Microbial Signatures in COVID-19: Distinguishing Mild and Severe Disease via Gut Microbiota

doi:10.21203/rs.3.rs-3341913/v1

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Research Article

Microbial Signatures in COVID-19: Distinguishing Mild and Severe Disease via Gut Microbiota

https://doi.org/10.21203/rs.3.rs-3341913/v1

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published 01 May, 2024

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Background

The global healthcare landscape has changed dramatically as a result of the COVID-19 pandemic caused by SARS-CoV-2. In addition to focusing on treatments and vaccines, it is crucial to investigate the effects of the virus on infected individuals. Recent studies have discovered that SARS-CoV-2 can infect the gut, affecting the gut microbiota and revealing a new aspect of the virus. This study examined the differences in gut microbiota composition between COVID-19 patients with severe and mild conditions.

Methods

In this study, we utilized 16S rRNA metagenomic sequencing to examine the fecal samples obtained from 49 patients with mild and 43 patients with severe cases of COVID-19 upon their admission to the hospital.

Results

Differential abundance analysis with respect to comorbidities on the 313 most abundant ASVs suggested an association of Enterococcus faecium, Rothia mucilaginosa, Akkermansia muciniphila, Schaalia odontolytica, Eubacterium limosum, and Slackia isoflavoniconvertens species with a severe condition, and Faecalibacterium prausnitzii, Ruminococcoides bili, Turicibacter sanguinis, Alistipes putredinis, Bacteroides vulgatus, Bacteroides stercoris, Lachnospiraceae bacterium sunii NSJ-8, Blautia faecis, and Anaerostipes hadrus with a milder course of the disease. Network analysis using SPIEC-EASI revealed keystone taxa and structural differences between the mild and severe groups, suggesting an increased level of bacterial connectivity disruption within the severe group.

Conclusion

Our study highlights the diverse impacts of SARS-CoV-2 on the gut microbiome among both mild and severe COVID-19 patients, showcasing a spectrum of microbial responses to the virus. Importantly, these findings align, to some extent, with observations from other studies on COVID-19 gut microbiomes, despite variations in methodologies.

Covid-19 severity

16S

human gut microbiome

faeces

SARS-CoV-2

The COVID-19 pandemic, caused by the highly contagious SARS-CoV-2 virus, was officially declared in March 2020 and lasted for three years until May 2023. In three years, the SARS-CoV-2 virus claimed millions of lives worldwide, becoming one of the deadliest viral diseases in decades[1].

Apart from respiratory symptoms, the SARS-CoV-2 virus is now recognized to have extrapulmonary effects on different organs and systems. Gastrointestinal symptoms are frequently observed, and emerging evidence suggests that COVID-19 patients undergo changes in their gut microbiome, encompassing bacteria, fungi, and viruses, persisting even in cases of long-term morbidity and post-COVID syndrome[2–4]. These alterations disrupt the ecological network, diminish diversity, and disturb homeostasis within the gut. Concurrent comorbidities compromise immune states, and individual factors further contribute to the overall disease severity[5].

SARS-CoV-2, the virus responsible for COVID-19, enters human intestinal cells through the ACE2 receptor, triggering an inflammatory response involving cytokines[6]. This infection can disrupt the mechanical barrier of the gastrointestinal tract by affecting the expression and function of tight junction (TJ) proteins, compromising the integrity of the intestinal paracellular barrier[7, 8]. Recent research in mice has directly proved that SARS-CoV-2 infection leads to dysbiosis in the gut microbiome, characterized by changes in Paneth cells, goblet cells, and markers associated with barrier permeability[9]. Additionally, the analysis of blood culture results, along with microbiome data, suggests the potential translocation of gut bacteria into the systemic circulation of COVID-19 patients[9].

Numerous investigations have assessed the disparities in gut microbiota composition between SARS-CoV-2 infected patients and healthy individuals, as well as among patients with varying disease severities[10–12]. In a study from Germany, the researchers examined 108 COVID-19, 22 post-COVID-19 patients, 20 pneumonia controls, and 26 asymptomatic controls by using 16S rRNA gene sequencing[13]. They divided SARS-CoV-2 infected patients into three groups (mild/severe/death) based on the World Health Organization (WHO) Ordinal scale for clinical improvement. Differential analysis between study groups, showed that zOTUs belonged to Faecalibacterium prausnitzii, Blautia luti, Dorea longicatena, Gemmiger formicilis, and Alistipes putredinis were significantly decreased in severe, death, and post COVID-19 groups. While, zOTUs belonging to Clostridium innocuum, Ruthenibacterium lactatiformans, and Alistipes finegoldii were increased in patients with a severe disease progression.

In another study[14], the researchers conducted a comparative analysis of the 16S gut microbiome composition among 30 COVID-19 patients, 24 influenza A (H1N1) patients, and 30 matched healthy controls (HC). In the COVID-19 group, the dominant genera in the gut microbiome were Streptococcus, Rothia, Veillonella, Erysipelatoclostridium, and Actinomyces. Whereas, the gut microbiome of the healthy control group (HC) was primarily dominated by the genera Romboutsia, Faecalibacterium, Fusicatenibacter, and the Eubacterium hallii group. Сompared to the H1N1 group the abundance of Prevotella, Ezakiella, Murdochiella, and Porphyromonas was higher in the H1N1 group than in the COVID-19 patients.

In our study, we investigated the composition of the gut microbiome in 92 inpatients with COVID-19 by using 16S rRNA gene sequencing of the V3-V4 region. To assess the severity of the disease, we utilized lung computed tomography (CT) scans to evaluate the degree of lung damage, and the WHO Performance Scale to assess the functional level of each individual. These assessments enabled us to classify the patients into two comparison groups based on the severity of their disease course.

Our objective was to identify microorganisms that are associated with the progression of the disease by performing differential abundance testing while considering patients' metadata as covariates. Furthermore, we examined the variations in the structure of the microbial interaction network between these two groups.

We anticipate that the findings from our study will enhance the understanding of the mechanisms underlying the impact of COVID-19 on the human gut microbiota.

Samples and data collection

The study included 92 patients with laboratory-confirmed novel coronavirus infection SARS-CoV-2 and clinical manifestations of COVID-19, who were treated at the Kuskovo Multiprofile Clinical Medical Center, A.I. Evdokimov Moscow State Medical University from April to June 2021. All the patients have signed informed consent to participate in the study. The study did not include patients diagnosed with cancer.

Inclusion criteria for the study group:

Participant's written consent to participate in the study
COVID-19 diagnosis confirmed by PCR test
Age older than 18 years

Exclusion criteria:

Severe life-threatening condition or contraindication, that prevents biomaterial sampling
Cancer diseases out of remission stage

The following metadata was collected based on questionnaires: whether or not the patient has health disorders such as hypertension, diabetes, obesity, and coronary artery disease (CAD).

Stool samples were collected in sterile containers in a sample volume of 5 to 15 ml. The samples were stored at − 70°C.

16S rRNA Sequencing

Total DNA was isolated using the MagMAX DNA Multi-Sample Ultra 2.0 Kit (Roche) and a KingFisher Flex automated isolation station. Libraries were prepared and sequenced according to the 16S metagenomic sequencing library preparation protocol for MiSeq, Illumina.

