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Short Communication

Analysis of the microbiota of sputum samples from patients with lower respiratory

tract infections

Yuhua Zhou 1, Ping Lin 2, Qingtian Li3,4, Lizhong Han3,4, Huajun Zheng 5, Yanxia Wei 1, Zelin Cui 1, Yuxing Ni 3,4,

and Xiaokui Guo 1*

1Department of Medical Microbiology and Parasitology, Institutes of Medical Sciences, Shanghai Jiao Tong University School of Medicine,

Shanghai 200025, China2

Medical Laboratory, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China3

Department of Laboratory Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China4

Department of Clinical Microbiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China5

Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai, Shanghai 201203,

China

*Correspondence address. Tel: þ86-21-64453285; Fax: þ86-21-64453285; E-mail: [email protected]

Sputum is the most common sample collected from

patients suffering from lower respiratory tract infections

and it is crucial for the bacterial identification of these

infections. In this study, we enrolled 101 sputum samples

from 101 patients with lower respiratory tract infections.

Initially, pyrosequencing of the 16S rDNA V3 hypervari-

able regions of the bacteria contained in the sputum was

utilized as a culture-independent approach for microbiota

analysis. For comparison, clinical laboratory tests using a

culture-dependent automated bacterial identification

system for the same cohort of sputum samples were also

done. By pyrosequencing, >70,000 DNA fragments were

found and classified into 129 bacterial genera after being

analyzed by the Ribosomal Database Project (RDP)

process. Most sequences belonged to several predominant

genera, such as Streptococcus and Staphylococcus, indicat-

ing that these genera play an important role in lower res-

piratory tract infections. In addition, some sequences

belonging to potential causative agents, such as

Mycoplasma, Haemophilus, and Moraxella, were also

found, but these sequences were not found by clinical lab-

oratory tests. For the nine genera detected by both

methods, the methods’ sensitivities were compared andthe results showed that pyrosequencing was more sensi-

tive, except for Klebsiella and Mycobacterium.

Significantly, this method revealed much more compli-

cated bacterial communities and it showed a promising

ability for the detection of bacteria.

Keywords 16S ribosomal DNA; lower respiratory

infection; microbiota; pyrosequence; sputum

Received: April 12, 2010 Accepted: July 14, 2010

Introduction

Lower respiratory tract infections, which include a series of

diseases, are a major concern in clinical practice and

medical research because of their contribution to the mor-

tality of older patients with co-morbid diseases. They are

also somewhat common and sometimes life-threatening

infections for the younger population, especially for infants

and children [1 – 3]. Identification of the causative agents

plays a key role in the treatment of the infection [4]. An

accurate and fast method for the identification may help

guide the use of narrow-spectrum antibiotics to reduce the

economic burden, relieve the dysbacteriosis, and avoid the

emergence of drug-resistant bacteria [5,6].

Although the common culture-dependent and culture-

independent methods provide much help in clinical labora-

tories, they cannot catch up with the advancing knowledge of

the causative agents of lower respiratory tract infections. The

view that a single causative agent causes these infections has

been replaced by the idea that more than one agent is respon-

sible, and the latter has become the dominant view [7 – 9].

Co-infections were given a new name, ‘the community as a

pathogen’, by Blaser and Falkow in a recent review article[10]. The routine methods seem to be unable to provide us

with an overview of the whole situation in a patient with

lower respiratory tract infections, because these methods aim

to find and identify limited, specific, and typical pathogens,

whereas the members of the normal flora or atypical

microbes that also contribute to the infections are neglected.

Thus, to unfold the complete panorama for further research, a

reliable, high-throughput method is needed.

The feasibility of 16S rDNA analysis using 454 sequen-

cing technology has already been proved in the research of

Acta Biochim Biophys Sin 2010, 42: 754–761 | ª The Author 2010. Published by ABBS Editorial Office in association with Oxford University Press on behalf of the

Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. DOI: 10.1093/abbs/gmq081.

