Lost microbes of COVID-19: Bifidobacterium, Faecalibacterium depletion and decreased microbiome diversity associated with SARS-CoV-2 infection severity

Abstract

Objective:
The study objective was to compare gut microbiome diversity and composition in SARS-CoV-2 PCR-positive patients whose symptoms ranged from asymptomatic to severe versus PCR-negative exposed controls.

Design:
Using a cross-sectional design, we performed shotgun next-generation sequencing on stool samples to evaluate gut microbiome composition and diversity in both patients with SARS-CoV-2 PCR-confirmed infections, which had presented to Ventura Clinical Trials for care from March 2020 through October 2021 and SARS-CoV-2 PCR-negative exposed controls. Patients were classified as being asymptomatic or having mild, moderate or severe symptoms based on National Institute of Health criteria. Exposed controls were individuals with prolonged or repeated close contact with patients with SARS-CoV-2 infection or their samples, for example, household members of patients or frontline healthcare workers. Microbiome diversity and composition were compared between patients and exposed controls at all taxonomic levels.

Results:
Compared with controls (n=20), severely symptomatic SARS-CoV-2-infected patients (n=28) had significantly less bacterial diversity (Shannon Index, p=0.0499; Simpson Index, p=0.0581), and positive patients overall had lower relative abundances of Bifidobacterium (p<0.0001), Faecalibacterium (p=0.0077) and Roseburium (p=0.0327), while having increased Bacteroides (p=0.0075). Interestingly, there was an inverse association between disease severity and abundance of the same bacteria.

Conclusion:
We hypothesise that low bacterial diversity and depletion of Bifidobacterium genera either before or after infection led to reduced proimmune function, thereby allowing SARS-CoV-2 infection to become symptomatic. This particular dysbiosis pattern may be a susceptibility marker for symptomatic severity from SARS-CoV-2 infection and may be amenable to preinfection, intrainfection or postinfection intervention.

Trial registration number:
NCT04031469 (PCR-) and 04359836 (PCR+).

Full text

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HazanS, etal. BMJ Open Gastro 2022;9:e000871. doi:10.1136/bmjgast-2022-000871
Lost microbes of COVID- 19:
Bidobacterium, Faecalibacterium
depletion and decreased microbiome
diversity associated with SARS- CoV- 2
infection severity
Sabine Hazan,1 Neil Stollman,2 Huseyin S Bozkurt,3 Sonya Dave ,4,5
Andreas J Papoutsis,1 Jordan Daniels,1 Brad D Barrows,1
Eamonn MM Quigley ,6 Thomas J Borody7
To cite: HazanS, StollmanN,
BozkurtHS, etal. Lost microbes
of COVID- 19: Bidobacterium,
Faecalibacterium depletion
and decreased microbiome
diversity associated with
SARS- CoV- 2 infection
severity. BMJ Open Gastro
2022;9:e000871. doi:10.1136/
bmjgast-2022-000871
►Additional supplemental
material is published online
only. To view, please visit the
journal online (http:// dx. doi.
org/ 10. 1136/ bmjgast- 2022-
000871).
Received 3 January 2022
Accepted 28 March 2022
For numbered afliations see
end of article.
Correspondence to
Dr Sabine Hazan;
DrHazan@ progenabiome. com
Infection
© Author(s) (or their
employer(s)) 2022. Re- use
permitted under CC BY- NC. No
commercial re- use. See rights
and permissions. Published
by BMJ.
ABSTRACT
Objective The study objective was to compare gut
microbiome diversity and composition in SARS- CoV- 2
PCR- positive patients whose symptoms ranged from
asymptomatic to severe versus PCR- negative exposed
controls.
Design Using a cross- sectional design, we performed
shotgun next- generation sequencing on stool samples
to evaluate gut microbiome composition and diversity in
both patients with SARS- CoV- 2 PCR- conrmed infections,
which had presented to Ventura Clinical Trials for care
from March 2020 through October 2021 and SARS- CoV- 2
PCR- negative exposed controls. Patients were classied
as being asymptomatic or having mild, moderate or
severe symptoms based on National Institute of Health
criteria. Exposed controls were individuals with prolonged
or repeated close contact with patients with SARS-
CoV- 2 infection or their samples, for example, household
members of patients or frontline healthcare workers.
Microbiome diversity and composition were compared
between patients and exposed controls at all taxonomic
levels.
Results Compared with controls (n=20), severely
symptomatic SARS- CoV- 2- infected patients (n=28)
had signicantly less bacterial diversity (Shannon
Index, p=0.0499; Simpson Index, p=0.0581), and
positive patients overall had lower relative abundances
of Bidobacterium (p<0.0001), Faecalibacterium
(p=0.0077) and Roseburium (p=0.0327), while having
increased Bacteroides (p=0.0075). Interestingly, there
was an inverse association between disease severity and
abundance of the same bacteria.
Conclusion We hypothesise that low bacterial diversity
and depletion of Bidobacterium genera either before
or after infection led to reduced proimmune function,
thereby allowing SARS- CoV- 2 infection to become
symptomatic. This particular dysbiosis pattern may be a
susceptibility marker for symptomatic severity from SARS-
CoV- 2 infection and may be amenable to preinfection,
intrainfection or postinfection intervention.
Trial registration number NCT04031469 (PCR−) and
04359836 (PCR+).
INTRODUCTION
The abundance of Bifidobacterium decreases
with increasing age and body mass index
(BMI)1 and Bifidobacterium is the active ingre-
dient of many probiotics. In vitro studies
have demonstrated the benefits of these
Summary box
What is already known about this subject?
►The gut microbiome is intrinsically related to host
immune response (eg, inammation, Th1 vs Th2)
and susceptibility to infection.
What are the new ndings?
►Patients with SARS- CoV- 2 infections possess
signicantly less bacterial diversity, lower abun-
dance of Bidobacterium and Faecalibacterium
and increased abundance of Bacteroides at the
genus level compared with SARS- CoV- 2- exposed
controls. There are inverse associations between
disease severity and the Shannon and Simpson
diversity indices and also with Bidobacterium and
Faecalibacterium abundance. There is also a di-
rect association between severity and Bacteroides
abundance.