Genomic libraries were prepared using the Tersus PCR kit (Evrogen, Russia), primers 341F and 801R were used to amplify the V3-V4 region of the 16s rRNA gene, unique combinations of indexing primers, which were analogs of the primers from the Nextera XT Index kit v2, were used to barcode samples.

The sequencing procedure was performed on a HiSeq 2500 instrument using the HiSeq Rapid SBS Kit v2 (500) consumable kit (Illumina) according to the manufacturer's recommendations.

16s rRNA data processing

Leftover adapters were removed using Trimmomatic v0.36[15], and quality filtering of reads was performed with the filterAndTrim function from the DADA2 package[16]. Denoising, merging and chimera removal was carried out with DADA2 v1.24.0 software with the following parameters: learnErrors: nbases = 1e + 09, randomize = TRUE, MAX_CONSIST = 2, dada: pool = TRUE, mergePairs: minOverlap = 18, removeBimeraDenovo: allowOneOff = FALSE, method="consensus".

Taxonomy annotation was carried out against the SILVA v138 reference database[17].

Potential contaminants were removed with the “frequency” method using the package decontam[18] version 1.10.0. In total the 1600 samples were decontaminated (X samples from this project and Y samples from other projects with similar objects for analysis) to better identify contaminant sequences.

The resulting dataset contained 6235 ASVs, with 2744 ASVs for the target dataset (n = 265). The mean number of reads per sample was 48126. Samples with < 1,000 reads were removed. The scheme of analysis is presented in the Additional file 1: Fig. S1.

Statistical analysis

Statistical analysis of the microbiome data was conducted using R version 4.0.5. The vegan package [19] and phyloseq package [20] were employed for the analysis. To filter out low-represented ASVs (Amplicon Sequence Variants), the core_members function from the microbiome package was applied with the criteria of detection threshold set at 15 and prevalence threshold at 10%. This filtering process resulted in 313 ASVs for further analysis.

The diversity composition of the bacterial microbiome was assessed using α-diversity, specifically the Shannon index, utilizing the plot_richness function from the phyloseq package.

The Dirichlet multinomial mixtures (DMM) approach on a genus-level matrix of samples from all participants was performed to stratify the joint microbiome dataset into community types[21].

Permutational multivariate variance analysis (PERMANOVA) was performed to identify associations between taxa and host parameters. The adonis function from the vegan package was used, running PERMANOVA with 1000 permutation tests on weighted UniFrac distance.

For differential abundance analysis at the ASV level, the DESeq2 package[22] in R and Songbird utility[23] in Python were used. In DESeq2, the default Wald test with Benjamin-Hochberg p-value correction was used to assess the statistical significance of log2FoldChange. A significance cut-off of p.adjusted < 0.05 and Log2FoldChange ≥ 1.5 was applied. Songbird was also utilized to determine differential rankings of microbes between the mild and severe groups. The overlapping set of differentially represented ASVs identified by both DESeq2 and Songbird was selected for further analysis.

PICRUSt2 v2.5.1 tool was used to reconstruct the phylogenic tree [24].

The co-abundance networks of microbial ASVs were generated using the SPIEC-EASI[25] algorithm with the Meinshausen-Bühlmann method for detecting correlations from the NetCoMi package[26]. The following parameters were utilized: 100 subsamples, 25 lambda iterations, and a minimum lambda value of 0.001. From the resulting network, clusters of co-abundant ASVs were identified using the Louvain method. Eigenvector centrality and degree centrality were used for defining hubs/keystone taxa (nodes with a centrality value above the empirical 95% quantile).

Study cohort

The final group included samples from 92 patients, ranging in age from 20 to 88 years old, with a distribution of 48 females and 44 males. After admission to the hospital, each patient underwent a lung computed tomography (CT) scan to assess the degree of lung damage. The CT-based severity classification system provides an assessment of the extent of COVID-associated lung abnormalities observed on CT scans. The classification ranges from CT1 (up to 25% involvement) to CT2 (up to 50% involvement), CT3 (up to 75% involvement), and CT4 (up to 100% involvement)[27]. Among subjects 43 (43/92, 46.7%) possessed CT1 severity score, 40 (40/92, 43.5%) - CT2, and 9 (3/92, 9.7%) - CT3.

In addition to the CT score the patients' condition at the time of hospitalization was evaluated using the WHO Performance Scale (WHO-PS)[28] according to the hospital's internal regulations. This scale measures the patient's functional level in terms of their ability to engage in self-care, daily activities, and physical capabilities. The scale comprises five levels of assessment:

0: The patient is fully active and can perform all tasks as before the disease.

1: The patient cannot perform heavy work but can handle light or sedentary tasks (e.g., light housework or clerical work).

2: The patient receives outpatient treatment, can take care of themselves, but is unable to work, and spends over 50% of their waking time in an upright position.

3: The patient can only manage limited self-care, spends time in a chair or bed, and more than 50% of their waking time in this state.

4: The patient is disabled, completely reliant on others for care, and confined to an armchair or bed.

5: The patient has passed away.

Based on this classification, out of the 92 subjects, 6 (6/92, 6.52%) had a WHO-PS scale of 0, 7 (7/92, 7.6%) had a WHO-PS scale of 1, 7 (7/92, 7.6%) had a WHO-PS scale of 2, 57 (57/92, 61.9%) had a WHO-PS scale of 3, and 15 (15/92, 16.3%) had a WHO-PS scale of 4.

To facilitate further comparative analysis based on CT and WHO-PS scores, the 92 patients were categorized into two groups. The first group, labeled as "mild" (n = 49), consisted of patients with a mild disease course (CT1 + WHO-PS 0–3; CT2 + WHO-PS 0–2; CT3 + WHO-PS 0–2). The second group, labeled as "severe" (n = 43), comprised patients with a severe disease course (CT2 + WHO-PS 3–4; CT1 + WHO-PS 4; CT3 + WHO-PS 3–4) (Fig. 1A).

We analyzed the distribution of some comorbidities in the groups of patients with mild and severe COVID-19. We did not observe any statistically significant differences in the distribution of covariates between groups (Fig. 1B-H).

Taxonomy of the gut microbiome of patients infected with SARS-CoV-2

The resulting dataset consists of 313 ASVs that belong to 34 families, with an average of 36276 read count per sample. Among the microbial composition of samples collected from 92 patients with varying degrees of COVID-19 severity, the top 7 dominant families were identified as Lachnospiraceae, Ruminococcaceae, Bacteroidaceae, Streptococcaceae, Coriobacteriaceae, Akkermansiaceae, and Oscillospiraceae (Fig. 2). The taxonomic profile at the ASV level is shown in the Additional file 1: Fig. S2.

Alpha and beta diversity shows no significant differences between patients with mild and severe COVID-19

Microbial alpha diversity, assessed using the Shannon metric, was utilized to examine changes in the microbiota community structure among patients categorized into mild and severe groups. Figure 3A displays the outcomes, indicating that no statistically significant variations (Wilcoxon test) were observed in the alpha diversity indices between the two patient groups. Beta-diversity was calculated through Principal coordinates analysis (PCoA) by using the Weighted Unifrac metric and showed no statistically significant variations (Adonis test) between the two groups Fig. 3B.

Determining gut biotope community types in mild and severe COVID-19 patients

We employed the Dirichlet multinomial mixtures method to analyze a dataset comprising 134 genera at the genus level. This analysis revealed the presence of two distinct microbiota community types based on taxonomic composition (Additional file 1: Fig. S3A). Community type 1 consisted of 62 patients (mild = 31, severe = 31), while Community type 2 included 30 patients (mild = 18, severe = 12). The dissimilarity of community types 1 and 2 is shown in Fig. 4.