Advance Access Publication 7 September 2010

Acta Biochim Biophys Sin (2010) | Volume 42 | Issue 10 | Page 754



microbiota in the oral [11,12], wound [13], and gastrointesti-

nal tract [14,15]. The diversity of the bacterial community,

unveiled by 454 sequencing, has yielded enough data sets to

analyze the constitution of the bacterial flora. Moreover, the

sputum, which can reflect the main part of the bacterial

community, is the most informative sample collected from

the patient with lower respiratory tract infections [16]. Thus,

in this study, we used the 16S rDNA library and 454 pyrose-quencing to investigate the complicated community of

sputum microbiota. Pneumonia is a severe lower respiratory

tract infection and the hospital-acquired pneumonia was

chosen as the theme of this research.

Materials and Methods

Experimental design

In our study, we enrolled 101 patients suffering from

hospital-acquired pneumonia as the candidates. For com-

parison, tests using a culture-dependent automated bacterial

identification system were also performed on the same

cohort of sputum samples. All of the sputum samples were

properly handled and the total DNA was extracted. One

hundred and one pairs of tagged primers, called barcoded

primers, were used for the enrichment of bacterial 16S

rDNA V3 hypervariable regions [17,18]. The enriched V3

regions were mixed and sequenced using the 454 platform.

All of the obtained data sets were analyzed for two pur-

poses. First, to calculate the number of detected sequences

belonging to each genus and then use the sequences to

analyze the constitution of bacterial flora in each patient.

Second, to use the genera detected by both methods to

evaluate the sensitivity and accuracy of each method.

Patients

One hundred and one patients from Shanghai Ruijin Hospital

were enrolled and their age ranged from 60 to 80. The infec-

tion happened .48 h after admission to the hospital. All of

the patients were tested for fever, cough, production of yel-

lowish sputum, presence of infiltrate lesions by X-ray, and a

rising white blood cell count in routine blood tests according

to the diagnosis standards for hospital-acquired pneumonia.

Clinical specimens and clinical laboratory work Fresh sputum samples were collected soon after the inocu-

lation for the culture-dependent routine test and for producing

a clinical report for comparison with the pyrosequencing

method. The identification of bacteria in our samples was

completed by the Vitek 2 Compact system. As a commercial

and standard system, its accuracy has been strictly evaluated.

Then, approximately five times the volume of the sample

of 2 mol/L sodium hydroxide solution was added to each

sample for liquefaction, and the samples were stored at 48C

for 24 h to ensure enough time for basic digestion.

DNA extraction from the sputum samples

Although the isolation of DNA was done predominantly in

accordance with the manufacturer’s instructions for the

UltraPureTM Genome DNA Kit (SbS, Beijing, China),

several crucial steps of the extraction, including the diges-

tions by lysozyme, lysostaphin, and proteinase K, were

enhanced, because sputum is difficult to work with. After

being stored at 48C for 24 h, each sample was centrifuged at 12,000 rpm for 10 min, all of the supernatant was removed,

and then the sample was resuspended in PBS buffer.

Lysozyme and lysostaphin were added to each diluted

sample, producing a final concentration of 5 mg/ml and 32

U/ml, respectively, and the samples were incubated at 378C

for 4 h. Then, proteinase K was added with a final concen-

tration of 0.1 mg/ml, and the samples were incubated at 658C

for 2 h. The samples were centrifuged again at 12,000 rpm

for 10 min, and all the supernatant, containing both bacterial

and human DNA, was collected and transferred to sterilized

1.5-ml EP tubes for further extraction. For the rest of the

extraction, the kit instructions were strictly followed and the

extracted DNA of the 101 samples was produced.

Sample DNA quantity detection

Because the quantity of DNA extracted from each sample

varied greatly, the DNA quantity of the freshly made

samples must be defined. The DNA concentration was

measured by UV spectrophotometer at 260 nm. The

average DNA concentration of the samples was 28.5 ng/ ml.

