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Quantitative PCR Analysis of Gut Disease-discriminatory Phyla for Diagnosing 1
Shrimp Disease Incidence 2
3
Weina Yua,b
, Jinxuan Caoa, Wenfang Dai
a,b, Qiongfen Qiu
a, Jinbo Xiong
a,b* 4
5
a School of Marine Sciences, Ningbo University, Ningbo, 315211, China 6
b Collaborative Innovation Center for Zhejiang Marine High-Efficiency and Healthy 7
Aquaculture, Ningbo University, Ningbo, 315211, China 8
9
*Corresponding author 10
Tel.: 86-574-87608368; Fax: 86-574-87608347 11
Jinbo Xiong, [email protected] 12
AEM Accepted Manuscript Posted Online 13 July 2018Appl. Environ. Microbiol. doi:10.1128/AEM.01387-18Copyright © 2018 American Society for Microbiology. All Rights Reserved.
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ABSTRACT 13
There is evidence that gut microbial signatures are indicative of host health status. 14
However, few efforts have been devoted to establish an applicable technique for 15
diagnosing disease incidence using the gut microbial signatures. Herein, we 16
established a quantitative PCR (qPCR) based approach to detect the relative 17
abundances of gut disease-discriminatory phyla, which in turn afforded independent 18
variables for quantitatively diagnosing the incidences of shrimp disease. Given the 19
temporal dynamics of gut bacterial communities as healthy shrimp aged, we identified 20
the disease-discriminatory phyla after ruling out the age-discriminatory phyla. The top 21
10 disease-discriminatory phyla contributed an overall 93.2% diagnosis accuracy (N = 22
103, confident health and disease shrimp), with 70% diagnosis accuracy at the disease 23
onset stage when symptoms or signs of disease were not apparent. Then, the 16S 24
rRNA gene-targeted group-specific primers of five disease-discriminatory phyla were 25
designed according to their compositions within shrimp gut microbiota, and other 26
primers were borrowed from references. Relative abundances of the 10 27
disease-discriminatory phyla assayed by qPCR exhibited a high consistency (r = 28
0.946, P < 0.001) with those detected by Illumina sequencing. Notably, using the 29
profiles of disease-discriminatory phyla assayed by qPCR and corresponding weight 30
coefficients as independent variables, we can accurately estimate the incidences of 31
future disease outcome. This work establishes an applicable technique to 32
quantitatively diagnose the incidence and onset of shrimp disease, which is a valuable 33
attempt to translate scientific research to practical application. 34
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IMPORTANCE 36
Current studies have identified gut microbial signatures of host health using 37
high-throughput sequencing (HTS) techniques. However, HTS is still expensive, 38
time-consuming and requires a high technical ability, thereby impeding its application 39
in routinely monitoring in aquaculture. Hence, it is necessary to seek an alternative 40
strategy to overcome these shortcomings. Herein, we establish a qPCR based 41
approach to detect the relative abundances of gut disease-discriminatory phyla, which 42
in turn afford independent variables to quantitatively diagnose the incidence and onset 43
of shrimp disease. Notably, there is a high consistency between the accuracy of 44
disease diagnosis as achieved by qPCR and HTS. This applicable technique makes 45
important progress towards defining a diseased state in shrimp and towards solving an 46
important animal health management driven economically problem. 47
48
Keywords: shrimp gut microbiota; disease-discriminatory phyla; independent 49
variable; diagnosis accuracy; disease incidence 50
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INTRODUCTION 51
Shrimp (Litopenaeus vannamei) is an important aquaculture species with a high 52
economic value on a global scale. In recent years, the development of high-density 53
zootechnology and recirculation shrimp farming systems impose enhanced stressors 54
on shrimp, resulting in an increased frequency of disease worldwide (1, 2). 55
Unfortunately, the occurrences of shrimp disease can cause massive mortalities within 56
a few days. Thus, it is urgently to establish effective strategies for rapidly diagnosing 57
the incidence of shrimp disease (3). 58
Increasing evidence has revealed that a balanced gut microbiota contributes 59
beneficial roles in shrimp health, i.e., stimulating immune response, increasing 60
nutrient acquisition and preventing pathogen colonization (4-6). Conversely, dysbiosis 61
in the gut microbial structures is concurrent with shrimp disease (7-10). It was 62
reported that the deviations in gut bacterial communities were tightly associated with 63
the severity of shrimp disease (11). Focusing on the most differentially abundant taxa 64
between healthy and diseased cohorts have identified gut microbial signatures of host 65
disease (5, 10-12). Notably, a recent work has exemplified that changes in the shrimp 66
gut microbial signatures are earlier than the emergence of apparent disease signs (13). 67
Putting these pieces together, it is feasible to apply disease-discriminatory lineages as 68
independent variables for diagnosing the incidence of shrimp disease (3). However, 69