How might it impact on clinical practice in the
foreseeable future?
►Boosting of Bidobacterium or Faecalibacterium
through probiotic supplementation or faecal mi-
crobiota transplant is worthy of exploration in the
management of patients with acute severe disease
or protracted infection. If the changes that we doc-
ument precede SARS- CoV- 2 infection in those who
are most severely affected, this therapeutic ap-
proach may be of particular interest. Conversely, if
the reduction follows infection, then repopulation of
the gut microbiome may reduce long- term effects
related to gut microbiome composition changes
with SARS- CoV- 2 infection.
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Gram- positive bacteria to include enhanced ATP produc-
tion, immune modulation and competence,2–8 mucosal
barrier integrity, restriction of bacterial adherence to and
invasion of the intestinal epithelium and modulation of
central nervous system activity.9 10 Additionally, Bifidobac-
terium have anti- inflammatory properties: Bifidobacterium
animalis, B. longum and B. bifidum decrease the function
of the ‘master switch’2 proinflammatory tumour necrosis
factor-α (TNF-α), increase the anti- inflammatory cyto-
kine IL- 10 and promote the Th1 while inhibiting the Th2
immune response.8 In a mouse model of inflammatory
bowel disease (IBD), B. bifidum and B. animalis reduced
proinflammatory cytokines and restored intestinal
barrier integrity.8
With respect to SARS- CoV- 2 infection, there is immu-
nologic coordination between the gut and lungs.11–13
Numerous studies have suggested that a healthy gut
microbiome may be associated with a decrease in SARS-
CoV- 2- related mortality14 and that probiotics should
be considered for prophylaxis15 and/or treatment of
SARS- CoV- 2 or its associated secondary infections.15–17
However, as of February 2022, despite the publication
of nearly 8000 studies on SARS- CoV- 2 infection, few
ongoing studies ( clinicaltrials. gov: NCT04443075 and
NCT04486482) and only five publications to date have
examined gut microbiome changes in SARS- CoV- 2-
infected patients. Nevertheless, an association between
the status of the gut microbiome and outcome from this
infection has been suggested. Accordingly, increased
abundances of the Streptococcus, Rothia, Veilonella and
Actinomyces genera were associated with inflammation,18
whereas increased abundances of Collinsella aerofaciens,
Collinsella tanakaei, Streptococcus infantis and Morganella
morganii were associated with faecal samples with high
SARS- CoV- 2 infectivity,19 and increased Lachnospira-
ceae and Enterobacterioaceae abundances were associated
with increased mortality and need for artificial ventila-
tion.19 Species potentially protective against SARS- CoV- 2
infection include Parabacteroides merdae, Bacteroides ster-
coris, Alistipes onderdonkii, Lachnospiracea bacterium19 and
F. prausnitzii,19 20 while vulnerability to infection and
increased severity were associated with decreased abun-
dance of B.bifidum.20 21 A recent study correlated aspects
of the gut microbiome with ‘Long- COVID’, including
reduced levels of F. prausnitzii on admission.22 In short,
there is still a compelling need to elucidate changes in
the human gut microbiome due to SARS- CoV- 2 and their
relationship with clinical outcomes.
The scientific community and lay public are increas-
ingly interested in the therapeutic potential of probi-
otics. Bifidobacteria have potential to improve clinical
conditions ranging from IBD23 to Clostridioides difficile
infections.23–26 Treatment with specific strains of Bifido-
bacterium in vitro has been shown to reduce toxins from
C. difficile .25 In vivo, Bifidobacterium can restore colonic
integrity,27 and B. longum administered intranasally in
mice prior to exposure to influenza has been associated
with reduction in mortality.4 Given that Bifidobacterium
are common component of several probiotic products
and appear to be associated with SAR- CoV- 2 infections,
one could ask if probiotics might have a role in SARS-
CoV- 2 therapy or prevention.
Herein, we evaluate the relationships between gut
microbiome diversity and composition compared with
clinical outcome in cross- sectional groups of SARS- CoV- 2
PCR- confirmed positive patients (ranging from asymp-
tomatic to severely symptomatic) versus SARS- CoV- 2
PCR- confirmed negative controls. Our controls are SARS-
CoV- 2 exposed persons who remained PCR negative
and asymptomatic. The controls likely had similar viral
exposures, but appeared protected against infection, and
our data suggest that some protection may reside in the
microbiome.
METHODS
Study design and patients
Individuals who were tested for SARS- CoV- 2 infection
either because they were symptomatic or had been
exposed to a ‘case’ were eligible for enrolment the week
following testing if either they or a household member
was positive. Controls eligible for enrolment were PCR
negative for SARS- CoV- 2, remained antibody negative for
3–6 months and asymptomatic for 6–12 months. Addition-
ally, controls had to either share a household with at least
one symptomatic SARS- CoV- 2- positive individual or be a
healthcare worker who had been repeatedly exposed to
symptomatic SARS- CoV- 2- positive patients or numerous
SARS- CoV- 2- positive samples. All exposed controls were
ones that, despite exposure to SARS- CoV- 2, chose not
to quarantine or take prophylaxis for SARS- CoV- 2 infec-
tion and none had yet been vaccinated. Patients did not
wear Personal Protective Equipment (PPE) inside their
homes and staff did not wear full PPE (ie, did not wear
masks) at the office because of its scarcity during this
global pandemic. Patients undergoing treatment with
total parenteral nutrition, or those with a history of signif-
icant gastrointestinal surgery (eg, bariatric surgery, total
colectomy with ileorectal anastomosis, proctocolectomy,
postoperative stoma, ostomy or ileoanal pouch) were
excluded.
