Influence of H7N9 virus infection and associated treatment on human gut microbiota
Influence of H7N9 virus infection and associated treatment on human gut microbiota
Nan Qin 0 1 4
Beiwen Zheng 0 1 4
Jian Yao 0 1 4
Lihua Guo 0
Jian Zuo 0
Lingjiao Wu 0
Jiawei Zhou 0 1
Lin Liu 0 1
Jing Guo 0
Shujun Ni 0
Ang Li 0
Yixin Zhu 0 1
Weifeng Liang 0 1
Yonghong Xiao 0 1
S. Dusko Ehrlich 2 3
Lanjuan Li 0 1
0 State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated College of Medicine, Zhejiang University , 310003 Hangzhou , China
1 Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University , 310003 Hangzhou , China
2 Metagenopolis, Institut National de la Recherche Agronomique , 78350, Jouy en Josas , France
3 King's College London, Centre for Host-Microbiome Interactions, Dental Institute Central Office, Guy's Hospital , London Bridge, London SE1 9RT , UK
4 These authors
OPEN Between March and June, 2013, forty H7N9 patients were hospitalized in our hospital. Nextgeneration sequencing technologies have been used to sequence the fecal DNA samples of the patient, the within sample diversity analysis, enterotyping, functional gene and metagenomic species analysis have been carried on both the patients and healthy controls. The influence of associated treatment in H7N9 infected patients is dramatic and was firstly revealed in species level due to deep sequencing technology. We found that most of the MetaGenomic Species (MGS) enriched in the control samples were Roseburia inulinivorans DSM 16841, butyrate producing bacterium SS3/4 and most of MGS enriched in the H7N9 patients were Clostridium sp. 7 2 43FAA and Enterococcus faecium. It was concluded that H7N9 viral infection and antibiotic administration have a significant effect on the microbiota community with decreased diversity and overgrowth of the bacteria such as Escherichia coli and Enterococcus faecium. Enterotype analysis showed that the communities were unstable. Treatment including antivirals, probiotics and antibiotics helps to improve the microbiota diversity and the abundance of beneficial bacteria in the gut.
proportion of fatal cases in other provinces was 39.4% (37/94 excluding Zhejiang patients, as of August
The indigenous microbiota plays a pivotal role for the prevention and treatment of microbial
infections, and it is sometimes referred to as a ?forgotten organ?6,7. Since next-generation sequencing
technologies emerged, much work has been done in gut microbiota field and greatly accelerated the research
in this field. The Human Microbiome Project (HMP) founded by NIH has sequencing microbiota from
different anatomical sites among 242 healthy individuals and generated the largest human microbiome
gene resource so far8. Metagenomcis of Human Intestinal Tract (MetaHIT) founded by European
commission has generated the first human gut gene catalogue and identified enterotypes which are
independent of geographic origin in the human gut microbiota9. Another gut microbiota study from 345 Chinese
individuals with type-2 diabetes (T2D) found 60,000 T2D-associated genes and reported a combination
of the genes that could be used to accurately diagnose the disease10.
Remarkably, chronic complex diseases have been associated with gut microbiota. Although the
intestinal microbiota is generally stable in healthy individuals over long periods of time, antibiotics can
significantly reshape the gut microbiota, allowing exogenous microbes to outgrow commensal bacteria and
cause permanent changes in varying states of disease6,11,12. Probiotic agents, which beneficially affect the
host by improving the gut microbial balance, have been used for the prevention and treatment of
respiratory tract diseases to avoid bacterial translocation13,14. It is noteworthy that gastrointestinal distress
symptoms were observed in some of our recently reported cases of H7N9 infection1,5. Additionally, most
of patients received antibiotic therapy within six hours after admission and also received probiotic agents
in our hospital. Influenza virus infection and subsequent therapies may affect human microbiota in a
model system15,16, however, the consequences of H7N9 infection in humans and the interplay between
viral pathogens and the microbiota of the host remains unknown.
In this study, we describe the influence of antibiotics and probiotics treatment on H7N9 patients? gut
microbiota, and show that viral infection and associated antibiotic and probiotics usage have a significant
effect on the microbiota.
