Open Access

Clinical detection of human probiotics and human pathogenic bacteria by using a novel high-throughput platform based on next generation sequencing

  • Chih-Min Chiu1,
  • Feng-Mao Lin1,
  • Tzu-Hao Chang2,
  • Wei-Chih Huang1,
  • Chao Liang1,
  • Ting Yang1,
  • Wei-Yun Wu1,
  • Tzu-Ling Yang1,
  • Shun-Long Weng1, 4, 5, 6Email author and
  • Hsien-Da Huang1, 3Email author
Contributed equally
Journal of Clinical Bioinformatics20144:1

DOI: 10.1186/2043-9113-4-1

Received: 31 May 2013

Accepted: 13 December 2013

Published: 13 January 2014

Abstract

Background

The human body plays host to a vast array of bacteria, found in oral cavities, skin, gastrointestinal tract and the vagina. Some bacteria are harmful while others are beneficial to the host. Despite the availability of many methods to identify bacteria, most of them are only applicable to specific and cultivable bacteria and are also tedious. Based on high throughput sequencing technology, this work derives 16S rRNA sequences of bacteria and analyzes probiotics and pathogens species.

Results

We constructed a database that recorded the species of probiotics and pathogens from literature, along with a modified Smith-Waterman algorithm for assigning the taxonomy of the sequenced 16S rRNA sequences. We also constructed a bacteria disease risk model for seven diseases based on 98 samples. Applicability of the proposed platform is demonstrated by collecting the microbiome in human gut of 13 samples.

Conclusions

The proposed platform provides a relatively easy means of identifying a certain amount of bacteria and their species (including uncultivable pathogens) for clinical microbiology applications. That is, detecting how probiotics and pathogens inhabit humans and how affect their health can significantly contribute to develop a diagnosis and treatment method.

Background

High throughput sequencing can analyze a large amount of sequences, enabling sequencing of 16S rRNA to identify complex bacteria species of pathogens and probiotic bacteria. Many naturally occurring bacteria form a complex population in the environment. The human body plays host to a vast array of bacteria, found in oral cavities, skin, gastrointestinal tract and the vagina. Some bacteria are harmful while others are beneficial to the host.

A pathogen is a microorganism that causes disease in its host. For example, bacterial pathogen include Corynebacterium diphtheria (causes diphtheria), Listeria monocytogenes (causes food poisons), and Legionella pneumophila (causes Legionnaires’ disease). Probiotics, another microorganism, benefit the host and has received considerable attention in recent years. A FAO report in 2001 [1] cited the advantages of probiotics as increasing immunity [2, 3], reducing gastrointestinal discomfort [4, 5], and protecting the flora within urogenital tract [6]. As is well known, probiotics can ameliorate symptoms of diseases [7] and reduce the risk of suffering from diseases [8, 9].

Despite the availability of many approaches to identify probiotics and pathogens, most of them are only applicable to specific and cultivable bacteria but time consuming. For instance, conventional methods detect growth of cultured bacteria in approximately two days, or an additional five days to obtain no-growth culture results [10], which is laborious. Besides, some bacteria cannot be cultured [11], subsequently increasing the difficulty of specifying pathogenic bacteria. Moreover, it is hard to determine whether an infection is caused by one or more bacteria types.

16S rRNA sequences, capable of identifying bacteria on a molecular level, can detect uncultivable bacteria [12]. Use of 16S rRNA sequencing can overcome some problems of conventional culture method [13]. Although 16S rRNA sequencing is a more effective means of identifying bacteria than conventional culture method, 16S rRNA sequencing takes a considerable amount of time in amplifying DNA sequences [14]. Sanger sequencing known as “first-generation” or “conventional” sequencing has been used for DNA sequencing for almost two decades. Next generation sequencing (NGS) can analyze large-scale sequences quicker, enable massively parallel analysis, reduce reagent costs and the size of sample components, and perform high throughput [15]. Thus NGS is more efficient than the Sanger method, which generates one read per sample. In addition, NGS of 16S rRNA more easily identify cultivable or uncultivable bacteria [12].

Because of the improvement of sequencing technology and Bioinformatics approaches, the accuracy in distinguishing bacteria with those methods has been increased. Based on high throughput sequencing technology, this work identifies 16S rRNA sequences of bacteria and analyzes bacteria species. High-throughput sequencing can sequence a large number of 16S rRNA sequence more efficiently; with high-throughput sequencing, researchers can acquire information to identify pathogens and probiotic bacteria [1618].

Results

Platform application: gut probiotics and pathogens detection

The read statistics of quality filtering and taxonomy assignment are demonstrated in Table 1. Figure 1A illustrated the percentage of probiotics detected by the proposed platform. Table 2 listed the quantities (matched sequenced reads) of probiotics identified in the samples in the case study. The top three identified probiotics in 12 samples are Lactococcus salivarius, Streptococcus thermophilus, and Bifidobacterium longum. Figure 1B and Table 3 listed the proportion and quantities of pathogens, of which top three pathogens are Escherichia coli, Salmonella enteric, and Haemophilus influenza.
Table 1

Results of quality filtering and taxonomy assignment

ID

Raw reads

QC

Bacteria identified

Probiotics

Pathogens

B011

125420

117451

93.65%

90952

77.44%

60

0.07%

3509

3.86%

B012

132240

120134

90.85%

94679

78.81%

3457

3.65%

20109

21.24%

B013

151876

142585

93.88%

99025

69.45%

3452

3.49%

21341

21.55%

B014

134619

126784

94.18%

95377

75.23%

611

0.64%

6665

6.99%

B016

135457

126507

93.39%

89407

70.67%

49

0.05%

20870

23.34%

B017

141682

131968

93.14%

89465

67.79%

1064

1.19%

8944

10.00%

B018

111228

102382

92.05%

56981

55.66%

910

1.60%

11630

20.41%

B019

128532

120719

93.92%

76877

63.68%

305

0.40%

2775

3.61%

B020

128441

121957

94.95%

89618

73.48%

123

0.14%

3673

4.10%

B031

140941

132311

93.88%

97962

74.04%

2129

2.17%

5194

5.30%

B033

142462

134554

94.45%

80548

59.86%

229

0.28%

2725

3.38%

B034

148854

140059

94.09%

106050

75.72%

9857

9.29%

15436

14.56%

Total

1621752

1517411

93.54%

1066941

70.31%

22246

2.09%

122871

11.52%

Figure 1

Relative abundance of probiotics and pathogenic bacteria from human gut of all samples. (A) The percentage of probiotics was identified in the samples. (B) The proportion of pathogenic bacteria was identified in the samples in the case study.

