on july 8, 2020 by guest · 7/9/2018 · 28 disease -discriminatory phyla assayed by qpcr...

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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

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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

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*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

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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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REFERENCES 484

1. Zhou J, Fang W, Yang X, Zhou S, Hu L, Li X, Qi X, Su H, Xie L. 2012. A 485

nonluminescent and highly virulent Vibrio harveyi strain is associated with 486

“bacterial white tail disease” of Litopenaeus vannamei shrimp. PLos One 487

7:e29961. 488

2. Leung TF, Bates AE. 2013. More rapid and severe disease outbreaks for 489

aquaculture at the tropics: implications for food security. J Appl Ecol 490

50:215-222. 491

3. Xiong J. 2018. Progress in the gut microbiota in exploring shrimp disease 492

pathogenesis and incidence. Appl Microbiol Biotechnol doi: 493

10.1007/s00253-018-9199-7. 494

4. Summerfelt ST, Sharrer MJ, Tsukuda SM, Gearheart M. 2009. Process 495

requirements for achieving full-flow disinfection of recirculating water using 496

ozonation and UV irradiation. Aquacult Eng 40:17-27. 497

5. Nie L, Zhou Q, Qiao Y, Chen J. 2017. Interplay between the gut microbiota 498

and immune responses of ayu (Plecoglossus altivelis) during Vibrio 499

anguillarum infection. Fish Shellfish Immunol 68:479-487. 500

6. Round JL, Mazmanian SK. 2009. The gut microbiota shapes intestinal 501

immune responses during health and disease. Nat Rev Immunol 9:313-323. 502

7. Rungrassamee W, Klanchui A, Maibunkaew S, Karoonuthaisiri N. 2016. 503

Bacterial dynamics in intestines of the black tiger shrimp and the Pacific white 504

shrimp during Vibrio harveyi exposure. J Invertebr Pathol 133:12-19. 505

8. Chen W, Ng TH, Wu J, Chen J, Wang H. 2017. Microbiome dynamics in a 506

shrimp grow-out pond with possible outbreak of acute hepatopancreatic 507

necrosis disease. Sci Rep 7:9395. 508

9. Cornejo-Granados F, Lopez-Zavala AA, Gallardo-Becerra L, 509

Mendoza-Vargas A, Sánchez F, Vichido R, Brieba LG, Viana MT, 510

Sotelo-Mundo RR, Ochoa-Leyva A. 2017. Microbiome of Pacific Whiteleg 511

shrimp reveals differential bacterial community composition between wild, 512

aquacultured and AHPND/EMS outbreak conditions. Sci Rep 7:11783. 513


.asm.org/

Dow

nloaded from

http://aem.asm.org/

25

10. Hou D, Huang Z, Zeng S, Liu J, Wei D, Deng X, Weng S, Yan Q, He J. 2018. 514

Intestinal bacterial signatures of white feces syndrome in shrimp. Appl 515

Microbiol Biotech 102:3701-3709. 516

11. Xiong J, Wang K, Wu J, Qiuqian L, Yang K, Qian Y, Zhang D. 2015. 517

Changes in intestinal bacterial communities are closely associated with shrimp 518

disease severity. Appl Microbiol Biotech 99:6911-6919. 519

12. Li T, Li H, Gatesoupe FJ, She R, Lin Q, Yan X, Li J, Li X. 2017. Bacterial 520

signatures of "red-operculum" disease in the gut of crucian carp (Carassius 521

auratus). Microb Ecol 74:510-521. 522

13. Xiong J, Zhu J, Dai W, Dong C, Qiu Q, Li C. 2017. Integrating gut microbiota 523

immaturity and disease-discriminatory taxa to diagnose the initiation and 524

severity of shrimp disease. Environ Microbiol 19:1490-1501. 525

14. Guinane CM, Cotter PD. 2013. Role of the gut microbiota in health and 526

chronic gastrointestinal disease: understanding a hidden metabolic organ. Ther 527

