Combining proteogenomics and metaproteomics for deep taxonomic and functional characterization of microbiomes from a non-sequenced host.
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ABSTRACT:
SUBMITTER: Gouveia D
PROVIDER: S-EPMC7275042 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
Publications
Combining proteogenomics and metaproteomics for deep taxonomic and functional characterization of microbiomes from a non-sequenced host.
Gouveia Duarte D Pible Olivier O Culotta Karen K Jouffret Virginie V Geffard Olivier O Chaumot Arnaud A Degli-Esposti Davide D Armengaud Jean J
NPJ biofilms and microbiomes 20200605 1
Metaproteomics of gut microbiomes from animal hosts lacking a reference genome is challenging. Here we describe a strategy combining high-resolution metaproteomics and host RNA sequencing (RNA-seq) with generalist database searching to survey the digestive tract of Gammarus fossarum, a small crustacean used as a sentinel species in ecotoxicology. This approach provides a deep insight into the full range of biomasses and metabolic activities of the holobiont components, and differentiates between ...[more]
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Project description:IntroductionMetaproteomics is a rapidly advancing field that offers unique insights into the taxonomic composition and the functional activity of microbial communities, and their effects on host physiology. Classically, data-dependent acquisition (DDA) mass spectrometry (MS) has been applied for peptide identification and quantification in metaproteomics. However, DDA-MS exhibits well-known limitations in terms of depth, sensitivity, and reproducibility. Consequently, methodological improvements are required to better characterize the protein landscape of microbiomes and their interactions with the host.MethodsWe present an optimized proteomic workflow that utilizes the information captured by Parallel Accumulation-Serial Fragmentation (PASEF) MS for comprehensive metaproteomic studies in complex fecal samples of mice.Results and discussionWe show that implementing PASEF using a DDA acquisition scheme (DDA-PASEF) increased peptide quantification up to 5 times and reached higher accuracy and reproducibility compared to previously published classical DDA and data-independent acquisition (DIA) methods. Furthermore, we demonstrate that the combination of DIA, PASEF, and neuronal-network-based data analysis, was superior to DDA-PASEF in all mentioned parameters. Importantly, DIA-PASEF expanded the dynamic range towards low-abundant proteins and it doubled the quantification of proteins with unknown or uncharacterized functions. Compared to previous classical DDA metaproteomic studies, DIA-PASEF resulted in the quantification of up to 4 times more taxonomic units using 16 times less injected peptides and 4 times shorter chromatography gradients. Moreover, 131 additional functional pathways distributed across more and even uniquely identified taxa were profiled as revealed by a peptide-centric taxonomic-functional analysis. We tested our workflow on a validated preclinical mouse model of neuropathic pain to assess longitudinal changes in host-gut microbiome interactions associated with pain - an unexplored topic for metaproteomics. We uncovered the significant enrichment of two bacterial classes upon pain, and, in addition, the upregulation of metabolic activities previously linked to chronic pain as well as various hitherto unknown ones. Furthermore, our data revealed pain-associated dynamics of proteome complexes implicated in the crosstalk between the host immune system and the gut microbiome. In conclusion, the DIA-PASEF metaproteomic workflow presented here provides a stepping stone towards a deeper understanding of microbial ecosystems across the breadth of biomedical and biotechnological fields.
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Project description:Stool samples were collected from 4 children and subjected to deep gut metaproteomics analysis for in-depth understanding the functional compositions of human gut mcirobiome.
2017-08-10 | PXD005619 | Pride
Project description:Exploring microbial community compositions in humans with healthy versus diseased states is crucial to understand the microbe-host interplay associated with the disease progression. Although the relationship between oral cancer and microbiome was previously established, it remained controversial, and yet the ecological characteristics and their responses to oral carcinogenesis have not been well studied. Here, using the bacterial 16S rRNA gene amplicon sequencing along with the in silico function analysis by PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2), we systematically characterized the compositions and the ecological drivers of saliva microbiome in the cohorts of orally healthy, non-recurrent oral verrucous hyperplasia (a pre-cancer lesion), and oral verrucous hyperplasia-associated oral cancer at taxonomic and function levels, and compared them with the re-analysis of publicly available datasets. Diversity analyses showed that microbiome dysbiosis in saliva was significantly linked to oral health status. As oral health deteriorated, the number of core species declined, and metabolic pathways predicted by PICRUSt2 were dysregulated. Partitioned beta-diversity revealed an extremely high species turnover but low function turnover. Functional beta-diversity in saliva microbiome shifted from turnover to nestedness during oral carcinogenesis, which was not observed at taxonomic levels. Correspondingly, the quantitative analysis of stochasticity ratios showed that drivers of microbial composition and functional gene content of saliva microbiomes were primarily governed by the stochastic processes, yet the driver of functional gene content shifted toward deterministic processes as oral cancer developed. Re-analysis of publicly accessible datasets supported not only the distinctive family taxa of Veillonellaceae and Actinomycetaceae present in normal cohorts but also that Flavobacteriaceae and Peptostreptococcaceae as well as the dysregulated metabolic pathways of nucleotides, amino acids, fatty acids, and cell structure were related to oral cancer. Using predicted functional profiles to elucidate the correlations to the oral health status shows superior performance than using taxonomic data among different studies. These findings advance our understanding of the oral ecosystem in relation to oral carcinogenesis and provide a new direction to the development of microbiome-based tools to study the interplay of the oral microbiome, metabolites, and host health.
