We investigate interrelationships between gut microbes, metabolites, and cytokines that characterize COVID-19 and its complications, and we validate the results with follow-up, the Japanese 4D ...(Disease, Drug, Diet, Daily Life) microbiome cohort, and non-Japanese data sets.
We performed shotgun metagenomic sequencing and metabolomics on stools and cytokine measurements on plasma from 112 hospitalized patients with SARS-CoV-2 infection and 112 non–COVID-19 control individuals matched by important confounders.
Multiple correlations were found between COVID-19–related microbes (eg, oral microbes and short-chain fatty acid producers) and gut metabolites (eg, branched-chain and aromatic amino acids, short-chain fatty acids, carbohydrates, neurotransmitters, and vitamin B6). Both were also linked to inflammatory cytokine dynamics (eg, interferon γ, interferon λ3, interleukin 6, CXCL-9, and CXCL-10). Such interrelationships were detected highly in severe disease and pneumonia; moderately in the high D-dimer level, kidney dysfunction, and liver dysfunction groups; but rarely in the diarrhea group. We confirmed concordances of altered metabolites (eg, branched-chain amino acids, spermidine, putrescine, and vitamin B6) in COVID-19 with their corresponding microbial functional genes. Results in microbial and metabolomic alterations with severe disease from the cross-sectional data set were partly concordant with those from the follow-up data set. Microbial signatures for COVID-19 were distinct from diabetes, inflammatory bowel disease, and proton-pump inhibitors but overlapping for rheumatoid arthritis. Random forest classifier models using microbiomes can highly predict COVID-19 and severe disease. The microbial signatures for COVID-19 showed moderate concordance between Hong Kong and Japan.
Multiomics analysis revealed multiple gut microbe-metabolite-cytokine interrelationships in COVID-19 and COVID-19related complications but few in gastrointestinal complications, suggesting microbiota-mediated immune responses distinct between the organ sites. Our results underscore the existence of a gut-lung axis in COVID-19.
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Medication is a major determinant of human gut microbiome structure, and its overuse increases the risks of morbidity and mortality. However, effects of certain commonly prescribed drugs and multiple ...medications on the gut microbiome are still underinvestigated.
We performed shotgun metagenomic analysis of fecal samples from 4198 individuals in the Japanese 4D (Disease, Drug, Diet, Daily life) microbiome project. A total of 759 drugs were profiled, and other metadata, such as anthropometrics, lifestyles, diets, physical activities, and diseases, were prospectively collected. Second fecal samples were collected from 243 individuals to assess the effects of drug initiation and discontinuation on the microbiome.
We found that numerous drugs across different treatment categories influence the microbiome; more than 70% of the drugs we profiled had not been examined before. Individuals exposed to multiple drugs, polypharmacy, showed distinct gut microbiome structures harboring significantly more abundant upper gastrointestinal species and several nosocomial pathobionts due to additive drug effects. Polypharmacy was also associated with microbial functions, including the reduction of short-chain fatty acid metabolism and increased bacterial stress responses. Even nonantibiotic drugs were significantly correlated with an increased antimicrobial resistance potential through polypharmacy. Notably, a 2-time points dataset revealed the alteration and recovery of the microbiome in response to drug initiation and cessation, corroborating the observed drug-microbe associations in the cross-sectional cohort.
Our large-scale metagenomics unravels extensive and disruptive impacts of individual and multiple drug exposures on the human gut microbiome, providing a drug-microbe catalog as a basis for a deeper understanding of the role of the microbiome in drug efficacy and toxicity.
To identify gut and oral metagenomic signatures that accurately predict pancreatic ductal carcinoma (PDAC) and to validate these signatures in independent cohorts.
We conducted a multinational study ...and performed shotgun metagenomic analysis of fecal and salivary samples collected from patients with treatment-naïve PDAC and non-PDAC controls in Japan, Spain, and Germany. Taxonomic and functional profiles of the microbiomes were characterized, and metagenomic classifiers to predict PDAC were constructed and validated in external datasets.
Comparative metagenomics revealed dysbiosis of both the gut and oral microbiomes and identified 30 gut and 18 oral species significantly associated with PDAC in the Japanese cohort. These microbial signatures achieved high area under the curve values of 0.78 to 0.82. The prediction model trained on the Japanese gut microbiome also had high predictive ability in Spanish and German cohorts, with respective area under the curve values of 0.74 and 0.83, validating its high confidence and versatility for PDAC prediction. Significant enrichments of Streptococcus and Veillonella spp and a depletion of Faecalibacterium prausnitzii were common gut signatures for PDAC in all the 3 cohorts. Prospective follow-up data revealed that patients with certain gut and oral microbial species were at higher risk of PDAC-related mortality. Finally, 58 bacteriophages that could infect microbial species consistently enriched in patients with PDAC across the 3 countries were identified.