This study was performed between 1 March 2020 and
31 October 2021, with all but one subject recruited prior
to 1 June 2021. During that time, alpha and epsilon vari-
ants predominated in the USA.28
Assessments
A self- administered questionnaire solicited information
on symptom severity, previous medical history, current
medication and probiotic use and exposure to recre-
ational drugs or animals. Patients with SARS- CoV- 2 infec-
tion were further classified as being either asymptomatic
carriers or having mild, moderate or severe symptoms as
per National Institute of Health, Clinical Spectrum of
SARS- CoV- 2 Infection criteria.29 30 Asymptomatic PCR-
confirmed SARS- CoV- 2- positive household members of
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SARS- CoV- 2- infected patients were categorised as asymp-
tomatic carriers. Patients and controls were classified
as underweight, normal weight, overweight, obese or
severely obese based on BMI criteria of the Center for
Disease Control and Prevention.31
Stool sample collection and processing
Patients and controls within the same household
collected stool samples within a week of the index case
being positive. Patients had stool samples collected at
baseline, prior to any treatment, and within 48 hours of
symptom onset. No subjects had been commenced on
antibiotics, SARS- CoV- 2 infection treatments, over the
counter (OTC) remedies (e.g., vitamins, antipyretics,
analgesics) or supplemental oxygen between the time of
symptom onset or demonstration of PCR positivity and
stool collections. Subjects were instructed (and educated
on the procedure and sterile methods) to collect 1 mL
of fresh stool and place it directly in a DNA/RNA Shield
Fecal Collection Tube (Zymo Research, Tustin, Cali-
fornia) and then mix sample thoroughly. Here,1 mL of
faeces is more than sufficient to capture the microflora
of the gut accurately and consistently. This method is
chosen to eliminate the need for whole stool mixing and
aliquoting. The solution in the Fecal Collection Tube is
designed to preserve samples at ambient temperature
(4°C–25°C) for >2 years, or below −20°C indefinitely.
Once samples reached our laboratory, they were immedi-
ately frozen at −20°C.
Following faecal collection, each individual sample
DNA was extracted and purified with the Qiagen Power-
Fecal Pro DNA extraction kit. The isolated DNA was then
quantitated using the Quantus Fluorometer with the
QuantFluor ONE dsDNA kit. After DNA quantification,
the DNA was normalised, that is, all samples begin library
preparation (following DNA extraction and purification)
with 100 ng of input DNA. Libraries were then prepared
using shotgun methodology with Illumina’s Nextera Flex
kit. Samples then underwent the shotgun metagenomic
processing procedure of tagmentation, amplification,
indexing and purification. Following completion of
this shotgun metagenomic standard protocol, purified
libraries were again normalised to standardise sequencing
depth during the next- generation sequencing (NGS)
run on the NextSeq 500/550. We achieved consistency
of sequencing depth (ie, number of reads) by normal-
ising the samples’ pooling concentrations (ie, molarity),
loading the same number of samples per sequencing run,
consistently using the same NextSeq High Output kits.
After completion of sequencing on the Illumina
NextSeq with 500/550 High- Output Kits V.2.5 (300
cycles), the raw data were streamed in real time to Illumi-
na’s BaseSpace cloud for FASTQ (Fast Quality, a standard
text file type for storing biological sequence information)
conversion. The FASTQ files were then sent through
One Codex’s bioinformatics pipeline for metagenomic
annotation and analyses to elucidate the microflora
composition and relative abundances of the top genera
and species for all patients and controls.
Data analysis
We assessed differences in relative abundance across taxa
between the gut microbiome of SARS- CoV- 2- infected
patients and exposed controls and calculated Shannon
and Simpson alpha diversity indices with One Codex’s
bioinformatics analysis pipeline using Jupyter Notebook
in Python. Specifically, the One Codex Database consists
of ~114K complete microbial genomes (One Codex, San
Francisco, California). During processing, reads were first
screened against the human genome and then mapped
to the microbial reference database using a k- mer- based
classification. Individual sequences (NGS read or contig)
were compared against the One Codex Database (One
Codex) by exact alignment using k- mers, where k=31.
Based on the relative frequency, unique k- mers were
filtered to control for sequencing or reference genome
artefacts.
The sequencing depth followed ProgenaBiome’s
standard operating procedures and was 8 239 475 reads
on average for this study. One should note that shallow
metagenomic sequencing is typically only 0.5 million
reads but is still considered sufficient for taxonomic
phyla level analysis (and even genera for the most abun-
dant bacteria).
The relative abundance of each microbial taxonomic
classification was estimated based on the depth and
coverage of sequencing across every available refer-
ence genome. Beta- diversity was calculated as weighted
UniFrac distance visualised in a distance matrix using
the phylum- level relative abundance obtained from One
Codex. Thirteen genera were selected based on our
experience and knowledge of critical players in the gut
microbiome as well as similarity to other studies18 20 29
To compare patients across subgroups and patients
to exposed controls, Analysis of Variation (ANOVA),
Mann- Whitney U, Kruskal- Wallis tests and χ2 test statistics
were conducted using GraphPad V.8 with p values <0.05
considered as significant. Dunn’s post- hoc was used for
Kruskal- Wallis test, with correction for multiple compari-
sons in all situations.
All authors had access to study data and reviewed and
approved the final manuscript.
