Oral and gut microbiome biomarkers of susceptibility to respiratory tract infection in adults: a longitudinal cohort feasibility study

Background and aim: Respiratory tract infections (RTIs) are common in the community. There is some evidence that microbial biomarkers can be used to identify individuals most susceptible to RTI acquisition. We investigated the feasibility of recruiting healthy adults to collect at-home self-reported socio-demographic data and biological samples, saliva (oral) and stool (gut) at three time points (TPs): baseline/start of the study (TP-A), during an RTI (TP-B) and end of study (TP-C). Methods: Healthy adults were recruited from two urban Bristol GP practices. To identify respiratory pathogens in all saliva samples and RTI-S stool samples reverse transcriptase PCR (RT-PCR) was applied. We compared oral and gut samples from participants who developed RTI symptoms (RTI-S) and those who remained healthy (no-RTI) using 16S rRNA profiling microbiome analysis to identify the core microbiome, alpha and beta diversity, and biomarkers for susceptibility to RTIs from baseline samples (TP-A) when all participants were healthy. Results: We recruited 56 participants but due to the UK COVID-19 pandemic disruption we did not receive samples from 16 participants leaving 19 RTI-S and 21 no-RTI participants with socio-demographic and microbiome data. RT-PCR revealed coagulase-negative Staphylococcus carriage was significantly higher in RTI-S participants compared to those who remained healthy and RTI symptoms may have been due to viral influenzae and bacterial co-infection with Haemophilus influenzae. Core microbiomes of no-RTI participants contained a greater number of taxa compared to RTI-S participants. Microbial biomarkers of RTI susceptibility in the oral cavity were an increased abundance of the pathobiont Streptococcus sobrinus and decreased probiotic bacterium Lactobacillus salivarius whereas in the gut there was an increased abundance of the genus Veillonella and decreased abundance of Coprobacillus. Conclusion: In our feasibility study we found oral and gut microbial biomarkers for susceptibility to RTI acquisition. Strategies to identify those most vulnerable to RTI in the community could lead to novel interventions to decrease respiratory infection and associated health services burden.

Intestinal Microbiome Associated With Immune-Related Adverse Events for Patients Treated With Anti-PD-1 Inhibitors, a Real-World Study

AimImmune checkpoint inhibitors (ICIs) have updated the treatment landscape for patients with advanced malignancies, while their clinical prospect was hindered by severe immune-related adverse events (irAEs). The aim of this study was to research the association between gut microbiome diversity and the occurrence of ICI-induced irAEs.Patients and MethodWe prospectively obtained the baseline fecal samples and clinical data from patients treated with anti-PD-1 inhibitors as monotherapy or in combination with chemotherapy or antiangiogenesis regardless of treatment lines. The 16S rRNA V3-V4 sequencing was used to test the gene amplicons of fecal samples. The development of irAEs was evaluated and monitored from the beginning of therapy based on CTCAE V5.01.ResultsA total of 150 patients were included in the study and followed up for at least 6 months. A total of 90 (60%) patients developed at least one type of adverse effect, among which mild irAEs (grades 1–2) occurred in 65 patients (72.22%) and severe irAEs (grades 3–5) in 25 patients (27.78%). Patients with severe irAEs showed a visible higher abundance of Streptococcus, Paecalibacterium, and Stenotrophomonas, and patients with mild irAEs had a higher abundance of Faecalibacterium and unidentified_Lachnospiraceae. With the aid of a classification model constructed with 5 microbial biomarkers, patients without irAEs were successfully distinguished from those with severe irAEs (AUC value was 0.66).ConclusionCertain intestinal bacteria can effectively distinguish patients without irAEs from patients with severe irAEs and provide evidence of gut microbiota as an informative source for developing predictive biomarkers to predict the occurrence of irAEs.

Bacterial Communities Associated with Human Decomposition

<p>Human decomposition is a little-understood process with even less currently known about the microbiology involved. The aim of this research was to investigate the bacterial community associated with exposed decomposing mammalian carcasses on soil and to determine whether changes in this community could potentially be used to determine time since death in forensic investigations. A variety of soil chemistry and molecular biology methods, including molecular profiling tools T-RFLP and DGGE were used to explore how and when bacterial communities change during the course of a decomposition event. General bacterial populations and more specific bacterial groups were examined. Decomposition was shown to cause significant and sequential changes in the bacterial communities within the soil, and changes in the bacterial community often correlated with visual changes in the stage of decomposition. Organisms derived from the cadavers and carcasses were able to be detected, using molecular methods, in the underlying soil throughout the decomposition period studied. There was little correlation found between decomposition stage and the presence and diversity within the specific bacterial groups investigated. Organisms contributing to the changes seen in the bacterial communities using molecular profiling methods were identified using a cloning and sequencing based technique and included soil and environment-derived bacteria, as well as carcass or cadaver-derived organisms. This research demonstrated that pig (Sus scrofa) carcass and human cadaver decomposition result in similar bacterial community changes in soil, confirming that pig carcasses are a good model for studying the microbiology of human decomposition. The inability to control for differences between donated human cadavers made understanding the human cadaver results difficult, whereas pig carcass study allowed many variables to be held constant while others were investigated. The information gained from this study about the bacteria associated with a cadaver and how the community alters over the course of decomposition may, in the future, enable the development of a forensic post mortem interval estimation tool based on these changes in the bacterial community over time. The findings in this thesis suggest that high variability between human bodies and their microflora is likely to present a challenge to the development of such a tool, but further study with emerging high-throughput molecular tools may enable identification of microbial biomarkers for this purpose.</p>

