Comparison of the respiratory tract microbiome in hospitalized COVID-19 patients with different disease severity

Techniques for learning and transferring knowledge for microbiome-based classification and prediction: review and assessment

The volume of microbiome data is growing at an exponential rate, and the current methodologies for big data mining are encountering substantial obstacles. Effectively managing and extracting valuable insights from these vast microbiome datasets has emerged as a significant challenge in the field of contemporary microbiome research. This comprehensive review delves into the utilization of foundation models and transfer learning techniques within the context of microbiome-based classification and prediction tasks, advocating for a transition away from traditional task-specific or scenario-specific models towards more adaptable, continuous learning models. The article underscores the practicality and benefits of initially constructing a robust foundation model, which can then be fine-tuned using transfer learning to tackle specific context tasks. In real-world scenarios, the application of transfer learning empowers models to leverage disease-related data from one geographical area and enhance diagnostic precision in different regions. This transition from relying on "good models" to embracing "adaptive models" resonates with the philosophy of "teaching a man to fish" thereby paving the way for advancements in personalized medicine and accurate diagnosis. Empirical research suggests that the integration of foundation models with transfer learning methodologies substantially boosts the performance of models when dealing with large-scale and diverse microbiome datasets, effectively mitigating the challenges posed by data heterogeneity.

Bacteremia due to Lachnoanaerobaculum umeaense in a patient with acute myeloid leukemia during chemotherapy: A case report, and a review of the literature

The present case reports a bacteremia due to Lachnoanaerobaculum umeaense (a Gram-positive, filamentous, rod-shaped, anaerobic, spore-forming bacillus present in the human oral microbiota) in a patient treated for acute myeloid leukemia. After failed identification by MALDI-TOF, identification was done by sequencing of 16s rRNA. The patient was successfully treated with Amoxicillin-clavulanic acid and ciprofloxacin for seven days. Comparison of V1-V3 regions of the bacterial 16S rRNA gene gene with published sequences failed to classify the strain as pathogenic or non-pathogenic based on this phylogenetic classification alone. Although Lachnoanaerobaculum gingivalis are known to be associated with bacteremia in patients with acute myeloid leukemia, this clinical case of infection by L. umeaense argues for further studies that will lead to more efficient classification of the infection by these microorganisms.

Insights into the Naso-Oropharyngeal Bacterial Composition in Suspected SARS-CoV-2 Cases

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was the causative agent of the coronavirus disease 2019 (COVID-19) pandemic. While research on COVID-19 has mainly focused on its epidemiology, pathogenesis, and treatment, studies on the naso-oropharyngeal microbiota have emerged in the last few years as an overlooked area of research. Here, we analyzed the bacterial community composition of the naso-oropharynx in 50 suspected SARS-CoV-2 cases (43 detected, 7 not detected) from Veraguas province (Panama) distributed across five age categories. Statistical analysis revealed no significant differences (p < 0.05) in bacterial alpha and beta diversities between the groups categorized by SARS-CoV-2 test results, age, or patient status. The genera Corynebacterium, Staphylococcus, Prevotella, Streptococcus, and Tepidiphilus were the most abundant in both detected and not-detected SARS-CoV-2 group. The linear discriminant analysis effect size (LEfSe) for biomarker exploration indicated that Veillonella and Prevotella were enriched in detected and hospitalized patients with SARS-CoV-2 relative to non-detected patients, while Thermoanaerobacterium and Haemophilus were enriched in non-detected patients with SARS-CoV-2. The results also indicated that the genus Corynebacterium was found to decrease in patients with detected SARS-CoV-2 relative to those with non-detected SARS-CoV-2. Understanding the naso-oropharyngeal microbiota provides insights into the diversity, composition, and resilience of the microbial community in patients with SARS-CoV-2.

