Enrichment of human nasopharyngeal bacteriome with bacteria from dust after short-term exposure to indoor environment: a pilot study.

BACKGROUND: Indoor dust particles are an everyday source of human exposure to microorganisms and their inhalation may directly affect the microbiota of the respiratory tract. We aimed to characterize the changes in human nasopharyngeal bacteriome after short-term exposure to indoor (workplace) environments. METHODS: In this pilot study, nasopharyngeal swabs were taken from 22 participants in the morning and after 8 h of their presence at the workplace. At the same time points, indoor dust samples were collected from the participants' households (16 from flats and 6 from houses) and workplaces (8 from a maternity hospital - NEO, 6 from a pediatric hospital - ENT, and 8 from a research center - RCX). 16S rRNA sequencing analysis was performed on these human and environmental matrices. RESULTS: Staphylococcus and Corynebacterium were the most abundant genera in both indoor dust and nasopharyngeal samples. The analysis indicated lower bacterial diversity in indoor dust samples from flats compared to houses, NEO, ENT, and RCX (p < 0.05). Participants working in the NEO had the highest nasopharyngeal bacterial diversity of all groups (p < 0.05). After 8 h of exposure to the workplace environment, enrichment of the nasopharynx with several new bacterial genera present in the indoor dust was observed in 76% of study participants; however, no significant changes were observed at the level of the nasopharyngeal bacterial diversity (p > 0.05, Shannon index). These "enriching" bacterial genera overlapped between the hospital workplaces - NEO and ENT but differed from those in the research center - RCX. CONCLUSIONS: The results suggest that although the composition of nasopharyngeal bacteriome is relatively stable during the day. Short-term exposure to the indoor environment can result in the enrichment of the nasopharynx with bacterial DNA from indoor dust; the bacterial composition, however, varies by the indoor workplace environment.

Sex-dependent host-microbiome dynamics in zebrafish: Implications for toxicology and gastrointestinal physiology.

The physiology of males and females can be vastly different, complicating interpretation of toxicological and physiological data. The objectives of this study were to elucidate the sex differences in the microbiome-gastrointestinal (GI) transcriptome of adult zebrafish. We compared microbial composition and diversity in both males and females fed the same diet and housed in the same environment. There were no sex-specific differences in weight gain nor gastrointestinal morphology based on histopathology. There was no difference in gut microbial diversity, richness (Shannon and Chao1 index) nor predicted functional composition of the microbiome between males and females. Prior to post-hoc correction, male zebrafish showed higher abundance for the bacterial families Erythrobacteraceae and Lamiaceae, both belonging to the phyla Actinobacteria and Proteobacteria. At the genus level, Lamia and Altererythrobacter were more dominant in males and an unidentified genus in Bacteroidetes was more abundant in females. There were 16 unique differentially expressed transcripts in the gastrointestinal tissue between male and female zebrafish (FDR corrected, p < 0.05). Relative to males, the mRNA expression for trim35-9, slc25a48, chchd3b, csad, and hsd17b3 were lower in female GI while cyp2k6, adra2c, and bckdk were higher in the female GI. Immune and lipid-related gene network expression differed between the sexes (i.e., cholesterol export and metabolism) as well as networks related to gastric motility, gastrointestinal system absorption and digestion. Such data provide clues as to putative differences in gastrointestinal physiology between male and female zebrafish. This study identifies host-transcriptome differences that can be considered when interpreting the microgenderome of zebrafish in studies investigating GI physiology and toxicology of fishes.

Profiling Tryptophan Catabolites of Human Gut Microbiota and Acute-Phase Protein Levels in Neonatal Dried Blood Specimens.

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.

Identification and Characterization of Androgen-Responsive Genes in Zebrafish Embryos.

Responsive genes for fish embryos have been identified so far for some endocrine pathways but not for androgens. Using transcriptome analysis and multiple concentration-response modeling, we identified putative androgen-responsive genes in zebrafish embryos exposed to 0.05-5000 nM 11-ketotestosterone for 24 h. Four selected genes with sigmoidal concentration-dependent expression profiles (EC50 = 6.5-30.0 nM) were characterized in detail. The expression of cyp2k22 and slco1f4 was demonstrated in the pronephros; lipca was detected in the liver, and sult2st3 was found in the olfactory organs and choroid plexus. Their expression domains, the function of human orthologs, and a pathway analysis suggested a role of these genes in the metabolism of hormones. Hence, it was hypothesized that they were induced to compensate for elevated hormone levels. The induction of sult2st3 and cyp2k22 by 11-ketotestosterone was repressed by co-exposure to the androgen receptor antagonist nilutamide supporting a potential androgen receptor mediated regulation. Sensitivity (expressed as EC50 values) of sult2st3 and cyp2k22 gene expression induction after exposure to other steroidal hormones (11-ketotestosterone ∼ testosterone > progesterone > cortisol > ethinylestradiol) correlated with their known binding affinities to zebrafish androgen receptor. Hence, these genes might represent potential markers for screening of androgenic compounds in the zebrafish embryo.

High-throughput concentration-response analysis for omics datasets.

Omics-based methods are increasingly used in current ecotoxicology. Therefore, a large number of observations for various toxic substances and organisms are available and may be used for identifying modes of action, adverse outcome pathways, or novel biomarkers. For these purposes, good statistical analysis of toxicogenomic data is vital. In contrast to established ecotoxicological techniques, concentration-response modeling is rarely used for large datasets. Instead, statistical hypothesis testing is prevalent, which provides only a limited scope for inference. The present study therefore applied automated concentration-response modeling for 3 different ecotoxicotranscriptomic and ecotoxicometabolomic datasets. The modeling process was performed by simultaneously applying 9 different regression models, representing distinct mechanistic, toxicological, and statistical ideas that result in different curve shapes. The best-fitting models were selected by using Akaike's information criterion. The linear and exponential models represented the best data description for more than 50% of responses. Models generating U-shaped curves were frequently selected for transcriptomic signals (30%), and sigmoid models were identified as best fit for many metabolomic signals (21%). Thus, selecting the models from an array of different types seems appropriate, because concentration-response functions may vary because of the observed response type, and they also depend on the compound, the organism, and the investigated concentration and exposure duration range. The application of concentration-response models can help to further tap the potential of omics data and is a necessary step for quantitative mixture effect assessment at the molecular response level.