A total of 26 patients were enrolled, we were not able to collect the fecal samples from the other 14
patients due to different condition of each patient. In total, 93 stool samples were taken from 26 patients
(Table S1); the first sample was taken on the day of admission. The median age of patients is 57 years
(range, 30 to 80). Most of the patients were elderly, with 42.3% aged older than 65 years, 5 patients
were over 75 years of age (19.2%). Males predominate in number over females, about 69.2% were men.
Of the 26 patients, 24 were discharged from hospital and 2 died. This study represents 65% (26/40)
of all lab confirmed cases in our hospital. 17 patients (65.4%) received antibiotic therapy within six
hours after admission. Commonly used antibiotics included piperacillin-tazobactam (n = 12),
moxifloxacin (n = 9), sulbactam-cefoperazone (n = 3), imipenem-cilastatin (n = 4), vancomycin (n = 7), and
piperacillin-sulbactam (n = 3). Remarkably, 24 patients (92.3%) were treated with Clostridium tablets, 2
patients (7.7%) received B. Subtilis and E. faecium enteric coated capsules and Clostridium tablets, and 1
patient received Clostridium tablets and Bacillus capsules (Tables?1 and S1).
In order to compare gut microbial communities in H7N9 patients with those in healthy individuals,
we selected 31 samples from a previous Chinese gut microbiota study matching for age, gender and BMI
(Table S2)10. Only one sample from each patient (26) and healthy individual (31) was taken for
comparison. The clean reads from the sequencing data (Table S3) were aligned against the reference genomes
from NCBI and HMP database (Table S4). The relative abundance of phylum, genus and species among
healthy control (H) groups, patients treated with antibiotic (AB) and without antibiotic (NAB) are shown
in Fig.? 1. Phylotypes with a median relative abundance greater than 0.01% (p < 0.01), calculated using
the Wilcoxon rank-sum test in either the H group or H7N9 patients were included for comparison. At
the phylum level, Bacteroidetes, Firmicutes and Proteobacteria were dominant in the faecal microbial
communities of all groups (Fig.?1a,c,e). Compared with H group, H7N9 patients had fewer Bacteroidetes
in NAB group and fewer Firmicutes in AB group, but higher levels of Proteobacteria in both groups.
At the genus level, Bacteroides was dominant in all groups (Fig.? 1b,d,f). In AB group, Escherichia and
Parabacteroides were the second and third genus in faecal microbial communities respectively whereas
in NAB group, Clostridium and Parabacteroides were the second and third. In contrast, in H group
Eubacterium and Roseburia were the second and third, respectively. The top 20 species are shown in
Fig.? 2. Ofthe top 20 species in AB group and NAB group, Escherichia coli represents the most and
second most abundant species respectively, indicating that Escherichia coli are potentially pathogenic
bacteria and whose abundance levels may correlate with the progression of H7N9 infection and associated
treatment. We also found Enterococcus faecium as enriched in patient groups. It is worthy to note that
Faecalibacterium prausnitzii, which is recognized as an anti-inflammatory probiotics17, was dramatically
decreased in AB group. These results show large differences in the microbial communities between the
H7N9 patients and the healthy individuals.
At the genus level, Eubacterium, Ruminococcus, Bifidobacterium and Roseburia dramatically decreased
in patient groups compared with controls and a similar trend was seen for Faecalibacterium and
Haemophilus (Fig.? 2a). In contrast, in AB and NAB patient groups, the proportion of Escherichia were
higher than in controls (5.5% versus 1.2%versus 0.4%, respectively) as was Salmonella, Enterococcus and
Clostridium tablets, B.
Subtilis and E. faecium
enteric coated capsules
B. Subtilis and E. faecium
enteric coated capsules
Clostridium tablets, B.
