High-calorie diets lead to hepatic steatosis and to the development of non-alcoholic fatty liver disease (NAFLD), which can evolve over many years into the inflammatory form of non-alcoholic steatohepatitis (NASH), posing a risk for the development of hepatocellular carcinoma (HCC). Due to diet and liver alteration, the axis between liver and gut is disturbed, resulting in gut microbiome alterations. Consequently, detecting these gut microbiome alterations represents a promising strategy for early NASH and HCC detection. We analyzed medical parameters and the fecal metaproteome of 19 healthy controls, 32 NASH patients, and 29 HCC patients, targeting the discovery of diagnostic biomarkers. Here, NASH and HCC resulted in increased inflammation status and shifts within the composition of the gut microbiome. An increased abundance of kielin/chordin, E3 ubiquitin ligase, and nucleophosmin 1 represented valuable fecal biomarkers, indicating disease-related changes in the liver. Although a single biomarker failed to separate NASH and HCC, machine learning-based classification algorithms provided an 86% accuracy in distinguishing between controls, NASH, and HCC. Fecal metaproteomics enables early detection of NASH and HCC by providing single biomarkers and machine learning-based metaprotein panels.

The gut microbiome is an important contributor to the development and the course of inflammatory bowel disease (IBD). While changes in the gut microbiome composition were observed in response to IBD therapy using biologics, studies elucidating human and microbial proteins and pathways in dependence on therapy success are sparse. Fecal samples of a cohort of IBD patients were collected before and after 14 weeks of treatment with three different biologics. Clinical disease activity scores were used to determine the clinical response and remission. Fecal metaproteomes of remitting patients (n = 12) and of non-remitting patients (n = 12) were compared before treatment and changes within both groups were assessed over sampling time to identify functional changes and potential human and microbial biomarkers. The abundance of proteins associated with neutrophilic granulocytes, and immunoglobulins significantly decreased in remitting patients. There were changes in pathways of microbial metabolism in samples from patients with remission after therapy, including an increased butyrate fermentation. Distinct changes of proteins related to gut inflammation and gut microbiome metabolism showed whether IBD remission was achieved or not. This suggests that metaproteomics could be a useful tool for monitoring remission in IBD therapies. SIGNIFICANCE: IBD is rising in incidence, especially in newly industrialized countries, and the microbiome is an important contributor to its pathogenesis. Despite manifold therapeutical options, achieving remission is often ineffective, and choosing new alternative drugs remains often empirical. Therefore, efficient tools for monitoring therapeutic response and assessing the effectiveness of drugs in specific patients are mandatory. In the present study, we show that the use of metaproteomics is a promising avenue to address these challenges. We observed the amelioration of inflammation and restoration of a healthy microbiome in remitting patients in contrast to non-remitting patients. Therefore, metaproteomics is a valuable tool for monitoring the therapy success in IBD.

Background Fecal samples are widely used as a proxy for studying gut microbiome composition in both human and animal research. Fecal metaproteomics provides valuable insights by tracking changes in the relative abundance of microbial taxa and their protein functions. To ensure reliable results, it is crucial to minimize alterations in the metaproteome occurring from sample collection to protein extraction. Therefore, employing effective stabilization methods is essential to preserve the integrity of the fecal metaproteome from sample collection to laboratory analysis, particularly over long distances or when rapid freezing options are not readily available. In line with these needs, the second edition of the Critical Assessment of MetaProteome Investigation (CAMPI-2) was specifically focused on improving sample stabilization prior to metaproteomic analysis. Results This collaborative multicenter study assessed the ability of five different stabilization methods, based on two commercial devices and three specific reagents (acetone, lithium dodecyl sulfate, and an RNAlater-like buffer), respectively, to stabilize the fecal metaproteome during room-temperature storage (14 days) and shipment to mass spectrometry facilities. The five methods were tested simultaneously by eight different laboratories across Europe, using aliquots from the same fecal sample. After protein extraction and digestion, duplicate aliquots of the resulting peptides were analyzed independently by two mass spectrometry facilities at distinct international locations. Analysis of the mass spectrometric data using two different search engines revealed that the fecal metaproteome profile differed considerably depending on the stabilization method used in terms of richness, alpha and beta diversity, reproducibility, and quantitative distribution of main taxa and functions. Although each method showed unique strengths and weaknesses, a commercial swab-based device stood out for its remarkable reproducibility and ranked highest for most of the metrics measured. Conclusions CAMPI-2 allowed a robust evaluation of five different methods for preserving fecal metaproteome samples. The present investigation provides useful data for the design of metaproteomics and multi-omics studies where fecal sampling cannot be immediately followed by long-term storage at −80°C. Further optimization of the tested protocols is necessary to improve stabilization efficiency and control bias in the taxonomic and functional profile of the gut microbiome.

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.

Background: Metaproteomics enables functional insight into microbial communities by identifying and quantifying proteins in complex samples. Yet, heterogeneous analytical workflows and the lack of standardization across experimental and bioinformatic stages hinder reproducibility and comparability, limiting integration with other omics data. Results: We here present a community-developed reporting checklist tailored to the specific needs of metaproteomics. We also outline current efforts to enable structured and interoperable metadata capture, drawing on standards from proteomics and microbiome research wherever possible. Conclusions: By promoting transparent reporting and advancing metadata practices, our recommendations aim to align metaproteomics more closely with FAIR principles and support reproducible and interoperable research practices.

Background The gut microbiome is an important contributor to the development and the course of inflammatory bowel disease (IBD). While changes in the gut microbiome composition were observed in response to IBD therapy using biologics, studies elucidating human and microbial proteins and pathways in dependence on therapy success are sparse. Methods Fecal samples of a cohort of IBD patients were collected before and after 14 weeks of treatment with three different biologics. Clinical disease activity scores were used to determine the clinical response and remission. Fecal metaproteomes of remitting patients (n=12) and of non-remitting patients (n=12) were compared before treatment and changes within both groups were assessed over sampling time to identify functional changes and potential human and microbial biomarkers. Results The abundance of proteins associated with the intestinal barrier, neutrophilic granulocytes, and immunoglobulins significantly decreased in remitting patients. In contrast, an increase of those proteins was observed in non-remitting patients. There were significant changes in pathways of microbial metabolism in samples from patients with remission after therapy. This included, for example, an increased abundance of proteins from butyrate fermentation. Finally, new potential biomarkers for the prediction and monitoring of therapy success could be identified, e.g. human lysosome-associated membrane glycoprotein 1, a cytotoxicity marker, or microbial anthranilate synthase component 2, a part of the tryptophan metabolism. Conclusions Distinct changes of proteins related to gut inflammation and gut microbiome metabolism showed whether IBD remission was achieved or not. This suggests that metaproteomics could be a useful tool for monitoring remission in IBD therapies.