medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
1 Temporal dynamics of human respiratory and gut microbiomes during the course of COVID- 2 19 in adults 3 4 Running title: Microbiome dynamics of COVID-19 adults
5
6 Rong Xu1, 4, #, Renfei Lu2, #, Tao Zhang3, #, Qunfu Wu3, #, Weihua Cai2, Xudong Han2, Zhenzhou
7 Wan5, Xia Jin1 *, Zhigang Zhang3 *, Chiyu Zhang1,4 *
8
9 1. Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China 10 2. Clinical Laboratory, Nantong Third Hospital Affiliated to Nantong University, Nantong 226006,
11 China
12 3. State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, School of
13 Life Sciences, Yunnan University, Kunming, Yunnan 650091, China
14 4. Pathogen Discovery and Evolution Unit, Institut Pasteur of Shanghai, Chinese Academy of
15 Sciences, Shanghai 200031, China.
16 5. Medical Laboratory of Taizhou Fourth People’s Hospital, Taizhou 225300 China
17
18 # These authors contributed equally to this work.
19
20 * Correspondence: Chiyu Zhang, Shanghai Public Health Clinical Center, Fudan University,
21 Shanghai 201508, China. Email: [email protected]
22 Or Zhigang Zhang, State Key Laboratory for Conservation and Utilization of Bio-Resources in
23 Yunnan, School of Life Sciences, Yunnan University, No. 2 North Cuihu Road, Kunming, Yunnan
24 650091, China. Email: [email protected].
25 Or Xia Jin, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China.
26 Email: [email protected]
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
27 Abstract
28 SARS-CoV-2 infects multiple organs including the respiratory tract and gut. Whether regional
29 microbiomes are disturbed significantly to affect the disease progression of COVID-19 is largely
30 unknown. To address this question, we performed cross-sectional and longitudinal analyses of throat
31 and anal swabs from 35 COVID-19 adults and 15 controls by 16S rRNA gene sequencing. The results
32 allowed a partitioning of patients into 3-4 categories (I-IV) with distinct microbial community types
33 in both sites. Lower-diversity community types often appeared in the early phase of COVID-19, and
34 synchronous fast restoration of both the respiratory and gut microbiomes from early dysbiosis towards
35 late near-normal was observed in 6/8 mild COVID-19 adult patients despite they had a relatively slow
36 clinical recovery. The synchronous shift of the community types was associated with significantly
37 positive bacterial interactions between the respiratory tract and gut, possibly along the airway-gut
38 axis. These findings reveal previously unknown interactions between respiratory and gut
39 microbiomes, and suggest that modulations of regional microbiota might help to improve the recovery
40 from COVID-19 in adult patients.
41
42 Keywords: SARS-CoV-2/COVID-19; respiratory microbiota; gut microbiota, dysbiosis; adults; co-
43 occurrence network. medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
44 Introduction
45 COVID-19, a severe respiratory disease caused by a novel virus SARS-CoV-21,2, has led to a devastating
46 global pandemic. It typically presents as asymptomatic infection and mild respiratory symptoms, but
47 in those older than 60 years or having comorbidities, COVID-19 can develop into severe pneumonia
48 and cause death3,4. The biological mechanisms behind the varied clinical presentation of disease are
49 not fully understood. Because the microbiota plays a major role in the maintenance of human health
50 by shaping the immune system and maintaining homeostasis5, and the microbial composition in the
51 respiratory tract and the gut have been linked to the occurrence and severity of various respiratory
52 viral infections, and affects subsequent respiratory health6,7, such as increased airway susceptibility
53 to infection by other RVs and/or colonization of pathogenic bacteria8-10, it is reasonable to posit that
54 the new respiratory infection COVID-19 may also interact with microbiota.
55 Recent studies show that SARS-CoV-2 infects human gut enterocytes and causes diarrhea11,12,
56 and COVID-19 patients have altered gut microbiota featuring an enrichment of opportunistic
57 pathogens and a depletion of beneficial bacteria13,14. However, the effect of COVID-19 on respiratory
58 microbiome has never been evaluated. Although one study reported persistent alterations in the gut
59 microbiota using longitudinal stool samples collected during COVID-19 patients’ hospitalization13,
60 but no study has been performed to simultaneously investigate the temporal dynamics of both the
61 respiratory and gut microbiota at various stages of disease. In this study, we investigated the dynamics
62 of both the respiratory and gut microbiomes in a cohort of COVID-19 patients and controls, and
63 presented a consistent pattern of changes in these two organs from early dysbiosis towards late
64 incomplete restoration in the majority of COVID-19 adults. medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
65 Results 66 Study cohort
67 The study subjects included 35 adult COVID-19 patients from 17 to 68 years of age, 15 healthy
68 adults, and 10 non-COVID-19 patients with other diseases. All COVID-19 patients (except patient
69 p09) had mild clinical symptoms. A total of 146 specimens including 37 pairs of both throat and anal
70 swabs were collected from COVID-19 patients (Supplementary Fig. S1). High-throughput
71 sequencing of the V4-region of bacterial 16S rRNA gene was performed for all samples (See
72 Supplementary Methods).
73
74 Respiratory microbiome dynamics in COVID-19
75 The 16S-rRNA gene sequences of all throat swabs were resolved into 3,126 amplicon sequence
76 variants (ASVs) representing 17 known phyla including 209 known genera (Supplementary Table
77 S1). Six throat microbial community types (or clusters) were identified using the Dirichlet
78 Multinomial Mixtures (DMM) modelling based on lowest Laplace approximation (Fig. 1a) and
79 visualized by Nonmetric Multidimensional Scaling (NMDS) based on Bray-Curtis distance (Fig. 1b).
80 Healthy adults (H) and non-COVID-19 patients (NP) formed independent clusters. The vast majority
81 of the specimens of COVID-19 patients were divided into four community types, called I-IV, and 6
82 specimens were included in the NP type (Fig. 1a). All COVID-19-related community types, as well
83 as the NP type, were significantly separated from the H type. Community types III and IV were not
84 only significantly separated from the types I and II, but also from each other (Fig. 1b). Consistently,
85 significantly lower richness and evenness were observed in community types III and IV, compared
86 with the H type (Fig. 1c).
