16S Rdna Sequencing Analysis of Upper Respiratory Tract ﬂora in Patients with Inﬂuenza H1N1 Virus Infection

Frontiers in Laboratory Medicine xxx (2017) xxx–xxx

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Frontiers in Laboratory Medicine

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16S rDNA sequencing analysis of upper respiratory tract ﬂora in patients with inﬂuenza H1N1 virus infection

Yanhua Li a,1, Jianbing Ding b,1, Yunfeng Xiao c, Bin Xu d, Wenfang He a, Yuqi Yang a, Liu Yang a, ⇑ ⇑ Mingquan Su a, Xiaoke Hao a, , Yueyun Ma a, a Department of Clinical Laboratory Medicine, Xijing Hospital, The Fourth Military Medical University, Xi’an, Shaanxi 710032, PR China b Department of Immunology, Xinjiang Medical University, Urumqi, Xinjiang, 810034, PR China c Department of Pharmacy, Tangdu Hospital, The Fourth Military Medical University, Xi’an, Shaanxi 710038, PR China d Shandong International Trust Co., Ltd, Jinan, Shandong 250013, PR China article info abstract

Article history: Background: We analyzed respiratory tract bacterial flora in patients with influenza H1N1 virus infection, Received 13 December 2016 and investigated the role of H1N1 virus in secondary bacterial infection. Received in revised form 13 January 2017 Method: A total of 12,766 operational taxonomic units (OTUs) were obtained, of which, 12,127 were Accepted 17 January 2017 identified to phylum level and 10,494 to genus level. We used next-generation sequencing technology Available online xxxx to evaluate bacterial abundance in swab specimens from patients infected with influenza H1N1 virus or Non-H1N1 influenza and from healthy controls. Data analysis was carried out by using alpha analysis Keywords: (Shannon-Wiener index and Rarefaction-Curve), beta analysis [UniFrac(abundance) and Metastats 16S rDNA analysis], and Community-and-Phylogenesis analysis. Influenza H1N1 Upper respiratory tract flora Results: At phylum level, Proteobacteria in patients with H1N1 virus infection (99.928 ± 0.008%) and Next-generation sequencing common cold (89.019 ± 1.845%) were significantly higher than in healthy controls (26.103 ± 2.495%) (p < 0.01). In contrast, proportions of Firmicutes, Bacteroidetes, Actinobacteria, Candidate division TM7, Fusobacteria and SR1 were down-regulated (p < 0.01) in patients with H1N1 virus infection. At genus level, Pseudomonas increased >500-fold in patients with H1N1 virus infection compared with healthy controls. Ochrobactrum, Brevundimonas, Caulobacter, Aquabacterium and Serratia also increased significantly in H1N1 virus infection, while Neisseria, Prevotella, Veillonella, Actinomyces, Porphyromonas, Streptococcus, Haemophilus and Acinetobacter decreased. Conclusion: Our data indicated that microbial abundance of the upper respiratory tract decreased in patients with H1N1 virus infection. Pseudomonas was the dominant genus among the upper respiratory tract bacterial flora in H1N1-infected patients. The changes in upper respiratory tract flora probably be closely related to the occurrence and progression of secondary bacterial infection. Ó 2017 Published by Elsevier B.V. on behalf of Laboratorial Medicine Committee of the Chinese Research Hospital Association. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Introduction that has a serious negative impact on economic development and human health. The early symptoms of H1N1 influenza are similar Influenza A virus subtype H1N1 [denoted as A(H1N1) or H1N1] to Non-H1N1 influenza, however, progression is rapid and may is one of the most commonly found influenza viruses in humans.26 lead to secondary and severe bacterial pneumonia, respiratory fail- Epidemic H1N1 influenza spreads rapidly in autumn and winter in ure and multiple organ injury and even death. Secondary bacterial the Northern Hemisphere, such as North America, Europe and infection accounted for 26–33% of deaths caused by H1N1 virus Asia.22,25 This has become a major social and public health problem infection,13,20,28,41,8 and 30–33% of patients with H1N1 virus in intensive care might have secondary bacterial infection.35,36 ⇑ H1N1 virus was the leading main cause of death in the three large Corresponding authors at: Department of Clinical Laboratory, Xijing Hospital, 30 The Fourth Military Medical University, 169 Changle West Road, Xi’an, Shaanxi influenza pandemics in 1918, 1957 and 1968. 710032, PR China. Secondary bacterial infection is attributed to the capacity of E-mail addresses: [email protected] (X. Hao), [email protected] H1N1 virus to replicate in the lower airways, causing extensive (Y. Ma). epithelial destruction, viral pneumonia, and secondary bacterial 1 These authors contributed equally to this study. http://dx.doi.org/10.1016/j.flm.2017.02.005 2542-3649/Ó 2017 Published by Elsevier B.V. on behalf of Laboratorial Medicine Committee of the Chinese Research Hospital Association. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Please cite this article in press as: Li Y., et al. 16S rDNA sequencing analysis of upper respiratory tract flora in patients with influenza H1N1 virus infection Frontiers in Laboratory Medicine (2017), http://dx.doi.org/10.1016/j.flm.2017.02.005 2 Y. Li et al. / Frontiers in Laboratory Medicine xxx (2017) xxx–xxx infections with increased lethality.22 Viral infection may enhance Guangzhou, China) and criteria for influenza infection were simul- bacterial pathogenesis and include impairment of mucociliary taneously met. According to the examination results, patients were clearance and increased bacterial adherence to epithelial cells. divided into two groups: G1, infection with H1N1 (n = 100), and Virus-induced epithelial damage facilitates bacterial translocation G2, infection without H1N1 but other type of influenza infection and dissemination and/or inhibition of antibacterial immune (Flu group or other influenza group) (n = 72). And the clinical fea- response. The healthy nose/nasopharynx has a bacterial tures are summarized in Table 1. community dominated by Actinobacteria, Firmicutes and Thirty healthy individuals were chosen as the normal control Proteobacteria.6 Secondary bacterial pneumonia is usually (N) group, whose average age was 26.5 years. Inclusion criteria: caused by Haemophilus influenzae, Pseudomonas aeruginosa, Staphy- despite their regular physical examinations, they were normal or lococcus epidermidis, Staphylococcus aureus and Corynebacterium healthy, without upper respiratory tract infection within the past tuberculostearicum.38,15,1,12,25 3 months. Exclusion criteria: same as for influenza. The occurrence and development of many human diseases are Pharyngeal swab specimens were collected with Copan 480CE usually closely related to changes in their bacterial flora.33,11 The (Copan, Brescia, Italy). In addition, we used five standard strains normal flora in the respiratory mucous membrane can be involved as positive controls: Streptococcus pneumoniae (ATCC 49619), in the regulation of adaptive immune response against influenza Escherichia coli (ATCC25922), P. aeruginosa (ATCC 27853), S. aureus virus.43,17,2 It can maintain a high tolerance threshold to modulate (ATCC 25923), and H. influenzae (ATCC 49247). the inflammatory response.29,37,23 So, it is reasonable to hypothe- size that the changes in respiratory tract flora caused by acute DNA extraction and PCR amplification H1N1 virus infection stimulate secondary and severe bacterial pneumonia, but the mechanisms are still not clear. The swabs were transferred to new 1.5-mL centrifuge tubes, The gold standard of identification by bacterial culture has been dissolved in PBS buffer by stirring, and centrifuged for 3 min at unable to meet the demand of bacterial diversity analysis, because 20,000g. The supernatant was discarded and 100–200 mL suspen- 34 >90% of the clinical bacteria cannot be cultured. Next-generation sion was left. Microbial DNA was extracted from swab samples sequencing (NGS) provides a new technology to study the micro- using the E.Z.N.A.Ò DNA Kit (Omega Bio-tek, Norcross, GA, USA). 16 biota and pathogenic mechanisms. The V3–V4 region of the bacterial 16S rDNA was amplified by 16S rDNA sequencing is a powerful tool for researching taxon- PCR (95 °C for 2 min, followed by 25 cycles at 95 °C for 30 s, omy and phylogeny of samples from a complex microorganism 55 °C for 30 s, and 72 °C for 30 s and a final extension at 72 °C for or environment. The generated operational taxonomic units 5 min) using primers 338F 50-barcode-ACTCCTACGGGAGG (OTUs) can be analyzed at seven levels: kingdom, phylum, class, CAGCA)-30 and 806R 50-GGACTACHVGGGTWTCTAAT-30, where 32 order, family, genus and species. Nasidze et al. found 39 bacteria barcode is an eight-base sequence unique to each sample. PCRs that have never been reported in the mouth through 16S rDNA were performed in triplicate with a 20-lL mixture containing 45 sequencing. Yang et al. analyzed salivary microorganisms in 19 4 lL5 FastPfu Buffer, 2 lL 2.5 mM dNTPs, 0.8 lL each primer individuals with active caries and 26 without caries with 16S rRNA (5 lM), 0.4 lL FastPfu Polymerase, and 10 ng template DNA. and whole genome sequencing technology. They found that the microbial changes were greater in patients with active caries. The present study applied NGS of 16S rDNA sequencing and Illumina MiSeq sequencing bioinformatics to analyze variation of the microbiome during H1N1 virus infection and might show how respiratory tract flora Amplicons were extracted from 2% agarose gels and purified changes cause severe secondary pulmonary infection during using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, H1N1 virus infection. Union City, CA, USA) and quantified using QuantiFluor-ST (Pro- mega, Madison, WI, USA). Purified amplicons were pooled in equimolar and paired-end sequenced (2 300) on an Illumina Materials and methods MiSeq platform.

