Aim: The role of intestinal fungi in human health and disease is becoming more evident. The mycobiota composition and diversity of preterm infants is affected by interactions with bacteria and clinical variables. In this study, we aimed to characterize the composition and the diversity of the preterm infant mycobiota and the effect of clinical variables on it in the first six postnatal weeks. Methods: Preterm infants (n = 50) and full-term infants (n = 6) admitted to Isala Women and Children’s hospital (Zwolle, The Netherlands) who were born during 24-36 or 37-40 weeks of gestation, respectively, were included in this study. Feces were collected during the first six postnatal weeks (n = 109) and their mycobiota composition and diversity were characterized by ITS2 amplicon sequencing. Results: Composition analyses identified fungi and other eukaryotic kingdoms, of which Viridiplantae was most abundant. Of the fungal kingdom, Ascomycota and Basidiomycota were the first and second most prominent phyla in early life of all infants. Candida was the most abundant genus in the first six weeks of life and increased with gestational and postnatal age. Fungal phylogenetic diversity remained stable in the first six postnatal weeks. The individuality and the mode of delivery were identified as significant predictors for the variation in the mycobiota composition. Vaginally delivered infants were enriched in Candida spp., whereas infants delivered through emergency C-section were characterized by Malassezia spp. Conclusion: These results indicate that fungi and other eukaryotic kingdoms are detected in the intestine of preterm and full-term infants in the first six postnatal weeks. Similar to the microbiota, colonization of the preterm intestine with fungi is determined by clinical variables including individuality and mode of delivery.

Metaproteomics, an emerging field among the omic techniques, holds great promise for unraveling the function of microbiomes in host health and our environment. Metaproteomics can also be a valuable addition to multiomics studies of the microbiome, complementing genome‐resolved metagenomics, metatranscriptomics, and metabolomics. The potential advancements from metaproteomics and multiomics research touch a breadth of disciplines, including ecology, biochemistry, immunology, medical microbiology, cell physiology, and medicine, and could lead to both fundamental and applied discoveries. However, there are significant roadblocks to widespread adoption of metaproteomics among microbiome researchers. In this Viewpoint article, we highlight the pivotal role of metaproteomics in microbiome research by showcasing its advantages, exploring opportunities to overcome challenges, and paving the way for its broader adoption as a mainstream technique. We hope that the recommendations provided in this Viewpoint article will inspire new, beneficial collaborations between proteomics experts, algorithm and infrastructure developers, biochemists, cell biologists, and microbiologists, enabling the construction of a knowledge base of microbiome function that can have an immediate and direct impact on host health and the environment.

Aim: The bacterial microbiota is well-recognized for its role in Clostridioides difficile colonization and infection, while fungi and yeasts remain understudied. The aim of this study was to analyze the predictive value of the mycobiota and its interactions with the bacterial microbiota in light of C. difficile colonization and infection. Methods: The mycobiota was profiled by ITS2 sequencing of fecal DNA from C. difficile infection (CDI) patients (n = 29), asymptomatically C. difficile colonization (CDC) patients (n = 38), and hospitalized controls with C. difficile negative stool culture (controls; n = 38). Previously published 16S rRNA gene sequencing data of the same cohort were used additionally for machine learning and fungal-bacterial network analysis. Results: CDI patients were characterized by a significantly higher abundance of Candida spp. (MD 0.270 ± 0.089, P = 0.002) and Candida albicans (MD 0.165 ± 0.082, P = 0.023) compared to controls. Additionally, they were deprived of Aspergillus spp. (MD -0.067 ± 0.026, P = 0.000) and Penicillium spp. (MD -0.118 ± 0.043, P = 0.000) compared to CDC patients. Network analysis revealed a positive association between several fungi and bacteria in CDI and CDC, although the analysis did not reveal a direct association between Clostridioides spp. and fungi. Furthermore, the microbiota machine learning model outperformed the models based on the mycobiota and the joint microbiota-mycobiota model. The microbiota classifier successfully distinguished CDI from CDC [Area Under the Receiver Operating Characteristic (AUROC) = 0.884] and CDI from controls (AUROC = 0.905). Blautia and Bifidobacterium were marker genera associated with CDC patients and controls. Conclusion: The gut mycobiota differs between CDI, CDC, and controls and may affect Clostridioides spp. through indirect interactions. The mycobiota data alone could not successfully discriminate CDC from controls or CDI patients and did not have additional predictive value to the bacterial microbiota data. The identification of bacterial marker genera associated with CDC and controls warrants further investigation.

Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science.

Oral vancomycin has a long history as the first-line treatment for Clostridioides difficile infection (CDI), but its use is associated with high relapse rates. Antibiotics that more selectively target C. difficile while sparing protective commensal gut bacteria, have the potential to prevent recurrent CDI (rCDI). Here, we investigate the experimental glycopeptide antibiotic, EVG7, in the context of rCDI. In vitro susceptibility assays reveal that clinical C. difficile isolates are up to 16-times more sensitive to EVG7 (MIC = 0.063–0.25 mg/L) compared to vancomycin (MIC = 0.5–2 mg/L). In a validated mouse model of rCDI in male mice, low dose oral EVG7 (0.04 mg/mL in drinking water) more effectively treats primary CDI and prevents recurrence, outperforming a 10-fold higher dose of vancomycin. Subsequent microbiome analysis and in vitro susceptibility testing reveal that EVG7 preserves Lachnospiraceae, a family of commensal bacteria associated with protection against C. difficile colonization.

The nutritional requirements of preterm infants are challenging to meet in neonatal care, yet crucial for their growth, development and health. Aberrant maturation of the gastrointestinal tract and the microbiota could affect the digestion of human milk and its nutritional value considerably. Therefore, the main objective of the proposed research is to investigate how the intestinal microbiota of preterm and full-term infants differ in their ability to extract energy and nutrients from oligosaccharides and glycoproteins in human milk. This pilot study will be an observational, single-center study performed at the Neonatal Intensive Care Unit at Isala Women and Children’s Hospital (Zwolle, The Netherlands). A cohort of thirty mother–infant pairs (preterm ≤30 weeks of gestation, n = 15; full-term 37–42 weeks of gestation, n = 15) will be followed during the first six postnatal weeks with follow-up at three- and six-months postnatal age. We will collect human milk of all mothers, gastric aspirates of preterm infants and fecal samples of all infants. A combination of 16S rRNA amplicon sequencing, proteomics, peptidomics, carbohydrate analysis and calorimetric measurements will be performed. The role of the microbiota in infant growth and development is often overlooked yet offers opportunities to advance neonatal care. The ‘From Mum to Bum’ study is the first study in which the effect of a preterm gut microbiota composition on its metabolic capacity and subsequent infant growth and development is investigated. By collecting human milk of all mothers, gastric aspirates of preterm infants and fecal samples of all infants at each timepoint, we can follow digestion of human milk from the breast of the mother throughout the gastrointestinal tract of the infant, or ‘From Mum to Bum’.