The Causal Effect of Gut Microbiota and Plasma Metabolome on Lung Cancer and the Heterogeneity across Subtypes: A Mendelian Randomization Study

The causal effect and pathways of gut microbiota and plasma metabolome on lung cancer have been important topics for personalized medicine; however, the heterogeneity of lung cancer subtypes has not gained enough attention in previous studies. This study sought to employ a Mendelian randomization analysis to screen the specific gut microbiota and plasma metabolome, which may have a causal effect on lung cancer. We further extended our analysis to estimate the effects of these exposures on various pathological subtypes of lung cancer. Furthermore, a mediation analysis was performed to identify the potential pathway underlying the influence of microbiota and metabolites. Our study identified 13 taxa and 15 metabolites with a causal association with the overall risk of lung cancer. Furthermore, we found 8 taxa and 14 plasma metabolites with a causal effect on lung adenocarcinoma, 4 taxa and 10 metabolites with a causal effect on squamous cell lung carcinoma, and 7 taxa and 16 metabolites with a causal effect on SCLC. We also identified seven mediation pathways that could potentially elucidate the influence of these microbiota and metabolites on overall lung cancer or special subtypes. Our study highlighted the heterogeneity of the gut microbiome and plasma metabolome in a lung cancer subtype and elucidated the potential underlying mechanisms. This could pave the way for more personalized lung cancer prevention and treatment.

Urbanization influences the indoor transfer of airborne antibiotic resistance genes, which has a seasonally dependent pattern

Over the last few years, the cumulative use of antibiotics in healthcare institutions, as well as the rearing of livestock and poultry, has resulted in the accumulation of antibiotic resistance genes (ARGs). This presents a substantial danger to human health worldwide. The characteristics of airborne ARGs, especially those transferred from outdoors to indoors, remains largely unexplored in neighborhoods, even though a majority of human population spends most of their time there. We investigated airborne ARGs and mobile genetic element (MGE, IntI1), plant communities, and airborne microbiota transferred indoors, as well as respiratory disease (RD) prevalence using a combination of metabarcode sequencing, real-time quantitative PCR and questionnaires in 72 neighborhoods in Shanghai. We hypothesized that (i) urbanization regulates ARGs abundance, (ii) the urbanization effect on ARGs varies seasonally, and (iii) land use types are associated with ARGs abundance. Supporting these hypotheses, during the warm season, the abundance of ARGs in peri-urban areas was higher than in urban areas. The abundance of ARGs was also affected by the surrounding land use and plant communities: an increase in the proportion of gray infrastructure (e.g., residential area) around neighborhoods can lead to an increase in some ARGs (mecA, qnrA, ermB and mexD). Additionally, there were variations observed in the relationship between ARGs and bacterial genera in different seasons. Specifically, Stenotrophomonas and Campylobacter were positively correlated with vanA during warm seasons, whereas Pseudomonas, Bacteroides, Treponema and Stenotrophomonas positively correlated with tetX in the cold season. Interstingly, a noteworthy positive correlation was observed between the abundance of vanA and the occurrence of both rhinitis and rhinoconjunctivitis. Taken together, our study underlines the importance of urbanization and season in controlling the indoor transfer of airborne ARGs. Furthermore, we also highlight the augmentation of green-blue infrastructure in urban environments has the potential to mitigate an excess of ARGs.

Gastrointestinal microbiota and metabolites possibly contribute to distinct pathogenicity of SARS-CoV-2 proto or its variants in rhesus monkeys

Gastrointestinal (GI) infection is evidenced with involvement in COVID-19 pathogenesis caused by SARS-CoV-2. However, the correlation between GI microbiota and the distinct pathogenicity of SARS-CoV-2 Proto and its emerging variants remains unclear. In this study, we aimed to determine if GI microbiota impacted COVID-19 pathogenesis and if the effect varied between SARS-CoV-2 Proto and its variants. We performed an integrative analysis of histopathology, microbiomics, and transcriptomics on the GI tract fragments from rhesus monkeys infected with SARS-CoV-2 proto or its variants. Based on the degree of pathological damage and microbiota profile in the GI tract, five of SARS-CoV-2 strains were classified into two distinct clusters, namely, the clusters of Alpha, Beta and Delta (ABD), and Proto and Omicron (PO). Notably, the abundance of potentially pathogenic microorganisms increased in ABD but not in the PO-infected rhesus monkeys. Specifically, the high abundance of UCG-002, UCG-005, and Treponema in ABD virus-infected animals positively correlated with interleukin, integrins, and antiviral genes. Overall, this study revealed that infection-induced alteration of GI microbiota and metabolites could increase the systemic burdens of inflammation or pathological injury in infected animals, especially in those infected with ABD viruses. Distinct GI microbiota and metabolite profiles may be responsible for the differential pathological phenotypes of PO and ABD virus-infected animals. These findings improve our understanding the roles of the GI microbiota in SARS-CoV-2 infection and provide important information for the precise prevention, control, and treatment of COVID-19.