RESULTS
Patient characteristics
Demographic and clinical characteristics of patients
(n=50) and exposed controls (n=20) are presented in
online supplemental tables 1 and 2, and summarised in
table 1. All patients were resident of USA, with states indi-
cated in online supplemental table 1. Twenty- four of 50
(48%) patients and 7 of 20 (35%) of exposed controls
were men. The mean±SEM age in years was 50.0±2.5 for
patients and 44.4±3.6 for exposed controls. Fourty- four
of 50 (88%) patients were non- Hispanic white; 5 of 50
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Table 1 Summary of demographics along with clinical and dietary characteristics of subjects
SARS- CoV- 2 negative;
exposed control
(n=20)
SARS- CoV- 2 positive
Asymptomatic
(n=4)
Mild
(n=6)
Moderate
(n=12)
Severe
(n=28)
Total
(n=50) P value
Demographics
USA resident 20 (100.00%) 4 (100.00%) 6 (100.00%) 12 (100.00%) 28 (100.00%) 50 (100.00%) ns
Male 7 (35.00%) 3 (75.00%) 2 (33.33%) 5 (41.66%) 14 (50.00%) 24 (48.00%) 0.5706
Mean age±SEM 44.40±3.62 47.50±9.40 37.67±4.65 50.58±5.92 52.82±3.33 50.00±2.50 0.5221
Median age 48 50.5 37 60 55.5 53
Race
White 17 (85.00%) 3 (75.00%) 5 (83.33%) 9 (75.00%) 27 (96.43%) 44 (88.00%) 0.3826
Hispanic 2 (10.00%) 1 (25.00%) 0 (0.00%) 3 (25.00%) 1 (3.57%) 5 (10.00%)
Black 1 (5.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%)
Other 0 (0.00%) 0 (0.00%) 1 (16.66%) 0 (0.00%) 0 (0.00%) 1 (2.00%)
Clinical/dietary characteristics
Severe COVID- 19
comorbidities*
12 (60.00%) 3 (75.00%) 3 (50.00%) 10 (83.33%) 16 (57.14%) 32 (64.00%) 0.5099
Normal stool
frequency
20 (100.00%) 4 (100.00%) 6 (100.00%) 10 (83.33%) 24 (85.71%) 44 (88.00%) 0.2891
Lack of appetite 0 (0.00%) 0 (0.00%) 0 (0.00%) 1 (8.33%) 2 (7.14%) 3 (6.00%) 0.6663
Omnivore diet 19†(100.00%) 4 (100.00%) 6 (100.00%) 12 (100.00%) 28 (100.00%) 50 (100.00%) ns
. Numbers in cells indicate number of subjects with percentage in categories, except for age and BMI, which indicate value. P values calculated via one- way ANOVA (age and BMI) or χ2
(others). Normal stool frequency is dened as absence of diarrhoea. Total refers to sum of SARS- CoV- 2- positive subjects, and it is not used in statistics.
*Comorbid conditions indicative of severe SARS- CoV- 2 infection (not including hypertension), based on Center for Disease Control (CDC) denitions.44
†Nineteen total subjects had data available for diet within exposed control.
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(10%) were Hispanic and 1 of 50 (2%), Native Amer-
ican and 17 of 20 (85.0%) of exposed controls were non-
Hispanic white; 2 of 20 (10.0%), Hispanic and 1 of 20
(5.0%), Black. Of patients, 28 of 50 (56%) had severe, 12
of 50 (24%) had moderate and 6 of 50 (12%) had mild
disease and 4 of 50 (8%) were asymptomatic. Thirty- two of
50 (64%) patients and 12 of 20 (60.0%) exposed controls
had underlying comorbidities considered risk factors for
increased severity of SARS- CoV- 2 infection by the Center
for Disease Control (CDC).1 The mean±SEM BMI of the
46 patients with available data was 27.1±0.98 compared
with 25.1±0.96 for the 20 exposed controls. There was no
significant difference (p>0.2) in gender, age, racial demo-
graphics, loss of appetite, change in stool frequency, diet
or presence of underlying comorbidities.
Of the exposed controls, 16 were household contacts of
SARS- CoV- 2- positive patients in the study, 2 were health-
care workers with extensive, non- protected, exposure
to SARS- CoV- 2- positive patients and 2 were laboratory
personnel exposed to thousands of SARS- CoV- 2 samples
(healthcare workers and laboratory personnel did not
wear full PPE, that is, did not wear a face mask, due its
scarcity; see the Methods section). During the timeframe
of the study, none of the patients or controls was on SARS-
CoV- 2 prophylaxis or treatment, and none had yet been
vaccinated. No patients or exposed control were positive
for SARS- CoV- 2 prior to the study.
Gut microbiome diversity and composition
Figure 1 depicts pie charts of the composition of the gut
microbiome for the exposed control at the phylum level
(figure 1A) and genus level (figure 1B). At phylum level,
Firmicutes and Bacteroides dominated, comprising 59.6%
(exposed control) and 54.7% (SARS- CoV- 2 positive) and
29.1% (exposed control) and 40.4% (SARS- CoV- 2 posi-
tive) of all phyla, respectively. At the level of genus, Bacte-
roides contributed 12.4% (exposed control) and 21.8%
(SARS- CoV- 2 positive), Alistipes 6.4% (exposed control)
and 7.2% (SARS- CoV- 2 positive) and Bifidobacterium 7.6%
(exposed control) and 1.5% (SARS- Cov- 2 positive).
Figure 2 shows two diversity indices for all subgroups
studied, namely, Shannon diversity (figure 2A) and
Simpson diversity index (figure 2B). The overall p value
for Shannon index (richness of bacterial composition)
demonstrated a significant (p=0.0499) decrease in diver-
sity with increased severity, and significance was seen for
exposed control versus severely symptomatic (p=0.0201),
analysed via Kruskal- Wallis test. The Simpson (even-
ness of bacterial composition) indexes showed a trend
(p=0.0581) of a decrease in diversity with increased
SARS- CoV- 2 severity.
Further metagenomic analysis comparing SARS- CoV- 2
patients and controls revealed significant differences
in relative abundance of specific bacteria. The relative
abundance of SARS- CoV- 2 positive (exposed control)
versus negative subjects is presented in table 2, along
with comparative p values via Mann- Whitney U test.
Patients with SARS- CoV- 2 infection showed a significantly
decreased relative abundance of Bifidobacterium and
Faecalibacterium, and significantly increased relative abun-
dance of Bacteroides (table 2).
Table 3 lists the genera/species relative abundances
(mean±SEM) for various levels of severity of SARS- CoV-
2- positive patients versus exposed control. Analysed via
Kruskal- Wallis test, the main effect (ie, overall p value)
of these changes are shown in the left column. Table 3
proceeds to compare, correcting for multiple compar-
ison, the three levels of severity in infected patients versus
exposed control and asymptomatic groups. Specifically,
increased disease severity was associated with decreased
relative abundance of Bifidobacterium, Faecalibacterium, F.
Figure 1 Distribution of bacterial relative abundance in
various (A) phlya and (B) genera for exposed control (n=20,
left) and SARS- CoV- 2 positive subjects (n=50, right).
