Host Factors Associated with Gut Mycobiome Structure.

Recent studies revealed a significant role of the gut fungal community in human health. Here, we investigated the content and variation of gut mycobiota among subjects from the European population. We explored the interplay between gut fungi and various host-related sociodemographic, lifestyle, health, and dietary factors. The study included 923 participants. Fecal DNA samples were analyzed by whole-metagenome high-throughput sequencing. Subsequently, fungi taxonomic profiles were determined and accompanied by computational and statistical analyses of the association with 53 host-related factors. Fungal communities were characterized by a high prevalence of Saccharomyces, Candida, and Sporisorium. Ten factors were found to correlate significantly with the overall mycobiota variation. Most were diet related, including the consumption of chips, meat, sodas, sweetening, processed food, and alcohol, followed by age and marital status. Differences in α- and/or ß-diversity were also reported for other factors such as body mass index (BMI), job type, autoimmunological diseases, and probiotics. Differential abundance analysis revealed fungal species that exhibited different patterns of changes under specific conditions. The human gut mycobiota is dominated by yeast, including Saccharomyces, Malassezia, and Candida. Although intervolunteer variability was high, several fungal species persisted across most samples, which may be evidence that a core gut mycobiota exists. Moreover, we showed that host-related factors such as diet, age, and marital status influence the variability of gut mycobiota. To our knowledge, this is the first large and comprehensive study of the European cohort in terms of gut mycobiota associations with such an extensive and differentiated host-related set of factors. IMPORTANCE The human gut is inhabited by many organisms, including bacteria and fungi, that may affect human health. However, research on human gut mycobiome is still rare. Moreover, the large European-based cohort study is missing. Here, we analyzed the first large European cohort in terms of gut mycobiota associations with a differentiated host-related set of factors. Our results showed that chips, meat, sodas, sweetening, processed food, beer, alcohol consumption, age, and marital status were associated with the variability of gut mycobiota. Moreover, our analysis revealed changes in abundances at the fungal species level for many investigated factors. Our results can suggest potentially valuable paths for further, narrowly focused research on gut mycobiome and its impact on human health. In the coming era of gut microbiome-based precision medicine, further research into the relationship between different mycobial structures and host-related factors may result in new preventive approaches or therapeutic procedures.

Machine learning on the road to unlocking microbiota's potential for boosting immune checkpoint therapy.

The intestinal microbiota is a complex and diverse ecological community that fulfills multiple functions and substantially impacts human health. Despite its plasticity, unfavorable conditions can cause perturbations leading to so-called dysbiosis, which have been connected to multiple diseases. Unfortunately, understanding the mechanisms underlying the crosstalk between those microorganisms and their host is proving to be difficult. Traditionally used bioinformatic tools have difficulties to fully exploit big data generated for this purpose by modern high throughput screens. Machine Learning (ML) may be a potential means of solving such problems, but it requires diligent application to allow for drawing valid conclusions. This is especially crucial as gaining insight into the mechanistic basis of microbial impact on human health is highly anticipated in numerous fields of study. This includes oncology, where growing amounts of studies implicate the gut ecosystems in both cancerogenesis and antineoplastic treatment outcomes. Based on these reports and first signs of clinical benefits related to microbiota modulation in human trials, hopes are rising for the development of microbiome-derived diagnostics and therapeutics. In this mini-review, we're inspecting analytical approaches used to uncover the role of gut microbiome in immune checkpoint therapy (ICT) with the use of shotgun metagenomic sequencing (SMS) data.

Metabolomics Comparison of Drug-Resistant and Drug-Susceptible Pseudomonas aeruginosa Strain (Intra- and Extracellular Analysis).

Pseudomonas aeruginosa is a common human pathogen belonging to the ESKAPE group. The multidrug resistance of bacteria is a considerable problem in treating patients and may lead to increased morbidity and mortality rate. The natural resistance in these organisms is caused by the production of specific enzymes and biofilm formation, while acquired resistance is multifactorial. Precise recognition of potential antibiotic resistance on different molecular levels is essential. Metabolomics tools may aid in the observation of the flux of low molecular weight compounds in biochemical pathways yielding additional information about drug-resistant bacteria. In this study, the metabolisms of two P. aeruginosa strains were compared-antibiotic susceptible vs. resistant. Analysis was performed on both intra- and extracellular metabolites. The 1H NMR method was used together with multivariate and univariate data analysis, additionally analysis of the metabolic pathways with the FELLA package was performed. The results revealed the differences in P. aeruginosa metabolism of drug-resistant and drug-susceptible strains and provided direct molecular information about P. aeruginosa response for different types of antibiotics. The most significant differences were found in the turnover of amino acids. This study can be a valuable source of information to complement research on drug resistance in P. aeruginosa.

Disease Differentiation and Monitoring of Anti-TNF Treatment in Rheumatoid Arthritis and Spondyloarthropathies.

Rheumatoid arthritis (RA), ankylosing spondylitis (AS), and psoriatic arthritis (PsA) are comprehensive immunological disorders. The treatment of these disorders is limited to ameliorating the symptoms and improving the quality of life of patients. In this study, serum samples from RA, AS, and PsA patients were analyzed with metabolomic tools employing the 1H NMR method in combination with univariate and multivariate analyses. The results obtained in this study showed that the changes in metabolites were the highest for AS > RA > PsA. The study demonstrated that the time until remission or until low disease activity is achieved is shortest (approximately three months) for AS, longer for RA and longest for PsA. The statistically common metabolite that was found to be negatively correlated with the healing processes of these disorders is ethanol, which may indicate the involvement of the gut microflora and/or the breakdown of malondialdehyde as a cell membrane lipid peroxide product.