Adv Gastroen 6:295-308. 528

15. Nordentoft S, Kabell S, Pedersen K. 2011. Real-time detection and 529

identification of Chlamydophila species in veterinary specimens by using 530

SYBR green-based PCR assays. Appl Microbiol Biotech 77:6323-6330. 531

16. Jost T, Lacroix C, Braegger CP, Chassard C. 2012. New insights in gut 532

microbiota establishment in healthy breast fed neonates. PLos One 7:e44595. 533

17. Guo X, Xia X, Tang R, Zhou J, Zhao H, Wang K. 2008. Development of a 534

real‐time PCR method for Firmicutes and Bacteroidetes in faeces and its 535

application to quantify intestinal population of obese and lean pigs. Lett Appl 536

Microbiol 47:367-373. 537

18. Yang Y, Chen M, Yang B, Huang X, Zhang X, He L, Zhang J, Hua Z. 2015. 538

Use of 16S rRNA gene-targeted group-specific primers for real-time PCR 539

analysis of predominant bacteria in mouse feces. Appl Environ Microbiol 540

81:6749-6756. 541

19. Teng F, Yang F, Huang S, Bo C, Xu Z, Amir A, Knight R, Ling J, Xu J. 2015. 542

Prediction of early childhood caries via spatial-temporal variations of oral 543


.asm.org/

Dow

nloaded from

http://aem.asm.org/

26

microbiota. Cell Host Microbe 18:296-306. 544

20. Xiong J, Yu W, Dai W, Zhang J, Qiu Q, Ou C. 2018. Quantitative prediction 545

of shrimp disease incidence via the profiles of gut eukaryotic microbiota. Appl 546