Subtilis and E. faecium
enteric coated capsules
Veillonella (Fig.? 3a). At the species level, Escherichia coli, the most common species, was significantly
more abundant in AB group than in NAB group and H group (5.5% versus 1.2%versus 0.4%,
respectively). Enterococcus faecium and Veillonella parvula were more abundant in the patient groups as was
Clostridium butyricum, likely because of its administration as probiotic. In contrast, Bacteroides vulgates
was observed to be the most abundant in H group, significantly more than in AB and NAB groups
(Figs?2a and 3b). In addition, we also identified adramatical decline of species such as Bacteroidesovatus,
Faecalibacterium prausnitzii, Roseburia intestinalis, Eubacteriumeligens, Bifidobacterium longum and also
Haemophilus parainfluenzae in patient groups.
Effects of influenza virus infection on gut microbial communities have not been previously reported.
In contrast, it is known that antibiotic administration has an immediate effect, with reducing microbiota
diversity11. Here we compared the within-sample diversity (Shannon index) among healthy controls
and H7N9 infected patients treated with (n = 17) or without (n = 9) antibiotics (Fig.? 4a). Very
interestingly, viral infection led to a significant decrease of diversity, there was significant difference in the
within-sample diversity between NAB group and control (p < 0.01, Student t-test), also between AB
group and control (p < 0.001, Student t-test). The decreased diversity is further exacerbated by antibiotics
administration, although the difference was not statistically significant between AB and NAB group
(p = 0.26 by the Student t-test).
To assess the effects of treatments on gut microbial communities, we selected patients from whom at
least one sample was obtained for >4 days of treatment (n = 7; Table S5). It has been shown previously
that antibiotic perturbation may shift the gut microbiota community structure to an alternatives state12.
We examined the enterotypes of all samples (n = 93) from H7N9 virus infected patients and controls
(n = 31) by PAM clustering with four distance metrics method (Fig. S1). The best cluster number at the
genus level was three (Fig. S1). Two enterotypes were driven by the genera Bacteroides and Prevotella
similar to previous studies in European and Chinese cohorts (Fig.?4c)9,10. The third enterotype was driven
by the genus Klebsiella and includes only the H7N9 infected patients. We hypothesize that the infection
causes the shift of the communities to this enterotype, which is different from those previously reported.
Interestingly, the samples from same patient fluctuate between different enterotypes within a short time
(<8 days; Table S3). Furthermore, assessment of the proximity of patients by the Spearman correlation
analysis indicates that different samples from the same patient do not cluster together (Fig. S2). We
conclude that the gut microbial communities in patients are unstable whereas in the healthy individuals
In this study, the next generation sequencing technology was applied to analyse, for the first time, the
gut microbiota of the H7N9 virus infected patients. For community structure, the changes at the
phylum genus and species-level were observed that caused by the H7N9 infection and associated treatment.
Interestingly, Escherichia coli and Enterococcus faecium could be inferred, on the basis of relationship
patterns, to be harmful, in line with previous studies, which concluded that they may cause or underlie
bacteraemia and intra-abdominal infections23,24. Interestingly, Clostridium butyricum was enriched in
both AB and NAB patient groups, furthermore, NAB group has a higher abundance than AB group,
indicating that Clostridium tablets may exert their functions in the gut (Fig.?3b). In contrast, Bacteroides
vulgatus, Bacteroidesovatus, Faecalibacterium prausnitzii, Roseburia intestinalis, Eubacteriumeligens,
Bifidobacterium longumand Haemophilus parainfluenzae may have a beneficial impact in this specific
biological context. In particular, the finding here that Haemophilus parainfluenzae was enriched in
healthy controls is consistent with the result from MGWAS study10, which imply an unknown function
of this known pathogen. In our study, patients with H7N9 of different treatment regimens had similar
gut community changes. These results led to a conclusion that composition changes observed between
H7N9 patients and healthy controls were mainly due to viral infection and associated treatment.
The within sample diversity of gut microbial communities was found to decrease in patients, treated
or not with the antibiotics. However, the treatment which included antivirals, probiotics and antibiotics,
appeared to help increase the diversity of the gut microbial communities. The clustering analysis of these
samples showed that they also could be grouped into three enterotypes as in previous studies but with
a different third enterotype driven by Klebsiella, which included the patients only. We suggest that this
enterotype may correspond to specific microbial communities that form in H7N9 infected individuals.
