aem.asm.org · 29 genera ( aspergillus, coniella, epicoccum, fusarium, giberella, lasiodiplodia,...
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Shifting populations in the root-zone microbiome of millet associated with enhanced crop 2
productivity in the Sahel 3
Spencer J. Debenport¹*, Komi Assigbetse2, Roger Bayala3,Lydie Chapuis-Lardy2, Richard P. Dick4, 4
Brian B. McSpadden Gardener¹ 5
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¹Department of Plant Pathology, The Ohio State University-OARDC, Wooster, OH; 2Instituit de 7
Recherche pour le Développement, Dakar, Senegal; 3Institut Sénégalais de Recherches Agricoles, 8
Dakar, Senegal; 4Department of Environment and Natural Resources, The Ohio State University, 9
Columbus, OH 10
11
*Corresponding author. Mailing address: Department of Plant Pathology, The Ohio State University, 12
OARDC, 1680 Madison Avenue, Wooster, OH, 44691. Phone: (330) 202-2719. Fax: (330) 263-3841. 13
E-mail: [email protected]. 14
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Keywords: Intercropping; soil microbiology; plant growth promotion; Senegal; Piliostigma reticulatum; 16
Guiera senegalensis; phytobiome. 17
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AEM Accepted Manuscript Posted Online 13 February 2015Appl. Environ. Microbiol. doi:10.1128/AEM.04122-14Copyright © 2015, American Society for Microbiology. All Rights Reserved.
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This study characterized specific changes in the millet root zone microbiome stimulated 19
by long term woody shrub intercropping at different sites in Senegal. At the two study sites, 20
intercropping with woody shrubs and shrub residue resulted in a significant increase in millet 21
(Pennisetum glaucum (L.) R. Br.) yield (P<0.05) and associated patterns of increased diversity in 22
both bacterial and fungal communities in the root zone of the crop. Across four experiments, 23
operational taxonomic units (OTUs) belonging to Chitinophaga were consistently significantly 24
(P<0.001) enriched in the intercropped samples, and Candidatus Koribacter were consistently 25
significantly enriched in samples where millet was grown alone. Those OTUs belonging to 26
Chitinophaga were enriched more than 30-fold in residue amended samples, and formed a 27
distinct subgroup from all OTUs detected in the genus. Additionally, OTUs belonging to 8 fungal 28
genera (Aspergillus, Coniella, Epicoccum, Fusarium, Giberella, Lasiodiplodia, Penicillium, and 29
Phoma) were significantly (P<0.005) enriched in all experiments at all sites in intercropped 30
samples. Four genera (Epicoccum, Fusarium, Giberella, and Haematonectria) contained OTUs 31
consistently enriched in millet grown in alone. Those OTUs enriched in intercropped samples 32
showed consistently large magnitude differences, ranging from 30- to 1000-fold increases in 33
abundance. OTUs consistently enriched in intercropped samples in the genera Aspergillus, 34
Fusarium, and Penicillium also formed phylogenetically distinct subgroups. These results suggest 35
that the intercropping system used here can influence the recruitment of potentially beneficial 36
microorganisms to the root zone of millet and aid subsistence farmers in producing higher 37
yielding crops. 38
39
Subsistence farmers in the Sahel region of sub-Saharan Africa struggle with the challenges of 40
drought, low organic matter in soils, and encroaching desertification (1). Resolving these issues is 41
critical for improving crop yields in this region. Woody shrubs that occur naturally in farmers fields 42
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might be used as part of an intercropping production system to improve crop yields. Two woody shrubs, 43
Guiera senegalensis and Piliostigma reticulatum, are distributed throughout the Sahel region (2,3). 44
Currently, most farmers manage these shrubs by coppicing, collecting, and burning above-ground 45
residue prior to planting crops. However, agricultural practices in the Sahel can be adjusted to utilize 46
shrub biomass as a natural source of fertilizer (4). Shrub residue can be added to the soil as a nutrient 47
rich organic amendment and decompose within an 8 month time period (5). Rhizosphere soils around 48
amended shrubs show increased nutrient content (6), improved soil moisture profiles due to hydraulic 49
lift (7), and a more diverse and complex microfauna soil food web (8). Intercropping with G. 50
senegalensis (9) and P. reticulatum (10) with the incorporation of shrub residue has also been shown to 51
improve crop yields at two long term field sites in Senegal. 52
Concomitantly with the physicochemical enrichments, microbial community composition and 53
activity respond positively to shrub-based amendments. Woody shrub intercropping drives an increase 54
in total PLFA content, for both bacterial and fungal markers, as well as increased soil enzyme activity 55
(11). DGGE profiling revealed similar patterns of increased soil microbial diversity in these 56
intercropping systems (Personal communication). While such profiling methods describe some 57
coarsely defined variations in soil microbial community structure, it is important to determine which 58
