Search

In the first days of life, the newborns' intestinal microbiota develops simultaneously with the intestinal gut barrier and follows intestinal immunity. The mode of delivery shows significant impact on microbial development and, thus, the initiation of the tryptophan catabolism pathway. Further antibiotics (ATB) treatment of mothers before or during delivery affects the microbial and tryptophan metabolite composition of stool of the caesarean- and vaginal-delivered newborns. The determination of microbiome and levels of tryptophan microbial metabolites in meconium and stool can characterize intestinal colonization of a newborn. From 134 samples from the Central European Longitudinal Studies of Parents and Children: The Next Generation (CELSPAC: TNG) cohort study, 16S rRNA gene sequencing was performed, and microbial tryptophan metabolites were quantified using ultra-high-performance liquid chromatography with triple-quadrupole mass spectrometry. Microbial diversity and concentrations of tryptophan metabolites were significantly higher in stool compared to meconium. Treatment of mothers with ATB before or during delivery affects metabolite composition and microbial diversity in stool of vaginal- and caesarean-delivered newborns. Correlation of microbial and metabolite composition shows significant positive correlations of indol-3-lactic acid, N-acetyl-tryptophan and indol-3-acetic acid with Bifidobacterium, Bacteroides and Peptoclostridium. The positive effect of vaginal delivery on newborns' microbiome development is degraded when mother is treated with ATB before or during delivery. KEY POINTS: • Antibiotic treatment diminishes the positive effects of vaginal delivery. • Antibiotic treatment affects metabolite and microbial composition in newborns. • Bifidobacterium and Peptoclostridium could be the producer of indole-lactic acid., (© 2024. The Author(s).)

It is becoming increasingly clear that the communities of microorganisms that populate the surfaces exposed to the external environment, termed microbiota, are key players in the regulation of pathogen-host cross talk affecting the onset as well as the outcome of infectious diseases. We have performed a multicenter, prospective, observational study in which nasal and oropharyngeal swabs were collected for microbiota predicting the risk of invasive fungal infections (IFIs) in patients with hematological malignancies. Here, we demonstrate that the nasal and oropharyngeal microbiota are different, although similar characteristics differentiate high-risk from low-risk samples at both sites. Indeed, similar to previously published results on the oropharyngeal microbiota, high-risk samples in the nose were characterized by low diversity, a loss of beneficial bacteria, and an expansion of potentially pathogenic taxa, in the presence of reduced levels of tryptophan (Trp). At variance with oropharyngeal samples, however, low Trp levels were associated with defective host-derived kynurenine production, suggesting reduced tolerance mechanisms at the nasal mucosal surface. This was accompanied by reduced levels of the chemokine interleukin-8 (IL-8), likely associated with a reduced recruitment of neutrophils and impaired fungal clearance. Thus, the nasal and pharyngeal microbiomes of hematological patients provide complementary information that could improve predictive tools for the risk of IFI in hematological patients.

National screening programs use dried blood specimens to detect metabolic disorders or aberrant protein functions that are not clinically evident in the neonatal period. Similarly, gut microbiota metabolites and immunological acute-phase proteins may reveal latent immune aberrations. Microbial metabolites interact with xenobiotic receptors (i.e., aryl hydrocarbon and pregnane-X) to maintain gastrointestinal tissue health, supported by acute-phase proteins, functioning as sensors of microbial immunomodulation and homeostasis. The delivery (vaginal or cesarean section) shapes the microbial colonization, which substantially modulates both the immune system's response and mucosal homeostasis. This study profiled microbial metabolites of the kynurenine and tryptophan pathway and acute-phase proteins in 134 neonatal dried blood specimens. We newly established neonatal blood levels of microbial xenobiotic receptors ligands (i.e., indole-3-aldehyde, indole-3-butyric acid, and indole-3-acetamide) on the second day of life. Furthermore, we observed diverse microbial metabolic profiles in neonates born vaginally and via cesarean section, potentially due to microbial immunomodulatory influence. In summary, these findings suggest the supportive role of human gut microbiota in developing and maintaining immune system homeostasis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Aust, Benesova, Vidova, Coufalikova, Smetanova, Borek, Janku, Budinska, Klanova, Thon and Spacil.)

The ability to predict invasive fungal infections (IFI) in patients with hematological malignancies is fundamental for successful therapy. Although gut dysbiosis is known to occur in hematological patients, whether airway dysbiosis also contributes to the risk of IFI has not been investigated. Nasal and oropharyngeal swabs were collected for functional microbiota characterization in 173 patients with hematological malignancies recruited in a multicenter, prospective, observational study and stratified according to the risk of developing IFI. A lower microbial richness and evenness were found in the pharyngeal microbiota of high-risk patients that were associated with a distinct taxonomic and metabolic profile. A murine model of IFI provided biologic plausibility for the finding that loss of protective anaerobes, such as Clostridiales and Bacteroidetes , along with an apparent restricted availability of tryptophan, is causally linked to the risk of IFI in hematologic patients and indicates avenues for antimicrobial stewardship and metabolic reequilibrium in IFI.

Exploratory mass spectrometry-based metabolomics generates a plethora of features in a single analysis. However, >85% of detected features are typically false positives due to inefficient elimination of chimeric signals and chemical noise not relevant for biological and clinical data interpretation. The data processing is considered a bottleneck to unravel the translational potential in metabolomics. Here, we describe a systematic workflow to refine exploratory metabolomics data and reduce reported false positives. We applied the feature filtering workflow in a case/control study exploring common variable immunodeficiency (CVID). In the first stage, features were detected from raw liquid chromatography-mass spectrometry data by XCMS Online processing, blank subtraction, and reproducibility assessment. Detected features were annotated in metabolomics databases to produce a list of tentative identifications. We scrutinized tentative identifications' physicochemical properties, comparing predicted and experimental reversed-phase liquid chromatography (LC) retention time. A prediction model used a linear regression of 42 retention indices with the cLogP ranging from -6 to 11. The LC retention time probes the physicochemical properties and effectively reduces the number of tentatively identified metabolites, which are further submitted to statistical analysis. We applied the retention time-based analytical feature filtering workflow to datasets from the Metabolomics Workbench (www.metabolomicsworkbench.org), demonstrating the broad applicability. A subset of tentatively identified metabolites significantly different in CVID patients was validated by MS/MS acquisition to confirm potential CVID biomarkers' structures and virtually eliminate false positives. Our exploratory metabolomics data processing workflow effectively removes false positives caused by the chemical background and chimeric signals inherent to the analytical technique. It reduced the number of tentatively identified metabolites by 88%, from initially detected 6940 features in XCMS to 839 tentative identifications and streamlined consequent statistical analysis and data interpretation.