The design of barcoded primers

The PCR enrichment of the 16S rDNA V3 hypervariable

region was performed with forward primer 50-CTCTTAC

T-TACGGGAGGCAGCAG-30 and reverse primer 50-CTC

TTACT-ATTACCGCGGCTGCTGG-30. For the primers as

mentioned, the 50 terminal of every primer contained an

eight-base-oligonucleotide tag (before the hyphen), while

the sequence after the hyphen was able to pare with the

sequences of the V3 end region. Because the terminal

sequences of the bacterial rDNA V3 region are conserved

among different bacteria, the 101 pairs of primers that con-

tained 101 different eight-base oligonucleotides and identi-

cal following sequences were used to lead the PCR

enrichment. The barcoded primers were synthesized by theShanghai Sangon Biological Engineering Technology &

Service Co., Ltd., Shanghai, China, PR.

PCR enrichment of the V3 region

To ensure that the quantity of the PCR product was enough

to harvest, a two-step PCR strategy was used. The first step

was in a 25 ml reaction volume containing 2.5 ml of PCR

buffer (TAKARA, Takara Bio Inc., Otsu, Shiga, Japan),

0.625 U ExTaq (TAKARA), 0.1 ml of BSA (TAKARA),

and 2 ml of primer solution, with 100 mmol of each forward

Sputum microbiota analysis using 16S rDNA pyrosequencing




and reverse primer. Fifty nanogram of extracted DNA was

added as a template. ddH2O was added to make up the

remaining volume of the reaction. Touchdown PCR con-

ditions were as follows: 5 min at 948C for initial denatura-

tion, 1 min at 948C for denaturation, 1 min at 658C for

annealing, and 1 min at 728C for extension, with the anneal-

ing temperature decreasing by 0.58C for each step of the last

20 cycles. The reaction volume in the second step of thePCR was 50 ml. The template for this reaction was the pro-

ducts from step one, with 5 ml of PCR buffer (TAKARA),

1.25 U ExTaq (TAKARA), 0.2 ml of BSA (TAKARA), and

24 ml of water, with 200 mmol of each barcoded forward

and reverse primer. The heating cycle used was 1 min at

948C for denaturation, 1 min at 558C for annealing, and

1 min at 728C for extension for five cycles with the tempera-

ture kept at 208C after the reaction [11,13].

Harvest of the purified products and establishment of

the pyrosequencing library

After the PCR reaction, the electrophoresis was immedi-

ately performed to isolate the enriched V3 region DNA

fragments from the reaction mixture. All of the products

were 200 bp in length and were harvested using a gel

extraction kit (OMEGA bio-tek, Norcross, USA) following

the manufacturer’s instructions. The quantity of the

enriched V3 region DNA was detected by UV spectropho-

tometer at 260 nm, and the average concentration was

39.9 ng/ ml. All of the enriched DNA was mixed in one EP

tube to establish the library. In this library, the quantity of

DNA taken from each sample was fixed at 50 ng.

Pyrosequencing using the 454 platform

Sequencing was undertaken by a Roche 454 FLX in

accordance with the manufacturer’s instructions, and the

gross sequencing data were arranged by the primer tags

[17,18]. All the sequencing experiments were performed at

the Chinese National Human Genome Center in Shanghai.

Taxonomy and statistical analysis

All of the data sets were taxonomically grouped using the

Ribosomal Database Project (RDP) classifier (the RDP’s

Naı̈ve Bayesian classifier) at a confidence level of 90%.

The sequences were assigned until the genus level in bac-teria domain was collected and screened in Microsoft

Excel [19,20]. The mean, standard deviation, and

chi-square tests were performed with SPSS 11.5 software.

