The impact of sequence database choice on metaproteomic results in gut microbiota studies

Tanca, Alessandro; Palomba, Antonio; Fraumene, Cristina; Pagnozzi, Daniela; Manghina, Valeria; Deligios, Massimo; Muth, Thilo; Rapp, Erdmann; Martens, Lennart; Addis, Maria Filippa; Uzzau, Sergio

doi:10.1186/s40168-016-0196-8

Cited by 111 publications

(145 citation statements)

References 54 publications

Supporting

Mentioning

133

Contrasting

Unclassified

Order By: Relevance

“…With our tailored database we obtained four times more peptides identified than using the NIH Gastrointestinal database. These results are consistent with previous studies on the use of metagenomic sequences for constructing proteogenomics databases [37].…”

Section: Metaproteomic Data Analysissupporting

confidence: 93%

gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms

Muñoz-Benavent

Hartkopf

Bossche

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Background The study of bacterial symbioses has grown exponentially in the recent past. However, existing bioinformatic workflows of microbiome data analysis do commonly not integrate multiple meta-omics levels and are mainly geared towards human microbiomes. Microbiota are better understood when analyzed in their biological context, which is together with their host or environment, but this is a limitation when studying non-model organisms mainly due to the lack of well-annotated sequence references. Results Here, we present gNOMO, a bioinformatic pipeline that is specifically designed to process and analyze non-model organism samples of up to three meta-omics levels: metagenomics, metatranscriptomics, and metaproteomics in an integrative manner. The pipeline has been developed using the Snakemake framework in order to obtain an automated and reproducible workflow. One of the key features is the on-the-fly creation of a tailored proteogenomic database based on metagenomics and metatranscriptomics data, leading to improved protein identification, taxonomic and functional analysis. gNOMO combines meta-omics analysis of the host with its bacterial population and allows to investigate both host and microbiome of non-model organisms with commonly insufficiently complete reference databases. Conclusions Using experimental datasets of the German cockroach Blattella germanica , a non-model organism with very complex gut microbiome, we show the capabilities of gNOMO with regard to meta-omics data integration, expression ratio comparison, taxonomic and functional analysis as well as intuitive output visualization. gNOMO includes functional information of metagenomics, metatranscriptomics, and metaproteomics data of the microbiome in the same visualization facilitating the interpretation of the results. Moreover, host data can be analyzed in parallel to obtain an equivalent output that allows to study the metabolic situation of the whole symbiotic system. Finally, the metaproteomics identification and annotation are optimized using a tailored proteogenomics database automatically obtained within the gNOMO workflow. In conclusion, gNOMO is a fully automated pipeline, for integrating and analyzing multiple meta-omics data and for producing useful output visualizations. In addition, it is specifically designed for data from non-model organisms. The gNOMO pipeline is freely available under the Apache 2.0 open-source license and can be downloaded from https://gitlab.com/rki_bioinformatics/gnomo .

show abstract

Section: Metaproteomic Data Analysissupporting

confidence: 93%

gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms

Muñoz-Benavent

Hartkopf

Bossche

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Functional analysis of metaproteomic data can be challenging. Database choice can have effects on the quality of results (Tanca et al, 2016), redundant peptides can lead to ambiguously identified proteins (Ning et al, 2016) and current methods of protein level analysis can lead to a loss of information. Typically, protein-centric workflows are used and can be considered analogous to a transcriptomic workflow where sequenced cDNA reads are mapped to genomic locations and analyses are completed on estimated transcript expression values.…”

Section: Discussionmentioning

confidence: 99%

pepFunk, a tool for peptide-centric functional analysis in metaproteomic human gut microbiome studies