General information, participant enrollment and sample collection Processing of sequencing data

We investigated patients who consulted the Division of Respira- Raw fastq files were demultiplexed, quality-filtered using tory Disease from the First Affiliated Hospital of The Fourth Mili- QIIME (version 1.9.1) with the following criteria: (1) 300-bp reads tary Medical University (Xi’an, China), from September 2009 to were truncated at any site receiving an average quality score <20 March 2010. The study was approved by the Ethics Committee of over a 50-bp sliding window, discarding the truncated reads that the First Affiliated Hospital of The Fourth Military Medical Univer- were shorter than 50 bp; (2) exact barcode matching, 2 nucleotide sity (KY20103129-1) and informed consent was obtained from all mismatch in primer matching, reads containing ambiguous charac- participants. Diagnostic criteria for influenza included: (1) emerg- ters were removed; and (3) only sequences that overlapped >10 bp ing cough and sputum; (2) worsening symptoms of pre-existing were assembled according to their overlap sequence. Reads that respiratory disease; (3) fever (>37.4 °C); (4) signs of consolidation could not be assembled were discarded. in the lung and/or moist rales; and (5) leukocyte count OTUs were clustered with 97% similarity cutoff using UPARSE >10 109/L or <4 109/L. Patients who met two or more criteria version 7.1 (http://drive5.com/uparse/), meanwhile, chimeric including criterion (5) were diagnosed as having influenza 39. sequences were identified and removed using UCHIME. If the Patients using immunosuppressive drugs, steroids, or antibiotics homology of whole length of 16S rDNA was 99%, bacteria could were excluded. We also excluded patients with systemic disease, be classified to species level. If the homology was 97–98.9%, bacte- HIV infection, hepatitis A, B or C, tuberculosis, pulmonary neo- ria generally belonged to the same genus but different species. If plasms, pulmonary embolism, atelectasis, pulmonary edema, the homology was <95%, bacteria were considered to be in differ- non-infectious interstitial lung disease, pulmonary vasculitis or ent genera. The taxonomy of each 16S rDNA sequence was ana- pulmonary eosinophil infiltration syndrome. lyzed by RDP Classifier (http://rdp.cme.msu.edu/) against the H1N1 virus infection was confirmed when positive laboratory silva (SSU123)16S rDNA database using confidence threshold of tests (real-time qPCR H1N1 virus nucleic acid test kit, DaAn Gene, 70%.3

Table 1 Clinical information and diagnosis of participating patients and whole sequencing data summary in this study.