Table 2

The quantities (matched sequenced reads) of probiotics identified in the samples in the case study

Probiotics

B011

B012

B013

B014

B016

B017

B018

B019

B020

B031

B033

B034

 

Bacillus coagulans

0

81

6

1

2

0

3

0

1

0

9

1

104

Bifidobacterium adolescentis

4

3

1520

81

1

372

185

177

1

5

0

375

2724

Bifidobacterium animalis

0

101

37

3

1

16

32

1

1

0

0

50

242

Bifidobacterium bifidum

0

3

3

0

0

84

2

0

0

0

0

21

113

Bifidobacterium breve

0

1092

465

96

6

102

212

13

2

9

18

79

2094

*Bifidobacterium longum

3

1859

1092

198

27

256

439

34

5

15

55

238

4221

Lactobacillus brevis

0

0

0

0

0

0

1

0

0

0

0

10

11

Lactobacillus casei

0

10

1

1

0

0

0

0

0

1

0

0

13

Lactobacillus fermentum

0

0

0

0

1

0

0

4

0

1

0

28

34

Lactobacillus gasseri

0

0

0

1

1

0

0

0

0

0

0

77

79

Lactobacillus johnsonii

0

0

0

0

0

0

0

0

0

0

0

7

7

Lactobacillus paracasei

0

1

2

0

0

0

0

0

0

0

0

1

4

Lactobacillus plantarum

1

0

0

2

0

0

0

0

0

0

0

0

3

Lactobacillus reuteri

0

0

0

1

0

0

0

0

0

0

0

1

2

Lactobacillus rhamnosus

0

1

0

0

0

0

0

0

0

0

0

2

3

*Lactobacillus salivarius

2

1

2

8

1

5

3

1

3

11

2

6753

6792

Lactococcus lactis

2

0

0

6

1

0

2

0

0

16

1

10

38

*Streptococcus thermophilus

48

305

324

213

8

229

31

75

110

2071

144

2204

5762

 

60

3457

3452

611

49

1064

910

305

123

2129

229

9857

 

For each species, if the number of reads is 0 for all samples, that species was not shown.

*The leading three probiotics are Lactococcus salivarius, Streptococcus thermophilus and Bifidobacterium longum.

Table 3

The quantities (matched sequenced reads) of pathogens identified in the samples in the case study

Pathogens

B011

B012

B013

B014

B016

B017

B018

B019

B020

B031

B033

B034

 

Bordetella pertussis

0

1

0

0

0

1

0

0

0

0

0

0

2

Brucella abortus

0

0

0

0

0

0

0

0

0

0

0

0

0

Brucella melitensis

0

0

0

0

0

0

0

0

0

0

0

0

0

Campylobacter jejuni

0

0

0

11

0

0

0

0

0

11

1

40

63

Clostridium botulinum

0

38

5048

4

5

2

1

361

153

1211

115

59

6997

Clostridium difficile

0

0

1

0

0

0

0

0

0

0

0

0

1

Clostridium perfringens

0

1

2

0

0

0

0

3

10

24

12

93

145

Corynebacterium diphtheriae

0

1

0

1

0

0

0

0

0

1

0

0

3

Enterococcus faecalis

57

13

1

4

8

4

0

20

5

19

6

38

175

Enterococcus faecium

41

8

2

6

5

2

1

22

3

13

1

32

136

*Escherichia coli

1744

8560

7900

3637

10651

4404

5691

1424

1733

210

165

4483

50602

*Haemophilus influenzae

2

1771

2

1055

8

49

1

171

15

1802

2322

4502

11700

Neisseria meningitidis

0

2

0

3

1

0

1

1

1

1

1

1

12

Pseudomonas aeruginosa

1

6

6

4

2

2

3

1

3

0

0

3

31

*Salmonella enterica

1570

9291

7978

1849

9864

4209

5726

622

1658

303

44

4495

47609

Shigella sonnei

41

243

239

32

308

122

192

8

41

1

1

98

1326

Staphylococcus aureus

0

0

0

0

0

0

0

0

1

0

0

0

1

Staphylococcus epidermidis

0

0

0

0

0

0

0

1

1

0

0

0

2

Streptococcus agalactiae

0

69

3

0

5

0

1

0

0

3

6

5

92

Streptococcus pneumoniae

46

26

9

16

1

36

3

46

25

272

5

154

639

Streptococcus pyogenes

7

76

149

14

10

112

9

94

23

417

45

1428

2384

Vibrio cholerae

0

3

0

0

0

1

1

0

1

0

1

0

7

Yersinia pestis

0

0

1

29

2

0

0

1

0

906

0

5

944

 

3509

20109

21341

6665

20870

8944

11630

2775

3673

5194

2725

15436

 

For each species, if the number of reads is 0 for all samples, that species was not shown.

*The leading three pathogens are Escherichia coli, Salmonella enterica and Haemophilus influenzae.

Table 4 listed the results of disease risk evaluations. It showed that three diseases of two samples (B031 and B034) had similar distributions in the control group. The three diseases are obesity, colorectal cancer, and constipation. Sample B031 had reached the significance level with P-value 0.0333 and 0.0121 < 0.05 of distribution in constipation and colorectal cancer respectively compared to 98 samples control group using binomial test. Sample B034 had reached the significance level with P-value 0.00257 and 0.0121 < 0.05 of distribution in obesity and colorectal cancer. Evaluated by the association of bacterial risk markers and disease, the results suggested that these two samples had higher risk than 98 samples control group in constipation, colorectal cancer, and obesity. Their enterotypes of gut probiotics and pathogens may be one of risk factors which would cause disease.
Table 4

The result of disease risk evaluations of 12 samples

Disease

B011

B012

B013

B014

B016

B017

B018

B019

B020

B031

B033

B034

Constipation

2.67E-01

2.67E-01

2.67E-01

1.00E + 00

1.00E + 00

2.67E-01

1.00E + 00

1.00E + 00

2.67E-01

3.34E-02

2.67E-01

1.00E + 00

Obesity

1.34E-01

1.34E-01

1.00E + 00

1.34E-01

1.00E + 00

1.34E-01

1.34E-01

1.00E + 00

1.00E + 00

1.34E-01

1.00E + 00

2.57E-03

IBS

3.33E-01

7.06E-01

1.00E + 00

3.33E-01

1.00E + 00

3.33E-01

3.33E-01

1.00E + 00

1.10E-01

1.10E-01

7.06E-01

3.33E-01

Ulcerative colitis

9.30E-02

4.15E-01

1.00E + 00

4.15E-01

1.00E + 00

9.30E-02

4.15E-01

1.00E + 00

9.30E-02

1.00E + 00

1.00E + 00

1.00E + 00

Colorectal cancer

4.88E-01

2.59E-01

9.35E-01

7.47E-01

7.47E-01

7.47E-01

2.59E-01

4.88E-01

4.88E-01

1.22E-02

7.47E-01

1.22E-02

Atopic dermatitis

1.83E-01

1.00E + 00

1.00E + 00

1.00E + 00

1.00E + 00

1.83E-01

1.83E-01

1.00E + 00

1.00E + 00

1.00E + 00

1.00E + 00

1.83E-01

Allergic rhinitis

1.89E-01

1.00E + 00

1.00E + 00

1.00E + 00

1.00E + 00

1.89E-01

1.89E-01

1.89E-01

1.00E + 00

1.89E-01

1.00E + 00

1.89E-01

The bold numbers represent two samples had reached significance level with P-value less than 0.05 of distribution in three diseases compared to 98 sample control group using evaluation model.

Reproducibility and accuracy evaluation of proposed platform

Two replicated experiments of four samples were performed to estimate the reproducibility of the proposed platform. The results of repeated experiments were consistent. The similarity between two repeated experiments were calculated by using UniFrac [19]. Results of each sample pair (replicate 1 and 2) closely resemble each other. The similarity of UniFrac distance of each sample pair is higher than 0.96 (0.9617 for B014, 0.9872 for B018, 0.9914 for B020, 0.9722 for B033). This implies that the analysis results are reproducible.