At the ASV level, the main drivers distinguishing Type 1 were ASV64_Blautia, ASV124_Bacteroides, ASV6_Streptococcus, ASV74_[Ruminococcus] torques group, and ASV138_Subdoligranulum. Conversely, Type 2 was characterized by ASV64_Blautia, ASV124_Bacteroides, ASV117_Faecalibacterium, ASV138_Subdoligranulum, and ASV114_Ruminococcus (Fig. 5).

Additionally, our analysis found no significant differences in the distribution of age, sex, severity group, obesity, and diabetes between the two community types (Additional file 1: Fig. S3B-I). This implies that these factors do not play a significant role in determining the division of a taxonomic composition into these clusters.

Exploring the relationship between the gut microbiome of SARS-CoV-2 infected patients and covariates

For testing the association between the microbiome and such covariates as age, sex, severity of, hypertension, diabetes, obesity, and CAD we applied PERMANOVA analysis (Fig. 6).

These covariates explained 17% of microbiome taxonomic composition with the most extensive and statistically significant contribution coming from the comparison group (4%), antibiotics (3%), diabetes (2%), and obesity(2%).

Analyzing microbial associations with disease severity in relation to age, gender, and comorbidities

In our research focused on exploring the potential impact of gut microbiome composition on the severity of COVID-19, we considered several important metadata variables, including the patient's age, sex, and comorbidities. Our objective was to identify ASVs that exhibited differential abundance patterns between patients with mild (n = 49) and more severe (n = 43) disease manifestations. To achieve this, we employed two distinct analysis methods: DESeq2 and Songbird.

During the DESeq differential abundance analysis, we observed significant differences in the abundance of 66 ASVs. Out of these, 42 ASVs were specifically associated with the mild degree group, while 24 ASVs exhibited associations with the severe degree group in the context of COVID-19 (Additional file 1: Fig. S4A). To further explore the findings, we used the Songbird tool and selected an equal number of ASVs (mild = 42, severe = 24) from each group derived from DESeq2 (Additional file 1: Fig. S4B). Then we selected ASVs associated with both the mild and severe course of COVID-19 that overlap the results of the two instruments (elected an equal number of ASVs (mild = 13, severe = 20) from each group derived from DESeq2 (Additional file 1: Fig. S5, Additional file 2: Table S1). The different ASVs selected by both tools (n = 4) that aligned to the same species were also added to the analysis (Additional file 2: Table S2).

Our analysis revealed a significant enrichment of 13 specific ASVs associated with the severe course of the disease. These ASVs originate from genera such as Enterococcus, Rothia, Akkermansia, Agathobacter, Actinomyces, Alloprevotella, Slackia, Prevotella9, [Ruminococcus] gauvreauii group, Eubacterium, Eisenbergiella, and Christensenellaceae R-7 group. Upon performing a BLASTN alignment of the ASV sequences against the nt database, we have identified the corresponding matches for each ASV as follows: ASV29 corresponds to Enterococcus hirae/Enterococcus faecium, ASV55 to Rothia mucilaginosa, ASV60 to Akkermansia muciniphila, ASV62 to [Eubacterium] rectale, ASV134 to Schaalia odontolytica, ASV822 from the Alloprevotella genus to an Uncultured bacterium, ASV938 to Slackia isoflavoniconvertens, ASV1016 to Prevotella copri, ASV1444 from the [Ruminococcus] gauvreauii group genus to an Blautia fasciola (identity = 99.5%)/Lachnospiraceae bacterium (identity = 99.7%), ASV1564 to Eubacterium limosum, ASV1629 to Enterococcus faecium, ASV1650 to Eisenbergiella tayi and ASV2021 from Christensenellaceae R-7 group as Uncultured bacterium.

We found 24 ASVs from Veillonella, Haemophilus, Alistipes, Atopobium, CAG-352 Bacteroides, Streptococcus, Romboutsia, Prevotella_9, Anaerostipes, [Eubacterium] xylanophilum group, Paraprevotella, Blautia, Turicibacter, [Eubacterium] hallii group, [Eubacterium] ventriosum group, Faecalibacterium genera that were associated with the mild group. ASV4 has been identified as Veillonella dispar/Veillonella nakazawae, ASV14 as Haemophilus parainfluenzae, ASV35 aligning with an Uncultured organism (identity = 100%)/Atopobium parvulum (identity = 99.5%), ASV50 as Ruminococcoidesnbili/Ruminococcus sp, ASV52 as Streptococcus gordonii, ASV177 Romboutsia ilealis, ASV202 as Alistipes putredinis, ASV284 as Bacteroides vulgatus, ASV277 and ASV347 both aligned with Prevotella copri, ASV545 as Lachnospiraceae bacterium sunii, ASV773 as Bacteroides stercoris, ASV1277 from the Bacteroides genus as Phocaeicola sp. (identity = 100%)/Bacteroides massiliensis (identity = 99.7%), ASV1326 as Blautia faecis, ASV177 as Anaerostipes hadrus, ASV1797 as Eubacterium sp., ASV1827 as Anaerostipes hadrus, ASV1925 from the Paraprevotella genus as Uncultured bacterium, ASV2019 as Turicibacter sanguinis, ASV2362 as Blautia sp.. Among different ASVs selected by both tools ASV1870 and ASV1360 were identified as Anaerobutyricum hallii, ASV1494 and ASV556 as Eubacterium ventriosum, ASV707 and ASV998 as Faecalibacterium prausnitzii, ASV949 and ASV1067 as Prevotella copri.

Figure 7 shows all differentially abundant ASVs associated with the COVID-19 severity course analyzed by the Deseq2 and Songbird utilities.

The gut microbiota's network structure shows variations across COVID-19 patients with differing severity levels

We utilized SPIEC-EASI to investigate bacterial interactions and evaluate potential variations in the organization of microbial communities between the mild and severe groups. This analysis focused on 313 highly represented ASVs in the dataset, aiming to gain insights into the microbial dynamics and community structure associated with different disease severity levels. The network analysis revealed distinct characteristics in the microbial communities of the mild and severe groups (Fig. 8, Additional file 2: Table S3). The network of the mild group exhibited a higher total number of components (mild = 169, severe = 131), higher clustering coefficient (mild = 0.155, severe = 0.038), higher edge density (mild = 0.097, severe = 0.025), and natural connectivity (mild = 0.051, severe = 0.013) compared to the severe group. However, the relative size of the largest connected component (LCC) (mild = 0.080, severe = 0,27), shorter average path length (mild = 2.63, severe = 4.8 ), and modularity value (mild = 0.53, severe = 0.75 ) were smaller in the mild group.

The network analysis identified four hubs/keystone taxa for the mild group ASV432_Dorea, ASV372_Blautia, ASV387_Coprococcus, ASV655_UCG005, and three hubs/keystone taxa for the severe group: ASV2222_Lachnospiraceae FCS020 group, ASV1365_Lachnospiraceae FCS020 group, ASV2294_Marvinbryantia. After conducting a BLASTN alignment of the ASV sequences against the nt database, we have matched each ASV with its respective counterpart as follows: ASV432_Dorea as Dorea formicigenerans, ASV372_Blautia as Blautia obeum, ASV387_Coprococcus as Coprococcus comes, ASV655_UCG005 as uncultured bacterium, ASV2222_Lachnospiraceae FCS020 group as Lachnospiraceae bacterium, ASV1365_Lachnospiraceae FCS020 group as Lachnospiraceae bacterium, and ASV2294_Marvinbryantia as Jingyaoa shaoxingensis/uncultured bacterium respectively (Additional file 2: Table S4).