specific organisms are associated with the phenomenon of improved crop growth in the field. Thus, the 59
overall objective of this study was to identify and quantify specific changes in soil microbiomes 60
associated with shrub-based enhancement of annual crop growth. Specifically, this work provides the 61
first detailed description of the changes in the microbiome stimulated by long term intercropping of 62
woody shrubs with millet (Pennisetum glaucum (L.) R. Br.) in the Sahel region of Africa. 63
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MATERIALS AND METHODS 65
Study Sites, Sample Collection, and Agronomic Data. Sampling was conducted at two long 66
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term study sites near Keur Matar and Nioro, Senegal (9,10) (Figure S1) which, respectively, had Dior 67
(Rubic arenosol with 95% sand) and Deck-Dior soil (Haplic Ferric Lixisol) (FAO, 2006). The two 68
major crops cultivated at the sites are millet and peanut (Arachis hypogaea L.). Keur Matar is in the 69
northern region of the Peanut Basin (14o45 N, 16o51 W) with mean annual precipitation of 450 mm 70
and mean monthly temperatures ranging from 20 to 33°C. Nioro is in the southern region of the Peanut 71
Basin (13o45 N, 15o47 W) with a mean annual precipitation of 750 mm and mean monthly air 72
temperatures ranging from 20.0°C to 35.7°C. 73
At both sites, a field of approximately 0.5 ha (5,000 m²) with pre-existing shrubs was selected 74
that had been under local farmer management for at least the last 50 years. The sites had been cropped 75
continuously with a peanut-millet rotation then left fallow for three years prior to initiating the 76
experiment. The experimental design at both sites was a randomized complete block design with 77
presence or absence of shrub as the treatments with four replicates each. The main plots were 78
established in the winter (dry season) of 2003 by manually removing existing shrubs to establish no 79
shrub plots and transplanting shrub seedlings to achieve a standardized stand density. Specifically, 80
stand densities of 1500-1833 Pilisotigma reticulatum plants ha-1 and 888 to 1555 Guiera senegalensis 81
plants ha–1 were established at Keur Matar and Nioro, respectively. Shrubs were randomly but 82
relatively evenly distributed. The following summer, millet was planted on all plots and fertilized with 83
68.5 kg N, 15 kg P and 15 kg K ha-1 to allow plots to equilibrate for one year before initiation of the 84
experiments. Main plot sizes were 46 m X 6 m and subplot sizes were 10 m X 6 m. There was a 2-m 85
gap between adjacent plots and 3-m gap between blocks. Following the dominant farmer practices in 86
the region, all plots had a crop rotation of peanut (Arachis hypogaea var 55-437) and millet 87
(Pennissetum glaucum var Souna 3) from 2004 to 2013 (except for 2008 to 2010 when no crops were 88
planted). 89
For this study, the Nioro field site was sampled on August 13, 2013, and the Keur Matar site 90
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was sampled on September 10, 2013. Soil samples were taken from millet root zones of plots with and 91
without shrubs. Samples from two independent transects were taken at each location and analyzed as 92
four separate experiments. Five cores of 2.5 cm diameter (0 to 20cm depth) were taken near the base 93
of a single plant and composited into one bag. These composite samples were homogenized in the bag 94
and then passed through a 2mm sieve prior to subsampling for soil chemical analysis and DNA 95
extraction. Millet biomass was taken for each plot at the end of the growing season by cutting millet 96
shoots at soil level, and removing the panicle. Millet shoots were dried in a 65ºC over for 48 hours and 97
weighed to determine dry weight. At Keur Matar, rainfall deficit caused a failure of grain production, 98
so the weight of panicles was used to estimate millet productivity. At Nioro, yield was measured as 99
grain weight after threshing. 100
Soil chemical analyses. Soil chemical analyses were conducted at the Laboratoire des Moyens 101
Analytiques (LAMA), a laboratory of the Institut de Recherche pour le Développement (IRD) in Dakar, 102
Senegal. Total organic carbon was determined using a modified degtjareff method (12). 103
DNA extraction and Illumina MiSeq sequencing. DNA was extracted from 0.25g of each soil 104
sample using MoBio PowerSoil DNA Isolation Kits (MoBio Laboratories, Carlsbad, CA). 50μl of 105
extracted DNA was precipitated to pellet form using 3M sodium acetate for transport to Wooster, OH. 106
Upon arrival, DNA pellets were resuspended in 50μl Qiagen (Qiagen, Venlo, Netherlands) elution 107
buffer and stored in a -20ºC freezer. Amplicon libraries for the 16S rRNA V4 region were generated 108
using methods outlined by Caporaso et al. (14). Amplicon libraries for the ITS1 region were generated 109
using the protocol described in McGuire et al. (15). For each of these library preparation methods, we 110
created a single PCR replicate instead of triplicate replicates in the listed protocols. For each amplicon 111
type, all sample libraries were pooled together and purified using a Pippen Prep instrument (Sage 112