the above-mentioned studies solely relied on high-throughput sequencing (HTS) 70
techniques. Currently, HTS is still relative expensive and time-consuming, and 71
requires a high technical ability (14). These limitations impede its use in routine 72
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monitoring applications. In this regard, cheap, rapid and handy features of qPCR 73
could overcome these shortcomings (15). Indeed, qPCR is being used to detect the 74
dominant gut bacterial lineages in human (16) and pig (17). Further, the application of 75
qPCR analysis has efficiently distinguished the colitis-infected mice from healthy 76
ones, with high accuracy and specificity (18). Inspired by these studies, it is promising 77
to apply qPCR for detecting the relative abundances of gut microbial signatures, 78
thereby affording independent variables to diagnose the incidences of host disease (3). 79
A few attempts have shown that disease-discriminatory lineages at the species-level 80
contribute a higher accuracy than these at the coarser phylum level in diagnosing 81
caries (19), and shrimp disease (20). However, designing specific primers of bacterial 82
16S rRNA gene at the species level remains challenging. For example, two common 83
marine pathogenic strains, Vibrio parahemolyticus and V. sinaloensis, share identical 84
16S rRNA gene sequences (21, 22), thereby hampering the identification of the two 85
strains using 16S rRNA gene. However, at the bacterial phylum level, specific 86
degenerate primers have been designed according to the conserved regions of 16S 87
rRNA gene (23). For example, bacterial phylum-specific primers have been applied to 88
compare intestinal population of obese and lean pigs (17), and to analyze the gut 89
predominant bacterial lineages in humans (24). To balance the accuracy and 90
applicability, it is uncertain whether the microbial signatures at the coarse phylum 91
level could contribute a high accuracy in assessing shrimp health status, though 92
significant changes in the gut bacterial phyla have been detected between healthy and 93
diseased shrimp (10, 11). In addition, given the host specific gut microbiota, the 94
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primers from available literatures may be unsuitable for detecting the bacterial 95
lineages in shrimp. Therefore, it is mandatory to design the phylum-specific primers 96
according to the compositions in shrimp gut microbiota. 97
To identify the gut microbial signatures that are associated with shrimp disease, we 98
firstly ruled out the gut phyla that depend on shrimp ages. Subsequently, using the 99
designed and cited bacterial phylum-specific primers, qPCR was applied to assess the 100
relative abundances of disease-discriminatory phyla, thereby providing independent 101
variables for quantitatively diagnosing the incidence of shrimp disease. The main 102
purposes of this study were: (i) to evaluate whether the coarse disease-discriminatory 103
phyla could accurately diagnose shrimp health status; (ii) to assess the consistency of 104
diagnosis accuracies as achieved by qPCR and HTS; (iii) to test whether the gut 105
bacterial dysbiosis occurred earlier than disease symptoms are apparent. The results 106
exemplify that the qPCR assay contributed a comparable accuracy as that of HTS, 107
thereby providing an applicable strategy to quantitatively and rapidly diagnose shrimp 108
disease incidence. 109
110
RESULTS 111
112
Variations in bacterial community over shrimp ages and disease progression 113
The dominant (sub)phyla were Gammaproteobacteria (44.5% ± 25.0%) and 114
Alphaproteobacteria (14.1% ± 11.1%) in “confident health” shrimp (ConfidentH, N = 115
85), which shifted into the predominance of Gammaproteobacteria (78.5% ± 10.2%) 116
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and Bacteroidetes (16.1% ± 9.4%) in “confident disease” group (ConfidentD, N = 18) 117
(Table S1). These high standard deviations could be attributed to the profound 118
dynamics of dominant phyla as shrimp aged (Fig. S1). Consistently, a principal 119
coordinate analysis (PCoA) revealed temporal differences in the gut microbiota over 120
shrimp development based on the profiles of bacterial phyla (Fig. 1A). Although a 121
distinct clustering of gut microbiotas was undetected at the shrimp disease onset stage, 122
clear separations were apparent over disease exacerbation (Fig. 1B). These patterns 123
were further confirmed by an analysis of similarity (ANOSIM), revealing that the 124
structures of gut bacterial community significantly differed (P < 0.05) between each 125
pair, with the exception between H84 and D84 (P = 0.354, at disease onset stage) 126
(Table S2). Therefore, both shrimp age and health status were the determinant factors 127
in governing the gut microbiota. 128
129
Identification of shrimp gut disease-discriminatory phyla 130
To distinguish gut microbiota features that associate with shrimp health status from 131
ontogeny, we firstly identified age-discriminatory phyla in the 85 ConfidentH shrimp 132
using a random Forest machine learning algorithm. The top two age-discriminatory 133