Metagenomics targeting the gut and oral microbiomes can provide a powerful source of biomarkers for identifying individuals with PDAC and their prognoses. The identification of shared gut microbial signatures for PDAC in Asian and European cohorts indicates the presence of robust and global gut microbial biomarkers.
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This study analyzed the gut and oral microbiomes of patients with pancreatic cancer and showed the potential of the microbial species to be used as a biomarker for the disease and its prognosis.
Abstract
Indigenous bacteriophage communities (virome) in the human gut have a huge impact on the structure and function of gut bacterial communities (bacteriome), but virome variation at a ...population scale is not fully investigated yet. Here, we analyse the gut dsDNA virome in the Japanese 4D cohort of 4198 deeply phenotyped individuals. By assembling metagenomic reads, we discover thousands of high-quality phage genomes including previously uncharacterised phage clades with different bacterial hosts than known major ones. The distribution of host bacteria is a strong determinant for the distribution of phages in the gut, and virome diversity is highly correlated with anti-viral defence mechanisms of the bacteriome, such as CRISPR-Cas and restriction-modification systems. We identify 97 various intrinsic/extrinsic factors that significantly affect the virome structure, including age, sex, lifestyle, and diet, most of which showed consistent associations with both phages and their predicted bacterial hosts. Among the metadata categories, disease and medication have the strongest effects on the virome structure. Overall, these results present a basis to understand the symbiotic communities of bacteria and their viruses in the human gut, which will facilitate the medical and industrial applications of indigenous viruses.
High vaccine reactogenicities may reflect stronger immune responses, but the epidemiological evidence for coronavirus disease 2019 (COVID-19) vaccines is sparse and inconsistent. We observed that a ...fever of ≥38℃ after two doses of the BNT162b2 vaccine was associated with higher severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike IgG titers.
Abstract
The humoral and cellular immune responses against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) upon the coronavirus disease 2019 (COVID-19) vaccination remain to be ...clarified. Hence, we aimed to investigate the long-term chronological changes in SARS-CoV-2 specific IgG antibody, neutralizing antibody, and T cell responses during and after receiving the BNT162b2 vaccine. We performed serological, neutralization, and T cell assays among 100 hospital workers aged 22–73 years who received the vaccine. We conducted seven surveys up to 8 months after the second vaccination dose. SARS-CoV-2 spike protein-specific IgG (IgG-S) titers and T cell responses increased significantly following the first vaccination dose. The highest titers were observed on day 29 and decreased gradually until the end of the follow-up period. There was no correlation between IgG-S and T cell responses. Notably, T cell responses were detected on day 15, earlier than the onset of neutralizing activity. This study demonstrated that both IgG-S and T cell responses were detected before acquiring sufficient levels of SARS-CoV-2 neutralizing antibodies. These immune responses are sustained for approximately 6 to 10 weeks but not for 7 months or later following the second vaccination, indicating the need for the booster dose (i.e., third vaccination).
Rapid, simple, and accurate methods are required to diagnose coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study aimed to ...evaluate the performance of the QIAstat-Dx Respiratory SARS-CoV-2 Panel (QIAstat-SARS-CoV-2), a rapid multiplex PCR assay for SARS-CoV-2 detection.
Nasopharyngeal swabs (NPS) that were obtained from patients with COVID-19 who were diagnosed at the National Center for Global Health and Medicine were used in this study. When the NPS samples were found to be negative for SARS-CoV-2 after treatment, they were used as negative samples. We evaluated the performance of the QIAstat-SARS-CoV-2 comparing SARS-CoV-2 detection with the National Institute of Infectious Diseases in Japan-recommended real-time polymerase chain reaction (RT-PCR) method (NIID-RT-PCR).
In total, 45 NPS samples were analyzed. The proportion of overall agreement between QIAstat-SARS-CoV-2 and NIID-RT-PCR on 45 samples was 91.0% with a sensitivity of 84.0% (21/25), specificity at 100% (20/20), negative predictive value at 83.3% (20/24), and positive predictive value at 100% (21/21). There were no patients with co-infections with pathogens other than SARS-CoV-2.
QIAstat-SARS-CoV-2 showed a high agreement in comparison with the NIID-RT-PCR for the detection of SARS-CoV-2. The QIAstat-SARS-CoV-2 also provided a rapid and accurate diagnosis for COVID-19, even when the concurrent detection of other respiratory pathogens was desired, and therefore, has the potential to direct appropriate therapy and infection control precautions.
