Sci. Rep. 7, 44641. EMBRAPA, 1997. Manual de métodos de análises de solo. In: Rio de Janeiro, Ministério da Agricultura e do Abastecimento, 2. (212p.). Flint, H.J., Scott, K.P., Duncan, S.H., Louis, P., Forano, E., 2012. Microbial degradation of complex carbohydrates in the gut. Gut Microb 3, 289–306. Friedman, J., Alm, E.J., 2012. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687. Garland, J.L., 1996. Patterns of potential C source utilisation by rhizosphere communities. Soil Biol. Biochem. 28, 223–230. Geissinger, O., Herlemann, D.P.R., Mörschel, E., Maier, U.G., Brune, A., 2009. The ultramicrobacterium "Elusimicrobium minutum" gen. nov., sp. nov., the first cultivated representative of the Termite Group 1 phylum. Appl. Environ. Microbiol. 75, 2831–2840. Ghosh, P.K., Tripathi, A.K., Bandyopadhyay, K.K., Manna, M.C., 2009. Assessment of nutrient competition and nutrient requirement insoybean/sorghum intercropping system. Eur. J. Agron. 31, 43–50. Gransee, A., Wittenmayer, L., 2000. Qualitative and quantitative analysis of water-soluble root exudates in relation to plant species and development. J. Plant Nutr. Soil Sci. 163, 381–385. Hammer, Ø., Harper, D., Ryan, P., 2001. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 1–9. Houlden, A., Timms-Wilson, T.M., Day, M.J., Bailey, M.J., 2008. Influence of plant developmental stage on microbial community structure and activity in the rhizosphere of three field crops. FEMS Microbiol. Ecol. 65, 193–201. Isobe, K., Tsuboki, Y., 1998. Relationship between the amount of root exudate and the infection rate of arbuscular mycorrhizal fungi in gramineous and leguminous crops. Plant Prod. Sci. 1, 37–38. Isobe, K., Tateishi, A., Nomura, K., Inoue, H., Tsuboki, Y., 2001. Flavonoids in the extract and exudate of the roots of leguminous crops. Plant Prod. Sci. 4, 278–279. Janssen, P.H., 2006. Identifying the dominant soil bacterial taxa in libraries of 16S rRNA and 16S rRNA genes. Appl. Environ. Microbiol. 72, 1719–1728. Kawakoshi, A., Nakazawa, H., Fukada, J., Sasagawa, M., Katano, Y., Nakamura, S., Hosoyama, A., Sasaki, H., Ichikawa, N., Hanada, S., Kamagata, Y., Nakamura, K.Yamazaki, 2012. Deciphering the genome of polyphosphate accumulating actinobacterium Microlunatus phosphovorus. DNA Res. 19, 383–394. Kielak, A.M., Barreto, C.C., Kowalchuk, G.A., van Veen, J.A., Kuramae, E.E., 2016. The ecology of acidobacteria: moving beyond genes and genomes. Front. Microbiol. 7, 744. Ladygina, N., Hedlund, K., 2010. Plant species influence microbial diversity and carbon allocation in the rhizosphere. Soil Biol. Biochem. 42, 162–168. Lai, W.-A., Kampfer, P., Arun, A.B., Shen, F.-T., Huber, B., Rekha, P.D., Young, C.-C., 2006. Deinococcus ficus sp. nov., isolated from the rhizosphere of Ficus religiosa L. Intern. J. Syst. Evol. Microbiol. 56, 787–791. Lauber, C.L., Hamady, M., Knight, R., Fierer, N., 2009. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120. Li, X., Rui, J., Xiong, J., 2014. Functional potential of soil microbial communities in the maize rhizosphere. PLoS One 9, e112609. Liang, J., Sun, S., Ji, J., Wu, H., Meng, F., Zhang, M., et al., 2014. Comparison of the rhizosphere bacterial communities of Zigongdongdou soybean and a high-methionine transgenic line of this cultivar. PLoS One 9, e103343. Liebner, S., Harder, J., Wagner, D., 2008. Bacterial diversity and community structure in polygonal tundra soils from Samoylov Island, Lena Delta, Siberia. Int. Microbiol. 11, 195–202. Männistö, M.K., Kurhela, E., Tiirola, M., Häggblom, M.M., 2013. Acidobacteria dominate the active bacterial communities of Arctic tundra with widely divergent winter-time snow accumulation and soil temperatures, FEMS. Microbiol. Ecol. 84, 47–59. Mendes, R., Kruijt, M., de Bruijn, I., 2011. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science 332, 1097–1100. Mendes, R., Garbeva, P., Raaijmakers, J.M., 2013. The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol. Rev. 37, 634–663. Mendes, L.W., Kuramae, E.E., Navarrete, A.A., Van Veen, J.A., Tsai, S.M., 2014. Taxonomical and functional microbial community selection in soybean rhizosphere. ISME J 8, 1577–1587. Mendes, L.W., Raaijmakers, J.M., Hollander, M., Mendes, R., Tsai, S.M., 2018a. Influence of resistance breeding in common bean on rhizosphere microbiome composition and function. ISME J 12, 212–224. Mendes, L.W., Mendes, R., Raaijmakers, J.M., Tsai, S.M., 2018b. Breeding for soil-borne pathogen resistance impacts active rhizosphere microbiome of common bean. ISME J 12, 3038–3042. Merbach, W., Mirus, E., Knof, G., Remus, R., Ruppel, S., Russow, R., Gransee, A., Schulze, J., 1999. Release of carbon and nitrogen compounds by plant roots and their possible ecological importance. J. Plant Nutr. Soil Sci. 162, 373–383. Miller, H.J., Liljeroth, E., Henken, G., Van Veen, J.A., 1990. Fluctuations in the fluorescent pseudomonad and actinomycete populations of rhizosphere and rhizoplane during the growth of spring wheat. Can. J. Microbiol. 