Bridging the Gap between Gut Microbiota and Alzheimer's Disease: A Metaproteomic Approach for Biomarker Discovery in Transgenic Mice

Unraveling the gut-brain axis: the impact of steroid hormones and nutrition on Parkinson's disease

This comprehensive review explores the intricate relationship between nutrition, the gut microbiome, steroid hormones, and Parkinson's disease within the context of the gut-brain axis. The gut-brain axis plays a pivotal role in neurodegenerative diseases like Parkinson's disease, encompassing diverse components such as the gut microbiota, immune system, metabolism, and neural pathways. The gut microbiome, profoundly influenced by dietary factors, emerges as a key player. Nutrition during the first 1000 days of life shapes the gut microbiota composition, influencing immune responses and impacting both child development and adult health. High-fat, high-sugar diets can disrupt this delicate balance, contributing to inflammation and immune dysfunction. Exploring nutritional strategies, the Mediterranean diet's anti-inflammatory and antioxidant properties show promise in reducing Parkinson's disease risk. Microbiome-targeted dietary approaches and the ketogenic diet hold the potential in improving brain disorders. Beyond nutrition, emerging research uncovers potential interactions between steroid hormones, nutrition, and Parkinson's disease. Progesterone, with its anti-inflammatory properties and presence in the nervous system, offers a novel option for Parkinson's disease therapy. Its ability to enhance neuroprotection within the enteric nervous system presents exciting prospects. The review addresses the hypothesis that α-synuclein aggregates originate from the gut and may enter the brain via the vagus nerve. Gastrointestinal symptoms preceding motor symptoms support this hypothesis. Dysfunctional gut-brain signaling during gut dysbiosis contributes to inflammation and neurotransmitter imbalances, emphasizing the potential of microbiota-based interventions. In summary, this review uncovers the complex web of interactions between nutrition, the gut microbiome, steroid hormones, and Parkinson's disease within the gut-brain axis framework. Understanding these connections not only offers novel therapeutic insights but also illuminates the origins of neurodegenerative diseases such as Parkinson's disease.

A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease

Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification, and prioritization of microbial proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant [to generate peptide-spectral matches (PSMs) and quantification], PepQuery2 (to verify the quality of PSMs), Unipept (for taxonomic and functional annotation), and MSstatsTMT (for statistical analysis). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.

IMPORTANCE

Clinical metaproteomics has immense potential to offer functional insights into the microbiome and its contributions to human disease. However, there are numerous challenges in the metaproteomic analysis of clinical samples, including handling of very large protein sequence databases for sensitive and accurate peptide and protein identification from mass spectrometry data, as well as taxonomic and functional annotation of quantified peptides and proteins to enable interpretation of results. To address these challenges, we have developed a novel clinical metaproteomics workflow that provides customized bioinformatic identification, verification, quantification, and taxonomic and functional annotation. This bioinformatic workflow is implemented in the Galaxy ecosystem and has been used to characterize diverse clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness and availability for use by the research community via analysis of residual fluid from cervical swabs.

A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease

Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, which are usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification and prioritization of microbial and host proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant (to generate peptide-spectral matches (PSMs) and quantification), PepQuery2 (to verify the quality of PSMs), and Unipept and MSstatsTMT (for taxonomy and functional annotation). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.