Next, accuracy of the platform is evaluated by adding Lactobacillus reuteri to a stool sample (B050). Sample B050 contains 24,408 assigned taxons, and Lactobacillus reuteri has no detected count. Whether the counts of this species in positive control sample (B050S_L) are elevated must be determined. Analysis results indicate that 27,113 taxons are detected in sample B050S_L. In fact, the detected counts of Lactobacillus reuteri in sample B050S_L are 1,430, and the percentage of Lactobacillus reuteri markedly increases from 0% to 5%.

In short, our platform is accurate and reproducible in terms of detecting the quantities of bacterial species of the proposed platform. The results evaluate the accuracy and feasibility of proposed platform in order to identify probiotics and pathogens. While requiring only about one day for detection, not limited in identifying certain bacteria, the proposed platform can detect and quantify multiple bacteria simultaneously.

Discussion

Because of the constraint of costs and technical limitations, 16S rRNA sequences obtained in most databases are partial sequences. Many studies thus assign taxonomy by using partial 16S rRNA sequences. In our probiotics and pathogens 16S rRNA sequence database, 17,964 sequences are collected from NCBI nucleotide database, NCBI 16S microbial rRNA database, Greengenes database, and SILVA. Our probiotics and pathogens 16S rRNA database contain less than 39% of 16S rRNA sequences which are longer than 1400 bps. Only 9% of the sequences are close to full length.

This work extracts the V4 region from full length 16S rRNA of microbiome in the human gut as a platform application. Some 16S rRNA variable regions are more dependable than other regions in assigning taxonomy like V3 and V4 [20, 21]; in addition, some 16S rRNA variable regions are much conserved. The proportion and diversity of probiotics and pathogens may be made diverse by using different 16S rRNA variable regions. The proposed platform is also applicable to other 16S rRNA variable regions for taxonomy assignment. Importantly, a more appropriate region than others must be selected to produce an outcome that is close to full length 16S rRNA sequence.

This work further attempt is to collect common probiotics and pathogens from the literature. Although it may be incomplete, recent advances in sequencing technology make it possible to identify and define an increasing number of bacteria, implying an obvious increase in the number of identified probiotics and pathogens in the future. Efforts are underway in our laboratory to update the list of used probiotics and pathogens.

Previous studies [2224] identified pathogen or probiotic bacteria by using antibody, 16S rRNA gene microarrays, fluorescence in situ hybridization (FISH), and proteomic methods. In this work, the proposed platform can detect various pathogens and probiotics based on 16S rRNA (rDNA) sequences of bacteria using NGS and Bioinformatics method. An average of 126,451 reads was acquired per sample in this work. It is doubt that the sequencing depth is enough to detect a small amount of probiotics and pathogens. Although increasing the coverage of sequencing can advance the sensitivity of detecting probiotics and pathogens, the sequencing cost will increase. It is important to work out an appropriate coverage of sequencing for detecting probiotics and pathogens.

The results of disease risk evaluations revealed that most of 12 samples did not have resembled distributions of bacteria markers with control group. Only two samples had reached the significance level of distributions. The reason for the phenomenon may be the overlapped bacteria markers between diseases. 28 markers are used in colorectal cancer, and 17 markers are used in irritable bowel syndrome. Six markers are overlapped. For sample B031, the significant distributions in colorectal cancer were partly contributed to the significance in irritable bowel syndrome owing to the overlapped markers. Similarly, two overlapped markers for sample B034 were in colorectal cancer and obesity. In this kind of speculation, the influence of colorectal cancer to irritable bowel syndrome would be six (overlapped markers of CC and IBS) over seventeen (markers of IBS), and the influence of colorectal cancer to obesity would be two (overlapped markers of CC and obesity) over nine (markers of obesity). In addition, the influence of colorectal cancer to constipation and ulcerative colitis would be one over six and two over ten, respectively.

In addition to that some bacteria markers in species level are belong to the marker of genus level and species level, both genus marker and species markers may have associated with affecting the distributions mutually. Continually, collecting more markers and evaluating the distributions with markers in the same level are required for constructing a global prediction model in Taiwanese.

Conclusions

This work constructed a bacterial disease risk evaluation model for seven diseases and developed a novel platform by using NGS and Bioinformatics approach. Compared with the traditional bacteria culture method, our proposed platform can reduce experiment time. Besides, the proportion of probiotics and pathogens (including uncultivable pathogens) in the human body can be detected rapidly with 16 s RNA database of probiotics and pathogens. Furthermore, the proposed platform provides further insight into the cause of disease based on the relation of probiotics, pathogens, and disease. For instance, the type of antibiotics can be adjusted if the pathogens of disease are identified from infected patients. In addition, the proposed platform allows researchers to determine whether the intake of probiotics impacts the human body [2529]. In the future, this preliminary study will be continuously extended for more bacterial disease markers. For more comprehensive applications, this work will also collect bacteria from other parts of human body as control group data. In fact, a detective method of how the probiotics and pathogens inhabit human can provide new insight for human health. It could improve diagnosis and treatment method.

Methods

Figure 2 illustrates the bioinformatics system flow of the proposed platform, which includes analysis pipeline of NGS. The Figure 2 contains four parts: sequence quality filtering, construction of bacteria sequence database, taxonomy assignment, and disease risk model evaluation. The detailed components in the proposed platform are described below.
Figure 2

System flow of bioinformatics analysis in the proposed platform. The proposed platform comprises the analysis pipeline of NGS, construction of probiotics and pathogens database, bacterial disease risk model evaluation and the application of individualized bacteria sequencing profile.

Sample collection

In this study, stool samples of 98 Taiwan volunteers were gathered. The samples were collected by Sigma-transwab (Medical Wire) into a tube with Liquid Amies Transport Medium, and stored at 4°C until processing.

DNA extraction

In the case study, fresh faeces were obtained from participants. DNA was extracted directly on stool samples by using a QIAamp DNA Stool Mini Kit (Qiagen). A swab was vortexed vigorously and incubated at room temperature for 1 min. The sample was then transferred to microcentrifuge tubes containing 560 μl Buffer ASL, vortexed, and incubated at 37°C for 30 min. In addition, the suspension was incubated at 95°C for 15 min, vortexed, and centrifuged at 14,000 rpm for 1 min into pellet stool particles. Extraction was performed following the protocol of the QIAamp DNA Stool Mini Kit. The DNA was eluted with 50 μl Buffer AE, and centrifuged at 14,000 rpm for 1 min. Moreover, the DNA extract was stored at-20°C until further analysis. Finally, DNA extraction was performed, depending on the sample collected.