In our study, we utilized 16S rRNA metagenomic analysis to compare the taxonomic composition of the gut microbiota in COVID-19 patients upon admission to the hospital. The clinical samples from this investigation were processed and sequenced similarly to those from other COVID-19-related projects conducted in our lab. The results of the upper respiratory samples analysis have been published[29].

The actual study focused on the analysis of fecal samples from COVID-19 patients with complete metadata, including age, sex, CT lung scans, WHO-PS, and comorbidities such as hypertension, CAD, diabetes, and obesity. By combining WHO-PS scores and CT scans, we categorized patients according to disease severity. Although this approach reduced our sample size, it allowed for a more robust statistical analysis. In the end, 92 samples from COVID-19 inpatients were included in the analysis. To our delight, the selected severe (n = 43) and mild (n = 49) groups showed no differences in the distribution of such covariates as age, sex, hypertension, CAD, diabetes, and obesity.

This paper compares individuals with milder COVID-19 cases to those with more severe cases, rather than comparing healthy individuals to COVID-19-infected subjects. The decision to concentrate on disease severity is driven by the fact that the comparison of healthy individuals and COVID-19-infected individuals has been extensively studied in the literature. Moreover, the Anna Karenina principle, which highlights that the gut microbiome of healthy individuals tends to be less variable and more stable than that of individuals with various diseases[30], also supports our choice. Additionally, considering that SARS-CoV-2 can be present in healthy individuals who may be asymptomatic carriers, using asymptomatic individuals as a control group, especially during a pandemic, may introduce bias into the analyses. Therefore, we have opted to examine the gut microbiota of patients with different degrees of disease severity caused by SARS-CoV-2.

The taxonomic composition of fecal samples from COVID-19 patients aligns with previous studies on human colonic microbiota[31, 32]. Our samples exhibited a homogeneous taxonomic profile, as evident in the heatmaps of Supplementary Fig. 3, where we observed no apparent clustering based on the considered covariates.

When examining the alpha and beta diversity of the gut microbiota in mild and severe COVID-19 patients, we found no statistically significant differences between the two groups. Our results differ from others that compared patients with varying COVID-19 severity levels[13, 33]. Various factors can contribute to these inconsistencies, such as variations in study design, sample size, patient demographics, methodologies, and geographic locations. Furthermore, there is no standardized or consistent method to categorize patients into severity groups. The lack of a uniform approach can contribute to variations in results across different studies and highlights the challenges in accurately assessing and comparing COVID-19 severity.

We observed significant differences in the abundance of specific ASVs between patients in different severity groups.

Specifically, ASVs identified as Enterococcus hirae/Enterococcus faecium, Rothia mucilaginosa, Akkermansia muciniphila, Schaalia odontolytica, Eubacterium limosum, and Slackia isoflavoniconvertens showed notable variations in abundance among patients with severe COVID-19 cases.

Akkermansia is a genus of Gram-negative anaerobic bacteria that belongs to the phylum Verrucomicrobiota. These bacteria are known for colonizing the human intestinal mucosa and have a specific ability to degrade the mucin layer. It was initially proposed by Derrien et al. in 2004, with A. muciniphila designated as the type species. This bacteria is notably one of the most abundant single species in the human gut microbiota, accounting for approximately 0.5-5% of the total bacterial population [34]. A. muciniphila degrades intestinal mucin into mainly propionic and acetic acid. In addition, the bacterium expresses Amuc_1100, one of the most abundant pili-like proteins found on its outer membrane. Experiments on mice have shown that supplementation with A. muciniphila stimulates the proliferation of intestinal stem cells and enhances the differentiation of Paneth and goblet cells in the small intestine and colon in both healthy and injured mice [35]. In addition, the presence of A. muciniphila was associated with increased levels of acetic and propionic acids in the cecal contents of treated mice, suggesting its role in promoting intestinal mucosal repair, with SCFAs playing an important role in this process [36]. The outer membrane protein Amuc_1100 has also been shown to be involved in restoring intestinal barrier function, along with acetic and propionic acids, probably by interacting with TLR2 and restoring expression of the corresponding tight junctions [37, 38].

In contrast to reports supporting the beneficial effects of A. muciniphila, several studies suggest potential negative effects on gut health. In mouse models of acute intestinal inflammation induced by Salmonella enterica Typhimurium infection and acute colitis induced by dextran sodium sulfate (DSS), A. muciniphila exacerbated the inflammatory response by its ability to disturb host mucus homeostasis. Simultaneously, the study on DSS-induced colitis observed that the administration of extracellular vesicles derived from A. muciniphila decreased the severity of DSS-induced colitis[39]. However, a later study did not confirm these findings, highlighting the need for further research to understand the complex interactions of A. muciniphila in gut health[40].

Our study illustrated a higher representation of A. muciniphila in the group of severe COVID-19 patients. This finding is consistent with numerous studies that have shown a significant association between a higher prevalence of the genus Akkermansia and SARS-CoV-2 infected subjects compared to healthy individuals[9, 10, 41]. Notably, researchers reported a higher abundance of A. muciniphila in the severe group of COVID-19 patients than in the mild group and healthy individuals[33]. An overrepresentation of Akkermansiaceae was detected in SARS-CoV-2 infected patients and in K18-hACE2 mice[9]. The study also observed a significant increase in mucus-producing goblet cells and a decrease in Paneth cells specifically in the ileum of infected mice, with no such changes observed in the duodenum. The reduced Paneth cell population showed structural abnormalities, including deformed or misplaced granules, and downregulation of several antimicrobial factors such as lysozyme, defensins, Reg3γ, and serum amyloid A in the ileum. In addition, the researchers identified a striking positive correlation between the percentage of abnormal Paneth cells and the abundance of the Akkermansiaceae family in the mice model[9]. Due to the controversial perspectives on the role of Akkermansia in human gut homeostasis, we cannot definitively conclude whether A. muciniphila has remodeling functions or whether it exacerbates the inflammatory response in SARS-CoV-2 infected patients. Further research and investigation are required to understand its potential effects in these contexts better.

Enterococcus faecium is a Gram-positive bacterium commonly found as part of the commensal flora in the human gastrointestinal tract. Despite its commensal status, it exhibits opportunistic pathogenic behavior, posing a risk to individuals with compromised immune systems or underlying health conditions. One concerning characteristic of E. faecium is its ability to develop resistance to many antibiotics, including those commonly used in clinical practice. The bacterium's virulence strategy involves colonization and the secretion of various factors such as Esp, AS, cytolysin, and gelatinase[42]. Notably, it employs specific enzymes that facilitate adherence to host tissues and inhibit the growth of competing bacteria, enhancing its ability to persist and cause infection. In addition, clinical studies have shown a remarkable relationship between the relative abundance of Enterococcus and certain disease parameters in SARS-CoV-2-infected patients[43]. Increased levels of Enterococcus have been associated with prolonged hospitalization, prolonged stays in intensive care units, increased oxygen requirements, and elevated levels of D-dimer, ferritin, and IL-6 in the bloodstream [44]. Taking into account the severity of the patient's medical condition and the prior use of antibiotics before hospital admission, we attribute the higher prevalence of E. faecium in the sampled patients with severe conditions.