Upper respiratory tract microbiome profiles in SARS-CoV-2 Delta and Omicron infected patients exhibit variant specific patterns and robust prediction of disease groups

IMPORTANCE

The role of the upper respiratory tract (URT) microbiome in predicting lung health has been documented in several studies. The dysbiosis in COVID patients has been associated with disease outcomes by modulating the host immune system. However, although it has been known that different SARS-CoV-2 variants manifest distinct transmissibility and mortality rates in human populations, their effect on the composition and diversity of the URT microbiome has not been studied to date. Unlike the older variant (Delta), the newer variant (Omicron) have become more transmissible with lesser mortality and the symptoms have also changed significantly. Hence, in the present study, we have investigated the change in the URT microbiome associated with Delta and Omicron variants and identified variant-specific signatures that will be useful in the assessment of lung health and can be utilized for nasal probiotic therapy in the future.

The nasopharyngeal microbiome in COVID-19

The development of novel culture-independent techniques of microbial identification has allowed a rapid progress in the knowledge of the nasopharyngeal microbiota and its role in health and disease. Thus, it has been demonstrated that the nasopharyngeal microbiota defends the host from invading pathogens that enter the body through the upper airways by participating in the modulation of innate and adaptive immune responses. The current COVID-19 pandemic has created an urgent need for fast-track research, especially to identify and characterize biomarkers to predict the disease severity and outcome. Since the nasopharyngeal microbiota diversity and composition could potentially be used as a prognosis biomarker for COVID-19 patients, which would pave the way for strategies aiming to reduce the disease severity by modifying such microbiota, dozens of research articles have already explored the possible associations between changes in the nasopharyngeal microbiota and the severity or outcome of COVID-19 patients. Unfortunately, results are controversial, as many studies with apparently similar experimental designs have reported contradictory data. Herein we put together, compare, and discuss all the relevant results on this issue reported to date. Even more interesting, we discuss in detail which are the limitations of these studies, that probably are the main sources of the high variability observed. Therefore, this work is useful not only for people interested in current knowledge about the relationship between the nasopharyngeal microbiota and COVID-19, but also for researchers who want to go further in this field while avoiding the limitations and variability of previous works.

Bacterial Biomarkers of the Oropharyngeal and Oral Cavity during SARS-CoV-2 Infection

(1) Background: Individuals with COVID-19 display different forms of disease severity and the upper respiratory tract microbiome has been suggested to play a crucial role in the development of its symptoms. (2) Methods: The present study analyzed the microbial profiles of the oral cavity and oropharynx of 182 COVID-19 patients compared to 75 unaffected individuals. The samples were obtained from gargle screening samples. 16S rRNA amplicon sequencing was applied to analyze the samples. (3) Results: The present study shows that SARS-CoV-2 infection induced significant differences in bacterial community assemblages, with Prevotella and Veillonella as biomarkers for positive-tested people and Streptococcus and Actinomyces for negative-tested people. It also suggests a state of dysbiosis on the part of the infected individuals due to significant differences in the bacterial community in favor of a microbiome richer in opportunistic pathogens. (4) Conclusions: SARS-CoV-2 infection induces dysbiosis in the upper respiratory tract. The identification of these opportunistic pathogenic biomarkers could be a new screening and prevention tool for people with prior dysbiosis.

Predicting potential microbe-disease associations based on multi-source features and deep learning

Studies have confirmed that the occurrence of many complex diseases in the human body is closely related to the microbial community, and microbes can affect tumorigenesis and metastasis by regulating the tumor microenvironment. However, there are still large gaps in the clinical observation of the microbiota in disease. Although biological experiments are accurate in identifying disease-associated microbes, they are also time-consuming and expensive. The computational models for effective identification of diseases related microbes can shorten this process, and reduce capital and time costs. Based on this, in the paper, a model named DSAE_RF is presented to predict latent microbe-disease associations by combining multi-source features and deep learning. DSAE_RF calculates four similarities between microbes and diseases, which are then used as feature vectors for the disease-microbe pairs. Later, reliable negative samples are screened by k-means clustering, and a deep sparse autoencoder neural network is further used to extract effective features of the disease-microbe pairs. In this foundation, a random forest classifier is presented to predict the associations between microbes and diseases. To assess the performance of the model in this paper, 10-fold cross-validation is implemented on the same dataset. As a result, the AUC and AUPR of the model are 0.9448 and 0.9431, respectively. Furthermore, we also conduct a variety of experiments, including comparison of negative sample selection methods, comparison with different models and classifiers, Kolmogorov-Smirnov test and t-test, ablation experiments, robustness analysis, and case studies on Covid-19 and colorectal cancer. The results fully demonstrate the reliability and availability of our model.

