Bioinformatics For Metaproteomics And Functional Analysis Of Microbial Communities – Distinct microbial taxonomic signatures of the human gut revealed by different methods of sample preparation and microbial cell disruption for metaproteomic analysis.
Using metaproteomics to study the human gut microbiota can shed light on the taxonomic structure and functional role of the microbial community. However, methods of protein extraction from stool samples continue to evolve, in search of appropriate protocols for soaking and dispersing the stool sample and for disrupting bacterial cells, which are two important steps to ensure the recovery of stool proteins. good. Here, we reviewed different stool sample processing (SSP) and microbial cell disruption (CDM) methods. Combining a long digestion time of the core sample in the tube spinner with sonication increased the total number of identified peptides and proteins. Identification of Proteobacteria, Bacteroidetes, Planctomycetes and Euryarchaeota was favored by mechanical disruption of cells with glass beads. In contrast, the abundance of Firmicutes, Actinobacteria, and Fusobacteria was enhanced when sonication was performed prior to bead impact. Identification of Tenericutes and Apicomplexa was improved by wetting faecal samples during processing and disrupting cells with mixed medium-sized glass beads with or without sonication. Identification of human proteins was influenced by sonication. To test the reproducibility of this intestinal metaproteomic analysis, we analyzed samples from six healthy individuals using a protocol that had shown good taxonomic diversity and protein identification from Proteobacteria and humans. We also identified proteins involved in host-dependent and highly taxa-specific viral functions, such as B12 biosynthesis and short-chain fatty acid (SCFA) production, which are mainly carried out by members of the genera Prevotella and Firmicutes respectively. The taxonomic and functional profiles obtained with the different protocols described in this paper provide the researcher with important information for choosing the most appropriate protocol for the study of several pathologies suspected to be related to a specific taxon from the gut microbiota.
Bioinformatics For Metaproteomics And Functional Analysis Of Microbial Communities
The human gut microbiota is a complex community of microbes living in the human gut. The gut microbiota includes about a thousand species of bacteria, in addition to archaea, fungi, insects and viruses (Alarcón et al., 2016; Lloyd-Price et al., 2016). If this ecosystem is moderate and shows high diversity, its close relationship with the host provides beneficial effects, such as the digestion of volatile foods, protection from infection, immunomodulation and the production of vitamins and other useful products (Jandhyala et al ., 2015). However, despite its great resilience, the gut microbiota is affected by many factors, including diet, age, pollution and antibiotic use, among others (Jakobsson et al., 2010; David et al., 2014; Jin et al., 2014). al., 2017; Kim and Jazwinski, 2018; Adak and Khan, 2019). These factors can affect its composition and diversity, leading in some cases to dysbiosis. Although it remains unclear whether dysbiosis is a cause or consequence of pathological environments, the link between pathological environments and gut microbiota dysbiosis is a proven fact. Gut microbiota dysbiosis has previously been associated with several diseases, including gastrointestinal diseases (Ni et al., 2017; Saffouri et al., 2019), cancer (Wang et al., 2019), Alzheimer’s disease (Kowalski and Mulak , 2019), and even autism (Fattorusso et al., 2019). Furthermore, the microbiota-gut-brain axis is the link between gut microbiota and host pathologies related to mental states (Wang and Wang, 2016). This connection shows the importance of the metabolic processes carried out by the intestinal microbiota, which produce a high variety of enzymes and metabolites that perform several functions in different parts of the body, not only in the intestinal tract.
Predictive Functional Profiles Using Metagenomic 16s Rrna Data: A Novel Approach To Understanding The Microbial Ecology Of Aquaculture Systems
Currently, metagenomics is the most common “omic” method to study the microbiota, due to its ability to provide important information about microbial complexity (Jovel et al., 2016). However, this strategy is unable to provide useful information. In this context, metaproteomics is a promising method for studying the gut microbiota, both from a taxonomic and a functional point of view. Indeed, metaproteomics can reveal the key metabolic and functional roles played by the various microorganisms present in the gut microbiota (Zhang et al., 2016; Issa Isaac et al., 2019). Furthermore, this method can provide information about the proteins that mediate interactions between the gut microbiota and the host (Blackburn and Martens, 2016). Therefore, metaproteomics can provide a better understanding of the functional roles of the gut microbiota, compared to other “omic” methods (Zhang et al., 2019).