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Project description:Metaproteomics is the characterization of all proteins being expressed by a community of organisms in a complex biological sample at a single point in time. Applications of metaproteomics range from the comparative analysis of environmental samples (such as ocean water and soil) to microbiome data from multicellular organisms (such as the human gut). Metaproteomics research is often focused on the quantitative functional makeup of the metaproteome and which organisms are making those proteins. That is: What are the functions of the currently expressed proteins? How much of the metaproteome is associated with those functions? And, which microorganisms are expressing the proteins that perform those functions? However, traditional protein-centric functional analysis is greatly complicated by the large size, redundancy, and lack of biological annotations for the protein sequences in the database used to search the data. To help address these issues, we have developed an algorithm and web application (dubbed "MetaGOmics") that automates the quantitative functional (using Gene Ontology) and taxonomic analysis of metaproteomics data and subsequent visualization of the results. MetaGOmics is designed to overcome the shortcomings of traditional proteomics analysis when used with metaproteomics data. It is easy to use, requires minimal input, and fully automates most steps of the analysis-including comparing the functional makeup between samples. MetaGOmics is freely available at https://www.yeastrc.org/metagomics/.
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Project description:Ticks transmit the most diverse array of disease agents and harbor one of the most diverse microbial communities. Major progress has been made in the characterization of the taxonomic profiles of tick microbiota. However, the functional profiles of tick microbiome have been comparatively less studied. In this proof of concept we used state-of-the-art functional metagenomics analytical tools to explore previously reported datasets of bacteria found in male and female Ixodes ovatus, Ixodes persulcatus, and Amblyomma variegatum. Results showed that both taxonomic and functional profiles have differences between sexes of the same species. KEGG pathway analysis revealed that male and female of the same species had major differences in the abundance of genes involved in different metabolic pathways including vitamin B, amino acids, carbohydrates, nucleotides, and antibiotics among others. Partial reconstruction of metabolic pathways using KEGG enzymes suggests that tick microbiome form a complex metabolic network that may increase microbial community resilience and adaptability. Linkage analysis between taxonomic and functional profiles showed that among the KEGG enzymes with differential abundance in male and female ticks only 12% were present in single bacterial genera. The rest of these enzymes were found in more than two bacterial genera, and 27% of them were found in five up to ten bacterial genera. Comparison of bacterial genera contributing to the differences in the taxonomic and functional profiles of males and females revealed that while a small group of bacteria has a dual-role, most of the bacteria contribute only to functional or taxonomic differentiation between sexes. Results suggest that the different life styles of male and female ticks exert sex-specific evolutionary pressures that act independently on the phenomes (set of phenotypes) and genomes of bacteria in tick gut microbiota. We conclude that functional redundancy is a fundamental property of male and female tick microbiota and propose that functional metagenomics should be combined with taxonomic profiling of microbiota because both analyses are complementary.
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Project description:We examined metaproteome profiles from two Arctic microbiomes during 10-day shipboard incubations to directly track early functional and taxonomic responses to a simulated algal bloom and an oligotrophic control. Using a novel peptide-based enrichment analysis, significant changes (p-value < 0.01) in biological and molecular functions associated with carbon and nitrogen recycling were observed. Within the first day under both organic matter conditions, Bering Strait surface microbiomes increased protein synthesis, carbohydrate degradation, and cellular redox processes while decreasing C1 metabolism. Taxonomic assignments revealed that the core microbiome collectively responded to algal substrates by assimilating carbon before select taxa utilize and metabolize nitrogen intracellularly. Incubations of Chukchi Sea bottom water microbiomes showed similar, but delayed functional responses to identical treatments. Although 24 functional terms were shared between experimental treatments, the timing, and degree of the remaining responses were highly variable, showing that organic matter perturbation directs community functionality prior to alterations to the taxonomic distribution at the microbiome class level. The dynamic responses of these two oceanic microbial communities have important implications for timing and magnitude of responses to organic perturbations within the Arctic Ocean and how community-level functions may forecast biogeochemical gradients in oceans.
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Project description:With the technological advances of the last decade, it is now feasible to analyze microbiome samples, such as human stool specimens, using multi-omic techniques. Given the inherent sample complexity, there exists a need for sample methods which preserve as much information as possible about the biological system at the time of sampling. Here, we analyzed human stool samples preserved and stored using different methods, applying metagenomics as well as metaproteomics. Our results demonstrate that sample preservation and storage have a significant effect on the taxonomic composition of identified proteins. The overall identification rates, as well as the proportion of proteins from Actinobacteria were much higher when samples were flash frozen. Preservation in RNAlater overall led to fewer protein identifications and a considerable increase in the share of Bacteroidetes, as well as Proteobacteria. Additionally, a decrease in the share of metabolism-related proteins and an increase of the relative amount of proteins involved in the processing of genetic information was observed for RNAlater-stored samples. This suggests that great care should be taken in choosing methods for the preservation and storage of microbiome samples, as well as in comparing the results of analyses using different sampling and storage methods. Flash freezing and subsequent storage at -80 °C should be chosen wherever possible.
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