Figure 2 Diversity of gut microbiome composition of
SARS- CoV- 2 positive patients (severely symptomatic: n=28;
moderately symptomatic: n=12; mildly symptomatic: n =
6; asymptomatic: n=4) versus exposed controls (n=20). (A)
Shannon index (p=0.0499), (B) Simpson index (p=0.0581).
Differences between severely symptomatic positive and
exposed negative controls were analysed via Kruskal- Wallis
test Dunn’s post- hoc, correcting for multiple comparisons,
showing signicant for Shannon index at p=0.0201.
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prausnitizii and Roseburium, along with an increased rela-
tive abundance of Bacteroides.
Depicted in figure 3 are the 12 most abundant fami-
lies and the 12 most abundant genera for patients, strat-
ified by disease severity and in comparison to exposed
controls. Distinguished by colour, the bars represent
the relative per cent bacterial families and genera abun-
dance. Note the reduced diversity of the microbiome of
SARS- CoV- 2- positive patients shown in column B.
Figure 4 summarises the microbiome changes according
to SARS- CoV- 2 positivity and severity, with green boxes
depicting significant elevation and red boxes indicating
significant depletion in genera/species abundance asso-
ciated with SARS- CoV- 2.
Figure 5A,B exhibit the relative abundance of Bifidobac-
terium for each subject, grouped by SARS- CoV- 2 infection
severity. This diagram, with subjects groups ordered by
severity (from severe on left, to exposed controls on the
right), depicts how Bifidobacterium abundance increases
as severity decreases.
Analysis of the beta- diversity of subjects demonstrated
that the diversity of control subjects cluster separately
from that of patients. Figure 6A shows the beta- diversity-
weighted (quantitative) UniFrac analysis featuring phyla
bacterial profiles for all individuals in the study (n=70).
Figure 6A reveals that, although there is a range of
dissimilarity, the SARS- CoV- 2- negative individuals are
more similar to one another than they are to SARS- CoV-
2- positive patients. The matrix also highlights clusters
of similarity among SARS- CoV- 2- positive patients, and
darker quadrants of dissimilarity where positive and nega-
tive patients intersect. At a more granular level, figure 6B
used principal component (PC) analysis of genera, where
the axes depict the per cent of variance. In PC analysis,
points closer together are more similar (less divergence
with axis representing directions of divergence). Herein,
the PC1 accounts for 43.16% of the variation, whereas
PC2 accounts for 12.78%. This analysis reveals a clear
divergence of a subset of SARS- CoV- 2- positive patients
clustering on the right side tracking along the x- axis
(PC1), highlighting microbiota divergence as a function
of disease. Thus, figure 6 shows that exposed controls
cluster similar separately from SARS- CoV- 2 patients; that
is, patients are more similar in terms of their microbiome
to each other than to controls.
DISCUSSION
Immune function and health could be enhanced by bacterial
abundance
Interactions between the host and gut microbiota are
complex, numerous and bidirectional. Gut microbiota
regulate the development and function of the innate and
adaptive immune systems,32 potentially allowing them
to protect against infections and infection severity. The
primary findings of our study are that SARS- CoV- 2 posi-
tivity and infection severity are associated with decreased
levels of the protective Bifidobacterium and Faecalibacterium
genera and with decreased bacterial diversity, as exempli-
fied by the Shannon and Simpson indices. This accords
with studies showing bacterial diversity inversely relates to
the presence of various common disorders.33 Uniquely,
our study compared SARS- CoV- 2- exposed SARS- CoV-
2- negative persons (ie, controls) with symptomatic and
asymptomatic SARS- CoV- 2- positive patients. Thus, we
controlled for SARS- CoV- 2 exposure.
The genus Bifidobacterium has important immune func-
tions,8 is a major component of the microbiome and is
frequently used in probiotics.34 Bifidobacterum increase
Treg responses and reduce cell damage by inhibiting
Table 2 Relative abundances of Bacteroides increase and of Bidobacterium, Faecalibacterium and Roseburium decrease in
SARS- CoV- 2 positive subjects versus SARS- CoV- 2 negative exposed controls
Genus (±species)
Relative abundance (mean±SEM)
P valueExposed controls SARS- CoV- 2 positive
Alistipes 0.0639±0.0095 0.0721±0.0100 0.8709
Bacteroides 0.1235±0.0178 0.2183±0.0191 0.0025
Bidobacterium 0.0755±0.0219 0.0147±0.0051 <0.0001
Blautia 0.0261±0.0040 0.0524±0.0088 0.1349
Clostridium 0.0431±0.0075 0.0309±0.0039 0.9948
Collinsella 0.0146±0.0045 0.0158±0.0029 0.9948
Dorea 0.0137±0.0024 0.0185±0.0022 0.2777
Eubacterium 0.0441±0.0063 0.0402±0.0043 0.4786
Faecalibacterium 0.0550±0.0086 0.0310±0.0039 0.0137
F. prausnitzii 0.0542±0.0085 0.0313±0.0039 0.0153
Prevotella 0.0110±0.0086 0.0091±0.0066 0.6538
Roseburium 0.0329±0.0056 0.0195±0.0032 0.0097
Ruminococcus 0.0376±0.0079 0.0391±0.0056 0.9844
Mean±SEM relative abundances, as well as p value via Mann- Whitney U test are indicated, with bold marking p<0.05.
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Table 3 Relative abundance of Bacteroides increases and those of Bidobacterium, Faecalibacterium and Roseburium decrease with increasing severity of disease
Genus
(±species)
Relative abundance (mean±SEM)
Main effect Severe Moderate Mild Asymptomatic Exposed control
A. Relative abundance of genera/species for various severities of SARS- CoV- 2 positivity, as well as exposed controls (SARS- CoV- 2 negative). Overall p value of Kruskal- Wallis test is indicated in
‘Main Effect’ column.