Science, Beverly, MA). Purified library pools were quantified using the Qubit double-stranded DNA 113
high-sensitivity assay (Life Tecnologies, Guilford, CT). Amplicon libraries were sequenced on an 114
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Illumina MiSeq instrument using the 250-base paired-end kits at the Molecular and Cellular Imaging 115
Center housed at the Ohio Agricultural Research and Development Center in Wooster, OH. 116
16S rRNA amplicon sequence processing. The 16S sequence paired-end data set was 117
demultiplexed on the MiSeq instrument itself at the time of sequencing. Each pair of reads was joined 118
and quality filtered using the 'join_paired_ends.py' script provided in the Quantitative Insights Into 119
Microbial Ecology (QIIME) software suite. The open reference operational taxonomic unit (OTU) 120
picking protocol in QIIME was used. Briefly, sequences were clustered against the 2013 Greengenes 121
ribosomal database 97% reference dataset (http://greengenes. secondgenome.com/downloads). 122
Sequences that did not match any entries in this reference were subsequently clustered into de novo 123
OTUs at 97% similarity with UCLUST. Taxonomy was assigned to all OTUs using the RDP classifier 124
(16) within QIIME using the Greengenes reference dataset. 125
ITS1 amplicon sequence processing. The ITS data set was demultiplexed on the MiSeq 126
instrument itself at the time of sequencing. Not all paired end reads were overlapping due to the 127
variable length of the ITS1 region, so only the forward read from each sequence was used for 128
downstream analysis. Reads were clustered into OTUs using the same open reference OTU picking 129
protocol in QIIME as the 16S data set, using the UNITE+INSD (International Nucleotide Sequence 130
Databases; NCBI, EMBL, DDBJ) 97% reference database. Taxonomy was assigned to all OTUs using 131
the RDP classifier (16) within QIIME using the UNITE+INSD reference dataset. 132
Statistical analyses. Agronomic, soil, and sequence count data were subjected to analyses of 133
variance using the general linear model and Tukey's test in R (17). A two factor model, consisting of 134
treatment and block (n=4) for each site was used. At each site, two experiments, each with a 135
randomized complete block design and four blocks, was conducted (Figure S1). Comparisons of 136
individual OTU abundances were performed across soil treatments (shrub-crop vs. sole crop zones) for 137
each transect at each site. OTU tables containing read counts for each OTU in each sample, taxonomy 138
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information for each OTU, and sample metadata for each sample were exported from QIIME, and 139
imported into R using phyloseq (18). In order to exclude sequences observed with very low frequency, 140
OTUs representing less than 0.001% of the total number of sequences from each library were removed. 141
OTU tables were subsetted for each comparison and formatted for the DESeq2 package in R (19). 142
Differential abundance of each OTU by sample type was determined using DESeq2. Sequences for all 143
OTUs belonging to genera shared commonly between experiments were extracted from representative 144
sequence files in QIIME. Sequences for each genus were subsequently aligned using ClustalW (20) and 145
trees were constructed using the neighbor-joining method with 1,000 bootstrap replicates in Geneious 146
6.0.3 (Biomatters, available from http://www.geneious.com). Subsets of OTUs within a genus were 147
defined by bootstrap values over 90. 148
149
RESULTS 150
Effect of intercropping on millet growth and yield. The biomass of millet plants grown in 151
shrub-crop intercropping (MR plots) were significantly (P < 0.05) increased compared to millet grown 152
alone (MB plots) (Table 1). This pattern of enhanced millet growth in the intercropped plots was true 153
for the experiments conducted at both sites (Keur Matar and Nioro). Such results demonstrate the 154
consistent response of annual crop growth to woody shrubs and annual shrub residue incorporation 155
independent of the shrub species used. More importantly, the harvested yields of millet were also 156
significantly (P < 0.05) increased with residue amendment at both sites. 157
Soil organic C and overall microbial community size and diversity. Soil organic C and the 158
root-zone microbiomes were increased by intercropping and the addition of shrub residues at both long 159
term field sites (Table 2). As expected, intercropping increased soil organic C levels measured in the 160
root zone of millet crops (P<0.05 for three of four independent experimental comparisons). At Keur 161
Matar the increases in organic C ranged from 21% to 26%. Similarly, at Nioro, the increases ranged 162
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from 24% to 53%. Associated with these enrichments in soil carbon, an approximately 2-fold increase 163
in the amount of microbial DNA recovered from root zone soils of the MR plots was noted. While the 164
average amount of DNA recovered per sample was higher in the MR plots as compared to the MB plots 165