Results

The 454 test detected 73,225 PCR amplicons that were

200 bp in length, with 725 reads of each tag (sample) on

average. All of the 200 bp-long sequences were regrouped

according to the tags linked to the primers, forming 101 data

sets that contained the same tags, each data set representing a

single patient. The distribution of sequences for each sample is

shown in Fig. 1. After analysis by RDP, 53,980 (73.7%) could

be classified into the bacterial domain and in the territory of

bacterial sequences (53,980 sequences), out of which 44,817

sequences could be assigned into the existing hierarchy to the

genus level including 129 bacterial genera, and the sequences

of each sample contained 18 genera on average. Thenumber of sequences of each genus was counted and the

top 10 genera that had the most sequences were Streptococcus,

Rothia, Staphylococcus, Prevotella, Pseudomonas, Gemella,

Granulicatella, Acinetobacter, Actinomyce, and Lactobacillus

(Table 1). The standard deviation was used to describe the

variation of each genus among patients. It seems that

the greater number of genera a patient had, the higher the

deviation was seen in the samples. The descending order of

deviation of the genera is identical to that in Table 1, and

the Streptococcus, Rothia, and Staphylococcus genera once

again occupied the top three places on the list (Table 2).

As the sputum samples were difficult to work with, the

quantity of DNA extracted from each patient varied greatly.

Thus, the mean and the deviation based on the number of

sequences detected by 454 alone might be inaccurate and

unable to be compared. To correctly assess the samples, the

data must be weighted. The percentage of each genus

sequence in all of the bacterial sequences extracted from

each patient was calculated, and these data were used as the

standard for comparison. The top 20 genera by mean and

standard deviation of the percentage was slightly different

compared with the data shown in Tables 1 and 2, and it is

considered to be more accurate and meaningful (Table 3).

Comparison of the ability to identify bacteria between

pyrosequencing and the Vitek 2 Compact system was

undertaken. In our list of identified bacterial genera, 129

genera were found; meanwhile, based on the same cohort

of samples, 12 genera were identified by the Vitek 2

Compact system. Nine genera, Acinetobacter , Enterococcus,

Klebsiella, Mycobacterium, Neisseria, Pseudomonas,

Staphylococcus, Streptococcus, and Stenotrophomonas, were

found by both methods. Because the ratios of each of the

nine genera detected by both methods were remarkably

different, the chi-square test was undertaken to compare and

evaluate the sensitivity of these two methods. The P -valuesof the chi-square test shown in Table 4 indicated that except

for Klebsiella and Mycobacterium, the identification rate of

the other seven genera shows a great difference between the

two methods, as revealed by the extremely low P -values.

Discussion

One of the most obvious advantages of our method is its

complete quantitative analysis [13,21]. After being pro-

cessed in bulk by the pipeline tool of the RDP website, the





percentage of sequences belonging to each bacterial genus

could be calculated for every patient [20]. By combining

the ranked list of the tables described above, some basic

conclusions could be drawn.

Generally, the pathogens as common causative agents of

hospital-acquired pneumonia boast the following features: a

wide distribution, which means that most afflicted patients

possess similar clinical complaints; a high prevalence,

Figure 1 V3 DNA counts for each sputum sample Each column represents the quantity of V3 region DNA sequences detected by the 454 platform.

The red line shows the average number.

Table 1 Top 20 genera by quantity

Genus Sum Mean

Streptococcus 14,975 148.27

Rothia 5027 49.77

Staphylococcus 2695 26.68

Prevotella 2690 26.63 Pseudomonas 2352 23.29

Gemella 2177 21.55

Granulicatella 2080 20.59

Acinetobacter 1678 16.61

Actinomyces 1439 14.25

Lactobacillus 823 8.15

Stenotrophomonas 718 7.11

Neisseria 716 7.09

Veillonella 692 6.85

Enterococcus 687 6.80

Ensifer 577 5.71

Leptotrichia 495 4.90

Corynebacterium 383 3.79

Haemophilus 318 3.15

Fusobacterium 274 2.71

Limnobacter 261 2.58

Table 2 Top 20 genera by standard deviation

Genus n Standard deviation

Streptococcus 101 150.80

Rothia 101 75.80

Staphylococcus 101 75.39

Acinetobacter 101 68.91Gemella 101 54.79

Pseudomonas 101 49.74

Lactobacillus 101 48.45

Prevotella 101 45.54

Enterococcus 101 35.20

Granulicatella 101 32.39

Stenotrophomonas 101 30.58

Actinomyces 101 23.70

Corynebacterium 101 15.12

Neisseria 101 13.94

Leptotrichia 101 13.67

Veillonella 101 12.05

Ensifer 101 11.29

Haemophilus 101 8.43

Limnobacter 101 6.41

Fusobacterium 101 6.13





which helps them make up a large portion of the population;

and a large variance in presence, which reflects an unstable

proportion that is different from one patient to another, indi-

cating that they are not the normal lung residents and that

the populations shift constantly from one patient to another.