Alistipes 0.8119 0.0793±0.0166 0.0520±0.0084 0.0664±0.0196 0.0901±0.0272 0.0639±0.0095
Bacteroides 0.0075 0.2187±0.0272 0.2849±0.0305 0.0912±0.0226 0.2058±0.0606 0.1235±0.0178
Bifidobacterium <0.0001 0.0018±0.0006 0.0037±0.0015 0.0507±0.0244 0.0840±0.0308 0.0755±0.0219
Blautia 0.2098 0.0495±0.0095 0.0426±0.0132 0.1037±0.0510 0.0260±0.0067 0.0261±0.0040
Clostridium 0.2721 0.0331±0.0062 0.0334±0.0055 0.0265±0.0083 0.0145±0.0025 0.0431±0.0075
Collinsella 0.7476 0.0145±0.0035 0.0107±0.0038 0.0203±0.0126 0.0334±0.0174 0.0146±0.0045
Dorea 0.4820 0.0218±0.0035 0.0126±0.0032 0.0174±0.0046 0.0152±0.0015 0.0137±0.0024
Eubacterium 0.9619 0.0436±0.0070 0.0367±0.0064 0.0366±0.0085 0.0332±0.0058 0.0441±0.0063
Faecalibacterium 0.0077 0.0209+0.0037 0.0428+0.0093 0.0359±0.0139 0.0597+0.0098 0.0550±0.0086
F. prausnitzii 0.0054 0.0220±0.0041 0.0417±0.0092 0.0356±0.0137 0.0589±0.0095 0.0542±0.0085
Prevotella 0.1687 0.0149±0.0118 0.0000±0.0000 0.0050±0.0046 0.0023±0.0020 0.0110±0.0086
Roseburium 0.0327 0.0146±0.0037 0.0245±0.0075 0.0268±0.0138 0.0274±0.0081 0.0329±0.0056
Ruminococcus 0.8033 0.0384±0.0076 0.0293±0.0073 0.0620±0.0250 0.0394±0.0139 0.0376±0.0079
Genus
(±species)
P value vs exposed control P value vs asymptomatic
Mild Moderate Severe Asymptomatic Mild Moderate Severe
B. P value for various levels of severity of symptomatic infection, compared to negative exposed controls and positive asymptomatic subjects, using Dunn’s post- hoc.
Alistipes >0.9999 >0.9999 >0.9999 >0.9999 >0.9999 >0.9999 >0.9999
Bacteroides >0.9999 0.0078 >0.9999 >0.9999 >0.9999 >0.9999 >0.9999
Bifidobacterium >0.9999 0.0006 0.0026 >0.9999 >0.9999 0.0006 <0.0001
Blautia 0.4116 >0.9999 0.6373 >0.9999 >0.9999 >0.9999 >0.9999
Clostridium >0.9999 >0.9999 >0.9999 0.4944 >0.9999 0.9841 >0.9999
Collinsella >0.9999 >0.9999 >0.9999 >0.9999 >0.9999 >0.9999 >0.9999
Dorea >0.9999 >0.9999 >0.9999 >0.9999 >0.9999 >0.9999 >0.9999
Eubacterium >0.9999 >0.9999 >0.9999 >0.9999 >0.9999 >0.9999 >0.9999
Faecalibacterium >0.9999 >0.9999 0.0082 >0.9999 >0.9999 >0.9999 0.1422
F. prausnitzii >0.9999 >0.9999 0.0109 >0.9999 >0.9999 >0.9999 0.1568
Prevotella >0.9999 >0.9999 >0.9999 >0.9999 >0.9999 0.8916 >0.9999
Roseburium >0.9999 >0.9999 0.0196 >0.9999 >0.9999 >0.9999 >0.9999
Ruminococcus >0.9999 >0.9999 >0.9999 >0.9999 >0.9999 >0.9999 >0.9999
For A and B, bold values indicate p<0.05. Note, no apparently signicant (p<0.05) p values were observed in post- hocs, with main effects non- signicant.
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TNF-α and macrophages.35 Bifidobacterium also protects
against intestinal epithelial cell damage independently
from their effects on TNF-α production. The exopolysac-
charide coat, which is a feature of some Bifidobacterium,
plays a significant role in this protective effect.36 These
immune functions of Bifidobacterium could be critical in
relation to its SARS- CoV- 2 infection- prevention effects.
Evidence has accumulated to support a beneficial
effect from supplementation with Bifidobacterium in
numerous disease states.37 The numbers of commensal
Bifidobacterium have been shown to decrease with age
and obesity, major SARS- CoV- 2 infection risk factors. We
demonstrate that patients with a more severe course of
viral infection had decreased abundance of Bifidobac-
terium. However, it should be noted that there are no
definitive studies concerning what constitutes a normal
baseline abundance of Bifidobacterium in a ‘healthy’
individual.
Figure 3 Graphic of relative abundance of the 12 most common (A) families and (B) genera. The top group represents the
SARS- CoV- 2 positive samples (n=50), stratied by severity. The bottom group represents the exposed control samples (n=20).
The coloured boxes represent the fraction of the entire rectangle composed of the given family/genera of bacteria.
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The abundance of Faecalibacterium genus and F. praus-
nitzii species was also inversely related to SARS- CoV- 2
positivity and SARS- CoV- 2 infection severity in this
analysis. Age and diabetes are risk factors for SARS- CoV- 2
infection, and F. prausnitzii levels decline markedly in
elder and diabetic populations.37 In fact, Faecalibacterium
levels have been considered an indirect ‘indicator’ of
overall human health.38 The abundance of F. prausnitzii
is reduced by the ‘Western’ diet (consumption of more
meat, animal fat, sugar, processed foods and low fibre),
while it is enhanced by the high- fibre containing ‘Medi-
terranean’ diet of vegetables and fruits with low meat
intake.39 Preliminary studies showed that reduced inges-
tion of a Mediterranean diet within the same country
is associated with increased SARS- CoV- 2- related death
rates.40 In short, we show that F. prausnitzii levels nega-
tively correlated to SARS- CoV- 2 infection severity and
prior studies show that reduced F. prausnitzii is associated
with SARS- CoV- 2 infection vulnerabilities such as age,
diabetes, obesity and possibly diet.
SARS- CoV- 2 positivity and severity were also associated
with decreased abundance of Roseburium and increased
abundance of Bacteroides. The implications of these
changes remain unclear.