phyla (Verrucomicrobia and Alphaproteobacteria) were closely linked with healthy 134
shrimp age. The relative abundance of Alphaproteobacteria was negatively associated 135
(r = -0.672, P < 0.001) with shrimp age, while that of Verrucomicrobia was peaked at 136
the juvenile stage (Fig. S2). After ruling out the two age-discriminatory phyla, we 137
identified 10 disease-discriminatory phyla based on their feature importance using the 138
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85 “ConfidentH” and the 18 “ConfidentD” samples. The most important phylum was 139
Planctomycetes, with a normalized relative importance of 30.8%, followed by 140
Gammaproteobacteria (15.0%) and Tenericutes (13.9%) (Table 2). The model was 141
applied for the enrolled 103 samples (assayed by Illumina sequencing), which 142
contributed an overall 93.2% diagnosis accuracy. In ConfidentH subjects, 82 in 85 143
cases (96.5% diagnosis accuracy) were correctly diagnosed as healthy. In ConfidentD 144
cohorts, 14 in 18 cases (77.8% diagnosis accuracy) were correctly diagnosed as 145
diseased (Table 1, Fig. S3). Then, we tested whether the gut bacterial dysbiosis 146
occurred earlier by employing the model to test whether a subject with RelativeD 147
microbiota would develop to disease. The model was applied to the 10 RelativeD 148
samples at the disease onset stage. Seven in 10 RelativeD samples (70% diagnosis 149
accuracy) were correctly diagnosed as diseased (Fig. 3). In addition, a PCoA biplot 150
depicted a separation of gut microbiotas between healthy and diseased cohorts using 151
the profiles of the 10 disease-discriminatory phyla (Fig. S4). 152
To evaluate to what extent the diagnosis accuracy was affected by the taxonomic 153
level of disease-discriminatory lineage, the model was further constructed at bacterial 154
class, order, family, genus, or species level, respectively. Intriguingly, the diagnosis 155
accuracies were comparable based on the disease-discriminatory lineages at each 156
taxonomic level, ranging from 92.2% to 95.1% (Table 1). The accuracy diagnosed by 157
the profiles of disease-discriminatory phyla (93.2%) was slightly compromised as 158
compared to these of disease-discriminatory taxa (95.1%). To facilitate subsequent 159
detection, bacterial signatures at the phylum level were selected in the final model for 160
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diagnosing shrimp health status. 161
162
Designing specific primers for disease-discriminatory phyla 163
We firstly searched phylum-specific primers from the literatures. The efficiency of 164
primer pair for Planctomycetes, Gammaproteobacteria, Tenericutes, Actinobacteria, 165
Deltaproteobacteria, Cyanobacteria, Bacteroidetes or Firmicutes, was respectively 166
tested using shrimp gut microbial DNA as template. The primer pairs of Tenericutes, 167
Actinobacteria, Deltaproteobacteria, Bacteroidetes and Firmicutes specifically 168
amplified their targets and yielded the expected sizes, while the other three primer 169
pairs had no amplification. In addition, the primer pairs of Chlamydiae and 170
Chloroflexi have not been reported yet. Thus, five primer pairs (Planctomycetes, 171
Gammaproteobacteria, Chlamydiae, Chloroflexi and Cyanobacteria) were newly 172
designed according to their compositions in shrimp gut microbiota (Table 2, see 173
details in Methods). The specificity of these primers was checked in silico by the 174
Probe Match program. Results showed that the expected amplification products of 175
designed primers had well homology with the 16S rRNA genes of these 176
corresponding phyla and exhibited very little cross-hybridization outside of their 177
targeted phyla (Table 2). To perform all PCRs simultaneously, the primers were 178
modified to function at the same annealing temperature (54°C). The specific primer 179
pairs yielded their corresponding target phyla with clear straps, specificity amplicons 180
and expected sizes (Fig. S5). Illumina sequencing of the amplicons further confirmed 181
the primers’ specificity (the proportion of amplicons affiliated with its target > 91% in 182
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every cases) for targeting corresponding phyla (Table 2). The amplification efficiency 183
of primer pairs ranged from 92.5% to 99.3% (Fig. S6). 184
185
Consistent diagnosis accuracy between qPCR and HTS 186
To evaluate the reliability of qPCR, we selected 67 samples (39 ConfidentH subjects: 187
three healthy samples were selected from each shrimp age; 10 RelativeD subjects at 188
the disease onset stage; and 18 ConfidentD subjects over disease exacerbation) to 189
detect relative abundances of the 10 disease-discriminatory phyla using their 190
dichotomous DNA as templates. Then, we compared the detected relative abundances 191
as assayed by qPCR with those detected by HTS (Fig. 2). Notably, there was a high 192
consistency (r = 0.946, P < 0.001, Pearson correlation coefficient) between the two 193
approaches (Fig. S7). For example, the relative abundances of Gammaproteobacteria 194
(48.5% ± 22.7% in qPCR vs. 52.1% ± 26.5% in HTS, paired t-test, N = 67, P = 0.211), 195
Actinobacteria (10.9% ± 17.9% vs. 9.52% ± 20.2%, P = 0.591) and Bacteroidetes 196