Characterization of upper airway microbiome across severity of COVID-19 during hospitalization and treatment

Longitudinal studies on upper respiratory tract microbiome in coronavirus disease 2019 (COVID-19) without potential confounders such as antimicrobial therapy are limited. The objective of this study is to assess for longitudinal changes in the upper respiratory microbiome, its association with disease severity, and potential confounders in adult hospitalized patients with COVID-19. Serial nasopharyngeal and throat swabs (NPSTSs) were taken for 16S rRNA gene amplicon sequencing from adults hospitalized for COVID-19. Alpha and beta diversity was assessed between different groups. Principal coordinate analysis was used to assess beta diversity between groups. Linear discriminant analysis was used to identify discriminative bacterial taxa in NPSTS taken early during hospitalization on need for intensive care unit (ICU) admission. A total of 314 NPSTS samples from 197 subjects (asymptomatic = 14, mild/moderate = 106, and severe/critical = 51 patients with COVID-19; non-COVID-19 mechanically ventilated ICU patients = 11; and healthy volunteers = 15) were sequenced. Among all covariates, antibiotic treatment had the largest effect on upper airway microbiota. When samples taken after antibiotics were excluded, alpha diversity (Shannon, Simpson, richness, and evenness) was similar across severity of COVID-19, whereas beta diversity (weighted GUniFrac and Bray-Curtis distance) remained different. Thirteen bacterial genera from NPSTS taken within the first week of hospitalization were associated with a need for ICU admission (area under the receiver operating characteristic curve, 0.96; 95% CI, 0.91-0.99). Longitudinal analysis showed that the upper respiratory microbiota alpha and beta diversity was unchanged during hospitalization in the absence of antimicrobial therapy.

Clinical identification and microbiota analysis of Chlamydia psittaci- and Chlamydia abortus- pneumonia by metagenomic next-generation sequencing

Introduction

Recently, the incidence of chlamydial pneumonia caused by rare pathogens such as C. psittaci or C. abortus has shown a significant upward trend. The non-specific clinical manifestations and the limitations of traditional pathogen identification methods determine that chlamydial pneumonia is likely to be poorly diagnosed or even misdiagnosed, and may further result in delayed treatment or unnecessary antibiotic use. mNGS's non-preference and high sensitivity give us the opportunity to obtain more sensitive detection results than traditional methods for rare pathogens such as C. psittaci or C. abortus.

Methods

In the present study, we investigated both the pathogenic profile characteristics and the lower respiratory tract microbiota of pneumonia patients with different chlamydial infection patterns using mNGS.

Results

More co-infecting pathogens were found to be detectable in clinical samples from patients infected with C. psittaci compared to C. abortus, suggesting that patients infected with C. psittaci may have a higher risk of mixed infection, which in turn leads to more severe clinical symptoms and a longer disease course cycle. Further, we also used mNGS data to analyze for the first time the characteristic differences in the lower respiratory tract microbiota of patients with and without chlamydial pneumonia, the impact of the pattern of Chlamydia infection on the lower respiratory tract microbiota, and the clinical relevance of these characteristics. Significantly different profiles of lower respiratory tract microbiota and microecological diversity were found among different clinical subgroups, and in particular, mixed infections with C. psittaci and C. abortus resulted in lower lung microbiota diversity, suggesting that chlamydial infections shape the unique lung microbiota pathology, while mixed infections with different Chlamydia may have important effects on the composition and diversity of the lung microbiota.

Discussion

The present study provides possible evidences supporting the close correlation between chlamydial infection, altered microbial diversity in patients' lungs and clinical parameters associated with infection or inflammation in patients, which also provides a new research direction to better understand the pathogenic mechanisms of pulmonary infections caused by Chlamydia.