Library construction and sequencing for V4 region of 16S ribosomal DNA

The PCR primers, F515 (5′-GTGCCAGCMGCCGCGGTAA-3′) and R806 (5′-GGACTACHVGGGTWTCTAAT-3′), were designed to amplify the V4 domain of bacterial 16S ribosomal DNA as described previously [30]. PCR amplification was performed in a 50 μl reaction volume containing 25 μl 2X Taq Master Mix (Thermo Scientific), 0.2 μM of each forward and reverse primer, and 20 ng DNA template. The reaction conditions consisted of an initial 95°C for 5 min, followed by 30 cycles of 95°C for 30 sec, 54°C for 1 min, and 72°C for 1 min, as well as a final extension of 72°C for 5 min. Next, amplified products were checked by 2% agarose gel electrophoresis and ethidium bromide staining. Amplicons were purified using the AMPure XP PCR Purification Kit (Agencourt), and quantified using Qubit dsDNA HS Assay Kit (Qubit) on Qubit 2.0 Fluorometer (Qubit)–all according to respective manufacturer instructions. For V4 library preparation, Illumina adapters were attached to the amplicons using the Illumina TruSeq DNA Sample Preparation v2 Kit. Purified libraries were applied for cluster generation and sequencing on the MiSeq system. The raw sequence files are available for download at http://clinic.mbc.nctu.edu.tw/.

16S rRNA (rDNA) sequence data quality filtering

The raw fastq files obtained by Illumina sequencing machine were quality-filtered using the FASTX-Toolkita. The paired-end 150 bp reads were performed using the minimum acceptable phred quality score of 20, as well as the 70% of bases that must exceed 20 phred quality score. Sequence shorter than 100 nucleotides would be omitted after quality trimming from reads tail. Notably, reads containing ambiguous characters were discarded.

Construction of probiotics and pathogens database

The list of probiotics and pathogens were obtained from literatures or the claims of official departments. Additional file 1: Table S1 lists species of probiotics which were adapted from both literatures [7, 9] and the claims of official departments, such as Taiwan Food and Drug Administration [31] and Health Canada [32]. 99 bacterial pathogens were collected from literature [25, 26, 3342] and Taiwan Food and Drug Administration [31] (Additional file 1: Table S2).

The 16S rRNA sequences of probiotics and pathogens used for taxonomy mapping were retrieved from the NCBI nucleotide database, NCBI 16S microbial rRNA database, Greengenes database [43] and SILVA [44]. Following sequence data collection, we assemble partial sequences which used the same species classification and removed redundant sequences. Additionally, we also removed the unique sequence from only one research support with 3% similarity which shared the same species classification with other sequence.

Taxonomy mapping

To generate taxonomy assignments, the proposed platform invoked a modified Smith-Waterman algorithm from miRExpress [45], which can compare pairs of sequences in parallel, for mapping reads to taxons. miRExpress was designed for identifying the best similarity between sequencing reads and miRNA precursor sequences. In our model, it was modified for identifying multiple hits of 16S rRNA sequence mapping results with similarity threshold 0.97. In order to reduce the storage space of output, the SAM format [46] was used to replace the original miRExpress output format for storing alignment results. Furthermore, two kinds of output format were designed. One format records whole mapped sequencing reads based on taxons. The other one records which taxons could be assigned based on sequencing reads. These two kinds of output could support the important information for assigning sequencing reads to suitable taxon. miRExpress was originally designed for dealing with single-end sequencing data. Therefore, the additional program was added for processing paired-end sequencing data. In this part, both end sequencing reads need to be assigned to the same taxon. If paired-end sequencing reads were mapped to different taxons, this paired sequence would be dropped. The probiotics and pathogens 16S rRNA sequence from our database were built in FASTA format. Following quality filtering, all paired-end sequences were aligned to the probiotics and pathogens database with whole read aligned from one end to the other end. Reads were then truncated with an identity lower than 97%, according to previous research in order to achieve a better compromise between sequences from PCR sequencing errors and taxonomic relatedness [27].

The construction of Bacterial disease risk evaluation model (BDREM)

To study the associations between bacteria and diseases, we collected related information from literatures. We concerned bacteria that are associated with seven diseases: constipation [28, 47, 48], obesity [29, 4952], irritable bowel syndrome (IBS) [28, 5358], ulcerative colitis (UC) [53, 5961], colon cancer (CC) [6264], Atopic Dermatitis (AD) and Allergic rhinitis (AR), were collected positive correlation and negative correlation data, and the individual risk of disease was evaluated.

The association data were majorly collected from case–control studies which the quantities of bacteria were obtained from NGS data, and few well-known bacteria validated by multiple studies through cultural experiments were also included. We further eliminated some conflicted data with both positive and negative correlation between bacteria and disease in different studies.

Health Asians stool samples of 98 Taiwan volunteers were gathered. Following deep sequencing and sequencing data processing, the proportion of 78 bacteria from control group was applied as risk markers (constipation: 6, obesity: 9, IBS: 17, UC: 10, CC: 28, AD: 4, AR: 4) to predict disease risk to seven diseases in this study (Table 5).
Table 5