Bacterial Communities Associated with Human Decomposition

<p>Human decomposition is a little-understood process with even less currently known about the microbiology involved. The aim of this research was to investigate the bacterial community associated with exposed decomposing mammalian carcasses on soil and to determine whether changes in this community could potentially be used to determine time since death in forensic investigations. A variety of soil chemistry and molecular biology methods, including molecular profiling tools T-RFLP and DGGE were used to explore how and when bacterial communities change during the course of a decomposition event. General bacterial populations and more specific bacterial groups were examined. Decomposition was shown to cause significant and sequential changes in the bacterial communities within the soil, and changes in the bacterial community often correlated with visual changes in the stage of decomposition. Organisms derived from the cadavers and carcasses were able to be detected, using molecular methods, in the underlying soil throughout the decomposition period studied. There was little correlation found between decomposition stage and the presence and diversity within the specific bacterial groups investigated. Organisms contributing to the changes seen in the bacterial communities using molecular profiling methods were identified using a cloning and sequencing based technique and included soil and environment-derived bacteria, as well as carcass or cadaver-derived organisms. This research demonstrated that pig (Sus scrofa) carcass and human cadaver decomposition result in similar bacterial community changes in soil, confirming that pig carcasses are a good model for studying the microbiology of human decomposition. The inability to control for differences between donated human cadavers made understanding the human cadaver results difficult, whereas pig carcass study allowed many variables to be held constant while others were investigated. The information gained from this study about the bacteria associated with a cadaver and how the community alters over the course of decomposition may, in the future, enable the development of a forensic post mortem interval estimation tool based on these changes in the bacterial community over time. The findings in this thesis suggest that high variability between human bodies and their microflora is likely to present a challenge to the development of such a tool, but further study with emerging high-throughput molecular tools may enable identification of microbial biomarkers for this purpose.</p>

Machine-Learning Model for Resistance/Relapse Prediction in Immune Thrombocytopenia Using Gut Microbiota and Function Signatures

Introduction Growing evidence has implicated gut microbiota in the pathogenesis of immune thrombocytopenia (ITP). In a previous research study, we found dysbiosis in the phylogenetic composition and function of gut microbiome in ITP and that corticosteroid treatment may have a strong effect on gut microbiota [Sci China Life Sci, 2020]. Corticosteroids have been widely used in the initial treatment of newly diagnosed ITP patients, but most adult patients relapse upon cessation of steroid treatment. Patients on agents in subsequent therapy may improve at any time, but which patients improve and when is unpredictable. The gut microbiome has been increasingly used in the assessment and prediction of immunomodulatory therapy in autoimmune diseases and cellular immunotherapy in cancers. Here, we provide evidence that gut microbiota and function signatures can be used to predict immune thrombocytopenia patients at high risk of relapse/resistance after corticosteroid treatment and to identify patients that are more likely to benefit from TPO-RAs in subsequent therapy. Methods Seventy-five fecal samples from 60 patients with newly diagnosed ITP (60 specimens before corticosteroid therapy and 15 specimens after corticosteroid therapy) and 41 samples from persistent/chronic ITP before and after treatment with TPO-RAs, including eltrombopag and avatrombopag were collected for deep shotgun metagenomic sequencing. To identify the microbial biomarkers related to relapse/resistance after corticosteroid treatment, we constructed a random forest classifier using machine learning to determine the risk of relapse/resistance of a training cohort of 30 patients from baseline samples and validated the classifier for 30 patients. Patients with persistent/chronic ITP were divided into responders and nonresponders according to their response to TPO-RA treatment in subsequent therapy. After identifying the microbial species and functional biomarkers related to the response to TPO-RA therapy, a random forest classifier was constructed using a training set of 20 patients and validated using a validation set of 21 patients. Results We used a metagenomic sequencing technique to investigate the differences among gut microbiota associated with relapse within 3 months of corticosteroid treatment. We observed that the diversity and composition of the microbial community in ITP patients after corticosteroid therapy (Post-C) changed significantly from the baseline (Pre-C), whereas the gut microbiota of the remission group was similar to that of the HC group, which implies that a shift in the gut microbiome could represent a return to homeostasis. To identify the microbial biomarkers related to early relapse after corticosteroid treatment, the Pre-C samples were divided into a remission group and a resistant/relapse group according to the response to corticosteroid therapy within 3 months. Nine significant associations with the microbial species and function were identified between the remission and resistant/relapse groups. A risk index built from this panel of microbes and functional pathways was used to differentiate remission from resistant/relapsed patients based on the baseline characteristics. The receiver operating characteristic (ROC) curve demonstrated that the risk index was a strong predictor of treatment response, with an area under the curve (AUC) of 0.87. Furthermore, to predict the response to TPO-RAs in subsequent therapy, the baseline gut microbiomes of responders and nonresponders before TPO-RA treatment were compared. Patients who responded to treatment exhibited an increase in Ruminococcaceae, Clostridiaceae and Bacteroides compared to nonresponders, with elevated abundance of the phosphotransferase system, tyrosine metabolism and secondary bile acid biosynthesis pathways according to KEGG analysis. Our prediction model based on the gut microbiome for TPO-RA response was robust across the cohorts and showed 89.5% and 79.2% prediction accuracy for persistent/chronic ITP patients in the training and validation sets, respectively. Conclusions The gut microbiome and function signatures based on machine learning analysis are novel potential biomarkers for predicting resistance/relapse after corticosteroid treatment and response to TPO-RAs, which may have important manifestations in the clinical. Disclosures No relevant conflicts of interest to declare.