Innate immunity could be enhanced by increased level of
beneficial bacteria
The pathological impact of SARS- CoV- 2 infection
includes both direct effects from viral invasion and
complex immunological responses including, in its most
severe form, the ‘cytokine storm’. The cytokine storm
is the result of a sudden increase in circulating levels
of proinflammatory cytokines produced by activated
macrophages, mast cells, endothelial cells and epithelial
cells during innate immune responses, which appear to
be modulated by the abundance of Bifidobacterium and
Faecalibacterium and bacterial diversity (5, 23, 25). Steroid
treatment has situational success in SARS- CoV- 2 infec-
tion, based on suppressing this over activation of the
innate immune system, reviewed by Tang et al41
Zhao reported that elevated serum levels of proin-
flammatory cytokines such as IL- 16 and IL- 17 predict
poor prognoses in patients with SARS- CoV- 2 infection.42
Also, Tao et al showed that changes in gut microbiota
composition might contribute to SARS- CoV- 2- induced
Figure 4 Diagram of taxa comparing the gut microbiome of SARS- CoV- 2 patients and exposed controls. Red or green
background indicates a signicant depletion or increase (due to positivity or severity), respectively, of the genus or species in
SARS- CoV- 2 positive subjects.
Figure 5 Relative abundance of Bidobacterium in SARS-
CoV- 2 positive patients (n=50) versus SARS- CoV- 2 negative
exposed controls (n=20). Data are plotted as (A) mean with
error bars for 95% CI and (B) individual points of relative
abundance for varying SARS- CoV- 2 infection severity.
Analysed via Kruskal- Wallis test, there were signicant
reductions in Bidobacterium relative abundance for
severely (p<0.0001) and moderately (p=0.0002) symptomatic
patients. Subjects 1–28 = severely symptomatic; subjects
29–40 = moderately symptomatic; subjects 41–46 = mildly
symptomatic subjects; subjects 47–50 = asymptomatic;
subjects 51–70 = exposed control. Figure A,B depicts same
data.
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production of inflammatory cytokines in the intestine,
which may lead to cytokine storm onset.29 Both authors
report significantly reduced gut microbiota diversity
and increased opportunistic pathogens in patients with
SARS- CoV- 2. Interestingly, the bloom of opportunistic
pathogens positively correlated with the number of
Th17 cells. Bozkurt and Quigley reported that IL- 6 and
IL- 17 promote viral persistence by immune interactions
through cellular autophagy via the inositol- requiring
enzyme 1 pathway.16 Additionally, some species of Bifido-
bacterium are likely to prevent the replication of coro-
naviruses by reducing endoplasmic reticulum stress,
also through the inositol- requiring enzyme 1 pathway.
Reduced Bifidobacterium abundance has been observed
in the gut microbiome of patients with IBD, which has
mechanisms involving IL- 17.31 Furthermore, the direct
endoscopic delivery of Bifidobacterium has been shown
to be effective in promoting symptom resolution and
Figure 6 SARS- CoV- 2 positive patients’ microbiome is more similar to each other than to that of exposed controls. (A)
Weighted UniFrac distance matrix of phylum level SARS- CoV- 2 positive (n=50) and exposed negative control samples (n=20).
Distance of microbiome differences increases with increasing blue colour intensity (see legend top right). The centre of the
diagram consists of negative subjects on both axis and is yellow indicative of less distance (ie, lessdifference in microbiome).
The central area of the left as well as central- top side of diagram, consists of negative subjects on one axis and positive on
the other, and are darker blue, indicative of more distance (more difference in microbiome). (B) Principal component analysis of
microbiota from SARS- CoV- 2 positive (n=50) and exposed negative controls (n=20). Dots closer in distance are more similar in
microbiome composition. Axes depict the per cent of variance explained by principal component (PC) 1 and 2. Plots are based
on bacterial genera relative abundance proles.
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mucosal healing in IBD—an effect likely to be associated
with the anti- Th17 effect of Bifidobacterium.(8) Figure 7
demonstrates how Bifidobacterium might hypothetically
quell a heightened immune response by dampening the
effect of the master switch TNF-α.
CONCLUSIONS
Given our cross- sectional study design, it is not possible
to determine whether the differences in Bifidobacterium
levels observed between patients and exposed controls
preceded or followed infection. If preceding infection,
they could be a marker of susceptibility, and boosting
Bifidobacterium levels might decrease the risk or severity
of SARS- CoV- 2 infection. If these changes followed infec-
tion, alteration of the gut microbiome (such as through
faecal microbiota transplantation or possibly probi-
otic supplementation) to increase Bifidobacterium could
be an area worth exploring for improved outcomes. If
future studies can demonstrate improved outcomes, such
therapy can be considered for complex cases of SARS-
CoV- 2 infection, such as ‘long- haulers’, and those with
severe disease. Developing outbreaks within tightly closed
communities such as nursing homes might be a good
setting in which to assess susceptibility: faecal samples
could be collected during the outbreak and run post hoc
on ‘cases’ and ‘controls’. Future studies of individuals
with baseline prepandemic microbiome data would be
highly valuable, although acquiring such baseline prein-
fection microbiome data is still costly.
With the lack of data on the gut microbiome prior to
onset of SARS- CoV- 2 infection, we cannot completely rule
out the confounding effect of illness on the microbiome.
Nonetheless, we eliminated effects of treatment on the
gut microbiome by sampling prior to administration of
SARS- CoV- 2 infection therapeutics of any kind and within
48 hours of symptom onset. Specifically, no subjects were
given antibiotics, antivirals, anti- inflammatory medicines,
oxygen or any other therapeutic agent between symptom
onset or PCR positivity and stool sampling. We also note
that the prevalence of appetite changes, alterations of
stool frequency and gastrointestinal (GI) symptoms, in
general, were not significantly different between any of
the severity groups or controls (table 1), although the
small sample sizes for some groups should be considered
in evaluating these statistics.