Although biomarkers to predict coronavirus disease 2019 (COVID-19) severity have been studied since the early pandemic, no clear guidelines on using them in clinical practice are available. Here, we ...examined the ability of four biomarkers to predict disease severity using conserved sera from COVID-19 patients who received inpatient care between January 1, 2020 and September 21, 2021 at the National Center for Global Health and Medicine, collected at the appropriate time for prediction. We predicted illness severity in two situations: 1) prediction of future oxygen administration for patients without oxygen support within 8 days of onset (Study 1) and 2) prediction of future mechanical ventilation support (excluding non-invasive positive pressure ventilation) or death of patients within 4 days of the start of oxygen administration (Study 2). Interleukin-6, IFN-λ3, thymus and activation-regulated chemokine, and calprotectin were measured retrospectively. Other laboratory and clinical information were collected from medical records. AUCs were calculated from ROC curves and compared for the predictive ability of the four biomarkers. Study 1 included 18 patients, five of whom had developed oxygen needs. Study 2 included 45 patients, 13 of whom required ventilator management or died. In Study 1, IFN-λ3 showed a good predictive ability with an AUC of 0.92 (95% CI 0.76-1.00). In Study 2, the AUC of each biomarker was 0.70-0.74. The number of biomarkers above the cutoff showed the possibility of good prediction with an AUC of 0.86 (95% CI 0.75-0.97). When two or more biomarkers were positive, sensitivity and specificity were 0.92 and 0.63, respectively. In terms of biomarker testing at times when prognostication may be clinically useful, IFN-λ3 was predictive of oxygenation demand and a combination of the four biomarkers was predictive of mechanical ventilator requirement.
Aim: This study was designed to evaluate whether the oral administration of lactobacilli could change the bacterial population in supra/subgingival plaque.
Material and Methods: Sixty‐six healthy ...volunteers without severe periodontitis were randomized into two groups to receive lactobacilli or placebo for 8 weeks (8W): the test group (n=34) received 2.01 × 109 CFU/day of Lactobacillus salivarius WB21 and xylitol in tablets; the control group (n=32) received placebo with xylitol. Supra/subgingival plaque samples were collected at the baseline and after 4 weeks (4W) and 8W. The bacterial amounts in plaque samples were analysed by quantitative real‐time polymerase chain reaction.
Results: The numerical sum of five selected periodontopathic bacteria in the test group was decreased significantly in subgingival plaque at 4W odds ratio (OR)=3.13, 95% confidence intervals (CI)=1.28–7.65, p=0.012. Multivariate analysis showed that significantly higher odds were obtained for the reduction of Tannerella forsythia in subgingival plaque of the test group at both 4W (OR=6.69, 95% CI=2.51–17.9, p<0.001) and 8W (OR=3.67, 95% CI=1.45–9.26, p=0.006).
Conclusion: Oral administration of probiotic lactobacilli reduced the numerical sum of five selected periodontopathic bacteria and could contribute to the beneficial effects on periodontal conditions.
The current gold standard in coronavirus disease (COVID-19) diagnostics is the real-time reverse transcription-polymerase chain reaction (RT-PCR) assay for detecting severe acute respiratory syndrome ...coronavirus 2 (SARS-CoV-2) RNA in nasopharyngeal swab (NPS) samples. Alternatively, nasal swab (NS) or saliva swab (SS) specimens are used, although available data on test accuracy are limited. We examined the diagnostic accuracy of NPS/NS/SS samples for this purpose.
Ten patients were included after being tested positive for SARS-CoV-2 RT-PCR in NPS samples according to the National Institute of Infectious Disease guidelines. In comparison with this conventional diagnostic method, NPS/NS/SS samples were tested using the cobas 6800 systems RT-PCR device. To investigate the usefulness of the cobas method and the difference among sample types, the agreement and sensitivity were calculated. Five to six samples were collected over a total period of 5-6 d from each patient.
Fifty-seven sets of NPS/NS/SS samples were collected, of which 40 tested positive for COVID-19 by the conventional method. Overall, the concordance rates using the conventional method were 86.0%/70.2%/54.4% for NPS/NS/SS samples (cobas); however, for samples collected up to and including on Day 9 after disease onset (22 negative and one positive specimens), the corresponding rates were 95.7%/87.0%/65.2%. The overall sensitivity estimates were 100.0%/67.5%/37.5% for NPS/NS/SS samples (cobas). For samples up to 9 d after onset, the corresponding values were 100.0%/86.4%/63.6%.
NS samples are more reliable than SS samples and can be an alternative to NPS samples. They can be a useful diagnostic method in the future.
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