The enterotypes are not stable, possibly due to the conjunction of viral infection and low diversity. The
MGS analysis was performed in this study and this was reported possibly to be markers for specific
disease20. We also found that many MGSs enriched in healthy controls or patients and most of them
are consistent with the finding in genus or species abundances changes in this study, such as MGS
Clostridium butyricum and Roseburia intestinalis.
There is also some limitations in this study. First, not all the H7N9 patients in our hospital were
enrolled. Second, we were not able to collect the fecal samples from patients in the early stage of
infection, because at the time that most of patients were not admitted or transferred to our hospital.
Finally, our study didn?t get the stool samples from patients after they discharged from hospital. Further
collection is planned in order to assess long-term effects of the infection and the associated treatment
in gut microbiota.
Materials and Methods
Patient information and sampling. We describe patients who were hospitalized in the First
Affiliated Hospital of Zhejiang University for H7N9 virus infection, as confirmed by a real-time RT-PCR.
Patients with confirmed H7N9 infection were interviewed by staff according to a standardized
questionnaire to obtain clinical and epidemiologic information, and medical records were reviewed. All
participants provided written informed consent for collection and testing of fecal sample prior to entering
the study. Research protocols conformed to the ethical guidelines of the 1975Declaration of Helsinki
and were approved and monitored by the Institutional Review Board of the First Affiliated Hospital of
The fresh fecal samples were collected by nurses from the H7N9 infected patient. Each fresh sample
was delivered immediately from the ward to lab in a larger cooler with ice packs where it was divided
into aliquots of 200 mg and was immediately stored at ?80 ?C until next step.
DNA extraction, library construction and sequencing. A frozen aliquot (200 mg) of each fecal
sample was performed by Qiagen QIAamp DNA Stool mini kit according to manufacturer?s
introduction. DNA concentration was measured by nanodrop (Thermo Scientific) and its molecular size was
estimated by agarose gel electrophoresis. Then DNA libraries were constructed using Illumina TruSeq
DNA Sample Prep Kit according to the manufacturer?s instruction. Illumina TruSeq PE Cluster and SBS
Kit were used to perform cluster generation, template hybridization, isothermal amplification,
linearization, blocking and denaturization and hybridization of the sequencing primers. Paired-end sequencing
2 ? 100 bp was performed to sequence all libraries. The base-calling pipeline (Casava 1.8.2 with
parameters?use-bases-mask y100n, I6n, Y100n, ?mismatches 1, ?adapter-sequence) was used to process the
raw fluorescent images and call sequences. The insert size inferred by Agilent 2100 was used for all
libraries (ranging from 275 to 450).
Quality control of reads. Reads that mapable to human genome together with their mated reads were
removed from each sample using BWA with parameters ?n 0.225. Then quality control was preceded
with following criteria: a. Reads contained more than 3 N bases were removed, b. Reads contained more
than 50 bases with low quality (Q2) were removed, c. Reads contained no more than 10 bases with low
quality (Q2) or N base in the tail of reads were trimmed. Resulting filtered reads were considered for
Reference genomes set collection, abundance profiling and enterotyping. The reference
Bacteria and Archaea genomes genomes were downloaded from NCBI database (version 20120810),
including draft genomes, bacterial genomes from HMP (version 2012.6) were also downloaded and
intergreted. Soap align 2.21 was used to align paired-end clean reads against reference genomes with
parameters ?r 2 ?m 200 ?x 1000. Reads with alignments on same reference genomes were assigned
according to the following rules:
A, reads aligned to only one genome, these reads were denoted as unique reads. B, reads aligned to
more than one genome, if these genomes come from one species, we denote these reads as unique reads.
If the genomes are from more than one species, we denote these reads as multiple reads.
For species S, if its abundance is Ab(S), it might have alignments with unique reads set U and
multi-position match reads M, the computation of Ab(S) is as follows.
Ab (S) = Ab (U) + Ab (M)
Ab (Us ) = N (U)/l
?N (M) ??
Ab (M) = ??? ? Co ( j) ???/l
?? j=1 ??