Eubacterium limosum is a producer of short-chain fatty acids, including butyrate. Increased butyrate production by this bacterium has been observed in diseases such as ulcerative colitis and experimental colitis[45]. The increase in E. limosum levels observed in both COVID-19 and colitis-associated gut microbiota could potentially be interpreted as an adaptive mechanism to enhance butyrate synthesis in response to a simultaneous decrease in the overall population of traditional butyrate-producing bacteria, such as Fecalibacterium and Blautia.

In patients from the mild group, we observed an enrichment of ASVs identified as Faecalibacterium prausnitzii, Ruminococcoides bili, Turicibacter sanguinis, Alistipes putredinis, Bacteroides vulgatus, Bacteroides stercoris, Lachnospiraceae bacterium sunii NSJ-8, Blautia faecis, and Anaerostipes hadrus.

Across various studies, it has been consistently observed that genera such as Faecalibacterium, Blautia, Alistipes, Lachnospiraceae, and Bacteroides tend to be reduced in SARS-CoV-2-infected patients and are more strongly associated with a healthy microbiome state [2, 11, 46].

In contrast to the severe group, the microbiome of mild patients exhibited an overrepresentation of flora that produces short-chain fatty acids (SCFAs), including Anaerostipes hadrus, Faecalibacterium prausnitzii, Lachnospiraceae bacterium sunii NSJ-8, and Blautia faecis[47]. SCFAs group encompasses butyrate, acetate, and propionate, these short-chain fatty acids are acknowledged as advantageous by-products of bacterial activity that contribute to the well-being of the host. The significant role played by butyrate in constraining the expansion of opportunistic pathogens, preserving the integrity of the intestinal mucosal barrier, triggering the adaptive immune response, and reinforcing the body's defenses against viruses is notably evident[48].

Positive association of B. vulgatus, as well as B. stercoris with gut flora of SARS-CoV-2 infected patients, were detected by several papers[49–51]. The research papers highlight the potential of specific B. vulgatus strains to positively impact the immune response, gut barrier integrity, and inflammatory processes, particularly in the context of ulcerative colitis in mice. These effects might be attributed to their ability to modulate cytokine expression, interact with colonic tissue, and influence immune cell populations[52, 53]. Also, B. vulgatus produces multiple proteases, showing higher activity than other Bacteroides species. These proteases, either individually or in combination, have the potential to disturb the colonic epithelium. This disruption can result in the migration of innate immune cells, notably neutrophils, into the affected area, intensifying the inflammation associated with colitis. However, it's crucial to note that this effect was primarily observed in co-culture scenarios, as the supernatant from B. vulgatus alone did not display any adverse impact on the integrity of the colonic epithelial barrier and did not cause disruption of the membrane integrity[54, 55]. According to recent research[56] B. stercoris and B. vulgatus, in conjunction with Prevotella copri (which is linked to both mild and severe cases in our study), function as markers of the microbiota's resilience to structural alterations. Interestingly, species belonging to the Bacteroidetes genus have been linked to the inhibition of colonic ACE2 expression, a host cell entry point for SARS-CoV-2, as demonstrated in a mouse model[57].

Through this study, we discovered an increased presence of two bile-resistant species, Turicibacter sanguinis, and Ruminococcoides bili, in the microbiome of individuals with mild COVID-19 disease course.

The R. bili strains demonstrate significant resistance to bile salts, potentially facilitated by various efflux transporters that could be involved in bile export. It is also capable of metabolizing resistant starches, resulting in the synthesis of formate, lactate, and acetate. This metabolic process contributes to the well-being of other bacteria and generates beneficial short-chain fatty acids (SCFAs) for the host[58]. Turicibacter sanguinis strains in the gut microbiota impact host bile and lipid compositions in a strain-specific manner. These strains possess bile salt hydrolases that influence distinct bile deconjugation patterns. Introducing Turicibacter strains led to changes in host bile acid profiles, similar to in vitro results. Mice colonized with another bacterium expressing genes from these strains exhibited reduced serum cholesterol, triglycerides, and adipose tissue mass[59].

Interestingly, we detected a higher prevalence of bacteria commonly found in the human respiratory tract. Severe patients exhibited higher levels of Schaalia odontolytica and Rothia mucilaginosa, while Streptococcus gordonii, Haemophilus parainfluenzae, and Veillonella dispar/Veillonella nakazawae were found to be more abundant in mild patients. The SARS-CoV-2 virus induces oral dysbiosis, with an increase in oral pathobionts, and intestinal dysbiosis, weakening the barrier to ingested microorganisms. These conditions are critical for successful colonization of the gut by oral pathobionts, which in turn exacerbates intestinal inflammation[60]. Among these oral pathobionts, R. mucilaginosa emerges as a potentially important factor in COVID-19. It should be considered in the diagnosis of pneumonia, regardless of the immune status of the host, because of its significant correlations with the disease[61]. Among the various bacterial taxa associated with SARS-CoV-2 infection, R. mucilaginosa stands out, with increased abundance observed in both the oral and gut microbiomes[62]^,[63].

Similarly increased bacterial accumulation in the gut is observed in patients with gastric achlorhydria and gastroesophageal reflux disease due to long-term proton pump inhibitors (PPI) therapy[64]. However, detailed information regarding PPI therapy is not available in our cohort.

The network analysis highlighted differences in the microbiome structure between the mild and severe groups. In the mild group, there were more microbial interactions, a higher clustering coefficient, and increased edge density, indicating a densely interconnected microbial network with efficient information transfer. In contrast, the severe group had fewer interactions, lower clustering coefficient, and edge density, suggesting a more densely connected microbial community. These results are similar to network characteristics of respiratory microbiota from COVID-19 patients with different levels of severity[65]. These network characteristics provide insights into the organization and dynamics of microbial communities in the context of disease severity. The differences observed between the mild and severe groups suggest distinct microbial network patterns associated with different disease severities.

Besides, network analysis has been used to identify keystone taxa (hubs) within the microbiomes of COVID-19 patients. These keystone taxa are specific microorganisms that have been found to have a significant impact on the overall structure and composition of the microbial community within COVID-19 patients.

In cases of mild course of infection, three taxa, such as Dorea formicigenerans, Blautia obeum, and Coprococcus comes, came to the forefront as keystone species. D. formicigenerans belongs to the Lachnospiraceae family and is known for its prolific production of formic acid. Research has demonstrated that the administration of formic acid to pigs leads to elevated levels of beneficial microorganisms while concurrently inhibiting the growth of pathogenic members of the Enterobacteriaceae family[66]. Surprisingly, Blautia obeum and Coprococcus comes were associated with mild/moderate COVID-19 disease course and positively correlated with lymphoid-related markers, suggesting a possible interaction between these gut microbes and the regulation of lymphocytes[33].

In the severe group, two ASVs that belong to Lachnospiraceae bacterium and Jingyaoa shaoxingensis should be considered as taxa hubs. The role of Lachnospiraceae bacterium as a keystone species in the microbiome of severe patients may indicate an adaptive response to the reduction in the overall population of conventional butyrate-producing bacteria, similar to E. limosum.