Table 3 is a combination of the different ranking lists of the

top 20 genera for comparison. The left column of this table

shows the top 20 genera by distribution, the middle column

shows the top 20 genera with the highest percentage in vivo,

and the right column shows the 20 genera with the highest

standard deviation of the percentage. The genera that are

found in the three columns simultaneously and occupy high

positions on all three lists might have a tendency to be

the causative pathogen or, to some extent at least, they con-

tribute more to the abnormal environment in vivo. In

keeping with the common knowledge of pneumonia [5],

Streptococcus sits at the top of the list with a huge popu-lation, and proves that it is the unquestioned leader

in causing infection. Our study provides good evidence

to support this traditional concept. In the right

column, Streptococcus is followed by Staphylococcus,

Pseudomonas, Acinetobacter , and Rothia, which are all sus-

pected to cause disease. However, from our point of view,

we consider that Rothia may be less responsible for disease

compared with the three genera above it for two reasons.

First, it is common knowledge that Rothia is a genus routi-

nely found in the normal flora of the respiratory tract and

oral cavity [22]. Its population may be influenced greatly by

intruders, which would cause the higher standard deviation.

Second, according to Table 3, the position of Rothia tends

to descend from the left column to the right (no. 2 on the

left [L], no. 2 in the middle [M], and no. 5 on the right [R]).

Conversely, the rankings of the Staphylococcus (L: 10; M:

3; R: 2), Pseudomonas (L: 3; M: 4; R: 3), and Acinetobacter

(L: 15; M: 7; R: 4) ascend. This indicates that although

Rothia is widely distributed in most patients and its popu-

lation amounts to a high percentage of the total, the number

of bacteria of this genus is more stable than that of

Staphylococcus, Pseudomona, and Acinetobacter , and it

shows that Rothia is more inclined to be a part of the normal

flora than the three genera above it. However, from the

viewpoint of ‘community as a pathogen’, Rothia should not

be excluded from the possible pathogen list.

Certainly, it is necessary to compare our results con-cluded above with the normal bacteria flora in respiratory

tract. From the basic knowledge of medical microbiology,

we are told that in mouth, the prominent member of the

resident flora is Streptococcus and the rest are constituted

by some kinds of gram-negative diplococci ( Neisseria,

Moraxella). In the pharynx and trachea, the flora is similar

to that in the mouth, whereas few bacteria are found in

normal bronchi. The small bronchi and alveoli are normally

sterile [23]. So it is considered that saliva from the normal

people is reliable to act as the comparison.

Table 3 The top 20 genera by distribution (left), percentage in vivo (middle), and standard deviation of the percentage (right)

Ranking Number of infected patients Mean percentage (%) Standard deviation of mean percentage