87 To more directly demonstrate that the variation of throat microbial composition is an indicator
88 of COVID-19 disease stages, the community type-specific indicator taxa were identified based on the
89 top 30 microbial genera (Fig. 1d). The type H was characterized by bacterial genus Bacteroides
90 (predominant taxa in the lung of healthy individuals) and unclassified Comamonadaceae, whereas
91 the NP type was marked by pro-inflammatory Enterobacteriaceae members. In contrast, the indicator
92 bacteria of four COVID-19-related community types were Alloprevotella in I, Porphyromonas,
93 Neisseria, Fusobacterium and unclassified Bacteroidales in II, Pseudomonas in III, and
94 Saccharibacteria incertae sedis, Rothia and unclassified Actinomycetales in IV (Fig. 1d). It is clear medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
95 that the indicators in the types III and IV belong to putative pathogenic (e.g. Pseudomonas) and
96 opportunistic pathogenic bacteria (e.g. Rothia). Community type I contained Alloprevotella genus, as
97 well as abundant Bacteroides and Prevotella that typically present in the H type (Fig.1a). Pathogenic
98 bacteria Pseudomonas, Saccharibacteria incertae sedis and Rothia that are associated with
99 pneumonia and various human diseases were significantly enriched in community types III and IV,
100 reflecting an impaired microbiome (dysbiosis) in the respiratory tract15-17. Apart from beneficial
101 commensals (e.g. Bacteroidales), some opportunistic pathogenic bacteria such as Porphyromonas,
102 Fusobacterium, and Neisseria that typically exist in the nasopharynx, and are associated with
103 pneumonia or chronic periodontitis18-20, were over represented in type II, possibly reflecting an
104 intermediate state between the normal and dysbiosis in the respiratory microbiota (Supplementary
105 Table S1 and Fig. S2). According to indicator characteristics, community types I to IV reflected a
106 progressive imbalance of the respiratory microbiome.
107 Longitudinal analysis showed that lower-diversity community types often appeared in early
108 specimens (Fig. 1e), and throat microbiota was dominated by few putative pathogenic and
109 opportunistic bacteria at the early phase of COVID-19 (Supplementary Fig. S2). Prominent
110 microbiome community type shifts from early lower-diversity community types (NP, IV or II)
111 towards later higher-diversity types (II or I) were observed in 9/24 COVID-19 adults who had
112 specimens at two or more time points. For example, an obvious throat microbiome progression from
113 type (IV or II) in early specimens to type I in late specimens was observed in five patients (p17, p25,
114 p13, p11 and p05) with 4 or more consecutive specimens (Fig. 1e). Accompanied with the restoration
115 of throat microbiota, beneficial commensals appeared to occupy the niches, and the bacterial diversity
116 increased accordingly (Supplementary Fig. S2). However, a reversed pattern was observed in four
117 patients who had microbiome composition shift from early higher-diversity types (I or II) to later
118 lower-diversity type (II-IV), implying a worsening of the throat microbiome. For example, in the only
119 severe case (p09), the community type II on day 10 was shifted to type IV on day 27, and sustained
120 to at least day 33 after symptom onset (Fig. 1e), and pathogenic bacteria Saccharibacteria incertae
121 sedis and Rothia were significantly enriched at late stage (Supplementary Fig. S2). Furthermore, a
122 persistent Pseudomonas-dominated throat microbiota (community type III) was observed in three
123 patients (p02, p03 and p04). These results indicate that the change of the respiratory microbiome
124 might be closely associated with the disease progression in COVID-19 patients. medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
125 126 Gut microbiome dynamics in COVID-19
127 To expand the scope of this research, a total of 1,940 ASVs were recovered from the 16S-rRNA
128 gene sequences of all anal swabs, representing 13 known phyla including 182 known genera
129 (Supplementary Table S1). The gut microbial communities of COVID-19 patients formed three
130 distinct community types I-III (Fig. 2a-b). The richness and evenness of the gut microbiome
131 decreased from type I to III (Fig. 2c). Indicator analyses showed that type I was primarily
132 characterized by healthy gut bacteria including Bacteroides genus and several known butyrate-
133 producing bacteria (e.g. Faecalibacterium, Roseburia, Blautia, and Coprococcus) and one
134 opportunistic pathogenic bacterium (Finegoldia) (Fig. 2d)21-27. The indicators of type II mainly
135 contain various pathogenic or opportunistic pathogenic bacteria (e.g. Neisseria and Actinomyces). In
136 community type III, the gut microbiota was dominated by Pseudomonas, implying a severe dysbiosis.
137 We also used the gut community types I-III to reflect the progressive worsening of the microbiome.
138 A shift of the gut microbiome from the lower-diversity community type (II or III) towards a
139 higher-diversity type (I or II) was observed over time in 8/10 patients who had anal swabs at different
140 time points (Fig. 2e). Accompanied with the shift, a clear trend of increase of bacterial diversity and
141 the relative abundance of beneficial commensals (e.g. Bacteroides and Faecalibacterium) was
142 observed in the gut microbiota from early to late stages of COVID-19 (Supplementary Fig. S3),
143 indicating the restoration of gut microbiota. Only one patient had a reverse shift from higher-diversity
144 type II to lower-diversity community type III.
145 146 Association between the respiratory and gut microbiomes in COVID-19
147 Most paired throat and anal swabs showed the same or similar community type levels (Fig. 3a).
148 In particular, the direction of shift over time of the microbiome appeared to match between the throat
149 and the gut in 7/8 patients who had two or more paired specimens at different time points (Fig. 3a).
150 Synchronous improvements of both the respiratory and gut microbiomes from early lower-diversity
151 community type towards late higher-diversity type occurred in six patients (p05, p17, p13, p11, p25
152 and p29). One patient (p33) showed an improved respiratory microbiome but a stable gut community
153 type on day 24. Only one case (p07) had a worsen gut microbiome from day 24 to day 35 but remained
154 a stable respiratory community type. These results suggested that the microbial community dynamics medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
155 of either niches may reflect disease development and flora restoration. Because of no available anal
156 specimens, we were unable to assess whether the gut microbiota, like the respiratory microbiota,
157 shifted from higher-diversity type to lower-diversity type over time in this severe case (p09) (Fig. 1e).
158 Except for the duration of COVID-19, the respiratory and gut microbial community divergence
159 seemed not to be significantly correlated with age, gender, antibiotics use, and detection of SARS-
160 CoV-2 RNA (Supplementary Figs. S4 and S5).
161 We further assessed the changes of the relative abundance of several representative bacteria,
162 including two probiotics (i.e. Bifidobacterium and Faecalibacterium), and five pathogenic bacteria
163 (Fig. 3b). The relative abundances of Bifidobacterium and Faecalibacterium appeared to be
164 negatively correlated with the relative abundance of the five pathogenic bacteria. An obvious decrease
165 in the relative abundance of pathogenic bacteria was accompanied by the restoration of the respiratory
166 and gut microbiome over time in five patients having three or more longitudinal samples (Fig.3b
167 upper panel and Supplementary Fig. S2-S3). Moreover, significantly decreased abundance of
168 Pseudomonas was observed in both organs in another two patients (p23 and p29). The relative
169 abundance of Pseudomonas increased in the only patient (p07) who had a worsening gut microbiome.
170 171 Bacteria–bacteria co-occurrence networks
172 There were four indicator bacteria genera (Porphyromonas, Neisseria, and Fusobacterium in
173 type II and Pseudomonas in type III) in the throat microbiomes that had been identified as the
174 indicators of gut microbial community types II and III in COVID-19 patients (Supplementary Fig.
