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

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*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

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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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aem.asm.org · 29 genera ( aspergillus, coniella, epicoccum, fusarium, giberella, lasiodiplodia,...

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