in all four experiments, such differences were not individually significant but were significant over all 166
(P<0.10). A similar pattern was observed in the average number of bacterial sequences obtained using 167
our high throughput sequencing protocol. That is to say, the number of 16S sequences in MR and MB 168
varied by less than a factor of two in all four experiments, and, while not significant individually, the 169
results were significant across the entire data set (P<0.10). Similar patterns were noted for the ITS 170
sequences, though a significant difference (P<0.05) for the individual comparison from the Nioro B 171
transect was noted as well. 172
A more dramatic contrast was observed in the phylogenetic diversity of bacterial and fungal 173
sequences obtained from MR and MB samples (Table 2). Specifically, the number of bacterial and 174
fungal OTUs increased in the root zone soils of the intercropped plots as compared to millet-only plots. 175
At Keur Matar, significant (P<0.05) increases in the number of bacterial OTUs were observed in plots 176
intercropped with G. senegalensis. DNA samples from the intercropped plots contained an average of 177
174% and 131% more 16S OTUs, for transect A and B, respectively. The same pattern was observed at 178
Nioro, but the 63% and 25% increases were not noted to be individual significant. More strikingly, 179
higher numbers of fungal OTUs were detected in MR samples in three of the four experimental 180
transects (increases of 84% and 27% at Keur Matar, and 64% and 99% at Nioro). 181
Specific changes in soil bacterial community structure and diversity induced by woody 182
shrub intercropping. Analyses of sequence data using DESeq2 revealed small but meaningful 183
variations in the relative abundance of just a few bacterial taxa due to crop and residue (Table 3). 184
Between 962 and 1272 OTUs, each representing at least 0.001% of the total number of sequences 185
obtained, were considered in each experimental comparison. Due to these high numbers of OTUs in the 186
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analysis, an alpha of 0.001 was used to define which populations differed significantly between 187
treatments. Comparisons of the abundance of OTUs classified to genera were made. Populations 188
differing by at least 2-fold (i.e. log2=1) between millet samples grown in the MR and MB samples are 189
shown in Figure 1. The 16S markers for eight bacteria genera at KeurMatar_A, seven genera at 190
KeurMatar_B, twenty-two genera at Nioro_A, and twenty-five genera at Nioro_B were enriched 191
significantly (P<0.001) in the root zone of millet grown MR as compared to the MB plots. At Keur 192
Matar, three of these genera were enriched in both experiments (i.e. Chitinophaga, Opitutus, and 193
Rhodoplanes). At Nioro, 16S markers representing 13 bacterial genera were enriched in both 194
experiments (i.e. Adheribacter, Azohydromonas, Bacillus, Burkholderia, Chitinophaga, Cupriavidus, 195
Delftia, Dokdonella, Mesorhizobium, Novosphingobium, Pseudomonas, Ramlibacter, and 196
Stenotrosphomonas). Interestingly, all of the genera named above at Keur Matar, and 60% of those 197
named above at Nioro were enriched more than 4-fold in MR soils. 198
In contrast, the 16S markers for just three bacterial genera at KeurMatar_A, four genera at 199
KeurMatar_B, six genera at Nioro_A, and thirteen genera at Nioro_B were significantly (P<0.001) 200
enriched in the root zone of the millet grown alone. At Keur Matar, markers for two genera were 201
enriched in both experiments (i.e. Candidatus Koribacter and Pseudomonas). At Nioro, three bacterial 202
genera were enriched on both experiments (i.e. Bacillus, Candidatus Koribacter, and Candidatus 203
Solibacter). 204
Specific changes in soil fungal community structure and diversity induced by woody 205
shrub residues. Analysis with DESeq2 was also performed with fungal microbiome sequences (Table 206
4). The number of OTUs which were present at more than 0.001% of the total sequence abundance and 207
included in each analysis ranged between 292 and 374. Due to these lower numbers of OTUs, an alpha 208
of 0.005 was selected. Populations differing by at least 2-fold (i.e. log2=1) between millet root zone 209
samples from intercropped plots and millet-only plots are shown in Figure 2. ITS1 markers 210
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representing 29 genera of fungi at KeurMatar_A, 34 genera at KeurMatar_B, 35 genera at Nioro_A, 211
and 36 genera at Nioro_B were enriched significantly (P<0.005) in MR samples as compared to MB 212
samples. At Keur Matar, 16 of these genera and at Nioro, 19 fungal genera were enriched in both 213
experiments (Table 4). Interestingly, the ITS1 markers for all of these genera at both sites were 214
enriched more than 4-fold, an indication of a high degree of preference for growth in residue amended 215
plots. In contrast, there were markers for just 21 fungal genera at KeurMatar_A, 24 genera at 216
KeurMatar_B, 21 genera at Nioro_A, and 17 genera at Nioro_B that were significantly (P<0.005) 217
enriched in MB samples. At each Keur Matar and Nioro, seven genera were enriched in both 218
experiments. 219