175 S6). Apart from the shared indicators, oropharyngeal pathogenic bacteria Capnocytophaga and
176 Actinomyces were also identified as indicators of the gut microbial community type II (Figs. 1d and
177 2d)28,29. Because community types II and III often appeared in the early stage of COVID-19 (Figs. 1e
178 and 2e), the appearance of these oropharyngeal bacteria in the gut suggested that a cross-talk between
179 the respiratory and gut microbiomes occurred by frequent bacterial translocation during the early
180 stage.
181 To further investigate the association between the respiratory and gut microbiomes, we
182 performed co-occurrence network analysis using paired specimens from 13 patients. Significantly
183 positive interactions between the respiratory and gut microbiota were observed (FDR-adjusted
184 P<0.05 and spearman correlation r > 0.7), despite obvious separations of co-occurrence patterns medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
185 between different community types (Fig. 4). There were four patterns of co-occurrence networks
186 (Throat-IV, Throat-III, II-II and I-I) that had been identified, and only bacteria from the same
187 community types formed significant co-occurrence networks regardless of whether they were in the
188 throat or gut. Under the dysbiosis condition, a few dominant pathogenic bacteria (e.g.
189 Saccharibacteria incertae sedis, Rothia and Pseudomonas) mediated the bacterial interactions, and
190 drove the oropharyngeal bacterial translocation to the gut possibly due to increased mucosal
191 permeability of the damaged airway and gut (Fig. 4: Throat-III and Throat-IV). Some beneficial
192 bacteria from Firmicutes are the primary colonizers to prevent immune-mediated diseases21.
193 Accompanied with the increase of bacteria diversity, more gut bacteria started to interact with throat
194 microbiota, and some beneficial commensals (mainly Firmicutes) occupied the niches of the throat
195 and gut mucosa to compete with the pathogenic bacteria (Fig. 4: II-II), leading to a possible restoration
196 of the damaged microbiota in both organs. At the late stage of COVID-19, the species from Firmicutes
197 and Bacterioidetes dominated the bacterial community in both organs (Fig. 4: I-I). In particular, the
198 appearance of various probiotics (e.g. Bifidobacterium and Faecalibacterium) in both the throat and
199 gut implied further improvement and restoration of the respiratory and gut microbiomes25,30. medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
200 Discussion
201 Whether SARS-CoV-2 infection alters microbiota to affect COVID-19 disease progression is an
202 important question that needs answers. In this study, we made three major observations. First, the
203 respiratory and gut microbiota compositions of COVID-19 adults can be characterized by four (I- IV)
204 and three (I-III) community types, respectively, and these types reflect different levels of balance
205 between the near-normal microbiota (type I) and dysbiosis (type III/IV). Second, lower-diversity
206 community types III/IV often appears in the early phase of COVID-19, and a consistent pattern of
207 shift from early dysbiosis towards late near-normal coincides with the recovery of both the respiratory
208 and gut microbiomes in most patients with mild COVID-19. Third, the shift of community types is
209 synchronous in the respiratory tract and gut.
210 SARS-CoV-2 infects cells through ACE2 receptor2, which is highly expressed in respiratory and
211 intestinal epithelial cells31. SARS-CoV-2 infection can trigger the cytokine storm, cause local
212 pathological damage32,33. As an open system with direct contact with environment and the primary
213 site for respiratory infections, the respiratory tract microbiota is more easily affected by SARS-CoV-
214 2 infection, but has not been examined yet. We observed alterations of the respiratory microbiota in
215 COVID-19 adults, and presented data on the dynamic change of the respiratory microbiome
216 composition over time. The respiratory microbiome of the COVID-19 adults was characterized by
217 four bacterial community types I-IV, which reflect the different levels of the normal microbiome to
218 dysbiosis. The community type III dominated by Pseudomonas and type IV enriched for other three
219 pathogenic bacteria often appeared in early throat specimens (e.g. first several days after symptom
220 onset), indicating SARS-CoV-2 infection results in a very rapid dysbiosis in respiratory tract. A
221 restoration of the respiratory microbiome from dysbiosis towards near-normal was observed over
222 time in some with mild disease, whereas prolonged or worsening microbiome appeared in a few
223 others including the only one severe case (p09). The inconsistency indicates that the diversity
224 characteristics of the respiratory microbiome had been affected by COVID-19.
225 Intestinal enterocytes that express ACE2 are also the target of SARS-CoV-2 which further up-
226 regulates the expression of ACE2, leading to a longer viral RNA shedding time in the gut than
227 respiratory tract11,31. The baseline microbiome composition with abundant pathogenic bacteria (e.g.
228 Coprobacillus, Clostridium ramoaum and Clostridium hathewayi) had been associated with the fecal
229 levels of SARS-CoV-2 and COVID-19 severity in a previous study31. However, the sampling time medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
230 was relatively late in that study (about 14 days after symptom onset), therefore unable to determine
231 whether the baseline microbiome status is a consequence of SARS-CoV-2 infection, or a cause of
232 disease severity. We also observed alterations of the gut microbiota during COVID-19 in adults, and
233 found some pathogenic bacteria (e.g. Streptococcus, Rothia, Veillonella, Actinomyces, Bacteroides
234 and Actinomyces) similar to those reported in the previous observations13,14. However，distinct from
235 the previous studies, three community types I-III were identified to characterize the changes of gut
236 microbiome over time, and low-diversity community types II and III often appeared in early
237 specimens, supporting the early effect of SARS-CoV-2 on the microbiome. A fast restoration with
238 the community type shifted from low-diversity type II to high-diversity type I over time was observed
239 in at least 4 patients. Moreover, the Pseudomonas-dominated community type III appeared in earlier
240 specimens of patients, and showed a slow improvement towards community II in three patients. Of
241 particular importance is that temporal dynamic changes of the respiratory and gut microbiomes
242 matched with each other extremely well, indicating a close association in microbiota between both
243 body sites, possibly via the “airway-gut axis”34.
244 The reason for the fast dysbiosis in both the respiratory tract and gut of patients with COVID-
245 19 might be that the early-stage inflammation induced by SARS-CoV-2 infection damages the
246 mucosal tissues in airway, lung and gut7,35, and results in a fast loss of beneficial commensals, which
247 then augments the colonization and growth of pathogens (Supplementary Fig. S7). The use of
248 empirical antibiotics in some patient during the early stages of the pandemic may exacerbate the
249 dysbiosis in the respiratory tract and gut. Therefore, the microbiome composition with enrichment of
250 pathogenic bacteria (e.g. Pseudomonas and Neisseria) was observed in both throat and gut
251 microbiomes during the first several days after symptom onsets. Because the respiratory tract is more
252 receptive to both exogenous and indigenous microbes than the gut7,36, the dysbiosis of respiratory
253 microbiome appeared to be worse and occurred earlier than that of the gut microbiota, as manifested
254 by lower diversity and richness and more indicators of presumed pathogenic bacteria in the former
255 than in the latter. During this early phase, the damaged respiratory tract mucosa enables some invasive
256 respiratory pathogenic bacteria to be translocated to the gut, worsening the gut bacterial community
257 (Supplementary Fig. S7).