(16.4% ± 9.8% vs. 15.3% ± 10.0%, P = 0.302) were comparable as assayed by the 197
two approaches (Fig. 2). Thus, qPCR assay was reliable to detect the relative 198
abundances of shrimp disease-discriminatory phyla. 199
Using qPCR assayed relative abundances of the 10 disease-discriminatory phyla 200
and corresponding weight coefficients as independent variables, the model was 201
applied to diagnose the health status of selected 67 samples, which contributed an 202
overall 91.0% accuracy (Table S3). Specifically, 37 in 39 healthy shrimp were 203
accurately diagnosed as healthy, while 24 in 28 diseased individuals (10 RelatvieD 204
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and 18 ConfidentD samples) were correctly diagnosed as diseased. It should be 205
stressed that seven in 10 RelatvieD samples (that is, 70.0% accuracy) were correctly 206
diagnosed as diseased shrimp at the disease onset stage when disease signs were not 207
apparent (Fig. 3). In addition, no significant difference (paired t-test, N = 67, P = 208
0.176) in the diagnosed disease incidences was detected between the two approaches. 209
Under these premises, the established diagnosis model based on qPCR analysis was 210
applicable for diagnosing the incidence and onset of shrimp disease. 211
212
DISCUSSION 213
Currently, HTS has been widely applied to investigate the compositions of gut 214
microbiota in biological sciences. However, HTS is much more expensive, 215
time-consuming and technical as compared with qPCR analysis. These limitations 216
restrict its application in routine and rapid detection of the gut microbial signatures 217
that are indicative of shrimp health status. To overcome this obstacle, we established a 218
qPCR-based approach based on prior HTS information to detect the relative 219
abundances of disease-discriminatory phyla, which offers independent variables for 220
quantitatively diagnosing the incidences of shrimp disease. 221
222
Accurate diagnosis of shrimp health status via disease-discriminatory phyla 223
The gut bacterial communities exhibited high temporal dynamics as healthy shrimp 224
aged (Fig. 1A), mirroring what have been observed in fishes (25-27). Additionally, the 225
gut bacterial communities are markedly affected by the occurrences of shrimp disease 226
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(Fig. 1B). Thus, both shrimp age and disease are the driving factors in governing the 227
gut microbiota. For this reason, it is mandatory to distinguish the effects of shrimp age 228
from these of disease on the gut microbiota. By distinguishing between age- and 229
disease- discriminatory taxa, a recent study theoretically exemplifies that the profiles 230
of gut eukaryotic disease-discriminatory taxa can accurately stratify shrimp health 231
status (20). Similarly, gut bacterial signatures for red-operculum disease are identified 232
at the species level in crucian carp (12). However, given the conservatism of bacterial 233
16S rRNA gene, it is challenge, if not impossible, to design primers for targeting 234
specific strains, thereby hampering a rapid detection of disease-discriminatory taxa. 235
Instead, a few studies have designed 16S rRNA gene-targeted group-specific primers 236
for rapidly detecting the dominant gut bacterial phyla (15, 17, 18). Inspired by these 237
studies, age-discriminatory phyla, Alphaproteobacteria and Verrucomicrobia, were 238
firstly identified. The relative abundance of Alphaproteobacteria tended to be 239
decreased as healthy shrimp aged, while Verrucomicrobia peaked at the juvenile stage 240
(Fig. S2). Thus, it appears that the initial predominant gut bacteria do not sustain their 241
advantages over shrimp development, whereas are selected by shrimp. This assertion 242
agrees data from previous studies in which the gut microbial communities are distinct 243
over shrimp ontogenesis (13, 20, 29), and from rearing water (8, 9, 11). Similarly, the 244
gut microbiota of catfish undergoes significant temporal shifts, despite rearing water 245
microbial communities remain constant during the host development (26). 246
Further, we ruled out the effects of age-discriminatory phyla on the variations in 247
shrimp gut microbiota. After this optimization, profiles of the identified 10 248
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disease-discriminatory phyla contributed an overall 93.2% diagnosis accuracy of 249
shrimp health status (Table 1). Relative abundances of these disease-discriminatory 250
phyla differed significantly between healthy and diseased shrimp (Fig. 2). 251
Consistently, some shrimp diseases, i.e., white feces syndrome and mysis mold 252
syndrome, are known to be caused by dysbiosis in the gut microbiota, instead of one 253
pathogen, one disease (10, 28). It is worth emphasizing that the change pattern for a 254
given phylum is generally concordant with its known pathogenic or beneficial feature. 255
For example, Gammaproteobacteria members, such as Pseudoalteromonadaceae and 256
Vibrionaceae species, are common opportunistic pathogens in shrimp aquaculture (29, 257
30), which enriched significantly in the diseased cohorts (Table S1). In contrast, the 258