In summary, our findings suggest that the SARS-CoV-2 virus has distinct effects on the composition of the gut microbial community in both mild and severe groups of patients, indicating differential microbial responses to the infection.

Ethics approval and consent to participate

All patients signed an informed consent form to participate in the study and the study was approved by the Independent Interdisciplinary Committee for Ethical Review of Clinical Trials, protocol 01-21 of 28.01.2021.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by State Assignments No. 122040100011-0 and No. 122030900064-9

Data availability statement

Sequencing reads for 16s rRNA gene amplicons were deposited to NCBI BioProject under project name PRJNA989180.

Authors' contributions

YGS analyzed the 16S rRNA gene amplicon datasets, interpreted results, and wrote the manuscript. EVS, AIM, and DEF analyzed the 16S rRNA gene amplicon datasets and interpreted the results. OVS, PYZ, KMK, VAV, MDM, and DIB performed DNA extraction and library preparation for all samples and sequencing of the 16S rRNA gene amplicon samples. OOY, NIK, OVL, DNA, FSS, AKF, MKD, NGA, AVZ, PAB, and VVE collected samples and metadata. VMG contributed resources. IVM and AVP supervised the study. ENI designed, supervised the study, and critically reviewed the manuscript. All authors were given the opportunity to read and contribute to the text and approve the final manuscript.

Acknowledgments

The major part of this work was performed using the core facilities of the Lopukhin FRCC PCM “Genomics, proteomics, metabolomics” (http://rcpcm.org/?p=2806)