258 Gut microbiota plays an important role in human health by shaping local immunity and medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
259 remodeling mucosal tissues37. It is relatively more stable and plastic than the respiratory microbiota,
260 and it may affect the latter by cross-talk between these two organs along the airway-gut axis34,36, as
261 determined by the significantly positive co-occurrence relationship between the bacteria of the gut
262 and respiratory tract. In spite of longer duration of SARS-CoV-2 shedding in the gut than in the
263 respiratory tract, gut microbiota appeared to have a faster restoration with an increased bacterial
264 diversity and enrichment of beneficial commensals than the respiratory microbiota. The former then
265 promoted the restoration of the latter via bacterial cross-talk, and resulted in a synchronous restoration
266 of both organs (Fig. 3b, Supplementary Figs. S2 and S3).
267 There are at least three major stages reflecting the restoration or worsening of the microbiome
268 in COVID-19 adults. The restoration of the microbiome seemed to be independent of early
269 microbiome community types. For example, several patients remained microbiome community type
270 II for a long time. Age, gender and antibiotics use seemed not to be linked to restoration of the
271 microbiome (Supplementary Fig. S4-S5), implying potential contributions from other factors such as
272 diet and genetic background.
273 We noted that Pseudomonas-dominated bacterial community type III was difficult to restore
274 towards higher-diversity community types in the respiratory tract. Pseudomonas is a well-known
275 pathogenic bacterium, and rarely found in healthy individuals. The identification of early microbiome
276 dysbiosis community type III or IV dominated by Pseudomonas, Rothia, and Saccharibacteria in
277 COVID-19 patients might imply a need for microbiota-based personalized antibiotics treatment
278 against these specific pathogens. The community type II represents a crucial intermediate stage during
279 the restoration of the microbiome from dysbiosis towards near-normal. It was characterized by
280 Neisseria, Fusobacterium, and Porphyromonas. Fusobacterium. Porphyromonas are the common
281 commensals in the oropharynx and the gut18,20, while Neisseria generally presents in the lung. The
282 appearance of lung Neisseria in both the respiratory tract and the gut, implying bacteria translocations
283 along the “airway-lung-gut axis”. The bacteria translocations may be the consequence of increased
284 permeability among these organs caused by local inflammation38. The Bifidobacterium and some
285 butyrate-producing bacteria (e.g. Faecalibacterium) can improve the inflammatory conditions and
286 regulate innate immunity by down-regulating ACE2 expression, and activating the corresponding
287 signaling pathways25,30. During the restoration of the microbiota, these probiotics started to occupy
288 the ecological niches in the gut and respiratory tract in the type II stage, and governed the microbial medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
289 communities in both organs by mass replacement of pathogenic bacteria (e.g. Rothia and Neisseria)
290 in the type I stage (Fig. 3b and Supplementary Fig. S7). However, a progressively worsening in the
291 respiratory and gut microbiome might be associated with severe cases of COVID-19.
292 In summary, we revealed for the first time the associations between the respiratory and gut
293 microbiota and COVID-19 disease progression, and observed early dysbiosis towards later restoration
294 to near-normal microbiota in a proportion of adults with mild COVID-19. In the absence of specific
295 antiviral drugs and vaccines for COVID-19, our findings have important clinical implications. First,
296 some indicator bacteria (e.g. pathogenic pseudomonas and beneficial butyrate-producing bacteria)
297 can be used as crucial biomarkers for clinical treatment decision making and prognostic markers. The
298 measurement of predominant short-chain fatty acid (especially butyrate) concentration in fecal
299 samples will be particularly useful in early clinical diagnosis. Second, apart from the routine treatment
300 efforts (e.g. non-specific antiviral and supportive treatments)39, precision intervention and modulation
301 of the gut and respiratory microbiota may open up novel therapeutic alternatives, such as personalized
302 antibiotics therapy to inhibit certain pathogenic bacteria (e.g. pseudomonas). Third, COVID-19
303 tailored probiotics (e.g. Bifidobacterium and Faecalibacterium), prebiotics (e.g. xylooligosaccharide)
304 treatment, or symbiotic treatments might be applied to modulate the gut and respiratory microbiota
305 to facilitate the recovery of COVID-19 patients. Lastly, fecal microbiota transplantation may be
306 considered as another treatment choice. medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
307 Methods 308 Study population
309 A total of 63 subjects, including 35 laboratory-confirmed COVID-19 patients, 10 SARS-CoV-2
310 negative patients with various diseases (non-COVID-19) and 15 healthy adults were enrolled in this
311 study. Demographic and clinical characteristics of these patients were provided in Supplementary
312 Table S2 and S340. Specimens including throat swabs and anal swabs were collected from the patients
313 during hospitalization (10-40) at Nantong Third Hospital Affiliated to Nantong University and from
314 healthy adults when they visited the same hospital for physical examination. Sampling was performed
315 using flexible, sterile, dry swabs, which can reach the posterior oropharynx and anus easily
316 (approximately 2 inches) by the professionals at the hospital. At least two throat swabs at different
317 days were available for 32 of 38 COVID-19 patients (Supplementary Fig S1).
318 The study was approved by Nantong Third Hospital Ethics Committee (EL2020006: 28
319 February 2020). Written informed consents were obtained from each of the involved individuals. All
320 experiments were performed in accordance with relevant guidelines and regulations.
321 322 Confirmation of COVID-19 patients
323 COVID-19 was diagnosed in adult patients according to the National Guidelines for Diagnosis
324 and Treatment of COVID-19. The virus RNA was extracted from all samples using a Mag-Bind RNA
325 Extraction Kit (MACCURA, Sichuan, China) according to the manufacturer’s instructions. Then the
326 ORFlab and N genes of SARS-CoV-2 was detected using a Novel Coronavirus (2019-nCoV) Real
327 Time RT-PCR Kit (Liferiver, Shanghai, China) according to the manufacturer’s instructions. Only the
328 individuals who had at least two consecutive throat swabs been positive for both ORFlab and N genes
329 of SARS-CoV-2 were defined as COVID-19 patients. All positive specimens of COVID-19 patients
330 were confirmed by Nantong Center for Disease Control and Prevention (CDC) using recommended
331 real-time RT-PCR assay by China CDC.