1 Streptococcus 89 Streptococcus 23.86 Streptococcus 20.80

2 Rothia 84 Rothia 8.14 Staphylococcus 17.51

3 Pseudomonas 75 Staphylococcus 7.16 Pseudomonas 13.09

4 Prevotella 70 Pseudomonas 6.59 Acinetobacter 12.38

5 Granulicatella 68 Prevotella 4.33 Rothia 11.66

6 Ensifer 67 Gemella 3.62 Gemella 9.30

7 Actinomyces 66 Acinetobacter 3.58 Enterococcus 8.62

8 Gemella 64 Granulicatella 3.52 Lactobacillus 8.24

9 Veillonella 64 Actinomyces 2.48 Prevotella 6.79

10 Staphylococcus 57 Enterococcus 1.87 Granulicatella 5.57

11 Leptotrichia 48 Ensifer 1.59 Stenotrophomonas 4.84

12 Sphingomonas 48 Lactobacillus 1.43 Actinomyces 4.18

13 Fusobacterium 45 Neisseria 1.25 Streptobacillus 4.15

14 Neisseria 44 Stenotrophomonas 1.21 Corynebacterium 4.12

15 Acinetobacter 38 Veillonella 1.16 Ensifer 3.57

16 Capnocytophaga 36 Corynebacterium 0.85 Neisseria 2.52

17 Atopobium 36 Leptotrichia 0.78 Leptotrichia 2.08

18 Limnobacter 35 Limnobacter 0.68 Veillonella 1.87

19 Haemophilus 35 Streptobacillus 0.58 Limnobacter 1.70

20 Peptostreptococcus 34 Haemophilus 0.54 Haemophilus 1.41





Tian et al . [24] also used pyrosequencing-based method

to analyze the bacterial flora in normal people’s saliva.

Considering that both their research work and ours were

based on the same procedure and method, we used their

gross sequencing data as the normal control.

Also, the data set were processed by RDP in bulk and the percentage of every genus was calculated to reveal the

constitution of the flora. Table 5 is the list of top 20

genera with the highest percentage and when compared

with the middle column of Table 3, obvious difference can

be seen. Except the Streptococcus, the genera such as

Staphylococcus, Pseudomonas, and Acinetobacter , which

were considered to be suspiciously contributing much to

the infection, are absent in Table 5. The fact indicates that

they have extremely low population in normal people’s

saliva. Meanwhile the ranking of Rothia descended

obviously in Table 5. All of these strongly solidify the

conclusion drawn in last paragraph and prove the reliability

of the distribution-percentage-deviation combined analysis

that we discussed above.

Another aspect of this study was the evaluation of identi-

fication ability based on the 16S rDNA library and the 454

platform. As described above, all of the sputum samples

were subjected to identification using the culture-dependent

Table 5 Top 20 genera consisting of the normal saliva flora with the

highest percentage

Ranking Genus Mean percentage (%)

1 Streptococcus 22.93

2 Prevotella 10.96

3 Haemophilus 4.90

4 Veillonella 3.83

5 Fusobacterium 3.83

6 Granulicatella 3.55

7 Gemella 2.97

8 Capnocytophaga 2.95

9 Leptotrichia 2.48

10 Neisseria 2.09

11 Actinomyces 1.72

12 Rothia 1.14

13 Selenomonas 0.88

14 Porphyromonas 0.81

15 Aggregatibacter 0.72

16 Peptostreptococcus 0.71

17 Campylobacter 0.69

18 Parvimonas 0.68

19 Treponema 0.51

20 Atopobium 0.29

Table 4 Chi-square test for comparison of identification ability of the

two methods in the study

Pyro(þ) Pyro(2) Total Exact significance

(two-sided)

Acinetobacter 0.000

Culture(þ) 5 3 8

Culture(2) 33 60 93

Total 38 63 101

Enterococcus 0.000

Culture(þ) 1 0 1

Culture(2) 15 85 100

Total 16 85 101

Neisseria 0.049

Culture(þ) 11 18 29

Culture(2) 33 39 72

Total 44 57 101

Staphylococcus 0.000

Culture(þ) 9 1 10

Culture(2) 48 43 91

Total 57 44 101

Pseudomonas 0.000

Culture(þ) 5 0 5

Culture(2) 70 26 96

Total 75 26 101

Stenotrophomonas 0.000Culture(þ) 3 0 3

Culture(2) 18 80 98

Total 21 80 101

Streptococcus 0.000

Culture(þ) 29 1 30

Culture(2) 60 11 71

Total 89 12 101

Klebsiella 0.727

Culture(þ) 1 5 6

Culture(2) 3 92 95Total 4 97 101

Mycobacterium 1.000

Culture(þ) 1 1 2

Culture(2) 0 99 99

Total 1 100 101





method in a clinical laboratory before our procedure. Every

sputum sample had a clinical report made by standard hos-

pital methods. The chi-square test was used to evaluate the

different detection ratios of both methods, and Table 4

shows the detailed results of this comparison. According to

Table 4, except for Klebsiella and Mycobacterium, it

was noted that whereas pairs of pyrosequencing-positive/

culture-negative in the other seven genera were common, pyrosequencing-negative/culture-positive discordant pairs