Disease-related biomarkers of seven diseases

Disease

Marker

Correlation

Lower bound

Upper bound

Case

Control

Pubmed ID

Constipation

Escherichia coli

-

2.86E-03

1.52E-01

35

35

20039451

Roseburia

-

1.41E-03

4.61E-02

14

12

22315951

Lactobacillus

-

6.10E-05

9.45E-03

14

12

22315951

Bifidobacterium

-

5.39E-05

1.73E-02

14

12

22315951

Enterobacteriaceae

+

1.00E-02

4.26E-01

14

12

22315951

Ruminococcus bromii

+

1.16E-05

4.98E-03

8

15

20014457

Obesity

Prevotella

-

2.46E-03

5.36E-01

23

13

20876719

Bifidobacterium

-

5.39E-05

1.73E-02

33

30

19498350

Lachnospiraceae

-

3.11E-03

6.74E-02

3

3

19164560

Verrucomicrobiae

-

1.43E-05

1.78E-02

3

3

19164560

Akkermansia

-

1.43E-05

1.78E-02

3

3

19164560

Faecalibacterium prausnitzii

+

7.70E-04

2.15E-02

15

13

19849869

Lactobacillus

+

6.10E-05

9.45E-03

20

20

19774074

Coriobacteriaceae

+

3.26E-05

4.72E-03

3

3

19164560

Erysipelotrichaceae

+

1.35E-04

6.64E-03

3

3

19164560

Ulcerative colitis

Bacteroides uniformis

-

7.63E-04

5.44E-02

13

22

21073731

Bacteroides vulgatus

-

1.55E-03

4.21E-02

13

22

21073731

Parabacteroides distasonis

-

2.22E-05

1.68E-03

13

22

21073731

Faecalibacterium prausnitzii

-

7.70E-04

2.15E-02

13

27

19235886

Firmicutes

-

9.18E-02

4.50E-01

13

27

19235886

Clostridium

-

2.48E-03

6.03E-02

31

30

21253779

Clostridium leptum

-

9.65E-06

1.05E-03

13

27

19235886

Bifidobacterium

-

5.39E-05

1.73E-02

13

27

19235886

Bacteroides ovatus

-

2.04E-04

1.81E-02

13

22

21073731

Escherichia coli

+

2.86E-03

1.52E-01

9

9

16954244

Atopic dermatitis

Lactobacillus

-

6.10E-05

9.45E-03

68

256

17604093

Bifidobacteriales

-

8.09E-05

1.84E-02

7

27

20626364

Bacteroides

+

6.56E-02

6.37E-01

68

256

17604093

Clostridium perfringens

+

0.00E + 00

1.06E-04

15

15

21963389

Colorectal cancer

Bacteroides uniformis

-

7.63E-04

5.44E-02

46

56

21850056

Roseburia

-

1.41E-03

4.61E-02

46

56

21850056

Fusobacterium

-

3.32E-05

2.64E-02

50

38

7574628

Eubacterium

-

1.36E-03

7.92E-02

46

56

21850056

Coprococcus

-

1.91E-05

2.89E-03

21

23

20740058

Collinsella aerofaciens

-

2.39E-05

2.09E-03

50

38

7574628

Alistipes

-

4.07E-04

2.60E-02

46

56

21850056

Sutterellaceae

-

9.39E-04

4.85E-02

46

56

21850056

Escherichia

+

3.05E-03

1.85E-01

46

56

21850056

Shigella

+

1.51E-03

8.84E-02

46

56

21850056

Bacteroides fragilis

+

7.22E-06

1.92E-02

46

56

21850056

Porphyromonas

+

0.00E + 00

1.59E-05

46

56

21850056

Faecalibacterium prausnitzii

+

7.70E-04

2.15E-02

50

38

7574628

Ruminococcus albus

+

0.00E + 00

4.95E-04

50

38

7574628

Streptococcus

+

1.12E-04

6.83E-03

46

56

21850056

Blautia hansenii

+

0.00E + 00

6.77E-05

50

38

7574628

Enterococcus

+

0.00E + 00

1.19E-04

46

56

21850056

Bifidobacterium angulatum

+

0.00E + 00

2.60E-05

50

38

7574628

Blautia producta

+

0.00E + 00

1.10E-04

50

38

7574628

Ruminococcus gnavus

+

1.04E-05

2.64E-03

50

38

7574628

Eubacterium eligens

+

7.09E-05

2.05E-02

50

38

7574628

Eubacterium rectale

+

8.13E-05

1.34E-02

50

38

7574628

Bacteroides stercoris

+

4.87E-05

2.94E-02

50

38

7574628

Enterobacteriales

+

1.00E-02

4.26E-01

10

10

21647227

Erysipelotrichaceae

+

1.35E-04

6.64E-03

50

38

7574628

Dorea

+

5.67E-05

6.08E-03

21

23

20740058

Bifidobacterium longum

+

1.56E-05

3.60E-03

50

38

7574628

Faecalibacterium

+

1.66E-03

6.79E-02

21

23

20740058

Irritable bowel syndrome

Bacteroides uniformis

-

7.63E-04

5.44E-02

11

22

21073731

Bacteroides vulgatus

-

1.55E-03

4.21E-02

11

22

21073731

Parabacteroides distasonis

-

2.22E-05

1.68E-03

11

22

21073731

Faecalibacterium prausnitzii

-

7.70E-04

2.15E-02

23

23

22339879

Bacteroidetes

-

2.87E-01

7.95E-01

62

46

21820992

Bifidobacterium

-

5.39E-05

1.73E-02

62

46

21820992

Bacteroides ovatus

-

2.04E-04

1.81E-02

11

22

21073731

Faecalibacterium

-

1.66E-03

6.79E-02

62

46

21820992

Escherichia coli

+

2.86E-03

1.52E-01

14

18

22356587

Haemophilus

+

1.02E-05

1.69E-03

22

22

21741921

Fusobacterium

+

3.32E-05

2.64E-02

23

23

22339879

Gammaproteobacteria

+

1.75E-02

4.69E-01

22

22

21741921

Ruminococcus

+

1.22E-03

4.08E-02

62

46

21820992

Enterococcus

+

0.00E + 00

1.19E-04

23

23

22339879

Veillonella

+

1.12E-05

7.82E-03

26

26

19903265

Lactobacillaceae

+

6.10E-05

9.45E-03

23

23

22339879

Dorea

+

5.67E-05

6.08E-03

62

46

21820992

Allergic rhinitis

Lactobacillus

-

6.10E-05

9.45E-03

12

12

19714856

Bifidobacterium

-

5.39E-05

1.73E-02

67

20

101

Bacteroides fragilis

+

7.22E-06

1.92E-02

22

22

17893165

Faecalibacterium prausnitzii

+

7.70E-04

2.15E-02

22

22

17893165

The associations between bacterium and disease are majorly collected from case–control studies which the quantities of bacterium are obtained from deep sequencing data. The proportion of 78 bacteria from control group was applied as risk markers (constipation: 6, obesity: 9, IBS: 17, UC: 10, CC: 28, AD: 4, AR: 4) to predict disease risk to seven diseases in this study.

The mathematical formula of BDREM in this study was developed as the following steps. Let λ be a N × S matrix, where N is the number of markers selected in the prediction model of constipation and S is the number of health subjects in 7 prediction models. T i was defined as one of the two notches of median for each row of λ[65]. T i is a threshold to distinguish λ ij from normal proportion level to abnormal (fail to success in one trail of binomial distribution). Smaller notch was selected to T i when each marker was recorded as a negative association to the disease, and a success trail was identified when λ ij is smaller than T i . On the opposite, larger notch was selected when association was positive, and a success trail was identified when λ ij is larger than T i .
Ti = Medain of λ i 1 , λ i 2 , , λ iS + 1.58 × IQR S , positive association Medain of λ i 1 , λ i 2 , , λ iS - 1.58 × IQR S , negative association
Let P j be the probability of successful trails in the j th column of λ. The meaning of P j is the personal probability that abnormal proportion level happened.
P j = # success trails in the jth column of λ . N
Let P h be the mean of P j . It represents how frequent the abnormal proportion level happened to all P j in average, regarded as the hypothesized probability of success in each P j .
P h = j = 1 S P j N

Assume P j obey a binomial distribution, and let P h be the hypothesized probability (0.05051 for constipation, 0.07239 for obesity, 0.06952 for IBS, 0.05227 for UC, 0.09280 for CC, 0.04924 for AD, 0.05114 for AR). A binomial test was used to P j and P h . Alpha = 0.05 was choose to judge if a subject is significantly differently from the others in λ.

Figure 3 illustrated an example for evaluating the risk of obesity of B034 and B031. The model used lower and upper proportion bound of 9 markers from 98 control samples to define risk markers of these two samples following by using binomial test. Four markers of B034 exceed the lower bound and upper bound of obesity. The binomial test P-Value of B034 is 2.572e-03 < 0.05, Since P-Value < = hypothesized probability 0.07239, this case is specifically associated (significantly) with disease than random chance. There are two markers of B031 exceed lower bound of obesity. The P-Value of B031 is 0.1344 > 0.05, the case is no more associated with disease than random chance. As the results, we can assume that B034 had higher probability to cause Obesity.
Figure 3

An example for evaluating the risk of obesity by using bacterial disease risk evaluation model. The model used lower and upper proportion bound of 9 markers from 98 control samples to define risk markers of these two samples (B034 and B031) following by using binomial test.