SARS- CoV- 2 infection presentation variability
correlates with colon microbiome bacterial composition
and overall diversity. The same changes we observe due
to SARS- CoV- 2 infection, namely reduced Bifidobacterium
and/or Faecalibacterium abundance, are associated with
SARS- CoV- 2 infection risk factors, including old age,
obesity and diabetes.9 37 39 43 Thus, colon microbiome
diversity and relative abundance of Bifidobacterium and
Faecalibacterium should be explored as potential markers
for predicting SARS- CoV- 2 infection severity.
In summary, we demonstrate in a study of PCR- positive
and PCR- negative SARS- CoV- 2- exposed subjects, reduced
bacterial diversity and reduced levels of various genus/
species are highly associated with both SARS- CoV- 2
Figure 7 Potential mechanism for cytokine storm and immune hyper- response in SARS- CoV- 2 positive patients. In individuals
infected with SARS- CoV- 2, the macrophages become activated; these in turn activate T- cells, additional macrophages, and
neutrophils―all of which release cytokines, including TNF-α. Bidobacterium, when present in sufcient numbers, can bind to
TNF-α and prevent the subsequent cytokine storm. Therefore, patients with a bidobacterial dysbiosis characterised by low
levels of Bidobacterium lack this line of defense, which may lead to a cytokine storm.
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positivity and SARS- CoV- 2 infection severity. These find-
ings suggest that probiotic supplementation or faecal
microbiota transplantation should be explored as a
potential therapeutic avenue for patients with SARS-
CoV- 2. Additionally, individual colon microbiome eval-
uation may predict vulnerability to the development of
severe SARS- CoV- 2 infection. Finally, our data suggest a
new area for exploration: if SARS- CoV- 2 severity is found
to be dependent on the microbiome, then accounting
for microbiome differences could reduce variability in
outcomes for SARS- CoV- 2.
Author afliations
1N/A, ProgenaBiome LLC, Ventura, California, USA
2Division of Gastroenterology, Alta Bates Summit Medical Center, Berkeley,
California, USA
3Clinic of Gastroenterology, Istanbul Maltepe University, Istanbul, Turkey
4N/A, Microbiome Research, Inc, Ventura, California, USA
5Medical Writing and Biostatistics, North End Advisory, Smyrna, Georgia, USA
6Division of Gastroenterology and Hepatology, The Methodist Hospital, Weill Cornell
Medical College, Houston, Texas, USA
7N/A, Centre for Digestive Diseases, Five Dock, New South Wales, Australia
Acknowledgements Medical writing assistance was provided by Sonya Dave,
PhD (an author on the publication) and was funded by ProgenaBiome. The authors
thank all clinicians for their involvement and contribution to the study. The authors
thank Kate Hendricks, MD, MPH and TM for many helpful editorial suggestions.
Finally, the authors owe a depth of gratitude to the late Sydney M Finegold, MD
for mentorship that sparked the interest in the microbiome to many scientists,
including authors of this paper.
Contributors All authors (SH, NS, HSB, SD, AJP, JD, BDB, EMMQ and TJB)
participated in the drafting, critical revision, and approval of the nal version of
the manuscript. SH led study design. SH and AJP conducted the bioinformatic
analysis. SD conducted the statistical analysis and was a major contributor to
writing the paper. SH was primarily responsible for interpretation of the study
results, with contributions from all authors. EMMQ and TJB are senior authors who
provided overall direction and advice. SH acts as the guarantor and accepts full
responsibility for the work, has access to all data, and controlled the decision to
publish.
Funding The authors have not declared a specic grant for this research from any
funding agency in the public, commercial or not- for- prot sectors.
Competing interests SH declares that she has pecuniary interest in Topelia
Pty Ltd in Australia, and Topelia Pty Ltd in USA where development of COVID- 19
preventative/treatment options is being pursued. She has also led patents
relevant to Coronavirus treatments. She is the founder and owner of Microbiome
research foundation, Progenabiome and Ventura Clinical Trials. TJB declares that he
has pecuniary interest in Topelia Pty Ltd in Australia, and Topelia Therapeutics Inc.
in USA developing COVID- 19 preventative/treatment medications. He has also led
patents relevant to COVID- 19 treatments. SD declares she has corporate afliation
to McKesson Specialty Health/Ontada and North End Advisory, LLC. SD is unaware
of SARS- CoV- 2 and microbiome projects and not directly involved in COVID- 19
relevant projects at McKesson, but they may exist. AJP and BDB have corporate
afliations to Progenabiome. EMMQ serves as a consultant to Precisionbiotics,
Novazymes, Salix, Biocodex and Axon Pharma and has received research support
from 4D Pharma.
Patient consent for publication Not applicable.
Ethics approval The study was conducted in accordance with ethical principles of
the Declaration of Helsinki, the International Council for Harmonisation Harmonised
Tripartite Guideline for Good Clinical Practice and the Ethical and Independent Review
Board. This study involves human participants and was approved by the 'Ethical and
Independent Review Board' (IRB, IRB00007807). Participants gave informed consent
to participate in the study before taking part.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data are available upon reasonable request. Data
available upon reasonable request from corresponding author, Dr. Sabine Hazan.
Supplemental material This content has been supplied by the author(s). It
has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have
been peer- reviewed. Any opinions or recommendations discussed are solely
those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability
and responsibility arising from any reliance placed on the content. Where the
content includes any translated material, BMJ does not warrant the accuracy and
reliability of the translations (including but not limited to local regulations, clinical
guidelines, terminology, drug names and drug dosages), and is not responsible
for any error and/or omissions arising from translation and adaptation or
otherwise.
Open access This is an open access article distributed in accordance with
the Creative Commons Attribution Non Commercial (CC BY- NC 4.0) license,
which permits others to distribute, remix, adapt, build upon this work non-
commercially, and license their derivative works on different terms, provided the
original work is properly cited, appropriate credit is given, any changes made
indicated, and the use is non- commercial. See:http://creativecommons.org/
licenses/by-nc/4.0/.
ORCID iDs
SonyaDave http://orcid.org/0000-0002-7653-0388
Eamonn MMQuigley http://orcid.org/0000-0003-4151-7180
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