Co ( j) =
?i=j 1Ab (Ui )
Ab(U) and Ab(M) mean abundance of uniquely match reads (U) and multi-position match reads (M) of
species S, respectively, N(U) is the number of uniquely match reads, N(M) is the number of multi-position
match reads, l is length of relative genome.
For each multi-position match read j, which has alignments with Nj different species, there is a species
coefficient Co(j) that was calculated as follows.
Profiling table at genus level was generated by adding abundance of species into its genera or
phylum. For some species that do not have genera, they are denoted as NULL. Based on species profiling
and genus profiling table, each sample was assigned enterotype clusters by the three distance metrics:
Jensen-Shannon divergence (JSD), Root Jensen-Shannon divergence (rJSD), Bray-Curtis (BC)method
using partitioning around medoids Clustering introduced by Arumugam, M. et al. and Koren et al.9,26.
Within sample diversity. Based on the species profile, we calculated the within sample (alpha)
diversity to estimate the species richness of a sample. Shannon index was used for each sample with the
S = ??Ri ln Ri
where S is Shannon index, n is number of species found in each sample and R is relative abundance of
Gene set construction. MetaGeneMark (prokaryotic GeneMark.hmm version 2.8) was used to
predict ORFs in assembled proceeded scaffolds (without ambiguous bases). The non-redundant virus
infected patient gut gene set was built by pair-wise comparison of all the predicted ORFs using blat and
the redundant ORFs were removed using a criterion of 95% identity over 90% of the shorter ORF length,
which is consisted with the criterion used in the MetaHIT. We use MetageneMark to predict genes in
assembled contigs originally from MetaHIT, T2D and HMP study and performed comparison study
using the same method described above.
Gene markers selection. Gene markers are selected as differentially abundance between the patient
and the healthy groups. Wilcoxon tests were employed to compute the probabilities that frequency
profiles are not different between the patient and the healthy groups by chance. Then we used Benjamini
Hochberg as multiple tests to adjust it. As a result, we got 24,113 and 12,781 gene markers separately
based on 0.001 and 0.05 p value cutoff in patient and healthy control groups.
Gene clustering. We clustered the selected gene markers profiles based on above method which have
a similar abundance in one individual but different abundance in different individuals. First, the
spearman correlation coefficients of gene markers were computed among 57 individuals (26 patients and 31
healthy controls). The less spearman rho value, the more similar between the two genes. Then we used
the farthest method to put the genes with 90% similarity together. To validate it, clusters are
taxonomically annotated by blating each gene in one cluster to NCBI (complete and draft) and HMP database,
Lastly we chose the clusters, that contained more than 20 genes, and selected the taxonomical level by
requiring that at least 80% of the genes had a best hit to the same phylogenetic group. 12 and 4 MGS
were finally obtained in patient and health cohort.
We would like to thank other persons whose names not listed in the authors for assistance with sample
collection. We are thankful to MetaHIT project and Human Microbiome Project for generating the
reference genomes for human gut microbes. Financial support: This work was supported by the National
Program on Key Basic Research Project (2013CB531401), Natural Science Foundation of Zhejiang
Province (NO. LR15H030002) Important National Science and Technology Project Research of Clinical
Treatment for Emerging Severe Acute Respiratory Infectious Disease (2014ZX10004006), National
Natural Science Foundation of China (NO. 81301475, 81301461, 31401144) to N.Q., B.W.Z., J.Y., the
Science Fund for Creative Research Groups of the National Natural Science Foundation of China (NO.
81121002), Important National Science and Technology Project-Research of Clinical treatment for
Emerging severe acute respiratory infectious disease (2004ZX10004006), National Supporting Project
(KJYJ-2013-01-05), the Technology Group Project for Infectious Disease Control of Zhejiang Province
and the China National Mega-projects for Infectious Diseases (2012ZX10004-210). SDE
acknowledges the support by the Metagenopolis grant ANR-11-DPBS-0001.
Supplementary information accompanies this paper at http://www.nature.com/srep
Competing financial interests: The authors declare no competing financial interests.
How to cite this article: Qin, N. et al. Influence of H7N9 virus infection and associated treatment on
human gut microbiota. Sci. Rep. 5, 14771; doi: 10.1038/srep14771 (2015).
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