Consistently enriched genera across all experiments and sites. Because it is well established 220
that microbiome structure can differ dramatically by site and treatment, we screened the sequence data 221
for patterns that were consistent across multiple transects and experimental sites. Figure 1 and Table 3 222
show the identity and magnitude difference in abundance of bacterial OTUs which were consistently 223
enriched in both transects at each site. There were two genera observed which were responsive and 224
common to each location and common across locations; Chitinophaga in the shrub-crop plots, and 225
Candidatus Koribacter in the sole crop plots. 226
Amongst the fungal populations (Table 4) a large fraction (i.e. >10%) of the distinct OTUs were 227
responsive to intercropping and amendment with shrub residue. When comparing the root zone 228
populations of millet plants grown in shrub-crop plots and in sole crop plots, eight genera were 229
consistently enriched in the millet root zones grown in intercropped plots (i.e. Aspergillus, Coniella, 230
Epicoccum, Fusarium, Gibberella, Lasiodiplodia, Penicillium, and Phoma) and four genera were 231
enriched in the millet-only plots (i.e. Epicoccum, Fusarium, Gibberella, and Haemonectria) at both 232
sites. It should be noted that the OTUs that comprise those genera that appear as significantly different 233
in each comparison are different from one another. The consistency of responses by these noted 234
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bacterial and fungal genera indicate that they are more sensitive to the woody shrub intercropping than 235
other soil and environmental variables. 236
Sequence variation of select taxa enriched in intercropped MR microbiomes. Phylogenetic 237
analyses were performed with sequences from select taxa enriched in MR microbiomes in order to 238
identify targets for marker-assisted recovery (21). In the 16S dataset, only one genus, Chitinophaga, 239
contained OTUs which were consistently enriched in MR samples in all four experiments. In total, 12 240
individual Chitinophaga OTUs were present in the >0.001% abundance datasets (Figure 3A). Analysis 241
with the DESeq2 package in R software revealed that two of these Chitinophaga OTUs (17% of all 242
types) were enriched in MR samples and both belong to a single cluster (Figure 3). The boxes in Figure 243
1 highlight the abundance of these OTUs which is within a range of three to six log 2-fold change 244
higher in MR samples over MB samples (i.e. 8- to 64-fold enrichment). OTU 218242 was significantly 245
enriched in all four experiments, while OTU 1138370 was significantly enriched in one of the four 246
experiments. 247
In the ITS dataset, eight genera (Aspergillus, Coniella, Epicoccum, Fusarium, Gibberella, 248
Lasiodiplodia, Penicillium, and Phoma) contained OTUs which were consistently enriched in MR 249
samples in all four experiments. There were a total of 24 Aspergillus OTUs in the >0.001% abundance 250
dataset (Figure 4A). DESeq2 analysis revealed 13 OTUs (54%) enriched in MR samples over MB 251
samples. Clustering analysis revealed two groups, A and D shown in Figure 4A, as containing only 252
OTUs enriched in MR samples. There were a total of three Coniella OTUs in the dataset, with OTU 253
HQ166057 shared between all sample types at all sites, and the two additional OTUs (NCRO14621 and 254
NRO1232) that were unique to the MR samples at Keur Matar. Seventeen of fifty-two Epicoccum 255
OTUs (33%) were found to be enriched in MR samples over MB samples after DESeq2 analysis. 256
However, there were no distinct clusters of Epicoccum OTUs enriched in either MR or MB samples 257
(Figure 4B). 258
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There were 103 Fusarium OTUs detected in our experiment (Figure 4C). Analysis with 259
DESeq2 found 38 OTUs (37%) significantly enriched in MR samples over MB samples. Phylogentic 260
analysis revealed two groups (A and C in Figure 4C) that consisted of only those OTUs enriched in MR 261
samples or not significantly differing between MR and MB samples. Five of twelve total Gibberella 262
OTUs identified in the total dataset (42%) were found to be significantly enriched in MR samples 263
compared to MB samples (Figure 4D). Group A contained only those OTUs enriched in MB samples 264
(NRO 53, NCRO20791, and NCRO1301), and the Gibberella OTUs enriched in MR samples 265
(NCRO6499, NCRO30244, NCRO8037, and NCRO30877) did not meet the standard set to be 266
considered a distinct cluster. 267
For Lasiodiplodia, 46 total OTUs were identified (Figure 4F). A total of 22 Lasiodiplodia 268
OTUs (48%) were significantly enriched in MR samples. Only one distinct subgroup was identified, 269
but the OTUs enriched in MR samples were not concentrated in that group. In total, 34 Penicillium 270
OTUs were identified (Figure 4G). Analysis with DESeq2 revealed 16 OTUs (47%) were significantly 271
enriched in MR samples over MB samples. Two clusters were identified, both containing OTUs 272
enriched in MR samples. A total of 25 Phoma OTUs were identified (Figure 4H) with 11 OTUs (44%) 273
significantly enriched in MR samples over MB samples. Among these, a total of three distinct groups 274