332 333 16S rRNA gene sequencing
334 Bacterial DNA was extracted from the swabs using a QIAamp DNA Microbiome Kit (QIAGEN,
335 Düsseldorf, Germany) according to the manufacturer’s instructions, and eluted with Nuclease-free
336 water and stored at -80℃ until use. The V4 hypervariable region (515-806 nt) of the 16S rRNA gene medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
337 was amplified universal bacterial primers41. To pool and sort multiple samples in a single tube of
338 reactions, two rounds of PCR amplifications were performed using a novel triple-index amplicon
339 sequencing strategy as described previously42. The first round of the PCR (PCR1) amplification was
340 performed with a reaction mixture containing 8 μL Nuclease-free water, 0.5 μL KOD-Plus-Neo
341 (TOYOBO, Osaka Boseki, Japan), 2.5 μL of 1 μM PCR1 forward primer, 2.5 μL of 1 μM PCR1
342 reverse primer, and 5 μL DNA template. The products of the PCR1 reactions were verified using a
343 1.5% agarose gel, purified using Monarch DNA Gel Extraction Kit (New England Biolabs, Ipswich,
344 MA, USA), and quantified by a Qubit® 4.0 Fluorometer (Invitrogen, Carlsbad, CA, USA). Equal
345 amounts of purified PXR1 products were pooled, and subjected to the secondary round of PCR (PCR2)
346 amplification. The PCR2 was performed with a reaction mix containing 21 μL Nuclease-free water,
347 1 μL KOD-Plus-Neo (TOYOBO, Osaka Boseki, Japan), 5 μL of 1 μM PCR2 forward primer, 5 μL of
348 1 μM PCR2 reverse primer, and 5 μL pooled PCR1 products. The PCR2 products were verified using
349 a 2% agarose gel, purified using the same Gel Extraction Kit and qualified using the Qubit® 4.0
350 Fluorometer. The amounts of the specific product bands were further qualified by Agilent 2100
351 Bioanalyzer (Agilent, Santa Clara, CA, USA). Equal molars of specific products were pooled and
352 purified after mixing with AMPure XP beads (Beckman Coulter, Pasadena, CA, USA) in a ratio of
353 0.8:1. Purified amplicons were paired-end sequenced (2x250) using Illumina-P250 sequencer.
354 355 Bioinformatic analysis of 16S rRNA gene sequence data 356 Sequenced forward and reverse reads were merged using USEARCH11 software43, then de- 357 multiplexed according to known barcodes using FASTX-Toolkit44. After trimming barcode, adapter 358 and primer sequences using USEARCH11, 19,096,003 sequences were retained with an average of 359 105508 sequences per sample. Samples with sequence <1000 were excluded from the following 360 analysis. 361 Because traditional OTU (operational taxonomic units) picking based on a 97% sequence 362 similarity threshold may miss subtle and real biological sequence variation45, several novel methods 363 such as DADA246 and Deblur47 were developed to resolve sequence data into single-sequence 364 variants. Here, the DADA2 was employed to perform quality control, dereplicate, chimeras remove 365 on Qiime2 platform48 with default settings except for truncating sequence length to 250bp. Finally, 366 an amplicon sequence variant (ASV) table, equivalent to OTU table, was generated and then spitted medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
367 into gut ASV table (2348 ASVs) and throat ASV table (4050 ASVs). The taxonomic classification of 368 ASV representative sequences was conducted by using the RDP Naive Bayesian Classifier 369 algorithm49 based on the Ribosomal Database project (RDP) 16S rRNA training set (v16) database50. 370 To eliminate sequencing bias across all samples, both the gut ASV table and throat ASV table were 371 subsampled at an even depth of 4700 and 3000 sequences per sample, respectively. The ASV coverage 372 of 82.6% (gut) and 77.2% (throat) were sufficient to capture microbial diversity of both sites. 373 374 Identification and characterization of microbial community types 375 Dirichlet multinomial mixtures (DMM)51 is an algorithm that can efficiently cluster samples 376 based on microbial composition, its sensitivity, reliability and accuracy had been confirmed in many 377 microbiome studies52-54. DMM clustering were conducted with bacterial genus abundance from throat 378 and gut microbiota using the command “get.communitytype” introduced by v1.44.1 of mothur55. The 379 appropriate microbial community type numbers (DMM clusters) were determined based on the lowest 380 Laplace approximation index. According to sample counts per cluster, the fisher exact test was applied 381 to discover significant associations between each cluster and host conditions (such as healthy controls, 382 COVID-19 patients, and Non-COVID-19 patients) under P values that are below 0.05 adjusted by 383 the False Discovery Rate (FDR). Conjugated with the Analysis of Similarities (ANOSIM), the 384 reliability of DMM clustering was further validated and then visualized by the Non-metric 385 multidimensional scaling (NMDS) based on the Bray-Curtis distance under bacterial genus level. 386 “The ANOSIM statistic “R” compares the mean of ranked dissimilarities between groups to the mean 387 of ranked dissimilarities within groups. An R value close to “1.0” indicates dissimilarity between 388 groups, whereas an R value close to “0” indicates an even distribution of high and low ranks within 389 and between groups”. The ANOSIM statistic R always ranges between −1 to 1. The positive R values 390 closer to 1 suggest more similarity within sites than between sites, and that close to 0 represent no 391 difference between sites or within sites56. ANOSIM p values that are lower than 0.05 imply a higher 392 similarity within sites. Richness (Observed OTUs/ASVs) and Pielou's or Species evenness for each 393 community type were calculated for estimating the difference of alpha diversity. The analyses of alpha 394 diversity, NMDS and ANOSIM were performed using R package “vegan” v2.5-6. Dynamic change 395 of community types was showed according to collected dates of specimens with R package ‘P 396 heatmap’. For association between community types and potential confounding factors such as sex, 397 age, virus existence and antibiotic use, the fisher exact test based on sample count was performed and medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
398 the association with FDR-corrected p value <0.05 was considered significant. 399 400 Indicator analysis in throat and gut community types 401 According to the definition given by the United Nations Environment Programme (1996), the 402 indicator species are a group of species whose status provides information on the overall condition of 403 the ecosystem and of other species in that ecosystem, reflecting the quality and changes in 404 environmental conditions as well as aspects of community composition. To obtain the reliable 405 indicator genus that is specific to each community type, we performed the Indicator Species Analysis 406 using the indicspecies package (ver.1.7.8) 57 in R platform with top 30 genus contributing to DMM 407 clustering in both throat (accounting for 66% cumulative difference) and gut (68% cumulative 408 difference). Dynamic changes of indicator genera corresponding to each throat community type were 409 showed in all COVID-19 patients using the pheatmap package in R and only gut indicator genera 410 with indicator values that were above 0.05 were presented in the patients. 411 412 Co-occurrence network analysis of a crosstalk between throat and gut microbiota 413 Based on microbial genus abundances of both throat and gut samples collected from 13 COVID-
414 19 patients at the same time point, we calculated the Pearson Correlation Coefficient (Pearson’s r)
415 among the throat & gut microbial genera. The Pearson’s r with P values that were below 0.05 after
416 the FDR adjustment were considered significant correlations. Co-occurrence network of significantly
417 correlated microbial genus pairs was visualized using Cytoscape v3.8.058. 418 419 Data availability
420 The raw data of 16S rRNA gene sequences are available at NCBI Sequence Read Archive (SRA)
421 (https://www.ncbi.nlm.nih.gov/sra/) at BioProject ID PRJNA639286.