Simopoulos

Ning

Zhang

et al. 2019

Preprint

View full text Add to dashboard Cite

AbstractEnzymatic digestion of proteins before mass spectrometry analysis is a key process in metaproteomic workflows. Canonical metaproteomic data processing pipelines typically involve matching spectra produced by the mass spectrometer to a theoretical spectra database, followed by matching the identified peptides back to parent proteins. However, the nature of enzymatic digestion produces peptides that can be found in multiple proteins due to conservation or chance, presenting difficulties with protein and functional assignment. To combat this challenge, we developed a peptide-centric metaproteomic workflow focused on the analysis of human gut microbiome samples. Our workflow includes a curated peptide database annotated with KEGG terms and a pathway enrichment analysis adapted for peptide level data. Analysis using our peptide-centric workflow is fast and identifies more enriched KEGG pathways than protein-centric analysis. Our workflow is open source and available as a web application or source code to be run locally.

show abstract

“…All quantified peptides were subjected to Unipept 4.0 for taxonomic assignment based on the principle of Lowest Common Ancestor (LCA) [37]. Cluster of Orthologous Groups of proteins (COGs) is used to functionally classify different bacterio-proteins.…”

Section: Methodsmentioning

confidence: 99%

Dietary supplementation with products of Citrus reticulata "Chachi" for improving the fecal microbiome of weaned piglets

Lin¹,

Li²,

Wang³

et al. 2020

Preprint

View full text Add to dashboard Cite

23Nutritional interventions play a critical role in modifying the intestinal microbiome of 24 host animals. This study was conducted to interrogate the physiological effects on 2 25 fecal microflora of weaned piglets via the dietary supplemented with two types of 26 products of Citrus reticulata "Chachi", respectively. For this purpose, A total of 72 27 piglets with uniform sizes were randomly assigned to four dietary treatment groups 28 consisted of a negative control group (NCG), a fermented citrus reticulata "Chachi" 29 pulp group (FCRPG), a Citri Reticulatae Pericarpium group (CRPG) and a positive 30 control group (PCG) in a 21-day feeding trial. After the raising experiment, fresh 31 feces of piglets were analyzed systematically using multi-omics technologies. 32Metagenomics method with high-throughput compositional characterization indicated 33 that the architecture and diversity of fecal microbiome were both influenced by these 34 two additives and compound antibiotics. Metabolite analysis showed that FCRPG 35 have an significant effects on fecal short-chain fatty acids (SCFAs) among four 36 treatment groups. Results of functional proteomics approaches found that FCRPG 37 presented the highest butyrate metabolic level, and CRPG showed the highest flavone 38 and flavonol biosynthesis level in feces. In addition, NCP produced an effective effect 39 on adjusting fecal microbiota profile. Consequently, our findings demonstrate that 40 dietary supplementation with FCRP or CRP modulates the microbial taxa, metabolic 41 and proteomic alterations in fecal microbiota of weaned piglets for health 42 maintaining.43 44 3 48 metabolism, immune regulation and gene expression. The vast majority of commensal 49 micro-bacteria are well adapted to the host environment but also can be shaped by 50 various factors such as nutrient import and external circumstances [2, 3]. Weanling 51 period is a special developmental stage throughout the entire lifecycle of pig, during 52 which piglets have exhibited rapid growth, high demand for nutrition, fast energy 53 harvesting capability [4], and succession of gut microbial composition [5-7]. However, 54 common diseases such as digestive disorders and intestinal inflammation, often 55 plague the piglet farming during this rearing phase. Gut microbiota obviously play the 56 function of facilitating adaption of weanling piglets to fibrous ingredients and 57 reducing the risk of colonization of enteric pathogens after weaning [8]. Actually, 58 although antibiotics have been pervasively applied many decades for antimicrobial 59 application and growth promoting for livetocks in China, European Union has banned 60 their prophylactic use in modern swine industry for the potential public hazard of 61 bacterial resistances and drug residues since 2006 [9, 10]. Thus, nutritional strategies 62 have been employed as an efficient way to improve intestinal microbiota architecture 63 for the purpose of restricting the use of antibiotics and enhancing the health of piglets 64 except for alternative ...

show abstract

The impact of sequence database choice on metaproteomic results in gut microbiota studies

Cited by 111 publications

References 54 publications

gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms

gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms

pepFunk, a tool for peptide-centric functional analysis in metaproteomic human gut microbiome studies

Dietary supplementation with products of Citrus reticulata "Chachi" for improving the fecal microbiome of weaned piglets

Contact Info

Product

Resources

About