Characteristics Group1 (pH1N1 infection) Group2 (other inﬂuenza Group3 p-Value1 p-Value2 p-Value3 virus infection) (Healthy control) (n = 100) (n = 72) (n = 30) (G1 vs 2) (G2 vs 3) (G1 vs 3) Demographic features Age (years) (mean ± SD) 15.63 ± 9.37 13.44 ± 5.56 16.50 ± 9.56 0.079 0.215 0.344 2 0(0) 1(1.4) 0(0) 3–14 59(59) 47(65.3) 10(33.3) 15–30 37(37) 23(31.9) 10(33.3) 31–40 1(1) 1(1.4) 10(33.3) 41–60 2(2) 0(0) 0(0) 61 1(1) 0(0) 0(0) Male/female 62/38 42/30 16/14 Nation Han 97(97) 70(97.2) 30(100) Others 3(3) 2(2.8) 0(0) Underlying conditions Lung disease 21(21) 8(11.1) Diabetes mellitus 0(0) 2(2.8) Hypertension 9(9) 7(9.7) Heart cerebrovascular disease 0(0) 6(8.3) Liver disease 0(0) 0(0) Other chronic disease 0(0) 0(0) Smoker 6(6) 23(31.9) History of similar exposure 29(29) 17(23.6) Symptoms Temperature(°C) (mean ± SD) 38.86 ± 1.57 37.93 ± 1.38 36.5 ± 0.61 0.235 0.192 0.103 <37.3 35(35) 25(34.7) 30(100) 37.3–38 13(13) 14(19.5) 38.1–39 5(5) 27(37.5) 39.1–40 46(46) 6(8.3) >40 1(1) 0(0) Headache 48(48) 25(34.7) Myalgia 30(30) 8(11.1) Fatigue 40(40) 5(6.9) Coryza 29(29) 17(23.6) Sore throat 35(35) 18(25) Dry cough 25(25) 20(27.8) Sputum 65(65) 52(72.2) Chest pain Dyspnoea 15(15) 15(20.8) Gastrointestinal symptoms 11(11) 21(29.2) Blood cell count, 109, (mean ± SD) 41(41) 8(11.1) Leucocyte 10.38 ± 6.86 8.32 ± 3.98 6.12 ± 3.78 0.596 0.171 0.049 Neutrophil 7.98 ± 5.79 6.47 ± 3.69 4.23 ± 2.68 0.304 0.241 0.056 Lymphocyte 1.56 ± 0.83 1.78 ± 0.65 1.81 ± 0.44 0.788 0.166 0.037 Primary diagnosis Pneumonia 82(82) 52(72.2) Acute bronchitis 1(1) 14(19.4) Upper respiratory tract infection 9(9) 6(8.3) ARDS 8(8) 0(0) Data summary(mean ± SD) Raw sequences(106) 14.98 ± 4.43 15.03 ± 3.88 10.73 ± 3.08 0.026 0.000 0.000 Valid reads(104) 3.60 ± 0.71 3.88 ± 0.97 6.21 ± 1.72 0.002 0.000 0.000 OTUs 163.15 ± 57.89 417.57 ± 218.48 1026.07 ± 173.22 0.000 0.176 0.000 Shannon index 0.69 ± 0.49 2.12 ± 1.08 4.16 ± 0.38 0.000 0.000 0.015 Rarefaction 122.11 ± 39.23 323.33 ± 176.02 759.91 ± 154.73 0.000 0.299 0.000

Note: p1: significance between group 1 and group 2; p2: significance between group 2 and group 3; p3: significance between group 1 and group 3

The differences in community of the dominant bacteria in G1, obtained according to the barcode and primer sequence. We cre- G2 and N groups were further statistically analyzed using SPSS ver- ated a merged data set according to variable region V3–V4 sion 17.0 software. Experimental data were displayed as mean plus sequences of bacterial 16S rDNA. We described flora changes in standard deviation. Student’s t-test, single factor variance analysis the upper respiratory tract in 208 samples. 10,370,176 raw and multiple comparisons were used for statistical analysis. sequences were obtained, which means there were 34,689– p < 0.05 indicated statistical significance. 58,087 reads for each sample. A total of 7,777,632 valid reads with high quality were clustered into 12,766 OUTs. A total of 12,127 Results OUTs were identified to phylum level and 10,494 to genus level.