422 423 Supplemental information
424 Supplemental information includes supplemental Experimental Procedures, six figures, and three
425 tables and can be found with this article.
426
427 Acknowledgements medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
428 We thank Miss Yingying Ma at Shanghai Public Health Clinical Center, Fudan University, and
429 Mr. Kai Liu and Mrs Xiuming Wu at Institut Pasteur of Shanghai, Chinese Academy of Sciences for
430 their technical support. This work was supported by grants from the National Key Research and
431 Development Program of China (2017ZX10103009-002, 2019YFC1200603 and 2018YFC2000500),
432 Special fund for COVID-19 diagnosis and treatment of Nantong Science and Technology Bureau
433 (SFCDT3-2), the Second Tibetan Plateau Scientific Expedition and Research (STEP) program
434 (2019QZKK0503), the Key Research Program of the Chinese Academy of Sciences (FZDSW-219),
435 and the Chinese National Natural Science Foundation (31970571).
436
437 Author contributions
438 C.Z. conceived the study idea. C.Z. and Z.Z. designed and supervised the study. R.L., W.C. and
439 X.H. collected clinical samples and data. R.X. and R.L. performed the experiments. T.Z. and Q.W.
440 processed and analyzed the raw sequencing data. R.X., R.L. and Z.W. analyzed the clinical data. Z.Z.
441 and R.X. generated the figures. C.Z., Z.Z. and X.J. interpreted the data. C.Z., Z.Z., R.X., and T.Z.
442 wrote the first draft of the manuscript. X.J. contributed to critical revision. All authors contributed to
443 the final manuscript.
444
445 Competing interests: The authors have not conflict of interests.
446
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566 Figure legends 567 Figure 1. DMM clustering of 16S rRNA gene sequencing data of throat microbiota (N = 112).
568 Dirichlet multinomial mixtures (DMM) modelling was applied to 16S rRNA gene sequencing. The
569 entire dataset formed six distinct clusters based on lowest Laplace approximation. Bacterial taxa
570 marked by the stars represent unclassified bacteria genera.
571 a. Heat map showing the relative abundance of the 30 most dominant bacterial genera per DMM
572 cluster. The stars represent unclassified genera. NP, enriched in Non-COVID-19 patients. H, enriched
573 in Healthy individuals. I-IV enriched in COVID-19 patients.
574 b. Nonmetric multidimensional scaling (NMDS) visualization of DMM clusters using Bray-Curtis
575 distance of throat bacterial genera. The ANOSIM statistic R closer to 1 with < 0.05 P value suggest
576 significant separation of microbial community structures. The stress value that was lower than 0.2
577 provides a good representation in reduced dimensions.
578 c. Box plots showing the alpha diversity (richness and evenness) per each DMM cluster.
579 d. Indicators of airway microbial community types (DMM clusters) identified from top 30 genus
580 contributing to throat microbial community typing (DMM clustering) in a. * P <0.05, ** P <0.01, and
581 *** P < 0.001.
582 e. Dynamic shift of four throat microbial community types (DMM clusters) in different COVID-19
583 stages. Empty boxes indicate samples were unavailable in COVID-19 patients. Ages (years) were
584 shown in parenthesis. NA, unavailable.
585
586 Figure 2. DMM clustering of 16S rRNA gene sequencing data of gut microbiota (N = 45).
587 Dirichlet multinomial mixtures (DMM) modelling was applied to 16S rRNA gene sequencing. The
588 entire dataset formed three distinct clusters based on lowest Laplace approximation. All samples were
589 collected from COVID-19 patients. Bacterial taxa marked by the stars represent unclassified bacteria
590 genera.
591 a. Heat map showing the relative abundance of the 30 most dominant bacterial genera per DMM
592 cluster.
593 b. Nonmetric multidimensional scaling (NMDS) visualization of DMM clusters using Bray-Curtis
594 distance of gut bacterial genera. The ANOSIM statistic R closer to 1 with < 0.05 P value suggest
595 significant separation of microbial community structures. The stress value that was lower than 0.2 medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
596 provides a good representation in reduced dimensions.
597 c. Box plots showing the alpha diversity (richness and evenness) per each DMM cluster.
598 d. Indicators of gut microbial community types (DMM clusters) identified from top 30 genus
599 contributing to gut microbial community typing (DMM clustering) in a. * P <0.05, ** P <0.01, and
600 *** P < 0.001.
601 e. Dynamic shift of gut microbial community types (DMM clusters) in different COVID-19 stages.
602 Empty boxes indicate samples were unavailable in COVID-19 patients. Ages (years) were shown in
603 parenthesis.
604
605 Figure 3. Dynamic change of both DMM clusters (a) and seven key taxa (b) in respiratory tract 606 and gut of patients with mild COVID-19
607 a, Co-variation dynamics of throat and gut microbial communities of 13 COVID-19 patients. Filled
608 circles indicate the presence of microbial community types. Positive or Negative detections of SARS-
609 -CoV-2 in gut or throat are implicated by + or - symbols, respectively. Age (months) of each COVID-
610 19 adult was shown in brackets.
611 b, key taxa of DMM clusters were shown in nine mild COVID-19 adults with at least two time points
612 of sampling. Linked to Fig.1a, Fig.2a, and Supplementary Figs.2-3.
613
614 Figure 4. Co-occurrence networks of gut and throat microbiota within 13 COVID-19 patients.
615 Pearson correlation was employed to calculate correlation coefficient (r) between bacterial genus
616 pairs based on their relative abundances. Co-occurred pairs with r > 0.7 under FDR-adjusted P < 0.05
617 were shown and visualized by Cytoscape version 3.8.0. Negative correlations that were not significant
618 were not shown. Edges were sized based on r values. The stars represented unclassified bacteria
619 genera. Four co-occurrence patterns mediated by throat-IV indicator, throat-III indicator, throat-II &
620 gut-II indicators, and throat-I& gut-I indicators, respectively. The bigger squares or circles were
621 indicators in Figs. 1d and 2d. medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
622 Supplementary materials 623 Supplementary table S1. Throat and gut microbial abundances (phyla and genera). 624 625 Supplementary table S2. Clinical index of COVID-19 patients in this study. 626 627 Supplementary table S3. Demographic information of COVID-19 patients in this study. 628 629 Supplementary figure S1. COIVD-19 patient admission and discharge time as well as the point 630 of detection of SARS-CoV-2. a. the hospitalization of p13 was 40 days. b. the information of these
631 patients was unavailable. DAY 1 was the day of symptom onset.