relative abundance of Actinobacteria in healthy shrimp was higher than that in 259
diseased hosts (Table S1). A similar pattern has been observed between normal and 260
stunted growth shrimp (the body size significantly smaller than normal shrimp) (31), 261
and between healthy and white feces syndrome shrimp (10). It has been proposed that 262
shrimp gut bacterial communities have a low functional redundancy (32). Thus, 263
dramatic shifts in the gut microbiota could alter the microbiome-conferred 264
functionalities, such as digestive activity (33), focal adhesion and disease infection 265
(32), which in turn results in the occurrence of shrimp disease. It is likely that the 266
divergences in gut microbiota between healthy and diseased shrimp are more apparent 267
at finer bacterial taxonomic level (13). Thus, we further evaluated the effects of 268
taxonomic level on diagnosis power of gut disease-discriminatory lineages. 269
Intriguingly, there were comparable diagnosis accuracies that achieved by gut 270
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signatures at the bacterial species and phylum levels (95.1 % vs. 93.2%, Table 1). 271
Hence, it is rational and feasible to select phylum level as the final indicators for 272
diagnosing the incidences of shrimp disease. 273
274
Designing group-specific primers for shrimp gut microbiota 275
Given the inherent socio-economic and health impacts, a key challenge will be how to 276
efficiently and quantitatively detect the gut bacterial signatures. Here, we applied 277
qPCR to detect the relative abundances of the 10 disease-discriminatory phyla. Indeed, 278
16S rRNA gene-targeted group-specific primers have been designed for detecting the 279
gut dominant phyla in pig (17), mouse (18), and humans (24, 34). However, it should 280
be noted that the gut microbiotas in these higher vertebrates are dominated by 281
Bacteroidetes and Firmicutes phyla, which are distinct from these in shrimp, with a 282
predominance of Proteobacteria (Fig. S1). These apparent differences raise the 283
question of whether available phylum-specific primers are also efficient for targeting 284
shrimp gut microbiota? Indeed, three primer pairs from the literatures did not work. 285
For example, Planctomycetes-specific primer has successfully detected this lineage in 286
the human gut microbiota (34), while no amplicons were obtained in detecting the 287
shrimp gut microbiota. Therefore, we designed the 16S rRNA gene-targeted 288
group-specific primers for the five disease-discriminatory phyla according to their 289
compositions in shrimp gut microbiota (Table 2). A striking feature of these designed 290
primers was that the 16S full-length sequences were collected according to the 291
representative sequences from HTS. This trick enabled us to design primers that were 292
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specific for targeting shrimp gut microbiota. After optimizing the reaction conditions, 293
the qPCR assay allowed the primer pairs to react in a shared thermocycling condition, 294
thereby facilitating a rapid data acquisition. Furthermore, in silico analysis and 295
sequencing of amplicons confirmed that these primers exhibited high specificity for 296
their targets (Table 2). 297
298
Consistent diagnosis accuracy between qPCR and HTS 299
To further validate the feasibility of qPCR for detecting the disease-discriminatory 300
phyla, we evaluated the consistency between the pattern assayed by qPCR and HTS 301
across the selected 67 samples. As expected, there was a significant and positive 302
correlation between relative abundances of the 10 disease-discriminatory phyla 303
assayed by the two approaches (Fig. S7). Intriguingly, although the diagnosed 304
incidences of shrimp disease were differed to a certain extent for a few samples, the 305
overall diagnosis accuracies were comparable (Fig. 3). Thus, qPCR assay can be 306
applied as an applicable approach to detect the relative abundances of shrimp gut 307
disease-discriminatory phyla, thereby affording independent variables for 308
quantitatively diagnosing the incidences of shrimp disease. 309
310
Disease-discriminatory phyla are diagnostic of shrimp disease onset 311
Ample evidence has shown that dysbiosis in the gut microbiota are concurrent with 312
shrimp diseases (8, 10, 11, 13). This raises the question of whether changes in 313
disease-discriminatory phyla are premonitory at disease onset stage. It should be 314
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stressed that differences in the gut bacterial communities were undetectable between 315
ConfidentH and RelatvieD shrimp (at the disease onset stage, Fig. 1B). However, 316
relative abundances of the 10 disease-discriminatory phyla significantly changed in 317
the RelatvieD shrimp as compared with ConfidentH cohorts (Table S1). Similarly, it 318
has been reported that some commensals are more sensitive to starvation stress, 319
whereas the overall gut microbiota of shrimp is unchanged (35). The 10 320
disease-discriminatory phyla contributed 70% diagnosis accuracy of shrimp health 321
status at this ‘transition’ from healthy to diseased stage (Fig. 3, estimated by both 322