OUT summary of all samples Difference of OTU distribution among groups (samples)

Miseq synthesizes and sequences simultaneously from both Venn diagrams can directly reflect the degree of similarity of ends of DNA. According to the overlap correlation of paired-end OTUs among the samples. We statistically analyzed and calculated (PE) reads, paired reads splice (merge) into a sequence. After eval- shared OTUs among groups (samples) according to their informa- uating mosaic effect and quality control filtering, valid reads were tion of classification, and drew the map of Wayne (Venn diagram).

Each color represents a group or sample, and the overlapping sec- within groups (G1, G2, N and P group) (Fig. 1Ca–c) were 35, 17, tion refers to shared OTUs of the adjacent groups (samples). The 190 and 0, respectively. But there were almost no shared OTUs numbers represent OTU count of a unique group (sample) or among all samples from different groups (Fig. 1Ce–h), which indi- shared among groups (samples). Fig. 1A shows the distribution dif- cated the individual differences were more significant. ferences in OTU number among the four groups. The OUT counts of the G1, G2, N and P groups were 2788, 7538, 6177 and 401, respec- Microbial diversity of different sequencing quantity (Shannon–Wiener tively. The OUT counts of shared bacteria numbers in all four index and Rarefaction curve) groups was 27. There were 181 OTUs shared among the G1, G2 and N groups. There were 1905 OTUs shared between the G1 and The Shannon–Wiener index was used to construct the microbial G2 groups, which accounted for 68.3% of the G1 group. There were diversity curve according to index of each sample in different 196 OTUs shared between the G1 and N groups. There were 1915 sequencing quantity, so as to reflect the variation of species diver- OTUs shared between the G2 and N groups. The unique OTUs in the sity with the sequencing process. Shannon index (In S) is character- G1, G2, N and P groups were 868, 3743, 4237 and 98, respectively ized by the complexity or diversity of species in communities. (Fig. 1B). In S was calculated with the following formula: P Fig. 1C illustrates the OTU differences between the five samples 1 ¼ s H 1 i¼1pi ln pi, where pi represents the proportion of the i-th among each group (Fig. 1Ca–c), and the OTU differences between species. Thus, when there is only one population in a community, different groups (Fig. 1Ce–h). Shared counts of bacterial phyla its In S should be a minimum value of 0. When a community has

Fig. 1. OUT distribution among all samples. (A) The merged data were set according to variable region V3–V4 of the sequence of bacterial 16S rDNA. (B) Distribution differences in OTU counts among the four groups: each color represents a group; the overlapping part refers to shared OTUs of adjacent groups; each number represents the unique OUT count of a group or the counts of several groups. The OUT counts of groups G1, G2, N and P were 2788, 7538, 6177 and 401, respectively; shared bacterial count of all four groups was 27. G1: H1N1 group; G2: group with other type of inﬂuenza; N: normal control group; P: positive control group. C: Difference in OTU distribution among samples (among and within groups): each color represents a sample; the overlapping part refers to shared OTUs of adjacent samples; each number represents the unique OUT of a sample or counts of several samples. The shared OTUs within groups G1 (a), G2 (b), N (c) and P (d); shared OTUs of samples among groups G1, G2, N and P (e–h).

Fig. 2. Microbial diversity of different sequencing quantity. (A) Microbial diversity curve for different amount of sequencing. The x axis represents selected sequencing data; the y axis represents corresponding In S; and different colors represent different samples. (B) Rarefaction curve of all samples. x axis represents the sequencing data and the y axis represents the OTU counts in the order of the x axis. (C) Weighted UnifracPCoA analysis. Every spot in the ﬁgure indicates a sample; x axis represents the ﬁrst dimension; and y axis represents the second dimension.