632
633 Supplementary figure S2. Time-scale changes of indicators of throat microbial community 634 types. Color sectors represent relative abundance of indicators in different COVID-19 stages. Linked 635 to Figure 1. 636 637 Supplementary Figure S3. Time-scale changes of indicators of gut microbial community types.
638 Color sectors represent relative abundance of indicators in different COVID-19 stages. Linked to
639 Figure 2.
640
641 Supplementary Figure S4. Enrichment analysis of impact factors on throat microbial 642 community typing. a) Sex, b) Age, c) Virus detection, and d) Antibiotic uses. Enrichment analysis 643 was performed by using the Fisher’s exact test under FDR-adjusted P < 0.05. Sample numbers were
644 shown on the bar. Only COVID-19 patients were used for this analysis.
645
646 Supplementary Figure S5. Enrichment analysis of impact factors on gut microbial community 647 typing. a) Sex, b) Age, c) Virus detection, and d) Antibiotic uses. Enrichment analysis was performed 648 by using the Fisher’s exact test under FDR-adjusted P < 0.05. No significant enrichment was observed.
649 Sample numbers were shown on the bar.
650
651 Supplementary Figure S6. Comparisons of indicator genera between throat and gut microbial medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
652 clusters. 653 654 Supplementary Figure S7. Putative restoration model of the respiratory and gut microbiomes 655 over time in adults with mild COVID-19. SARS-CoV-2 infection resulted in a fast dysbiosis in
656 both the respiratory tract and gut at the very early phase of the disease. A fast restoration of both the
657 respiratory and gut microbiomes from early dysbiosis towards late near-normal status was observed
658 in most adults with mild COVID-19 albeit they seemed to have a relatively slow clinical recovery.
659 This model reflects the major microbiome dynamic change in most adults with mild COVID-19 but
660 not all features in all patients, especially in those with severe disease. medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Fig. 1
a NP H Ⅰ Ⅱ Ⅲ Ⅳ DMM Cluster b Pseudomonas Rothia Prevotella Bacteroides R = 0.687 Saccharibacteria incertae sedis P = 0.001 Pasteurellaceae* Stress = 0.169 Actinomyces 0.8 NP Neisseria 1 2 H Leptotrichia Streptococcus 0.6 Veillonella Porphyromonas Ⅲ Fusobacterium 0.4 0
Gemella MDS2 Campylobacter Ⅰ Lactobacillales* 0.2 Alloprevotella Ⅱ Relative abundance Lachnospiraceae* 0 Corynebacterium Ⅳ Lachnoanaerobaculum Comamonadaceae* Oribacterium Faecalibacterium Ruminococcaceae* Bacteroidales* Actinomycetales* Capnocytophaga −3 −2 −1 Leptotrichiaceae* Enterobacteriaceae* −2 0 2 4 Stomatobaculum N=10 N=15 N=16 N=44 N=10 N=4 MDS1 c e 250 p34 (NA) p18 (NA) Ⅰ p21 (NA) 200 *** p05 (17) Ⅱ p17 (20) p20 (26) Ⅲ 150 p10 (26)
*** p13 (29) Ⅳ DMM Cluster 100 p15 (29) p16 (33) p23 (39) 50 *** ** p11 (41) p08 (42) 0 p07 (46) Richness(observed species) p31 (48) HNP Ⅰ Ⅱ Ⅲ Ⅳ p06 (50) p03 (52) 0.8 p36 (52) p02 (52) p04 (53) * 0.6 *** *** p35 (54) p24 (57) *** p28 (57) 0.4 p32 (58) COVID-19 patients (Age/years) p22 (58) p19 (59) Pielou Evenness 0.2 p25 (63) p30 (64) p09 (68) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
HNP Ⅰ Ⅱ Ⅲ Ⅳ Days after symptom onset
d H NP Ⅰ Comamonadaceae* ** Enterobacteriaceae* ** Alloprevotella ** Bacteroides *** 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 Porphyromonas Ⅱ Ⅲ Saccharibacteria Ⅳ ** incertae sedis *** Neisseria * Pseudomonas Rothia *** Fusobacterium * *** Actinomycetales* *** Bacteroidales* * 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 Indicator values Indicator values Indicator values medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Fig. 2 a c Ⅰ Ⅱ Ⅲ DMM Cluster Pseudomonas 300 Bacteroides Prevotella *** Lachnospiraceae* Neisseria Leptotrichia 200 0.8 Pasteurellaceae* Rothia Actinomyces *** 0.6 Gemella 100 Streptococcus Blautia Richness(observed species) 0.4 Campylobacter Faecalibacterium Anaerostipes Ⅰ Ⅱ Ⅲ 0.2 Finegoldia 0.8 Relative abundance Porphyromonas 0 Lactobacillales* ** Dorea Parabacteroides 0.6 Roseburia Lachnoanaerobaculum Ruminococcaceae* Oribacterium Leptotrichiaceae* 0.4 Coprococcus Pielou Evenness Veillonella Fusicatenibacter Capnocytophaga 0.2 Ruminococcus2 Ⅰ Ⅱ Ⅲ N=15 N=24 N=6 b e 3 p05 (17) R = 0.627 p17 (20) P = 0.001 Stress = 0.169 p20 (26) p13 (29) 2 Ⅱ p15 (29) p23 (39) p11 (41) 1 p08 (42) Ⅰ p27 (43)
MDS2 p29 (44) 0 p07 (46) p06 (50) p03 (52) −1 p36 (52) Ⅲ p04 (53) p35 (54) Ⅰ COVID-19 patients (Age /years) −2 p32 (58) Ⅱ −1 0 1 2 p25 (63) Ⅲ MDS1 p33 (66) DMM Cluster 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
d Days after symptom onset Ruminococcus2 * Ⅰ Pasteurellaceae* ** Ⅱ Ⅲ Ruminococcaceae* * Neisseria ** *** Roseburia Campylobacter ** Lachnospiraceae* *** Leptotrichiaceae* * Fusicatenibacter *** Leptotrichia ** Pseudomonas Finegoldia ** Streptococcus * Faecalibacterium ** Oribacterium ** *** Dorea *** Lactobacillales* * Coprococcus *** Lachnoanaerobaculum * Blautia *** Gemella ** Anaerostipes ** Porphyromonas Parabacteroides ** ** Capnocytophaga * Bacteroides *** Actinomyces * 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 Indicator values Indicator values Indicator values medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Fig. 3 a + Locations p33 (66) − − − Throat − − − − − p25 (63) − − − − − Gut