Humoral Response to Microbial Biomarkers in Rheumatoid Arthritis Patients

Background/Objective: Chronic humoral immune response against multiple microbial antigens may play a crucial role in the etiopathogenesis of rheumatoid arthritis (RA). We aimed to assess the prevalence and magnitude of antibody response against various bacterial and viral immunogen peptides in the sera of RA patients compared with the general population. Methods: Polyclonal IgG antibodies (Abs) specific for peptides derived from Porphyromonas gingivalis (RgpA, Kpg), Aggregatibacter actinomycetemcomitans (LtxA1, LtxA2), Mycobacterium avium subsp. paratuberculosis (MAP4027), Epstein–Barr virus (EBNA1, EBVBOLF), and human endogenous retrovirus (HERV-W env-su) were detected by ELISA in serum samples from 148 consecutive RA patients and 148 sex and age-matched healthy controls (HCs). In addition, the presence of a relationship between the positivity and the titer of antibodies and RA descriptors was explored by bivariate correlation analysis. Results: RA patients exhibit a higher prevalence of humoral immune response against all tested peptides compared to HCs with a statically significant difference for MAP4027 (30.4% vs. 10.1%), BOLF (25.7% vs. 8.1%), RgpA (24.3% vs. 9.4%), HERV W-env (20.3% vs. 9.4%), and EBNA1 (18.9% vs. 9.4%) peptides. Fifty-three (35.8%) out of 148 RA serum and 93 (62.8%) out of 148 HCs were negative for all pathogen-derived peptides. There was a significant correlation between OD values obtained by ELISA test against all peptides (p < 0.0001). We also found an increased titer and prevalence of Abs against LtxA1 and LtxA2 in seropositive vs. seronegative RF (p = 0.019, p = 0.018). Conclusion: This study demonstrates a significantly increased humoral response against multiple pathogens in patients with RA and implies that they could be an important factor in the pathogenesis of the disease. Therefore, the role of each individual pathogen in RA needs to be further investigated.

Investigating the Reciprocal Interrelationships among the Ruminal Microbiota, Metabolome, and Mastitis in Early Lactating Holstein Dairy Cows

Mastitis in dairy cow significantly affects animal performance, ultimately reducing profitability. The reciprocal interrelationships among ruminal microbiota, metabolome, and mastitis combining early inflammatory factors (serum proinflammatory cytokines) in lactating dairy cows has not been explored, thus, this study evaluated these reciprocal interrelationships in early lactating Holstein dairy cows to identify potential microbial biomarkers and their relationship with ruminal metabolites. The ruminal fluid was sampled from 8 healthy and 8 mastitis cows for the microbiota and metabolite analyses. The critical ruminal microbial biomarkers and metabolites related to somatic cell counts (SCC) and serum proinflammatory cytokines were identified by the linear discriminant analysis effect size (LEfSe) algorithm and Spearman’s correlation analysis, respectively. The SCC level and proinflammatory cytokines positively correlated with Sharpea and negatively correlated with Ruminococcaceae UCG-014, Ruminococcus flavefaciens, and Treponema saccharophilum. Furthermore, the metabolites xanthurenic acid, and 1-(1H-benzo[d]imidazol-2-yl) ethan-1-ol positively correlated with microbial biomarkers of healthy cows, whereas, xanthine, pantothenic acid, and anacardic acid were negatively correlated with the microbial biomarkers of mastitis cows. In conclusion, Ruminococcus flavefaciens and Treponema saccharophilum are potential strains for improving the health of dairy cows. The current study provides a novel perspective to assist in targeting the ruminal microbiota with preventive/therapeutic strategies against inflammatory diseases in the future.