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Proteomics Approaches to Uncover the Drug Resistance Mechanisms of Microbial Biofilms
Published in Chaminda Jayampath Seneviratne, Microbial Biofilms, 2017
Chaminda Jayampath Seneviratne, Tanujaa Suriyanarayanan, Lin Qingsong, Juan Antonio Vizcaíno
Most of the existing studies on microbial biofilms have been performed under in vitro conditions simulating in vivo biofilms. Notwithstanding the insight gained from in vitro studies, it is obvious that in vitro biofilms are not equivalent to their in vivo counterparts. Therefore, it is worth looking into the data derived from in vivo or clinical study samples. The introduction of metaproteomics has made such studies possible. Metaproteomics can be defined as the large-scale characterisation of the entire protein complement of environmental microbiota at a given point in time. It has been used to study complex samples derived from clinical settings, and natural ecosystems such as wastewaters, or acid mine drainage, among many other applications [82,162]. Metaproteomics tools have facilitated the study of microbial communities both at a functional biomolecular and a whole-community level [163,164].
Gut Microbiome
Published in Nathalie Bergeron, Patty W. Siri-Tarino, George A. Bray, Ronald M. Krauss, Nutrition and Cardiometabolic Health, 2017
Brian J. Bennett, Katie A. Meyer, Nathalie Bergeron, Patty W. Siri-Tarino, George A. Bray, Ronald M. Krauss
The classification schemes described earlier focus on 16S rRNA characterizations of the microbiota. An alternative approach is to focus on the overall functional characteristics of the microbiota. Although relatively fewer in number, studies that have conducted whole-genome metagenomics have shown that the large variation observed in compositional measures of the microbiota does not imply large variability in the functional potential or activity of the microbiota. In fact, these studies point to clear redundancy in gene presence and expression, suggesting a core set of activities that can be fulfilled by different microbiota (Turnbaugh et al. 2009, Qin et al. 2010, Human Microbiome Project, Consortium 2012). We know from shotgun metagenomics studies that the total microbial community DNA encompasses a rich set of genes involved with carbohydrate and amino acid metabolism, illustrating a core functional role of the gut microbiome in digestion and metabolism. The presence of specific microbial genes only reflects functional potential, and to truly characterize functional activity of the microbiome, it will be necessary to employ microbial measures of messenger RNA (metatranscriptomics), proteins (metaproteomics), and metabolites (metametabolomics) (Integrative 2014). These approaches may reveal variability related to health and disease not captured through 16S rRNA characterization of the microbiota and contribute to understanding a healthy core microbiome from a functional perspective. For example, a study utilizing metagenomics and metatranscriptomics analysis revealed that there is greater variability in gene expression than in gene presence (Franzosa et al. 2014). Similarly, proteins related to carbohydrate metabolism have been shown to be expressed at a level greater than expected from metagenomics profiles (Verberkmoes et al. 2009). Furthermore, there is an indication that copy-number variation may also impact the functional capacity of the microbiota (Greenblum, Carr, and Borenstein 2015). Understanding the factors that regulate differences in microbiota gene expression and their relationship to disease is still a critical gap in our knowledge.
Advances in the clinical use of metaproteomics
Published in Expert Review of Proteomics, 2023
Maximilian Wolf, Kay Schallert, Luca Knipper, Albert Sickmann, Alexander Sczyrba, Dirk Benndorf, Robert Heyer
In recent years, the impact of human microbiomes on health and disease has been recognized, and metaproteomics has evolved into an important method to investigate in particular the functional composition of these microbiomes. Due to the novelty of this approach, several explorative studies on a broad spectrum of diseases and matrices have been published, focusing on reaching a comprehensive insight into clinical samples and improving the sensitivity of the analysis. The technical improvements discussed above show that there is now an emerging interest in improving the validity of research results for clinical implementation in research studies and diagnostics. These improvements already pave the road to high throughput, reproducibility, and sensitivity, especially enabled by 96-well formats, automation, advances in mass spectrometry, and carefully planned bioinformatics solutions.
An overview of technologies for MS-based proteomics-centric multi-omics
Published in Expert Review of Proteomics, 2022
Andrew T. Rajczewski, Pratik D. Jagtap, Timothy J. Griffin
Another emerging area that fits in the scope of MS-based proteomic-centered multi-omics is the field of metaproteomics [152]. Metaproteomics incorporates metagenome information on microbial communities from a wide-variety of settings – from human host samples to complex samples (e.g. wastewater, soil) relevant to environmental studies. These multi-omic data can be used to create large protein sequence databases of potential microbe-derived proteins within these samples, which are then used to search for MS/MS data generated from these samples. When analyzed with specialized multi-omic tools [153], the results provide a unique snapshot of the functional proteins expressed by microbial communities, which may drive host biology or regulate characteristics of complex ecological systems. These results can also help identify potential metabolic pathways and small molecules generated by the microbiota that play a role in interactions and regulatory mechanisms. Metaproteomics also expands the reach of proteomic-centered multi-omics to studying flora, fauna, and microbial communities responding to environmental factors (e.g. climate change, pollution [154], bioremediation [155]) in addition to biomedical applications [156].
An integrated workflow for enhanced taxonomic and functional coverage of the mouse fecal metaproteome
Published in Gut Microbes, 2021
Nicolas Nalpas, Lesley Hoyles, Viktoria Anselm, Tariq Ganief, Laura Martinez-Gili, Cristina Grau, Irina Droste-Borel, Laetitia Davidovic, Xavier Altafaj, Marc-Emmanuel Dumas, Boris Macek
To prioritize the KEGG categories, we selected eight categories differing significantly in terms of gene-protein correlation in comparison to the overall correlation (Figures 4c and S4D). Among the KEGG categories displaying higher abundance in the metaproteome compared to the metagenome were the membrane transport, translation, signaling and cellular processes, and genetic information processing. Conversely, transcription, carbohydrate metabolism and antimicrobial drug resistance exhibited lower abundance. The KEGG Orthology (KO) entries differing significantly in abundance between the metagenomes and metaproteomes were identified via t-test and used for gene set enrichment analysis (GSEA). GSEA revealed an enrichment of a number of overlapping KEGG pathways, with 19 and 6 pathways positively and negatively enriched, respectively (Figure 4d, Table S4). Interestingly, we found the ribosome pathway enriched in protein with increased abundance (between metaproteome and metagenome datasets), therefore highlighting the functional activation of this pathway (Figure 4e and S4E). Conversely, homologous recombination, DNA replication and mismatch repair were enriched in protein with decreased abundance, suggesting no or low activation of these pathways. Overall, our findings highlight the critical importance of metaproteomics to characterize microbiome samples particularly when it comes to their functional activity.