Despite the progress of metaproteomic techniques in recent years (Zhang and Figeys, 2019), the complexity of microbiota samples, usually obtained from stool samples, has made it difficult to develop an appropriate protocol to enhance virus recovery. First, the microbial cells must be isolated and extracted from the fecal sample. In this regard, differential centrifugation was found to give good results in the enrichment of the bacterial fraction (> 90%), indicating the retention of the microbial cell and other fecal fractions (Apajalahti et al., 1998). Dance etc. (2015) also investigated the effectiveness of this method. In both tasks, and prior to this differential centrifugation, the faecal sample was moistened in the tube spinner for several minutes. In contrast, Zhang et al. (2018) replaced this several-minute tube circulating step of humectation and diffusion of the faecal sample with large-sized glass beads to save time in sample processing. In addition, an excellent microbial cell disruption method (CDM) is needed to ensure that protein identification is as complete as possible. Because protein extraction methods can affect metaproteomic results (Zhang et al., 2018), the protocol should be developed to produce representative taxonomic profiles and identify the most important metabolic functions performed by gut microbiota. Regarding the disruption of microbial cells, several methods have been used in microbiota studies by different authors using different lysis buffers and different mechanical disruption methods. Regarding lysis buffers, these studies showed that SDS-containing buffer was the one that gave the best results in protein yield compared to other buffers tested (Zhang et al., 2018). In terms of electrical perturbations, shock absorption and sonication are the two main methods for this purpose and have been widely used in various metaproteomic studies (Santiago et al., 2014; Tanca et al., 2014; Zhang et al., 2016). . . Regarding bead beating, the appropriate bead size to use in gut microbiota studies is not yet well defined. Small beads (0.5 mm) are often used. . used to damage yeast cells (Pitarch et al., 2008). A combination of different beads has been shown to better remove proteins from gram-positive bacteria and yeast (Hayoun et al., 2019). So we wanted to test several individual protein extraction processes using different bead size combinations or not with an additional sonication step prior to bead beating.
In addition to optimizing the extraction of microbial proteins from stool samples, bioinformatics is an important step in the development of metaproteomics. There are various software for peptide identification, such as MaxQuant (Cox and Mann, 2008) or X! TANDEM (Craig and Beavis, 2004). Moreover, there are several tools that allow the activity of identified peptides (Sajulga et al., 2020). There are also various options available that combine both peptide identification and functional expression, such as MetaProteomeAnalyzer (Muth et al., 2015), Unipept (Mesuere et al., 2016) or MetaLab (Cheng et al., 2017 ). These software are open source and easy to use tools. The use of this software has facilitated the analysis of large amounts of data generated in metaproteomic studies of the human gut microbiota. We used MetaLab software for the bioinformatic analysis performed in this work. This software takes a protein dataset that was generated from more than 1000 human microbiota samples, which allows for a high number of peptide/protein identifications. As explained earlier, the software also allows functional methods to be assigned to proteins identified at specific taxonomic levels.
Metaproteomics Insights Into Traditional Fermented Foods And Beverages
In this study, we aimed to compare the isolation and characterization of human gut microbiota by analyzing a stool sample with different protocols combining different sample processing and microbial CDM. We evaluated the effectiveness of one of these tested protocols by comparing and contrasting six samples of the human gut microbiome, according to their taxonomic profiles and protein functions identified in each sample.
To test different protocols, a stool sample from a healthy volunteer (henceforth referred to as H1) was used. To perform the metaproteomic study, stool samples were collected from 6 healthy adult volunteers with their informed consent. The six samples consisted of three women and three men, aged between 31 and 52 years and with names from S1 to S6. None of them had been under antibiotics during the previous year of sampling. Only one subject reported gastrointestinal problems within 3 months prior to sampling. The nutritional status of the volunteers is unknown. The supernatant was stored at -80°C until processing.
We included two different stool sample processing (SSP) and three CDM. All these protocols are shown in Figure 1. Each protocol was performed in triplicate.
Critical Assessment Of Metaproteome Investigation (campi): A Multi Laboratory Comparison Of Established Workflows
Figure 1. (A) Workflow of different SSP (stool sample processing) and
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