Endnote

Notes

Declarations

Acknowledgements

The authors would like to thank the National Science Council of the Republic of China for financially supporting this research under Contract No. NSC 101-2311-B-009-003-MY3 and NSC 100-2627-B-009-002. This work was supported in part by the UST-UCSD International Center of Excellence in Advanced Bio-engineering sponsored by the Taiwan National Science Council I-RiCE Program under Grant Number: NSC 101-2911-I-009-101 and NSC 102-2911-I-009-101, and Veterans General Hospitals and University System of Taiwan (VGHUST) Joint Research Program under Grant Number: VGHUST101-G5-1-1. This work was also partially supported by MOE ATU.

Authors’ Affiliations

(1)
Institute of Bioinformatics and Systems Biology, National Chiao Tung University
(2)
Graduate Institute of Biomedical Informatics, Taipei Medical University
(3)
Department of Biological Science and Technology, National Chiao Tung University
(4)
Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital
(5)
Mackay Medicine, Nursing and Management College
(6)
Department of Medicine, Mackay Medical College

References

  1. FAO/WHO: Health and nutritional properties of probiotics in food including powder milk with live lactic acid bacteria. Book Health and nutritional properties of probiotics in food including powder milk with live lactic acid bacteria. http://www.who.int/foodsafety/publications/fs_management/en/probiotics.pdf. City; 2001
  2. Kalliomaki M, Salminen S, Arvilommi H, Kero P, Koskinen P, Isolauri E: Probiotics in primary prevention of atopic disease: a randomised placebo-controlled trial. Lancet. 2001, 357: 1076-1079. 10.1016/S0140-6736(00)04259-8.View ArticlePubMedGoogle Scholar
  3. Isolauri E, Sutas Y, Kankaanpaa P, Arvilommi H, Salminen S: Probiotics: effects on immunity. Am J Clin Nutr. 2001, 73: 444S-450S.PubMedGoogle Scholar
  4. Gopal PK, Prasad J, Smart J, Gill HS: In vitro adherence properties of lactobacillus rhamnosus DR20 and bifidobacterium lactis DR10 strains and their antagonistic activity against an enterotoxigenic Escherichia coli. Int J Food Microbiol. 2001, 67: 207-216. 10.1016/S0168-1605(01)00440-8.View ArticlePubMedGoogle Scholar
  5. Ogawa M, Shimizu K, Nomoto K, Takahashi M, Watanuki M, Tanaka R, Tanaka T, Hamabata T, Yamasaki S, Takeda Y: Protective effect of Lactobacillus casei strain Shirota on Shiga toxin-producing Escherichia coli O157:H7 infection in infant rabbits. Infect Immun. 2001, 69: 1101-1108. 10.1128/IAI.69.2.1101-1108.2001.PubMed CentralView ArticlePubMedGoogle Scholar
  6. Reid G: Probiotic agents to protect the urogenital tract against infection. Am J Clin Nutr. 2001, 73: 437S-443S.PubMedGoogle Scholar
  7. Wan YM, Zhu YQ, Xia B, Luo J: Treating TNBS-induced colitis in rats with probiotics. Turk J Gastroenterol. 2011, 22: 486-493.PubMedGoogle Scholar
  8. Taipale T, Pienihakkinen K, Isolauri E, Larsen C, Brockmann E, Alanen P, Jokela J, Soderling E: Bifidobacterium animalis subsp. lactis BB-12 in reducing the risk of infections in infancy. Br J Nutr. 2011, 105: 409-416. 10.1017/S0007114510003685.View ArticlePubMedGoogle Scholar
  9. Wickens K, Black PN, Stanley TV, Mitchell E, Fitzharris P, Tannock GW, Purdie G, Crane J: A differential effect of 2 probiotics in the prevention of eczema and atopy: a double-blind, randomized, placebo-controlled trial. J Allergy Clin Immunol. 2008, 122: 788-794. 10.1016/j.jaci.2008.07.011.View ArticlePubMedGoogle Scholar
  10. Jordan JA, Durso MB: Real-time polymerase chain reaction for detecting bacterial DNA directly from blood of neonates being evaluated for sepsis. J Mol Diagn. 2005, 7: 575-581. 10.1016/S1525-1578(10)60590-9.PubMed CentralView ArticlePubMedGoogle Scholar
  11. Wilson M: Bacteriology of Humans: An Ecological Perspective. 2008, Hoboken: Wiley-BlackwellGoogle Scholar
  12. Goldenberger D, Kunzli A, Vogt P, Zbinden R, Altwegg M: Molecular diagnosis of bacterial endocarditis by broad-range PCR amplification and direct sequencing. J Clin Microbiol. 1997, 35: 2733-2739.PubMed CentralPubMedGoogle Scholar
  13. Woo PC, Lau SK, Woo GK, Fung AM, Ngan AH, Hui WT, Yuen KY: Seronegative bacteremic melioidosis caused by Burkholderia pseudomallei with ambiguous biochemical profile: clinical importance of accurate identification by 16S rRNA gene and groEL gene sequencing. J Clin Microbiol. 2003, 41: 3973-3977. 10.1128/JCM.41.8.3973-3977.2003.PubMed CentralView ArticlePubMedGoogle Scholar
  14. Fox GE, Magrum LJ, Balch WE, Wolfe RS, Woese CR: Classification of methanogenic bacteria by 16S ribosomal RNA characterization. Proc Natl Acad Sci U S A. 1977, 74: 4537-4541. 10.1073/pnas.74.10.4537.PubMed CentralView ArticlePubMedGoogle Scholar
  15. Metzker ML: Sequencing technologies–the next generation. Nat Rev Genet. 2010, 11: 31-46. 10.1038/nrg2626.View ArticlePubMedGoogle Scholar
  16. Kuczynski J, Lauber CL, Walters WA, Parfrey LW, Clemente JC, Gevers D, Knight R: Experimental and analytical tools for studying the human microbiome. Nat Rev Genet. 2012, 13: 47-58.View ArticleGoogle Scholar
  17. Woo PC, Lau SK, Teng JL, Tse H, Yuen KY: Then and now: use of 16S rDNA gene sequencing for bacterial identification and discovery of novel bacteria in clinical microbiology laboratories. Clin Microbiol Infect. 2008, 14: 908-934. 10.1111/j.1469-0691.2008.02070.x.View ArticlePubMedGoogle Scholar
  18. Lazarevic V, Whiteson K, Huse S, Hernandez D, Farinelli L, Osteras M, Schrenzel J, Francois P: Metagenomic study of the oral microbiota by Illumina high-throughput sequencing. J Microbiol Methods. 2009, 79: 266-271. 10.1016/j.mimet.2009.09.012.PubMed CentralView ArticlePubMedGoogle Scholar
  19. Lozupone C, Hamady M, Knight R: UniFrac–an online tool for comparing microbial community diversity in a phylogenetic context. BMC Bioinformatics. 2006, 7: 371-10.1186/1471-2105-7-371.PubMed CentralView ArticlePubMedGoogle Scholar
  20. Bokulich NA, Joseph CM, Allen G, Benson AK, Mills DA: Next-generation sequencing reveals significant bacterial diversity of botrytized wine. PloS one. 2012, 7: e36357-10.1371/journal.pone.0036357.PubMed CentralView ArticlePubMedGoogle Scholar