were identified of which A and B contained a majority of OTUs enriched in MR samples. 275
Sequence variation of select taxa enriched in sole crop (MB) microbiomes. In the 16S 276
dataset, only one genus was consistently enriched in MB samples, Candidatus Koribacter. Seven of the 277
44 related OTUs (16%) were found to be significantly enriched in MB samples compared to MR 278
samples (Figure 3B). OTU 723690 was significantly enriched in both MB transects at Keur Matar 279
(KM_A and KM_B) at a 2.5 log 2-fold change higher than MR samples, while NRO676 was found to 280
be enriched at both MB transects at Nioro (N_A and N_B) at a log 2-fold change ranging from 1.75 to 281
2.5 higher than MR samples. The remaining OTUs marked as significant in Figure 3 only appeared in 282
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one out of the four experiments. 283
In the ITS dataset, Epicoccum, Fusarium, Gibberella, and Haematonectria all contained OTUs 284
significantly enriched in MB samples compared to MR samples. A total of 19 Epicoccum (37%) 285
(Figure 4B), 18 Fusarium (17%) (Figure 4C), and 4 Gibberella (33%) (Figure 4D) OTUs were found to 286
be significantly enriched in MB samples after analysis with DESeq2. Within these genera, only group 287
A in the Gibberella genus was identified as a cluster of MB enriched OTUs. The genus 288
Haematonectria contained a total of 29 OTUs (Figure 4D). A total of 10 Haematonectria OTUs (34%) 289
were found to be significantly enriched in MB samples compared to MR samples. However, 290
phylogenetic analysis revealed no distinct groups in this genus. 291
292
DISCUSSION 293
In this study, we identified several highly significant associations between root zone inhabiting 294
soil microbial populations and improved millet growth mediated by woody shrub intercropping. 295
Specifically, bacterial OTUs in the genus Chitinophaga and fungal OTUs in the genera Aspergillus, 296
Coniella, Lasiodiplodia, Penicillium, and Phoma increased in response to crop growth promoting 297
intercropping and residue amendments. We also found contrasting responses of different populations of 298
Epicoccum, Fusarium, and Gibberella. In comparison to fungal communities, bacterial communities 299
showed fewer significant responses with only OTUs of Chitinophaga and Candidatus Koribacter 300
consistently responding to woody shrub intercropping. Our findings of increased diversity in soil 301
microbial communities following application of shrub residue expand upon previous work done at 302
these same sites using phospholipid fatty acid (PLFA) analysis (11). That previous study noted an 303
increase in all PLFA types (bacterial and fungal) in shrub-crop plots as compared to sole crop plots, 304
and higher response of fungal PLFAs over bacterial PLFAs to residue amendment. Millet growth 305
promotion was observed at both study sites in plots intercropped with woody shrubs. For the Keur 306
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Matar site, the results of this study are consistent with previous findings (9) where millet (and 307
groundnut) grown in the presence of G. senegalensis had substantially greater yields over 4 cropping 308
seasons compared to sole crop plots. In that study, the application of an intercropping system with the 309
addition of shrub residue took 4 cropping seasons to build the soil organic carbon and alter soil nutrient 310
status. The elevated millet grain and biomass yield at the Nioro site with P. reticulatum were somewhat 311
in contrast to previous research on these plots (10). In that previous study, there was no significant 312
difference in the sole crop and shrub-crop yield in two of the four study years with contrasting trends in 313
the other two years. These divergent results at Kuer Matar and Nioro may be due to the former location 314
having a more sandy soil allowing for a more dramatic and immediate response to shrub intercropping. 315
Compared to Nioro, which has heavier soils and greater rainfall, there was a less stressed environment 316
for crop growth. Thus, it could be that at Nioro it took longer to have a consistent crop growth response 317
due to the generally beneficial effects of P. reticulatum on crop yields. 318
Phylogenetic clustering of OTU sequences revealed distinct groups of microorganisms 319
associated with improved plant growth. These specific subgenera groups were found in the 320
Chitinophaga (Figure 3A), Aspergillus, Fusarium, Penicillium, and Phoma genera (Figure 4A, C, G, 321
and H). Additionally, we noted contrasting responses of various populations of certain genera. Previous 322
studies have shown that some isolates of Fusarium promoted plant growth while others acted to 323
suppress plant growth (22). Our data suggest the potential for similar phenomenon among millet grown 324
in Senegal. The Epicoccum, Fusarium, and Gibberella genera each contained multiple OTUs enriched 325
in either MR or MB samples. For the related Fusarium and Gibberella genera, we noted some 326
clustering of OTUs enriched in MR samples, shown in Figures 4C and 4D. There were also a small 327