qPCR and HTS). Consistently, disease severity and mortality rapidly increased in the 323
subsequent two samplings. This finding means that marked changes in the sensitive 324
gut disease-discriminatory phyla are premonitory for shrimp disease. In addition, the 325
nine ConfidentH samples (healthy control for the disease onset stage) were correctly 326
diagnosed as healthy, congruent with no disease emergence later. Though this pattern 327
requires validation in a larger sampling size, it exemplifies that the application of gut 328
disease-discriminatory phyla is promising to diagnose the onset of shrimp disease (i.e., 329
estimate the incidences of future disease outcome), when disease sign is not apparent. 330
331
Conclusion 332
This study attempts to apply qPCR to detect the gut bacterial signatures for 333
diagnosing the incidence of shrimp disease. To achieve this, disease-discriminatory 334
phyla are elegantly identified after ruling out the age-discriminatory phyla. In addition, 335
primer pairs for five disease-discriminatory phyla are designed according to their 336
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components in shrimp gut microbiota, which improved the specificity and sufficiency 337
for targeting the shrimp gut lineages. Notably, the diagnosis accuracies achieved by 338
qPCR and HTS are comparable. Intriguingly, the high diagnosis accuracy also holds 339
true for samples at the early, preclinical stage of transition. This work affords an 340
applicable approach for diagnosing the incidences and onset of shrimp disease. 341
However, additional work is required to validate the applicability of this novel 342
strategy in practice. 343
344
MATERIALS AND METHODS 345
346
Experimental design and sample collection 347
Shrimp samples (L. vannamei) were collected from greenhouse ponds at Zhanqi, 348
Ningbo, the Eastern China (29°31
′N, 121
°31
′E). After one week of inoculation, shrimp 349
samples were bi/weekly collected from selected six ponds. A disease occurred in three 350
of the monitored six ponds on 4th
July, 87 days after cultivation. The mortality rates 351
increased over disease exacerbation, thus experiment was forcefully terminated on 352
10th
July due to an urgent harvest. The diseased shrimp exhibited typical symptoms of 353
white feces syndrome, including inactivity, white guts, red hepatopancreas, and white 354
fecal strings (36). We traced back the three ponds with diseased shrimp as the disease 355
onset stage (without apparent physical symptoms, on 1st July, 84 days after 356
cultivation). To improve statistical power, pseudo-biological replicates of diseased 357
and healthy shrimp were collected on 1st July and thereafter. In total, 113 samples 358
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(including 85 ConfidentH, 10 RelativeD: disease onset stage, and 18 ConfidentD: 359
over disease exacerbation, on 4th
and 10th
July, samples) were enrolled to analyze the 360
shrimp gut bacterial community (Table S4). Shrimp were reared with water from the 361
corresponding pond and were aerated before transport to laboratory. 362
363
DNA extraction and bacterial 16S rRNA gene Illumina sequencing 364
On the sampling day, shrimp were washed with sterilized saline water, and wiped with 365
alcohol cotton ball before dissected intestines. Afterwards, intestines from every three 366
shrimp were dissected on ice with sterile forceps and were pooled to compose one 367
biological sample for extracting sufficient amount of DNA for each sample. 368
Genomic DNA was extracted using a QIAamp DNA Stool mini kit (Qiagen, GmbH, 369
Hilden, Germany) according to the instructions. The concentration and purity of DNA 370
were determined using a NanoDrop ND-2000 spectrophotometer (NanoDrop 371
Technologies, Wilmington, USA). The DNA of each sample was divided into two 372
parts: one was used as template for amplicons, and the other part was used for qPCR 373
assay. 374
The V3-V4 regions of bacterial 16S rRNA gene were amplified and sequenced 375
using an Illumina MiSeq platform (Illumina, San Diego, CA, USA). The paired-end 376
reads were spliced with FLASH (37), then were analyzed by Quantitative Insights 377
Into Microbial Ecology (QIIME v1.9.0) pipeline to generate the bacterial profiles, 378
including filtering sequences on the basis of quality score < 20, sequence length, 379
ambiguous sequence, chimera and primer mismatch thresholds (38). To eliminate the 380
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bias induced by unequal sequencing depth, we standardized the random selection of 381
21,000 sequences per sample in subsequent analysis. The raw sequences data in this 382
study were deposited in DDBJ under the accession number DRA005256. 383
384
Identification of shrimp gut disease-discriminatory phyla 385
Given the predominance of Proteobacteria in shrimp gut microbiota, it was divided 386
into subphyla. The relative abundances of all bacterial phyla in the 85 ConfidentH 387
samples were fitted against their chronologic age (days after inoculation) using the 388
“random Forest” package in R v3.3.3 (39). The “rfcv” function was implemented to 389
identify the minimal number of top ranking age-discriminatory phyla required for 390