  21. Claesson MJ, Wang Q, O’Sullivan O, Greene-Diniz R, Cole JR, Ross RP, O’Toole PW: Comparison of two next-generation sequencing technologies for resolving highly complex microbiota composition using tandem variable 16S rRNA gene regions. Nucleic Acids Res. 2010, 38: e200-10.1093/nar/gkq873.PubMed CentralView ArticlePubMedGoogle Scholar
  22. Russmann H, Kempf VA, Koletzko S, Heesemann J, Autenrieth IB: Comparison of fluorescent in situ hybridization and conventional culturing for detection of Helicobacter pylori in gastric biopsy specimens. J Clin Microbiol. 2001, 39: 304-308. 10.1128/JCM.39.1.304-308.2001.PubMed CentralView ArticlePubMedGoogle Scholar
  23. Barber JJ, Grichting WL: Australia’s media campaign against drug abuse. Int J Addict. 1990, 25: 693-708.PubMedGoogle Scholar
  24. Rigsbee L, Agans R, Foy BD, Paliy O: Optimizing the analysis of human intestinal microbiota with phylogenetic microarray. FEMS Microbiol Ecol. 2011, 75: 332-342. 10.1111/j.1574-6941.2010.01009.x.PubMed CentralView ArticlePubMedGoogle Scholar
  25. Maley MW, Kociuba K, Chan RC: Prevention of laboratory-acquired brucellosis: significant side effects of prophylaxis. Clin Infect Dis. 2006, 42: 433-434. 10.1086/499112.View ArticlePubMedGoogle Scholar
  26. Mantur BG, Amarnath SK, Shinde RS: Review of clinical and laboratory features of human brucellosis. Indian J Med Microbiol. 2007, 25: 188-202. 10.4103/0255-0857.34758.View ArticlePubMedGoogle Scholar
  27. Hummelen R, Fernandes AD, Macklaim JM, Dickson RJ, Changalucha J, Gloor GB, Reid G: Deep sequencing of the vaginal microbiota of women with HIV. PloS one. 2010, 5: e12078-10.1371/journal.pone.0012078.PubMed CentralView ArticlePubMedGoogle Scholar
  28. Lyra A, Rinttila T, Nikkila J, Krogius-Kurikka L, Kajander K, Malinen E, Matto J, Makela L, Palva A: Diarrhoea-predominant irritable bowel syndrome distinguishable by 16S rRNA gene phylotype quantification. World J Gastroenterol. 2009, 15: 5936-5945. 10.3748/wjg.15.5936.PubMed CentralView ArticlePubMedGoogle Scholar
  29. Furet JP, Kong LC, Tap J, Poitou C, Basdevant A, Bouillot JL, Mariat D, Corthier G, Dore J, Henegar C, et al: Differential adaptation of human gut microbiota to bariatric surgery-induced weight loss: links with metabolic and low-grade inflammation markers. Diabetes. 2010, 59: 3049-3057. 10.2337/db10-0253.PubMed CentralView ArticlePubMedGoogle Scholar
  30. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, Fierer N, Knight R: Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A. 2011, 108 (Suppl 1): 4516-4522.PubMed CentralView ArticlePubMedGoogle Scholar
  31. Khamis A, Raoult D, La Scola B: Comparison between rpoB and 16S rRNA gene sequencing for molecular identification of 168 clinical isolates of corynebacterium. J Clin Microbiol. 2005, 43: 1934-1936. 10.1128/JCM.43.4.1934-1936.2005.PubMed CentralView ArticlePubMedGoogle Scholar
  32. Lin J: Effect of antibiotic growth promoters on intestinal microbiota in food animals: a novel model for studying the relationship between gut microbiota and human obesity?. Front Microbiol. 2011, 2: 53-PubMed CentralPubMedGoogle Scholar
  33. Hegerle N, Guiso N: Epidemiology of whooping cough and typing of Bordetella pertussis. Future Microbiol. 2013, 8 (11): 1391-1403. 10.2217/fmb.13.111.View ArticlePubMedGoogle Scholar
  34. Seleem MN, Boyle SM, Sriranganathan N: Brucella: a pathogen without classic virulence genes. Vet Microbiol. 2008, 129: 1-14. 10.1016/j.vetmic.2007.11.023.View ArticlePubMedGoogle Scholar
  35. Zhan P, Suo LJ, Qian Q, Shen XK, Qiu LX, Yu LK, Song Y: Chlamydia pneumoniae infection and lung cancer risk: a meta-analysis. Eur J Cancer. 2011, 47: 742-747. 10.1016/j.ejca.2010.11.003.View ArticlePubMedGoogle Scholar
  36. Parkhill J, Wren BW, Mungall K, Ketley JM, Churcher C, Basham D, Chillingworth T, Davies RM, Feltwell T, Holroyd S, et al: The genome sequence of the food-borne pathogen campylobacter jejuni reveals hypervariable sequences. Nature. 2000, 403: 665-668. 10.1038/35001088.View ArticlePubMedGoogle Scholar
  37. Guidoboni M, Ferreri AJ, Ponzoni M, Doglioni C, Dolcetti R: Infectious agents in mucosa-associated lymphoid tissue-type lymphomas: pathogenic role and therapeutic perspectives. Clin Lymphoma Myeloma. 2006, 6: 289-300. 10.3816/CLM.2006.n.003.View ArticlePubMedGoogle Scholar
  38. Harvey RA, Champe PC, Fisher BD: Lippincott’s Illustrated Reviews: Microbiology. 2007, Philadelphia: Lippincott Williams & Wilkins, 2Google Scholar
  39. Fredlund H, Falk L, Jurstrand M, Unemo M: Molecular genetic methods for diagnosis and characterisation of Chlamydia trachomatis and Neisseria gonorrhoeae: impact on epidemiological surveillance and interventions. APMIS. 2004, 112: 771-784. 10.1111/j.1600-0463.2004.apm11211-1205.x.View ArticlePubMedGoogle Scholar
  40. Berdichevski T, Barshack I, Bar-Meir S, Ben-Horin S: Pseudomembranes in a patient with flare-up of inflammatory bowel disease (IBD): is it only clostridium difficile or is it still an IBD exacerbation?. Endoscopy. 2010, 42 (Suppl 2): E131-View ArticlePubMedGoogle Scholar
  41. Satterfield BA, Stewart AF, Lew CS, Pickett DO, Cohen MN, Moore EA, Luedtke PF, O’Neill KL, Robison RA: A quadruplex real-time PCR assay for rapid detection and differentiation of the Clostridium botulinum toxin genes A, B, E and F. J Med Microbiol. 2010, 59: 55-64. 10.1099/jmm.0.012567-0.View ArticlePubMedGoogle Scholar
  42. Brook I: The role of anaerobic bacteria in cutaneous and soft tissue abscesses and infected cysts. Anaerobe. 2007, 13: 171-177. 10.1016/j.anaerobe.2007.08.004.View ArticlePubMedGoogle Scholar
  43. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL: Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006, 72: 5069-5072. 10.1128/AEM.03006-05.PubMed CentralView ArticlePubMedGoogle Scholar
  44. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glockner FO: The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013, 41: D590-D596. 10.1093/nar/gks1219.PubMed CentralView ArticlePubMedGoogle Scholar