number of OTUs that were significantly enriched in both the MR and MB samples. These particular 328
OTUs could be showing variations in niche preference related to environmental factors differing 329
between treatments. The OTUs which were consistently enriched in MB plots could be negatively 330
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impacting millet growth as well, which would help to explain the variation in millet productivity we 331
observed. The techniques used in our study could be missing some of the variations in the observed 332
populations which might further distinguish subgenera clusters of OTUs. As high throughput 333
sequencing technology advances to allow for longer reads, more variable components of the ITS region 334
in particular will be accessible for differentiation of specific populations of fungi within a genus. 335
A pattern of both increased soil organic C and microbial diversity in plots treated with residue 336
was observed at both field sites. Soil organic matter has been shown to have an impact on arbuscular 337
mycorrhizal diversity in other semi-arid soils (23) and on bacterial communities in polar desert soil 338
(24). There was no detected response by the Glomeromycota in this study, but there were large 339
increases in fungal diversity with intercropping and residue amendment. The primer set used for the 340
ITS1 amplification and sequencing could be biased against the Glomeromycota, which would explain 341
why we did not see a response in this group compared to other studies (23). Van Horn et al. (24) also 342
demonstrated that in low to medium saline soils, the addition of organic matter consistently shifted the 343
bacterial community composition to a higher degree than increasing soil moisture through water 344
addition. This result has implications for the woody shrub intercropping system in semi arid soils, 345
where organic matter additions could be shaping the microbial communities. Ng et al. (25) also found 346
that organic matter amendment in the form of green waste significantly increased the number of PLFAs 347
observed, particularly those coming from fungal populations. They noted that the ratio of bacteria to 348
fungi was significantly reduced with green waste amendment, showing a large response of the fungal 349
community similar to what is shown in this study. Similarly, Cookson et al. (26) presented results 350
showing a significant increase in total PLFAs with the application of hay, which matches the findings 351
of Diedhiou et al. (11) and this study, indicating higher microbial diversity in residue-amended 352
intercropped soils. 353
In this study, fine scale resolution of differences in microbiome structure was resolved using 354
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Illumina sequencing of ribosomal sequences. Previous studies on the effect of intercropping on the 355
structure of microbiomes have used coarse community profiling techniques, such as terminal restriction 356
fragment length polymorphism (T-RFLP) (27, 28), phospholipid fatty acids (PLFA) (29), and 357
denaturing gradient gel electrophoresis (DGGE) (30, 31). Such methods were able to identify changes 358
across phyla or classes of microorganisms, but the sequence-based approach used here quantifies 359
population responsiveness of unique members of individual genera (Figures 1 and 2). By identifying 360
certain microorganisms associated with a particular ecological function (such as crop growth 361
promotion), it is possible to eventually recover those microorganisms of interest through marker-362
assisted selection (21). Parallel advances have been made in the analysis of functional diversity of 363
microbial communities. Such techniques have advanced from community level physiological profiling 364
(CLPP) with Biolog EcoPlates (32) to the use of microarray technology in the form of Geochip (33, 34) 365
to metatranscriptomic analysis through the use of high throughput sequencing (35). We used the 366
DESeq2 R package in order to perform our analyses following arguments in support of this 367
methodology (36). This package was originally written for differential expression analysis of RNA Seq 368
data and can be very simply adapted to analyze differential abundance of OTUs between samples. We 369
did not perform any rarification of our sequence libraries, instead preserving the original library size for 370
our analyses. When performing rarification, valid sequences are discarded from the analysis at random, 371
and using proportional abundance discards any data about library sizes from the analysis. These two 372
pitfalls were avoided in our analyses. 373
Intercropping with a shrub while using the coppicing residues as a mulch presents a workable 374
option for small farmers in Sub-Saharan Africa to improve crop growth. This study has shown that 375
intercropping and shrub residue application can have a substantial effect on microbial communities, 376
with individual OTUs expressing high levels of enrichment in intercropping systems (9, 10). Moving 377
forward from these associations between individual OTUs and plant health status to the identification 378