36, 254–258. Miranda, A.R.L., Antunes, J.E.L., Araujo, F.F., Melo, V.M.M., Bezerra, W.M., van Den Brink, P.J., Araujo, A.S.F., 2018. Less abundant bacterial groups are more affected than the most abundant groups in composted tannery sludge-treated soil. Sci. Rep. 8, 11755. Mougel, C., Offre, P., Ranjard, L., Corberand, T., Gamalero, E., Robin, C., Lemanceau, P., 2006. Dynamic of the genetic structure of bacterial and fungal communities at different developmental stages of Medicago truncatula gaertn. cv. jemalong line J5. New Phytol. 170, 165–175. Oliveros, J.C., 2007. VENNY. In: An interactive tool for comparing lists with Venn Diagrams. Parks, D.H., Tyson, G.W., Hugenholtz, P., Beiko, R.G., 2014. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124. Pérez-Jaramillo, J.E., Carrión, V.J., Bosse, M., Ferrão, L.F.V., Hollander, M., Garcia, A.A.F., Ramírez, C.A., Mendes, R., Raaijmakers, J.M., 2017. Linking rhizosphere microbiome composition of wild and domesticated Phaseolus vulgaris to genotypic and root phenotypic traits. ISME J 11, 2244–2257. Pylro, V.S., Roesch, L.F.W., Ortega, J.M., Amaral, A.M., Tótola, M.R., Hirsch, P.R., Rosado, A.S., Góes-Neto, A., Da Silva, A.L.C., Rosa, C.A., Morais, D.K., Andreote, F.D., Duarte, G.F., Melo, I.S., Seldin, L., Lambais, M.R., Hungria, M., Peixoto, R.S., Kruger, R.H., Tsai, S.M., Azevedo, V., 2014. Brazilian Microbiome Project: revealing the unexplored microbial diversity challenges and prospects. Microb. Ecol. 67, 237–241. Ramirez-Cabral, N.Y.Z., Kumar, L., Shabani, F., 2017. Global alterations in areas of A.S.F. de Araujo, et al. Applied Soil Ecology 143 (2019) 26–34 33

suitability for maize production from climate change and using a mechanistic species distribution model (CLIMEX). Sci. Rep. 7, 5910. Rao, P.R., Nammi, S., Raja, A.D.V., 2002. Studies on the antimicrobial activity of Heliotropicum indicum. J. Natural Remedies 2, 195–198.. Rinke, C., Schwientek, P., Sczyrba, A., Ivanova, N.N., et al., 2013. Insights into the phylogeny and coding potential of microbial dark matter. Nature 499, 431–437. Roesch, L.F.W., Fulthorpe, R.R., Riva, A., 2007. Pyrosequencing enumerates and contrasts soil microbial diversity. ISME J 1, 283–290. Rovira, A.D., 1969. Plant root exudates. Bot. Rev. 35, 35–57. Sanchez-Osuna, M., Barbé, J., Erill, I., 2016. Comparative genomics of the DNA damageinducible network in the Patescibacteria. Environ. Microbiol. 19, 3465–3474. Tamaki, H., Tanaka, Y., Matsuzawa, H., Muramatsu, M., Meng, X.Y., Hanada, S., Nori, K., Kamagata, Y., 2011. Armatimonas rosea gen. nov., sp. nov., of a novel bacterial phylum, Armatimonatedes phyl. Nov., formally called the candidate phylum OP10. Int. J. Syst. Evol. Microbiol. 61, 1442–1447. Turner, T.R., Ramakrishnan, K., Walshaw, J., Heavens, D., Alston, M., Swarbreck, D., Osbourn, A., Grant, A., Poole, P.S., 2013. Comparative metatranscriptomics reveals kingdom level changes in the rhizosphere microbiome of plants. ISME J 7, 2248–2258. Uroz, S., Buée, M., Murat, C., Frey-Klett, P., Martin, F., 2010. Pyrosequencing reveals a contrasted bacterial diversity between oak rhizosphere and surrounding soil. Environ. Microbiol. Rep. 2, 281–288. Weinert, N., Piceno, Y., Ding, G.C., 2011. PhyloChip hybridization uncovered an enormous bacterial diversity in the rhizosphere of different potato cultivars: many common and few cultivar-dependent taxa. FEMS Microbiol. Ecol. 75, 497–506. Weisskopf, L., Fromin, N., Tomasi, N., Aragno, M., Martinoia, E., 2005. Secretion activity of white lupin's cluster roots influences bacterial abundance, function and community structure. Pl. Soil. 268, 181–194. Yang, Y., Wang, N., Guo, X., Zhang, Y., Ye, B., 2017. Comparative analysis of bacterial community structure in the rhizosphere of maize by high-throughput pyrosequencing. PLoS One 12, e0178425. Zhang, Y., Du, B.-H., Jin, Z., Li, Z., Song, H., Ding, Y.-Q., 2011. Analysis of bacterial communities in rhizosphere soil of healthy and diseased cotton (Gossypium sp.) at different plant growth stages. Pl. Soil 339, 447–455. Zhou, Y., Zhu, H., Fu, S., Yao, Q., 2017. Variation in soil microbial community structure associated with different legume species is greater than that associated with different grass species. Front. Microbiol. 8, 1007. A.S.F. de Araujo, et al. Applied Soil Ecology 143 (2019) 26–34 34