Please cite this article in press as: Li Y., et al. 16S rDNA sequencing analysis of upper respiratory tract flora in patients with influenza H1N1 virus infection Frontiers in Laboratory Medicine (2017), http://dx.doi.org/10.1016/j.flm.2017.02.005 6 laect hsatcei rs s iY,e l 6 DAsqecn nlsso pe eprtr rc oai ainswt nunaHN iu inf virus H1N1 influenza with patients in flora tract respiratory upper of analysis sequencing rDNA 16S al. (2017), et Medicine Y., Laboratory Li in as: Frontiers press in article this cite Please

Table 2 Metastats results at the genus level.

Shared in all groups Shared in G1 and 2 Shared in G2 and NC Shared in G1 and NC Genus in G1 only Genus in G2 only Genus in NC only n =36 n =41 n =13 n =4 n =19 n =10 n =47 Neisseria Bacillus Atopobium TM7_ Mycobacterium Herbaspirillum Selenomonas Prevotella Caulobacter Oribacterium Rothia Alistipes Ralstonia Megasphaera Veillonella Methyloversatilis Peptostreptococcaceae_ Stenotrophomonas Arthrobacter Janthinobacterium Dialister Actinomyces Psychrobacter Leptotrichia Biﬁdobacterium Chryseobacterium Pelomonas Tannerella Porphyromonas Escherichia SR1_ Enterococcus Herminiimonas Peptococcus Streptococcus Albidiferax Lachnoanaerobaculum Helcococcus Flavihumibacter Parvimonas Campylobacter Pedobacter Treponema Clostridium_sensu_stricto_1 Blastocatella Aggregatibacter Haemophilus Dechloromonas Catonella Parabacteroides Xanthobacter Stomatobaculum http://dx.doi.org/10.1016/j.ﬂm.2017.02.005 Alloprevotella Achromobacter Gemella Parasutterella Finegoldia Bacteroidales_ Capnocytophaga Aquabacterium Gracilibacteria_bacterium_oral_taxon_873 Methylobacterium Sediminibacterium Filifactor

Peptostreptococcus Serratia Kingella Lactococcus Butyrivibrio xxx–xxx (2017) xxx Medicine Laboratory in Frontiers / al. et Li Y. Un-classiﬁed Paucibacter Bacteroidetes_ Rhodobacter Solobacterium Moraxella Limnobacter Peptoniphilus Micrococcus Abiotrophia Bergeyella Aquincola Atopobium Skermanella Granulicatella Acinetobacter Hydrogenophaga Oribacterium Cocos_nucifera_ Eikenella Sphingobium Ochrobactrum Peptostreptococcaceae_ Hydrotalea Mobiluncus Pseudomonas Pseudoxanthomonas Facklamia Mogibacterium Rhizobium Phenylobacterium Methylomonas Firmicutes_oral_clone_FM046 Lautropia Suttonella Cylindrotheca_closterium Shuttleworthia Corynebacterium Delftia Ottowia Bacteroides Pannonibacter Mitsuokella Devosia Methylotenera Slackia Fusobacterium Zoogloea Lachnospiraceae_ Brevundimonas Azospira Cardiobacterium Lactobacillus Thiothrix Acholeplasma Staphylococcus Curvibacter Clostridiales_ Dolosigranulum Actinotalea Alloscardovia Sphingopyxis Ferribacterium Alysiella Bosea Anaerococcus Anaeroglobus Propionibacterium Sulfuritalea Candidatus_Saccharimonas Incertae_Sedis Hyphomicrobium Veillonellaceae_ Sphingobacterium Oxobacter Cryptobacterium Lysobacter Woodsholea Peptoniphilaceae_ Sphingomonas Sphingosinicella Erysipelotrichaceae_ Paracoccus Lutispora Mollicutes_ Burkholderia Anaerospora Simonsiella Deinococcus Bulleidia Kerstersia Gracilibacteria_bacterium_canine_oral_taxon_364 Flavobacterium Desulfobulbus Nordella Fretibacterium Thermicanus Actinobaculum Bacteroidetes Olsenella Ruminococcaceae_ Clostridiales_bacterium_canine_oral_taxon_260 Microbacterium Scardovia ection Note: G1: infection with pH1N1; G2: infection with other type of inﬂuenza virus; NC: healthy control. Y. Li et al. / Frontiers in Laboratory Medicine xxx (2017) xxx–xxx 7