  45. Wang WC, Lin FM, Chang WC, Lin KY, Huang HD, Lin NS: miRExpress: analyzing high-throughput sequencing data for profiling microRNA expression. BMC Bioinformatics. 2009, 10: 328-10.1186/1471-2105-10-328.PubMed CentralView ArticlePubMedGoogle Scholar
  46. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Genome Project Data Processing S: The sequence alignment/map format and SAMtools. Bioinformatics. 2009, 25: 2078-2079. 10.1093/bioinformatics/btp352.PubMed CentralView ArticlePubMedGoogle Scholar
  47. Chmielewska A, Szajewska H: Systematic review of randomised controlled trials: probiotics for functional constipation. World J Gastroenterol. 2010, 16: 69-75.PubMed CentralPubMedGoogle Scholar
  48. Chassard C, Dapoigny M, Scott KP, Crouzet L, Del’homme C, Marquet P, Martin JC, Pickering G, Ardid D, Eschalier A, et al: Functional dysbiosis within the gut microbiota of patients with constipated-irritable bowel syndrome. Aliment Pharmacol Ther. 2012, 35: 828-838. 10.1111/j.1365-2036.2012.05007.x.View ArticlePubMedGoogle Scholar
  49. Schwiertz A, Taras D, Schafer K, Beijer S, Bos NA, Donus C, Hardt PD: Microbiota and SCFA in lean and overweight healthy subjects. Obesity (Silver Spring). 2010, 18: 190-195. 10.1038/oby.2009.167.View ArticleGoogle Scholar
  50. Balamurugan R, George G, Kabeerdoss J, Hepsiba J, Chandragunasekaran AM, Ramakrishna BS: Quantitative differences in intestinal Faecalibacterium prausnitzii in obese Indian children. Br J Nutr. 2010, 103: 335-338. 10.1017/S0007114509992182.View ArticlePubMedGoogle Scholar
  51. Million M, Maraninchi M, Henry M, Armougom F, Richet H, Carrieri P, Valero R, Raccah D, Vialettes B, Raoult D: Obesity-associated gut microbiota is enriched in lactobacillus reuteri and depleted in Bifidobacterium animalis and methanobrevibacter smithii. Int J Obes (Lond). 2012, 36: 817-825. 10.1038/ijo.2011.153.View ArticleGoogle Scholar
  52. Armougom F, Henry M, Vialettes B, Raccah D, Raoult D: Monitoring bacterial community of human gut microbiota reveals an increase in lactobacillus in obese patients and methanogens in anorexic patients. PloS one. 2009, 4: e7125-10.1371/journal.pone.0007125.PubMed CentralView ArticlePubMedGoogle Scholar
  53. Noor SO, Ridgway K, Scovell L, Kemsley EK, Lund EK, Jamieson C, Johnson IT, Narbad A: Ulcerative colitis and irritable bowel patients exhibit distinct abnormalities of the gut microbiota. BMC Gastroenterol. 2010, 10: 134-10.1186/1471-230X-10-134.PubMed CentralView ArticlePubMedGoogle Scholar
  54. Carroll IM, Ringel-Kulka T, Siddle JP, Ringel Y: Alterations in composition and diversity of the intestinal microbiota in patients with diarrhea-predominant irritable bowel syndrome. Neurogastroenterol Motil. 2012, 24: 521-530. 10.1111/j.1365-2982.2012.01891.x. e248PubMed CentralView ArticlePubMedGoogle Scholar
  55. Duboc H, Rainteau D, Rajca S, Humbert L, Farabos D, Maubert M, Grondin V, Jouet P, Bouhassira D, Seksik P, et al: Increase in fecal primary bile acids and dysbiosis in patients with diarrhea-predominant irritable bowel syndrome. Neurogastroenterol Motil. 2012, 24: 513-520. 10.1111/j.1365-2982.2012.01893.x. e246-517View ArticlePubMedGoogle Scholar
  56. Kerckhoffs AP, Ben-Amor K, Samsom M, van der Rest ME, de Vogel J, Knol J, Akkermans LM: Molecular analysis of faecal and duodenal samples reveals significantly higher prevalence and numbers of Pseudomonas aeruginosa in irritable bowel syndrome. J Med Microbiol. 2011, 60: 236-245. 10.1099/jmm.0.022848-0.View ArticlePubMedGoogle Scholar
  57. Saulnier DM, Riehle K, Mistretta TA, Diaz MA, Mandal D, Raza S, Weidler EM, Qin X, Coarfa C, Milosavljevic A, et al: Gastrointestinal microbiome signatures of pediatric patients with irritable bowel syndrome. Gastroenterology. 2011, 141: 1782-1791. 10.1053/j.gastro.2011.06.072.PubMed CentralView ArticlePubMedGoogle Scholar
  58. Rajilic-Stojanovic M, Biagi E, Heilig HG, Kajander K, Kekkonen RA, Tims S, de Vos WM: Global and deep molecular analysis of microbiota signatures in fecal samples from patients with irritable bowel syndrome. Gastroenterology. 2011, 141: 1792-1801. 10.1053/j.gastro.2011.07.043.View ArticlePubMedGoogle Scholar
  59. Sokol H, Seksik P, Furet JP, Firmesse O, Nion-Larmurier I, Beaugerie L, Cosnes J, Corthier G, Marteau P, Dore J: Low counts of faecalibacterium prausnitzii in colitis microbiota. Inflamm Bowel Dis. 2009, 15: 1183-1189. 10.1002/ibd.20903.View ArticlePubMedGoogle Scholar
  60. Andoh A, Imaeda H, Aomatsu T, Inatomi O, Bamba S, Sasaki M, Saito Y, Tsujikawa T, Fujiyama Y: Comparison of the fecal microbiota profiles between ulcerative colitis and Crohn’s disease using terminal restriction fragment length polymorphism analysis. J Gastroenterol. 2011, 46: 479-486. 10.1007/s00535-010-0368-4.View ArticlePubMedGoogle Scholar
  61. Sokol H, Lepage P, Seksik P, Dore J, Marteau P: Temperature gradient gel electrophoresis of fecal 16S rRNA reveals active Escherichia coli in the microbiota of patients with ulcerative colitis. J Clin Microbiol. 2006, 44: 3172-3177. 10.1128/JCM.02600-05.PubMed CentralView ArticlePubMedGoogle Scholar
  62. Wang T, Cai G, Qiu Y, Fei N, Zhang M, Pang X, Jia W, Cai S, Zhao L: Structural segregation of gut microbiota between colorectal cancer patients and healthy volunteers. ISME J. 2012, 6: 320-329. 10.1038/ismej.2011.109.PubMed CentralView ArticlePubMedGoogle Scholar
  63. Moore WE, Moore LH: Intestinal floras of populations that have a high risk of colon cancer. Appl Environ Microbiol. 1995, 61: 3202-3207.PubMed CentralPubMedGoogle Scholar
  64. Shen XJ, Rawls JF, Randall T, Burcal L, Mpande CN, Jenkins N, Jovov B, Abdo Z, Sandler RS, Keku TO: Molecular characterization of mucosal adherent bacteria and associations with colorectal adenomas. Gut Microbes. 2010, 1: 138-147. 10.4161/gmic.1.3.12360.PubMed CentralView ArticlePubMedGoogle Scholar
  65. Mcgill R, Tukey JW, Larsen WA: Variations of box plots. Am Stat. 1978, 32: 12-16.Google Scholar

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