+ Throat community p35 (54) − types + p36 (52) + Throat−Ⅰ
− Throat−Ⅱ p03 (52) − Throat−Ⅲ + − p07 (46) − − Throat−Ⅳ Diversity Increasing + − p29 (44) − − Throat−NP
− Gut community p08 (42) − types
+ + − − − − Gut−Ⅰ p11 (41) − + − − + −
COVID-19 p atients (Age/years) Gut−Ⅱ + p23 (39) − Gut−Ⅲ Diversity Increasing + − + + − − p13 (29) + − − + − − day40
− − − − − − p17 (20) − − − − − − Discharge time
+ + − p05 (17) − − − 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 b Days after symptom onset p05 (17 years) p17 (20 years) p13 (29 years) p11 (41 years) p25 (63 years) Gut Throat Gut Throat Gut Throat Gut Throat Gut Throat 0.08 0.10 0.02 0.2 0.04 0.05 0.1 0.04 0.00 0.00 0.0 0.00 0.00 0.4 0.8 0.4 0.4 0.2 0.2 0.4 0.2 0.0 0.0 0.0 0.0 0.0 Relative Abundance Relative 06 08 10 04 06 08 10 07 09 11 13 15 17 07 09 11 13 15 17 06 08 10 12 14 16 06 08 10 12 14 16 05 07 09 11 13 05 07 09 11 13 11 13 15 17 19 21 11 13 15 17 19 21 days days days days days
p23 (39 years) p29 (44 years) p07 (46 years) p33 (66 years) Key Genera Gut Throat Gut Throat Gut Throat Gut Throat Faecalibacterium -4 -4 Bifidobacterium 0.4 6×10 6×10 4×10-4 4×10-4 Pseudomonas 0.3 0.4 2×10-4 2×10-4 0 0 Neisseria 0.2 0.2 0.75 0.4 Rothia 0.1 0.50 0.25 0.2 Actinomyces 0.0 0.0 0.00 0.0 Streptococcus Relative Abundance Relative 02 15 02 12 07 22 07 16 22 24 35 24 35 02 24 02 24 days days days days medRxiv preprint doi: https://doi.org/10.1101/2020.07.21.20158758; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Fig. 4 (throat-Ⅳ) (Ⅱ-Ⅱ) (Ⅰ-Ⅰ) Carnobacteriaceae* Methylophilus Catonella Bacteroidales* Lactococcus Peptococcus Prevotella sedis(SR1) Peptostreptococcaceae* Porphyromonadaceae* Saccharibacteria sedis Flavobacteriaceae* Fusobacterium Acidovorax Yersinia Empedobacter Moraxella Burkholderiales* Eubacterium Ralstonia Clostridium_sensu_stricto Peptostreptococcaceae_incertae_sedis Rheinheimera Cyanobacteria (GpI) Shuttleworthia Gemmiger Gammaproteobacteria* Mogibacterium Neisseriaceae* Caulobacter Actinomycetales* Actinobacteria_C* Ezakiella Eikenella Slackia Rothia Solobacterium Pediococcus Dialister Rothia Comamonadaceae* Scardovia Fretibacterium Desulfovibrio Mobiluncus Saccharibacteria sedis Olsenella Bacillales* Enterococcus Proteus Atopobium Megamonas Staphylococcus Actinobacteria_P* Pasteurellaceae* Actinobacteria_C* Coriobacteriaceae* Treponema Paraprevotella Stenotrophomonas Dolosigranulum Firmicutes(XIII) Varibaculum Alistipes Bilophila Allisonella
Bacteroidetes* Spirochaetes* Oscillibacter Porphyromonas Lachnospiracea_incertae_sedis Phascolarctobacterium Mycoplasma Firmicutes* Microbacterium Leptotrichia Moraxella Micrococcaceae* Oscillibacter Clostridium_XlVb Intestinibacter Parvimonas Ralstonia Comamonadaceae* Selenomonadales* Parasutterella Eikenella Methylobacterium Faecalibacterium Acinetobacter Reyranella sedis(SR1) Peptoniphilus Bacteroidia* Kingella Psychrobacter Ruminococcaceae* Negativicoccus Undibacterium Flectobacillus Haemophilus Clostridium_XlVa Johnsonella Rheinheimera Actinobacteriaphylum Roseburia Coprobacter Finegoldia Filifactor Leuconostoc Eggerthella Parabacteroides Collinsella Ⅲ Desulfovibrio Campylobacter Sediminibacterium (throat- ) Brachybacterium Herbaspirillum Anaerococcus Lachnospiraceae* Thiobacillus Anaerococcus Cloacibacterium Carnobacteriaceae* Yersinia Tannerella Actinomycetaceae* air.Firmicutes.Blautia Atopobium Gemella Bifidobacterium Dorea Acidovorax Parasutterella Staphylococcus Clostridium_XVIII Betaproteobacteria* Mycobacterium Coprococcus Finegoldia Clostridium_sensu_stricto Fusicatenibacter Pseudomonas Comamonas Negativicoccus Peptoniphilus Solobacterium Bacilli* Bacteroides Lachnospiracea_incertae_sedis Veillonella Xanthomonas Anaeroglobus Anaerostipes Ruminococcus2 Mogibacterium Morococcus Bifidobacterium Eggerthella Collinsella Lachnoanaerobaculum Granulicatella Bacteroides Butyricicoccus
Roseburia Lachnospiraceae* Erysipelotrichaceae* Bacillales* Haemophilus Abiotrophia Peptostreptococcus Coprococcus Fusobacteriales* Clostridium_XlVb Kingella Anaerostipes Blautia Fusobacterium Gemella Clostridium_XlVa Bilophila Eubacterium Gammaproteobacteria* Lactobacillales* Clostridiales* Fusicatenibacter Desulfomicrobium Coprobacter Phascolarctobacterium Proteus Bradyrhizobium Clostridia Morococcus Lautropia Sutterella Enterobacteriaceae* Sporobacter Dorea Parabacteroides Negativicutes* Gemmiger Acinetobacter Oribacterium Clostridium_IV Barnesiella gut.Firmicutes.Romboutsia Alistipes Enterobacteriaceae* Abiotrophia Burkholderiales* Chryseobacterium Brevundimonas Faecalibacterium Positive Interaction Ezakiella Hungatella Clostridium_XVIII Stomatobaculum Varibaculum Ruminococcaceae*Ruminococcus Moryella Betaproteobacteria* Microbacterium Pediococcus Ruminococcus2 Fusobacteriales* Actinomyces Hungatella Neisseria Mobiluncus Vibrio Pseudoalteromonas Erysipelotrichaceae_incertae_sedis
Marinomonas Alloprevotella Throat Spirochaetes* Psychromonas
Veillonellaceae* air.Firmicutes.Butyrivibrio Bacteroidetes. Gut Firmicutes (XIII)
Bacteria Proteobacteria Fusobacteria Actinobacteria Tenericutes Cyanobacteria Candidatus Phyla SR1 Synergistetes Spirochaetes Firmicutes Bacteroidetes Saccharibacteria