Considerations for implementing and adjusting public health and social measures in the context of COVID-19 [Internet]. World Health Organization; 2023 [cited 2023 Aug 25]. Available from: https://www.who.int/publications/i/item/who-2019-ncov-adjusting-ph-measures-2023.1
Zhang F, Wan Y, Zuo T, Yeoh YK, Liu Q, Zhang L, et al. Prolonged Impairment of Short-Chain Fatty Acid and L-Isoleucine Biosynthesis in Gut Microbiome in Patients With COVID-19. Gastroenterology [Internet]. 2022 Feb;162(2):548–61.e4. Available from: http://dx.doi.org/10.1053/j.gastro.2021.10.013
Liu Q, Su Q, Zhang F, Tun HM, Mak JWY, Lui GCY, et al. Multi-kingdom gut microbiota analyses define COVID-19 severity and post-acute COVID-19 syndrome. Nat Commun [Internet]. 2022 Nov 10;13(1):6806. Available from: http://dx.doi.org/10.1038/s41467-022-34535-8
Liu Q, Mak JWY, Su Q, Yeoh YK, Lui GCY, Ng SSS, et al. Gut microbiota dynamics in a prospective cohort of patients with post-acute COVID-19 syndrome. Gut [Internet]. 2022 Mar;71(3):544–52. Available from: http://dx.doi.org/10.1136/gutjnl-2021-325989
Xu E, Xie Y, Al-Aly Z. Long-term gastrointestinal outcomes of COVID-19. Nat Commun [Internet]. 2023 Mar 7;14(1):983. Available from: http://dx.doi.org/10.1038/s41467-023-36223-7
Gu J, Gong E, Zhang B, Zheng J, Gao Z, Zhong Y, et al. Multiple organ infection and the pathogenesis of SARS. J Exp Med [Internet]. 2005 Aug 1;202(3):415–24. Available from: http://dx.doi.org/10.1084/jem.20050828
Wan Y, Shang J, Graham R, Baric RS, Li F. Receptor Recognition by the Novel Coronavirus from Wuhan: an Analysis Based on Decade-Long Structural Studies of SARS Coronavirus. J Virol [Internet]. 2020 Mar 17;94(7). Available from: http://dx.doi.org/10.1128/JVI.00127-20
Raya-Sandino A, Luissint AC, Kusters DHM, Narayanan V, Flemming S, Garcia-Hernandez V, et al. Regulation of intestinal epithelial intercellular adhesion and barrier function by desmosomal cadherin desmocollin-2. Mol Biol Cell [Internet]. 2021 Apr 15;32(8):753–68. Available from: http://dx.doi.org/10.1091/mbc.E20-12-0775
Bernard-Raichon L, Venzon M, Klein J, Axelrad JE, Zhang C, Sullivan AP, et al. Gut microbiome dysbiosis in antibiotic-treated COVID-19 patients is associated with microbial translocation and bacteremia. Nat Commun [Internet]. 2022 Nov 1;13(1):5926. Available from: http://dx.doi.org/10.1038/s41467-022-33395-6
Mańkowska-Wierzbicka D, Zuraszek J, Wierzbicka A, Gabryel M, Mahadea D, Baturo A, et al. Alterations in Gut Microbiota Composition in Patients with COVID-19: A Pilot Study of Whole Hypervariable 16S rRNA Gene Sequencing. Biomedicines [Internet]. 2023 Jan 27;11(2). Available from: http://dx.doi.org/10.3390/biomedicines11020367
Maeda Y, Motooka D, Kawasaki T, Oki H, Noda Y, Adachi Y, et al. Longitudinal alterations of the gut mycobiota and microbiota on COVID-19 severity. BMC Infect Dis [Internet]. 2022 Jun 24;22(1):572. Available from: http://dx.doi.org/10.1186/s12879-022-07358-7
Moreira-Rosário A, Marques C, Pinheiro H, Araújo JR, Ribeiro P, Rocha R, et al. Gut Microbiota Diversity and C-Reactive Protein Are Predictors of Disease Severity in COVID-19 Patients. Front Microbiol [Internet]. 2021 Jul 19;12:705020. Available from: http://dx.doi.org/10.3389/fmicb.2021.705020
Schult D, Reitmeier S, Koyumdzhieva P, Lahmer T, Middelhoff M, Erber J, et al. Gut bacterial dysbiosis and instability is associated with the onset of complications and mortality in COVID-19. Gut Microbes [Internet]. 2022 Jan-Dec;14(1):2031840. Available from: http://dx.doi.org/10.1080/19490976.2022.2031840
Gu S, Chen Y, Wu Z, Chen Y, Gao H, Lv L, et al. Alterations of the gut microbiota in patients with COVID-19 or H1N1 influenza. Clin Infect Dis [Internet]. 2020;71(10):2669–78. Available from: https://pdfs.semanticscholar.org/be47/a5668d6c9db9bbdc4daa2ae43862264a36f9.pdf
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics [Internet]. 2014 Aug 1;30(15):2114–20. Available from: http://dx.doi.org/10.1093/bioinformatics/btu170
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods [Internet]. 2016 Jul;13(7):581–3. Available from: http://dx.doi.org/10.1038/nmeth.3869
Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res [Internet]. 2007 Oct 18;35(21):7188–96. Available from: http://dx.doi.org/10.1093/nar/gkm864
Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome [Internet]. 2018 Dec 17;6(1):226. Available from: http://dx.doi.org/10.1186/s40168-018-0605-2
Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci [Internet]. 2003 Dec;14(6):927–30. Available from: https://onlinelibrary.wiley.com/doi/10.1111/j.1654-1103.2003.tb02228.x
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One [Internet]. 2013 Apr 22;8(4):e61217. Available from: http://dx.doi.org/10.1371/journal.pone.0061217
Holmes I, Harris K, Quince C. Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS One [Internet]. 2012 Feb 3;7(2):e30126. Available from: http://dx.doi.org/10.1371/journal.pone.0030126
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol [Internet]. 2014;15(12):550. Available from: http://dx.doi.org/10.1186/s13059-014-0550-8
Morton JT, Marotz C, Washburne A, Silverman J, Zaramela LS, Edlund A, et al. Establishing microbial composition measurement standards with reference frames. Nat Commun [Internet]. 2019 Jun 20;10(1):2719. Available from: http://dx.doi.org/10.1038/s41467-019-10656-5
Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol [Internet]. 2020 Jun;38(6):685–8. Available from: http://dx.doi.org/10.1038/s41587-020-0548-6
Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol [Internet]. 2015 May;11(5):e1004226. Available from: http://dx.doi.org/10.1371/journal.pcbi.1004226
Peschel S, Müller CL, von Mutius E, Boulesteix AL, Depner M. NetCoMi: network construction and comparison for microbiome data in R. Brief Bioinform [Internet]. 2021 Jul 20;22(4). Available from: http://dx.doi.org/10.1093/bib/bbaa290
Morozov SP, Andreychenko AE, Blokhin IA, Gelezhe PB, Gonchar AP, Nikolaev AE, et al. MosMedData: data set of 1110 chest CT scans performed during the COVID-19 epidemic. Digital Diagnostics [Internet]. 2020 Dec 30;1(1):49–59. Available from: https://journals.eco-vector.com/DD/article/view/46826
Oken MM, Creech RH, Tormey DC, Horton J, Davis TE, McFadden ET, et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol [Internet]. 1982 Dec;5(6):649–55. Available from: https://www.ncbi.nlm.nih.gov/pubmed/7165009
Galeeva JS, Starikova EV, Fedorov DE, Manolov AI, Pavlenko AV, Konanov DN, et al. Microbial communities of the upper respiratory tract in mild and severe COVID-19 patients: a possible link with the disease course. Frontiers in Microbiomes [Internet]. 2023;2. Available from: https://www.frontiersin.org/articles/10.3389/frmbi.2023.1067019
Ma ZS. Testing the Anna Karenina Principle in Human Microbiome-Associated Diseases. iScience [Internet]. 2020 Apr 24;23(4):101007. Available from: http://dx.doi.org/10.1016/j.isci.2020.101007
Ochoa-Reparaz J, Mangalam AK. The Role of the Gut Microbiota in Health and Inflammatory Diseases [Internet]. Frontiers Media SA; 2020. 176 p. Available from: https://play.google.com/store/books/details?id=9qAMEAAAQBAJ
Haller D. The Gut Microbiome in Health and Disease [Internet]. Springer; 2018. 356 p. Available from: https://play.google.com/store/books/details?id=c7hmDwAAQBAJ
Xu X, Zhang W, Guo M, Xiao C, Fu Z, Yu S, et al. Integrated analysis of gut microbiome and host immune responses in COVID-19. Front Med [Internet]. 2022 Apr;16(2):263–75. Available from: http://dx.doi.org/10.1007/s11684-022-0921-6
Derrien M, Vaughan EE, Plugge CM, de Vos WM. Akkermansia muciniphila gen. nov., sp. nov., a human intestinal mucin-degrading bacterium. Int J Syst Evol Microbiol [Internet]. 2004 Sep;54(Pt 5):1469–76. Available from: http://dx.doi.org/10.1099/ijs.0.02873-0
Kim S, Shin YC, Kim TY, Kim Y, Lee YS, Lee SH, et al. Mucin degrader Akkermansia muciniphila accelerates intestinal stem cell-mediated epithelial development. Gut Microbes [Internet]. 2021 Jan-Dec;13(1):1–20. Available from: http://dx.doi.org/10.1080/19490976.2021.1892441
Macchione IG, Lopetuso LR, Ianiro G, Napoli M, Gibiino G, Rizzatti G, et al. Akkermansia muciniphila: key player in metabolic and gastrointestinal disorders. Eur Rev Med Pharmacol Sci [Internet]. 2019 Sep;23(18):8075–83. Available from: http://dx.doi.org/10.26355/eurrev_201909_19024
Bian X, Wu W, Yang L, Lv L, Wang Q, Li Y, et al. Administration of Akkermansia muciniphila Ameliorates Dextran Sulfate Sodium-Induced Ulcerative Colitis in Mice. Front Microbiol [Internet]. 2019 Oct 1;10:2259. Available from: http://dx.doi.org/10.3389/fmicb.2019.02259