prediction by 999 iterations. To minimize age-effects on the identification of 391
disease-discriminatory phyla, the top-ranking age-discriminatory phyla were removed 392
from the dataset. The random Forest model was repeated to screen 393
disease-discriminatory phyla between the 85 ConfidentH and the 18 ConfidentD 394
groups. The diagnosis accuracy based on the profiles of disease-discriminatory phyla 395
was further calculated with a 10-fold cross-validation algorithm (40). To evaluate to 396
what extent the coarse phyla indicators affect diagnosis accuracy, we compared the 397
diagnosis accuracy using the profiles of disease-discriminatory lineages at bacterial 398
phylum, class, order, family, genus, or species level as described above. Finally, to 399
evaluate the capacity for diagnosing shrimp disease onset, we used the profiles of 400
disease-discriminatory lineages to stratify the 10 RelativeD samples. 401
402
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Design 16S rRNA gene-targeted specific primers 403
The 16S rRNA gene-targeted group-specific primers of the disease-discriminatory 404
phyla were searched from literatures. If the primer pairs were unavailable or 405
insufficient, we designed them according to their compositions in shrimp gut 406
microbiota. To design specific primers for the shrimp disease-discriminatory phyla, 50 407
representative sequences from each discriminatory phylum were selected from the 408
V3-V4 merged sequencing. Each sequence was blasted against the Ribosomal 409
Database Project II (RDP-II) (http://rdp.cme.msu.edu/probematch/search.jsp) to 410
collect its full length of 16S rRNA gene sequence (41). Sequences affiliated with each 411
phylum were clustered using ClustalX (42), and then the consensus regions were 412
identified using BioEdit (43). The primer pairs were designed with a length of 18 to 413
24 nt and a predicted melting temperature ranged from 50°C to 55°C using Primer 5 414
software. To minimize the nucleotide mismatches, the primers were designed with the 415
phyla-specific nucleotide(s) at the 3’ end (23, 44). The degenerate bases were 416
determined according to the bias of abundant sub-phyla in the shrimp gut microbiota. 417
418
Primer specificity and optimal temperature condition 419
The specificity of all primer sets was initially verified in silico using the tool ‘probe 420
match’ at the RDP-II platform (41) and was further validated by single band and 421
expected size using conventional PCR. To maximize amplification efficiency, PCRs 422
were performed for all primer pairs at gradient annealing temperatures from 50°C to 423
55°C (with an interval of 1°C) with following conditions: initial denaturing of 5 min 424
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at 94°C, 30 cycles of 94°C for 30 s, gradient annealing for 30 s and 72°C for 45 s, and 425
a final elongation step at 72°C for 10 min. Every 25 μL PCR reaction system 426
contained 0.2 U/μL of Taq polymerase, 2 μL of dNTP, 1 μL concentrations of each 427
primer, 2.5 μL of buffer, and 50 ng DNA as the templates. The expected sizes of 428
amplicons were checked by standard gel electrophoresis. After optimization of the 429
PCR annealing, it was found that 54°C maximized the specificity of all primer pairs. 430
To further verify the specificity of the primers, the amplicons of each 431
disease-discriminatory phylum were sequenced using an Illumina MiSeq platform. 432
Proportion of the sequences affiliated with its targeted phylum was evaluated in 433
Qiime pipeline as described above. 434
435
Quantitative PCR reaction condition 436
Quantitative PCRs were carried out in sealed 96-well optical plates on 7500 437
Real-Time PCR Systems (Applied Biosystems) with 2 × FastStart SYBR green mix 438
(SYBR Premix Ex TaqTM, TaKaRa). All qPCR mixtures contained 10 μL of 2 × 439
FastStart SYBR green, 0.4 μL of 50× ROX Reference Dye, 0.8 μL of each forward 440
and reverse primers (0.8 μM), and 8 μL of DNA template (equilibrated to 10 ng). The 441
amplification program was 95°C for 2 min, followed by 40 cycles of 95°C for 15 s, 442
54°C for 20 s and 72°C for 30 s. The threshold cycle (CT) and melting curve were 443
generated after amplification. The CT values and baseline settings were performed by 444
automatic analysis settings. To minimize qPCR-induced biases, each primer pair was 445
amplified in triplicate, and a negative control was set. 446
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447
Primer efficiency 448
To determine the amplification efficiency of each primer pair, standard procedures 449
were performed as described elsewhere (23), including dilution series of shrimp gut 450
microbial DNA, calculating a linear regression based on the CT values, and inferring 451
the efficiency from the slope of the regression line. Serial dilutions of gut DNA were 452
made of 1:1, 1:4, 1:16, 1:64, and 1:256. Every dilution and primer pair were amplified 453
in triplicate, respectively. A Non-Template Control (NTC) was set in each assay, 454
which eliminated interference with operation systems and pollution factors. 455
456
Quantitative analysis of the disease-discriminatory phyla of 16S rRNA genes 457