The salivary and nasopharyngeal microbiomes are associated with SARS-CoV-2 infection and disease severity

Emerging evidence suggests the oral and upper respiratory microbiota may play important roles in modulating host immune responses to viral infection. As the host microbiome may be involved in the pathophysiology of coronavirus disease 2019 (COVID-19), we investigated associations between the oral and nasopharyngeal microbiome and COVID-19 severity. We collected saliva (n = 78) and nasopharyngeal swab (n = 66) samples from a COVID-19 cohort and characterized the microbiomes using 16S ribosomal RNA gene sequencing. We also examined associations between the salivary and nasopharyngeal microbiome and age, COVID-19 symptoms, and blood cytokines. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection status, but not COVID-19 severity, was associated with community-level differences in the oral and nasopharyngeal microbiomes. Salivary and nasopharyngeal microbiome alpha diversity negatively correlated with age and were associated with fever and diarrhea. Oral Bifidobacterium, Lactobacillus, and Solobacterium were depleted in patients with severe COVID-19. Nasopharyngeal Paracoccus was depleted while nasopharyngeal Proteus, Cupravidus, and Lactobacillus were increased in patients with severe COVID-19. Further analysis revealed that the abundance of oral Bifidobacterium was negatively associated with plasma concentrations of known COVID-19 biomarkers interleukin 17F and monocyte chemoattractant protein-1. Our results suggest COVID-19 disease severity is associated with the relative abundance of certain bacterial taxa.

Comparison of the respiratory tract microbiome in hospitalized COVID-19 patients with different disease severity

Little is known about the characteristics of respiratory tract microbiome in Coronavirus disease 2019 (COVID-19) inpatients with different severity. We conducted a study that expected to clarify these characteristics as much as possible. A cross-sectional study was conducted to characterize respiratory tract microbial communities of 69 COVID-19 inpatients from 64 nasopharyngeal swabs and 5 sputum specimens using 16S ribosomal RNA gene V3-V4 region sequencing. The bacterial profiles were analyzed to find potential biomarkers by the two-step method, the combination of random forest model and the linear discriminant analysis effect size, and explore the connections with clinical characteristics by Spearman's rank test. Compared with mild COVID-19 patients, severe patients had significantly decreased bacterial diversity (p-values were less than 0.05 in the alpha and beta diversity) and relative lower abundance of opportunistic pathogens, including Actinomyces, Prevotella, Rothia, Streptococcus, Veillonella. Eight potential biomarkers including Treponema, Leptotrichia, Lachnoanaerobaculum, Parvimonas, Alloprevotella, Porphyromonas, Gemella, and Streptococcus were found to distinguish the mild COVID-19 patients from the severe COVID-19 patients. The genera of Actinomyces and Prevotella were negatively correlated with age in two groups. Intensive care unit admission, neutrophil count, and lymphocyte count were significantly correlated with different genera in the two groups. In addition, there was a positive correlation between Klebsiella and white blood cell count in two groups. The respiratory tract microbiome had significant differences in COVID-19 patients with different severity. The value of the respiratory tract microbiome as predictive biomarkers for COVID-19 severity deserves further exploration.

Untangling the link between the human gut microbiota composition and the severity of the symptoms of the COVID-19 infection

Recent pandemic infection caused by SARS-CoV-2 (COVID-19) led the scientific community to investigate the possible causes contributing to the physiopathology of this disease. In this context, analyses of the intestinal microbiota highlighted possible correlation between host-associated bacterial communities and development of the COVID-19. Nevertheless, a detailed investigation of the role of the human microbiota in the severity of the symptoms of this disease is still lacking. This study performed a comprehensive meta-analysis of 323 faecal samples from public and novel Italian data sets based on the shotgun metagenomic approach. In detail, the comparative analyses revealed possible differences in the microbial biodiversity related to the individual health status, highlighting a species richness decrease in COVID-19 patients with a severe prognosis. Moreover, healthy subjects resulted characterized by a higher abundance of protective and health-supporting bacterial species, while patients affected by COVID-19 disease displayed a significant increase of opportunistic pathogen bacteria involved in developing putrefactive dysbiosis. Furthermore, prediction of the microbiome functional capabilities suggested that individuals affected by COVID-19 subsist in an unbalanced metabolism characterized by an overrepresentation of enzymes involved in the protein metabolism at the expense of carbohydrates oriented pathways, which can impact on disease severity and in excessive systemic inflammation.