de Vos WM. Microbe Profile: Akkermansia muciniphila: a conserved intestinal symbiont that acts as the gatekeeper of our mucosa. Microbiology [Internet]. 2017 May;163(5):646–8. Available from: http://dx.doi.org/10.1099/mic.0.000444
Ganesh BP, Klopfleisch R, Loh G, Blaut M. Commensal Akkermansia muciniphila exacerbates gut inflammation in Salmonella Typhimurium-infected gnotobiotic mice. PLoS One [Internet]. 2013 Sep 10;8(9):e74963. Available from: http://dx.doi.org/10.1371/journal.pone.0074963
Ring C, Klopfleisch R, Dahlke K, Basic M, Bleich A, Blaut M. Akkermansia muciniphila strain ATCC BAA-835 does not promote short-term intestinal inflammation in gnotobiotic interleukin-10-deficient mice. Gut Microbes [Internet]. 2019;10(2):188–203. Available from: http://dx.doi.org/10.1080/19490976.2018.1511663
Gaibani P, D’Amico F, Bartoletti M, Lombardo D, Rampelli S, Fornaro G, et al. The Gut Microbiota of Critically Ill Patients With COVID-19. Front Cell Infect Microbiol [Internet]. 2021 Jun 29;11:670424. Available from: http://dx.doi.org/10.3389/fcimb.2021.670424
Șchiopu P, Toc DA, Colosi IA, Costache C, Ruospo G, Berar G, et al. An Overview of the Factors Involved in Biofilm Production by the Enterococcus Genus. Int J Mol Sci [Internet]. 2023 Jul 18;24(14). Available from: http://dx.doi.org/10.3390/ijms241411577
Sulayyim HJA, Ismail R, Hamid AA, Ghafar NA. Antibiotic Resistance during COVID-19: A Systematic Review. Int J Environ Res Public Health [Internet]. 2022 Sep 21;19(19). Available from: http://dx.doi.org/10.3390/ijerph191911931
Righi E, Lambertenghi L, Gorska A, Sciammarella C, Ivaldi F, Mirandola M, et al. Impact of COVID-19 and Antibiotic Treatments on Gut Microbiome: A Role for Enterococcus spp. Biomedicines [Internet]. 2022 Nov 2;10(11). Available from: http://dx.doi.org/10.3390/biomedicines10112786
Ragsdale SW. Not a “they” but a “we”: The microbiome helps promote our well-being. J Biol Chem [Internet]. 2022 Feb 1 [cited 2023 Aug 21];298(2). Available from: https://www.jbc.org/article/S0021-9258(21)01321-1/abstract
Zuo T, Zhang F, Lui GCY, Yeoh YK, Li AYL, Zhan H, et al. Alterations in Gut Microbiota of Patients With COVID-19 During Time of Hospitalization. Gastroenterology [Internet]. 2020 Sep;159(3):944–55.e8. Available from: http://dx.doi.org/10.1053/j.gastro.2020.05.048
Louis P, Flint HJ. Diversity, metabolism and microbial ecology of butyrate-producing bacteria from the human large intestine. FEMS Microbiol Lett [Internet]. 2009 May;294(1):1–8. Available from: http://dx.doi.org/10.1111/j.1574-6968.2009.01514.x
Parada Venegas D, De la Fuente MK, Landskron G, González MJ, Quera R, Dijkstra G, et al. Short Chain Fatty Acids (SCFAs)-Mediated Gut Epithelial and Immune Regulation and Its Relevance for Inflammatory Bowel Diseases. Front Immunol [Internet]. 2019 Mar 11;10:277. Available from: http://dx.doi.org/10.3389/fimmu.2019.00277
Yeoh YK, Zuo T, Lui GCY, Zhang F, Liu Q, Li AY, et al. Gut microbiota composition reflects disease severity and dysfunctional immune responses in patients with COVID-19. Gut [Internet]. 2021 Apr;70(4):698–706. Available from: http://dx.doi.org/10.1136/gutjnl-2020-323020
Li S, Yang S, Zhou Y, Disoma C, Dong Z, Du A, et al. Microbiome Profiling Using Shotgun Metagenomic Sequencing Identified Unique Microorganisms in COVID-19 Patients With Altered Gut Microbiota. Front Microbiol [Internet]. 2021 Oct 11;12:712081. Available from: http://dx.doi.org/10.3389/fmicb.2021.712081
Zuo T, Liu Q, Zhang F, Lui GCY, Tso EY, Yeoh YK, et al. Depicting SARS-CoV-2 faecal viral activity in association with gut microbiota composition in patients with COVID-19. Gut [Internet]. 2021 Feb;70(2):276–84. Available from: http://dx.doi.org/10.1136/gutjnl-2020-322294
Wang C, Xiao Y, Yu L, Tian F, Zhao J, Zhang H, et al. Protective effects of different Bacteroides vulgatus strains against lipopolysaccharide-induced acute intestinal injury, and their underlying functional genes. J Advert Res [Internet]. 2022 Feb;36:27–37. Available from: http://dx.doi.org/10.1016/j.jare.2021.06.012
Li S, Wang C, Zhang C, Luo Y, Cheng Q, Yu L, et al. Evaluation of the Effects of Different Bacteroides vulgatus Strains against DSS-Induced Colitis. J Immunol Res [Internet]. 2021 May 29;2021:9117805. Available from: http://dx.doi.org/10.1155/2021/9117805
Mills RH, Dulai PS, Vázquez-Baeza Y, Sauceda C, Daniel N, Gerner RR, et al. Multi-omics analyses of the ulcerative colitis gut microbiome link Bacteroides vulgatus proteases with disease severity. Nat Microbiol [Internet]. 2022 Feb;7(2):262–76. Available from: http://dx.doi.org/10.1038/s41564-021-01050-3
Riepe SP, Goldstein J, Alpers DH. Effect of secreted Bacteroides proteases on human intestinal brush border hydrolases. J Clin Invest [Internet]. 1980 Aug;66(2):314–22. Available from: http://dx.doi.org/10.1172/JCI109859
Chen J, Siliceo SL, Ni Y, Nielsen HB, Xu A, Panagiotou G. Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions. Microbiome [Internet]. 2023 Aug 8;11(1):178. Available from: http://dx.doi.org/10.1186/s40168-023-01604-z
Geva-Zatorsky N, Sefik E, Kua L, Pasman L, Tan TG, Ortiz-Lopez A, et al. Mining the Human Gut Microbiota for Immunomodulatory Organisms. Cell [Internet]. 2017 Feb 23;168(5):928–43.e11. Available from: http://dx.doi.org/10.1016/j.cell.2017.01.022
Molinero N, Conti E, Walker AW, Margolles A, Duncan SH, Delgado S. Survival Strategies and Metabolic Interactions between Ruminococcus gauvreauii and Ruminococcoides bili, Isolated from Human Bile. Microbiol Spectr [Internet]. 2022 Aug 31;10(4):e0277621. Available from: http://dx.doi.org/10.1128/spectrum.02776-21
Lynch JB, Gonzalez EL, Choy K, Faull KF, Jewell T, Arellano A, et al. Gut microbiota Turicibacter strains differentially modify bile acids and host lipids. Nat Commun [Internet]. 2023 Jun 20;14(1):3669. Available from: http://dx.doi.org/10.1038/s41467-023-39403-7
De R, Dutta S. Role of the Microbiome in the Pathogenesis of COVID-19. Front Cell Infect Microbiol [Internet]. 2022;12. Available from: https://www.frontiersin.org/articles/10.3389/fcimb.2022.736397
Maraki S, Papadakis IS. Rothia mucilaginosa pneumonia: a literature review. Infect Dis [Internet]. 2015 Mar;47(3):125–9. Available from: http://dx.doi.org/10.3109/00365548.2014.980843
Maddah R, Goodarzi V, Asadi-Yousefabad SL, Abbasluo M, Shariati P, Shafiei Kafraj A. Evaluation of the gut microbiome associated with COVID-19. Inform Med Unlocked [Internet]. 2023 Apr 3;38:101239. Available from: http://dx.doi.org/10.1016/j.imu.2023.101239
Wu Y, Cheng X, Jiang G, Tang H, Ming S, Tang L, et al. Altered oral and gut microbiota and its association with SARS-CoV-2 viral load in COVID-19 patients during hospitalization. NPJ Biofilms Microbiomes [Internet]. 2021 Jul 22;7(1):61. Available from: http://dx.doi.org/10.1038/s41522-021-00232-5
Kitamoto S, Kamada N. Untangling the oral-gut axis in the pathogenesis of intestinal inflammation. Int Immunol [Internet]. 2022 Sep 6;34(9):485–90. Available from: http://dx.doi.org/10.1093/intimm/dxac027
Hernández-Terán A, Mejía-Nepomuceno F, Herrera MT, Barreto O, García E, Castillejos M, et al. Dysbiosis and structural disruption of the respiratory microbiota in COVID-19 patients with severe and fatal outcomes. Sci Rep [Internet]. 2021 Oct 29;11(1):21297. Available from: http://dx.doi.org/10.1038/s41598-021-00851-0
Ren C, Wang Y, Lin X, Song H, Zhou Q, Xu W, et al. A Combination of Formic Acid and Monolaurin Attenuates Enterotoxigenic Escherichia coli Induced Intestinal Inflammation in Piglets by Inhibiting the NF-κB/MAPK Pathways with Modulation of Gut Microbiota. J Agric Food Chem [Internet]. 2020 Apr 8;68(14):4155–65. Available from: http://dx.doi.org/10.1021/acs.jafc.0c01414

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Microbial Signatures in COVID-19: Distinguishing Mild and Severe Disease via Gut Microbiota

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Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Materials and methods

Samples and data collection

16S rRNA Sequencing

16s rRNA data processing

Statistical analysis

Results

Study cohort

Taxonomy of the gut microbiome of patients infected with SARS-CoV-2

Alpha and beta diversity shows no significant differences between patients with mild and severe COVID-19

Determining gut biotope community types in mild and severe COVID-19 patients

Exploring the relationship between the gut microbiome of SARS-CoV-2 infected patients and covariates

Analyzing microbial associations with disease severity in relation to age, gender, and comorbidities

The gut microbiota's network structure shows variations across COVID-19 patients with differing severity levels

Discussion

Declarations

References

Additional Declarations

Supplementary Files

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