Fig. 3. Metastats analysis of significant differences between groups at phylum level. Community differences in groups G1 (A), G2 (B) and N (C) at phylum level. (D) Flora composition differences among groups G1, G2 and N at phylum level. pairwise samples, UniFrac distance matrices of samples were Metastats analysis of significant differences between groups obtained. Weighted Unifrac means that the sequence abundance is considered, which can further quantitatively detect the differ- Metastats is used to detect objective differences in characteris- ences in samples in different evolutionary lineages. tics of the abundance of metagenomic samples. To identify Principal co-ordinates analysis based on Unifrac shows the microorganisms with significant differences, comparing samples differences in individuals or populations. The smaller the distance of every two groups, we used mother and Metastats for between spots, the more similar the bacterial community that analysis.44,40 p < 0.05 was considered significant. We took OTUs they share. Fig. 2C shows that changes in the bacterial at phylum and genus levels for further analysis. community among samples of different groups were significantly Fig. 3 shows that, in the N group, the first four phyla of different. OTUs were Firmicutes (30.817 ± 1.209%), Proteobacteria (26.103 ±

Fig. 4. Metastats analysis of signiﬁcant differences between groups at genus level. Community differences in groups G1 (A), G2 (B) and N (C) at genus level. (D) Flora composition differences among groups G1, G2 and N at genus level. (E) Proportions of classiﬁed genera of S005 (a), S004 (b), NC025 (c) and NC027 (d).

2.495%), Bacteroidetes (25.256 ± 1.359%) and Actinobacteria Brevundimonas, Caulobacter, Aquabacterium and Serratia were (12.058 ± 1.077%). Candidate division TM7 (1.913 ± 0.269%), increased in G1 compared with N (p < 0.05). In contrast, Hae- Fusobacteria (1.626 ± 0.231%), and SR1 (1.338 ± 0.359%) were rela- mophilus, Streptococcus, Acinetobacter and Rhizobium declined sig- tively fewer. Spirochaetes (0.308 ± 0.054%), Saccharibacteria nificantly (p = 0.001). There was no significant difference in (0.191 ± 0.046%), BD1 (0.168 ± 0.087%) and Spirochaetae Corynebacterium, Devosia and Staphylococcus (p > 0.05). (0.143 ± 0.038%) were much less (Fig. 3C). Comparing the G2 and N groups, the percentages of Brevundi- However, Proteobacteria accounted for the largest proportion in monas, Caulobacter, Aquabacterium, Serratia and Dolosigranulum the G1 (99.928 ± 0.008%) (Fig. 3A) and G2 (89.019 ± 1.845%) groups increased significantly (p = 0.001). Neisseria, Prevotella, Veillonella, (Fig. 3B). It was higher than that in the N group (26.103 ± 2.495%) Actinomyces, Porphyromonas, Streptococcus, Campylobacter and (p = 0.001). Compared with the N group, Firmicutes Atopobium decreased significantly (p = 0.001). There were no sig- (0.030 ± 0.004%), Actinobacteria (0.022 ± 0.006%), Bacteroidetes nificant changes in Moraxella, Gracilibacteria bacterium oral taxon (0.007 ± 0.003%), Fusobacteria and Candidate division TM7 were 873, Kingella, Bacteroides, Sphingobium and Stenotrophomonas decreased in the G1 group (p < 0.01) (Fig. 3D). (p > 0.05). More than 5% of OTUs at genus level in the N group were There were two new infected cases S004 and S005 (Fig. 4Ea, b). focused in six genera: Neisseria (18.703 ± 2.332%), Prevotella The microbial communities of S004 and S005 were similar to sam- (14.313 ± 1.577%), Veillonella (8.558 ± 0.888%), Actinomyces ples NC025 and NC027 from group N (Fig. 4Ec, d). Fig. 4E illustrated (6.173 ± 0.833%), Porphyromonas (6.047 ± 0.677%) and Streptococ- the proportions of classified genus composition of samples S005, cus (5.739 ± 0.605%) (Fig. 4A–C). OTUs accounted for >1% but <5% S004, NC025 and NC027. The proportion of Haemophilus in S004 were distributed in Campylobacter, Atopobium, Haemophilus, and S005 was 6.68% and 27.26%, respectively. It increased com- Alloprevotella, Oribacterium, Capnocytophaga, Peptostreptococcus, pared with that in group N (3.305 ± 0.219%). This suggests that dis- Leptotrichia, SR1, Selenomonas, Moraxella and Megasphaera. There ease progression contributes to changes in flora composition. were fewer OTUs in other genera, such as Pseudomonas (0.076 ± 0.034%). Analysis of community structure at species level Fig. 4D shows details of shared genera in groups G1, G2 and N. The proportions of Pseudomonas in G1 and G2 were According to the difference in composition and abundance of (98.877 ± 0.176%) and (48.885 ± 5.096%), respectively, which OTU in each sample, we selected the classification results at spe- were significantly higher than in N (p = 0.001). Ochrobactrum, cies level. We took the top 30 species of each group (Fig. 5), which