were rare, which is similar to the results of Price et al . [13].

The explanation for this phenomenon is as follows. First,

molecular detection could find the 16S rDNA V3 regions

belonging to the unculturable or dead bacteria, which were

unable to be enriched on media in vitro. Second, molecular

detection could discover the bacteria that were proportion-

ately rare within the community and masked by the domi-

nant bacteria in culture media in vitro [13]. The most

possible reason for the unexpected results with

Mycobacterium is that in the very first step of our laboratory

procedure, the tough cell wall of Mycobacterium might not

have been destroyed completely to release the DNA.

However, currently, a reasonable explanation could not be

found for the bias measure of Klebsiella. Lazarevic et al .

[25] have also mentioned a similar bias in their research.

Several genera of important possible pathogens were

detected by the 454 platform but were absent from the list

obtained by the clinical method. According to our knowl-

edge and published articles, Mycoplasma, Haemophilus,

and Moraxella are referred to as atypical pathogens of pneu-

monia [5,26 – 28]. Compared with clinical reports, these

potential pathogens were only found sporadically by our

method in sputum samples. Although the atypical pathogens

were certain species below these three mentioned genera

and our analysis did not determine the species of them, the

result showed that this method is promising.

The weaknesses and limitations of our method should not

be ignored. Although it is a good tool to unveil the panor-

ama of the bacterial community in pneumonia, the method

provides us with data describing only the quantity of bac-

teria. For further research on the concept of ‘the community

as a pathogen’, a method simply based on calculation is far

from ideal, and many other methods should be designed to

help us understand this concept. All of the V3 regionsequences are cataloged at the genus level, and it is under-

stood that a new, more efficient method to satisfy the clini-

cal diagnosis standard must provide more accurate results,

which are able to reach the species level. Fortunately,

further research is already underway and we believe that

more precise results will be obtained in the near future.

From this work, at least two conclusions can be drawn.

First, in the point of view of ‘the community as a pathogen’,

the boundary between pathogenic agents and normal flora is

becoming vague. Thus, many factors should be considered

to evaluate which agent (or agents) contributes most to the

infection. As described above, with the combination of dis-

tribution in population, percentage of flora and variance

among the patients, we conclude that Streptococcus,

Staphylococcus, Pseudomonas, and Acinetobacter may play

the most important role in hospital-acquired pneumonia.

Second, according to the comparison between the

pyrosequencing-based method and the Vitek 2 Compact system, the former not only provides us with a more compli-

cated bacterial community, but also covers almost all the

results obtained by the clinical laboratory method (the pairs

of pyrosequencing-positive/culture-negative were common

and the pyrosequencing-negative/culture-positive discordant

pairs were rare). Although our method is far from the stan-

dard for clinical diagnosis use, with the development of

analysis algorithm, it is a promising and feasible method for

bacterial identification in an uncultured sample. Because

from both our conclusions, there did not exist obvious bias

between such culture-independent method and traditional

knowledge on pneumonia since almost all clinical result

were covered by our method and meanwhile, the barrier of

the requirement for culture in vitro was overcome and the

view of the observation was widened.

Funding

This work was supported by grants from the National

Natural Science Foundation of China (30770111,

30900051, and 30970125), the National Key Program for

Infectious Diseases of China (2008ZX10004 and

2009ZX10004), the Program of Shanghai Subject Chief Scientist (09XD1402700), and the Program of Shanghai

Research and Development (10JC1408200).

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