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of microorganisms which could be providing beneficial functions for improved crop growth will 379
require the isolation and functional characterization of strains corresponding to the OTUs identified 380
here. Previous studies have identified molecular markers associated with plant disease suppression in a 381
disease suppressive soil (37) using T-RFLP analysis (27). The molecular markers identified in that 382
study were cloned, sequenced, and then used to design an isolation strategy, resulting in the recovery of 383
novel species of Mitsuaria and Burkholderia with strong pathogen suppressing capacities (38). This 384
approach is generally referred to as marker assisted selection (39) and can draw upon sequences 385
obtained through any number of community profiling methods, including DGGE, T-RFLP, GeoChip, 386
and high throughput sequencing, as previously described. Subsequent work in Senegal can follow a 387
similar course using the taxonomic information embedded in the sequenced ribosomal gene markers to 388
design semi-selective media in order to isolate organisms of interest from the amended soils. Specific 389
primers can be designed using OTU sequences generated here which can be used to screen culture 390
collections isolated on semi-selective media. Isolates containing these markers can then be tested in the 391
laboratory, the greenhouse, and the field to determine their impact on crop plant growth. The OTUs in 392
the Chitinophaga, Aspergillus, Fusarium, Penicillium, and Phoma clusters described in this work 393
provide excellent targets for recovery and testing as novel inoculant strains. As the interest in 394
microbials as biopesticides and biofertilizers continues to rise, new approaches to the discovery of 395
beneficial microbes (21, 39) will help to streamline the process of identifying and introducing novel 396
microbial inoculants. These advances in sequencing technology, analytical tools, and microbiological 397
techniques offer new opportunities in phytobiome research and the development of novel and effective 398
microbial inputs for agriculture. 399
400
ACKNOWLEDGMENTS 401
We thank Matthew Bright, Chelsea Delay, and Sidy Diakhaté for important contributions during 402
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fieldwork. The help of Mariama Gueye and Lamine Dieng was greatly appreciated in the lab. Maria 403
Elena Hernandez and Jody Whittier at the Molecular and Cellular Imaging Center at the Ohio State 404
University Ohio Agricultural Research and Development Center (http://oardc.osu.edu/mcic/) provided 405
much assistance with the Illumina sequencing. Spencer Debenport was supported by the Ohio State 406
University Distinguished University Fellowship. This work was funded through the National Science 407
Foundation Partnerships for International Research and Education (PIRE) grant OISE-0968247. 408
409
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Figure 1. Bacterial OTUs occurring at >0.001% abundance which are significantly enriched in MR or MB 526
samples. OTUs from KM_A (A), KM_B (B), N_A (C), and N_B (D). Boxes highlight OTUs from genera consistently 527
significantly enriched in all experiments. A positive log2FoldChange value indicates the OTU is significantly 528
enriched in MR samples, and a negative log2FoldChange indicates the OTU is significantly enriched in MB 529
samples. Boxes are highlighting OTUs in genera consistently enriched across all experiments. 530
531
Figure 2. Fungal OTUs occurring at >0.001% abundance which are significantly enriched in MR or MB samples. 532
OTUs from KM_A (A), KM_B (B), N_A (C), and N_B (D). Boxes highlight OTUs from genera consistently 533
significantly enriched in all experiments. A positive log2FoldChange value indicates the OTU is significantly 534
enriched in MR samples, and a negative log2FoldChange indicates the OTU is significantly enriched in MB 535
samples. Boxes are highlighting OTUs in genera consistently enriched across all experiments. 536
537
Figure 3. Clustering of 16S OTUs within genera shown to be significantly enriched in MR or MB samples in all 538
experiments. Chitinophaga (A) and Candidatus Koribacter (B). Branches display bootstrap consensus values 539
after 1,000 replications. Groupings classified as having a bootstrap value > 90. OTUs marked with a carrot (^) 540
are significantly enriched (P < 0.001) in MR samples, and OTUs marked with an asterisk (*) are significantly 541
enriched (P< 0.001) in MB samples. 542
543
Figure 4. Clustering of ITS OTUs within genera shown to be significantly enriched in MR or MB samples in all 544
experiments. Aspergillus (A), Epicoccum (B), Fusarium (C), Gibberella (D), Haematonectria (E), Lasiodiplodia (F), 545
Penicillium (G), and Phoma (H). Coniella had too few OTUs to allow for clustering analysis. Branches display 546
bootstrap consensus values after 1,000 replications. Groupings classified as having a bootstrap value > 90. 547
OTUs marked with a carrot (^) are significantly enriched (P < 0.005) in MR samples, and OTUs marked with an 548
asterisk (*) are significantly enriched (P< 0.005) in MB samples. 549
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