Fig. 5. Analysis of community structure of significant differences between groups at species level. Community differences in groups G1 (A), G2 (B) and N (C) at species level. (D) Flora composition differences among groups G1, G2 and N at species level. accounted for >60% of all OTUs in each group. In the G1 group, the Discussion top 30 classified species were significantly different from those in the N group. Pseudomonas fluorescens (74.512 ± 2.451%) and Pseu- The composition of upper respiratory tract flora in the healthy domonas fulva (18.806 ± 1.939%) were increased in the G1 com- individuals was checked using 16S rDNA NGS. Upper respiratory pared with the N group (0.014 ± 0.008% and (0.004 ± 0.002%) tract flora is mainly composed of Firmicutes, Proteobacteria and Acti- (p = 0.001). In the G2 group, P. fluorescens (26.776 ± 4.119%) and nobacteria.21,22 We obtained a similar result in that a large propor- P. fulva (8.871 ± 1.659%) decreased compared with the G1 group tion of flora in the upper respiratory tract were in four phyla: (p = 0.001), but S. aureus (1.428 ± 0.533%) increased significantly Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria. Bac- (p = 0.001). P. aeruginosa and H. influenzae also increased signifi- teroidetes is not unexpected because it is a group that inhabits the cantly. However, when comparing with the N group, S. aureus human oral cavity. The similar results suggest that, although the (0.008 ± 0.007%) increased significantly. There was no significant upper respiratory tract contains heterogeneous sources of air- and difference in P. aeruginosa and H. influenzae between the G2 and food-borne microbes, the normal pharyngeal microbiota in different N groups. people is stable and large perturbations usually indicate infection.

We found that Proteobacteria was the dominant phylum in reagents/materials/analysis tools. YH L, YY M and XK H wrote the H1N1 virus infection. The proportions of flora correlated with paper. All authors read and approved the final manuscript. hypothermia and pneumonia, such as Pseudomonadaceae, Brucel- 19 4,9 laceae and Enterobacteriaceae in Proteobacteria increased in Funding patients within H1N1 virus infection. We confirmed that Pseu- domonas in Proteobacteria was the dominant genus in patients with National instruments major projects of China H1N1 virus infection. Ochrobactrum, Brevundimonas, Caulobacter, (2012YQ03026107). Aquabacterium and Serratia genera in Proteobacteria significantly increased. More specifically, several Pseudomonas such as P. fluo- Acknowlegements rescens, P. fulva, Pseudomonas lundensis and Pseudomonas peli were dominant species in patients with H1N1 virus infection. 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