The dichotomous DNA of each sample was amplified using universal primer (internal 458
reference) and a specific primer pair for a given disease-discriminatory phylum to 459
detect its relative abundance (X) using the following formula (23): 460
461
where the Eff.Univ and Eff.Spec were the amplification efficiencies of the total 16S 462
rRNA genes (reference gene as control for normalization, primer pair: 341F and 806R) 463
(45) and the target phylum (2 = 100% and 1 = 0%). Ctuniv and Ctspec are the threshold 464
cycles recorded by the thermocycler. 465
466
The applicability of qPCR for diagnosing the incidences of shrimp disease 467
To evaluate the incidence of shrimp disease, relative abundances of the 10 468
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disease-discriminatory phyla assayed by qPCR and corresponding weight coefficients 469
were used as independent variables in the random Forest model (39). Due to a large 470
sample size here, 67 samples were selected in qPCR assay (resulting in 2214 reactions, 471
that is, 67 samples × 11 targets × 3 repeats + 3 negative control). If the diagnosed 472
healthy probability > 50%, the sample was stratified as healthy, while the probability 473
< 50% was stratified as diseased. The consistency between the diagnosed and 474
observed health status was termed as correct diagnosis, otherwise termed as false 475
diagnosis. To evaluate the applicability of qPCR, we compared the diagnosis accuracy 476
of shrimp health status as achieved by qPCR and HTS. 477
478
ACKNOWLEDGEMENTS 479
This work was supported by the Public Welfare Technology Application Research 480
Project of Zhejiang Province (2016C32063), the Project of Science and Technology 481
Department of Ningbo (2017C10044), the Xinmiao Talent program of Zhejiang 482
Province (2018R405080), and the K.C. Wong Magna Fund in Ningbo University.483
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TABLE 1 The diagnosis accuracy based on profiles of the disease-discriminatory 631
lineages using samples from the 85 “ConfidentH” and the 18 “ConfidentD” groups at 632
each bacterial taxonomic level. Bold values represent the numbers of correct 633
diagnoses in each health status. 634
635
Taxonomic level Observed
status
Diagnosed health status Correct
number
Overall
accuracy (%) Healthy Diseased
Phylum Healthy 82 3 96 93.2
Diseased 4 14
Class Healthy 83 2 96 93.2
Diseased 5 13
Order Healthy 83 2 95 92.2
Diseased 6 12
Family Healthy 82 3 96 93.2
Diseased 4 14
Genus Healthy 83 2 98 95.1
Diseased 3 15
Species Healthy 84 1 98 95.1
Diseased 4 14
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TABLE 2 The disease-discriminatory phylum specific primers and their importance, specificity and efficiency 636
Target phylum Sequence (5′-3
′)
a Importance
scores (%)
Specificity
(%) b
Amplicons
belonging to
target (%)
Amplification
efficiency (%)
Reference
Planctomycetes F: GGCTGCAGTCGAGRATCT
R: GGCTGCTGGCACGDACTTAG
30.8 99.8 98.2±0.3 96.2 This study
Gammaproteobacteria F: GCTCGTGTTGTGAAATGTTGG
R: CGTAAGGGCCATGATGACTTG
15.0 99.8 98.6±0.4 94.6 This study
Tenericutes F: ATGTGTAGCGGTAAAATGCGTAA
R: CMTACTTGCGTACGTACTACT
13.9 95.0 96.3±0.2 96.8 (18)
Actinobacteria F: TACGGCCGCAAGGCTA
R: TCRTCCCCACCTTCCTCCG
11.7 91.3 95.6±0.4 99.3 (23)
Deltaproteobacteria F: GTGCNARCGTTGYTCGGA
R: CCGTCAATTCMTTTRAGTTT
6.8 83.5 91.2±0.7 92.5 (46)
Chlamydiae F: CCAACACTGGGACTGAGACACT
R: AGCTGCTGGCACGGAGTTAG
6.1 100 98.6±0.2 98.6 This study
Chloroflexi F: GGTGKAGTGGTGRAATGCGTAGA
R: TTCCTTTGAGTTTTARSCTTGC
5.9 92.9 97.3±0.9 96.4 This study
Cyanobacteria F: CGGTAAKACGGAGGAKGCA
R: TCGCCACTGGTGTTCTTCC
4.3 99.2 99.5±0.4 93.1 This study
Bacteroidetes F: CRAACAGGATTAGATACCCT
R: GGTAAGGTTCCTCGCGTAT
3.5 96.2 98.4±0.6 94.6 (23)
Firmicutes F: GGAGYATGTGGTTTAATTCGAAGCA
R: AGCTGACGACAACCATGCAC
2.1 99.6 99.2±0.3 93.8 (17)
a Nucleotide codes: M = A/C; R = A/G; S = C/G; Y = C/T; K = G/T; D = A/G/T; B = C/G/T; N = any nucleotide. 637
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b Proportion of ‘Probe Match’ hits that fall within the target phylum.638
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Figure Titles and Legends 639
640
Figure 1 Principal coordinate analysis (PCoA) of the shrimp gut bacterial phyla based 641
on Bray-Curtis distances over shrimp ages (A) and over disease progression (B). D: 642
diseased, H: healthy. Numbers are shrimp ages (days after inoculation). 643
644
Figure 2 Comparing the relative abundances (Means ± standard deviations) of the 10 645
disease-discriminatory phyla assayed by qPCR and HTS between healthy and 646
diseased shrimp. *: P < 0.05 between healthy and diseased shrimp assayed by each 647
approach using an unpaired t-test. 648
649
Figure 3 The diagnosed probabilities of shrimp health based on profiles of the 10 650
disease-discriminatory phyla assayed by qPCR (A) and HTS (B). The diagnosed 651
probability of health > 50% was stratified as healthy, while that < 50% was stratified 652
as diseased. The inconsistency between observed and diagnosed health status was 653
termed as false diagnose (solid symbols), while the consistency was termed as correct 654
diagnose (open symbols). 655
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