Microbiol Biotech 102:3315-3326. 547

21. García-Bernal M, Medina-Marrero R, Rodríguez-Jaramillo C, Marrero-Chang 548

O, Campa-Córdova ÁI, Medina-García R, Mazón-Suástegui JM. 2018. 549

Probiotic effect of Streptomyces spp. on shrimp (Litopenaeus vannamei) 550

postlarvae challenged with Vibrio parahaemolyticus. Aquacul Nutr 551

24:865-871. 552

22. Gomez-Gil B, Fajer-Avila E, Pascual J, Macia´n MC, Pujalte MaJ, Garay E, 553

Roque A. 2008. Vibrio sinaloensis sp. nov., isolated from the spotted rose 554

snapper, Lutjanus guttatus Steindachner, 1869. Int J Syst Evol Microb 555

58:1621-1624. 556

23. Gregoris TBD, Aldred N, Clare AS, Burgess JG. 2011. Improvement of 557

phylum- and class-specific primers for real-time PCR quantification of 558

bacterial taxa. J Microbiol Meth 86:351-356. 559

24. Matsuki T, Watanabe K, Fujimoto J, Takada T, Tanaka R. 2004. Use of 16S 560

rRNA gene-targeted group-specific primers for real-time PCR analysis of 561

predominant bacteria in human feces. Appl Environ Microbiol 70:7220-7228. 562

25. Burns AR, Stephens WZ, Stagaman K, Wong S, Rawls JF, Guillemin K, 563

Bohannan BJ. 2016. Contribution of neutral processes to the assembly of gut 564

microbial communities in the zebrafish over host development. ISME J 565

10:655-664. 566

26. Zhang Z, Li D, Refaey MM, Xu W, Tang R, Li L. 2018. Host age affects the 567

development of southern catfish gut bacterial community divergent from that 568

in the food and rearing water. Front Microbiol 9:495. 569

27. Yan Q, Li J, Yu Y, Wang J, He Z, Van Nostrand JD, Kempher ML, Wu L, 570

Wang Y, Liao L. 2016. Environmental filtering decreases with fish 571

development for the assembly of gut microbiota. Environ Microbiol 572

18:4739-4754. 573


.asm.org/

Dow

nloaded from

http://aem.asm.org/

27

28. Zhu J, Dai W, Qiu Q, Dong C, Zhang J, Xiong J. 2016. Contrasting ecological 574

processes and functional compositions between intestinal bacterial community 575

in healthy and diseased shrimp. Microb Ecol 72:975-985. 576

29. Zheng Y, Yu M, Liu J, Qiao Y, Wang L, Li Z, Zhang X, Yu M. 2017. 577

Bacterial community associated with healthy and diseased pacific white 578

shrimp (Litopenaeus vannamei) larvae and rearing water across different 579

growth stages. Front Microbiol 8:1362. 580

30. Goarant C, Reynaud Y, Ansquer D, Decker Sd, Saulnier D, Roux Fdrl. 2006. 581

Molecular epidemiology of Vibrio nigripulchritudo, a pathogen of cultured 582

penaeid shrimp (Litopenaeus stylirostris) in New Caledonia. Syst Appl 583

Microbiol 29:570-580. 584

31. Saulnier D, Avarre JC, Moullac GL, Ansquer D, Levy P, Vonau V. 2000. 585

Rapid and sensitive PCR detection of Vibrio penaeicida, the putative 586

etiological agent of syndrome 93 in New Caledonia. Dise Aquat Organ 587

40:109-115. 588

32. Xiong J, Dai W, Zhu J, Liu K, Dong C, Qiu Q. 2017. The underlying 589

ecological processes of gut microbiota among cohabitating retarded, 590

overgrown and normal shrimp. Microb Ecol 73:988-999. 591

33. Dai W, Yu W, Xuan L, Tao Z, Xiong J. 2018. Integrating molecular and 592

ecological approaches to identify potential polymicrobial pathogens over a 593

shrimp disease progression. Appl Microbiol Biotech 102:3755-3764. 594

34. Cayrou C, Sambe B, Armougom F, Raoult D, Drancourt M. 2013. Molecular 595

diversity of the Planctomycetes in the human gut microbiota in France and 596

Senegal. Apmis 121:1082-1090. 597

35. Dai W, Zhang J, Qiu Q, Chen J, Yang W, Ni S, Xiong J. 2018. Starvation 598

stress affects the interplay among shrimp gut microbiota, digestion and 599

immune activities. Fish Shellfish Immunol 80:191-199. 600

36. Thitamadee S, Prachumwat A, Srisala J, Jaroenlak P, Salachan PV, 601

Sritunyalucksana K, Flegel TW, Itsathitphaisarn O. 2016. Review of current 602

disease threats for cultivated penaeid shrimp in Asia. Aquaculture 452:69-87. 603


.asm.org/

Dow

nloaded from

http://aem.asm.org/

28

37. Magoč T, Salzberg SL. 2011. FLASH: fast length adjustment of short reads to 604

improve genome assemblies. Bioinformatics 27:2957-2963. 605

38. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello 606

EK, Fierer N, Peña AG, Goodrich JK, Gordon JI. 2010. QIIME allows 607

analysis of high-throughput community sequencing data. Nat Methods 608

7:335-336. 609

39. Liaw A, Wiener M. 2002. Classification and regression by randomForest. R 610

News 2:18-22. 611

40. Refaeilzadeh P, Tang L, Liu H. 2009. Cross-validation, in: Encyclopedia of 612

database systems. Springer 532-538. 613

41. Maidak BL, Cole JR, Lilburn TG, Parker CTJ, Saxman PR, Farris RJ, Garrity 614

GM, Olsen GJ, Schmidt TM, Tiedje JM. The RDP-II (Ribosomal Database 615

Project). Nucleic Acids Res 29:173-174. 616

42. Thompson JD, Gibson TJ, Plewniak F, Jeanmougin F, Higgins DG. 1997. The 617

CLUSTAL_X windows interface: flexible strategies for multiple sequence 618

alignment aided by quality analysis tools. Nucleic Acids Res 25:4876-4882. 619

43. Hall TA. BioEdit: a user-friendly biological sequence alignment editor and 620

analysis program for Windows 95/98/NT. Nucleic Acids Symp Ser 41:95-98. 621

44. Petruska J, Goodman MF, Boosalis MS, Sowers LC, Cheong C, Tinoco I. 622

1988. Comparison between DNA melting thermodynamics and DNA 623

polymerase fidelity. Proc Nat Acad Sci USA 85:6252-6256. 624

45. Roggenbuck M, Schnell IB, Blom N, Bælum J, Bertelsen MF, 625

Sicheritz-Pontén T, Sørensen SJ, Gilbert MTP, Graves GR, Hansen LH. 2015. 626

The microbiome of new world vultures. Nat Commun 5:5498. 627

46. Morales SE, Holben WE. 2009. Empirical testing of 16S rRNA gene PCR 628

primer pairs reveals variance in target specificity and efficacy not suggested 629

by in silico analysis. Appl Environ Microbiol 75:2677-2683. 630


.asm.org/

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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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