A peptidome-based phylogeny pipeline reveals differential peptides at the strain level within Bifidobacterium animalis subsp. lactis

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.fm.2016.06.015.Bifidobacteria are gut commensal microorganisms belonging to the Actinobacteria group. Some specific strains of Bifidobacterium animalis subsp. lactis are used in functional foods as they are able to exert health-promoting effects in the human host. Due to the limited genetic variability within this subspecies, it is sometimes difficult for a manufacturer to properly track its strain once included in dairy products or functional foods. In this paper, we present a peptidome-based analysis in which the proteomes of a set of B. animalis subsp. lactis strains were digested in silico with human gut endopeptidases. The molecular masses were compared along all the strains to detect strain-specific peptides. These peptides may be interesting towards the development of methodologies for strain identification in the final product.This research was funded by Grant AGL2013-44039-R from the Spanish “Plan Estatal de IþDþI”, and by Grant EM2014/046 from the “Plan Galego de investigaci on, innovaci on e crecemento 2011e2015”. Borja S anchez was recipient of a Ram on y Cajal postdoctoral contract from the Spanish Ministry of Economy and Competitiveness (RYC-2012-10052). This work was also partially funded by the [14VI05] Contract- Programme from the Unixikversity of Vigo and the Agrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/273). The research leading to these results has also received funding from the European Union’s Seventh Framework Programme FP7/REGPOT-2012-2013.1 under grant agreement no 316265, BIOCAPS. This document reflects only the author’s views and the European Union is not liable for any use that may be made of the information contained herein

MAHMI database: a comprehensive MetaHit-based resource for the study of the mechanism of action of the human microbiota

The Mechanism of Action of the Human Microbiome (MAHMI) database is a unique resource that provides comprehensive information about the sequence of potential immunomodulatory and antiproliferative peptides encrypted in the proteins produced by the human gut microbiota. Currently, MAHMI database contains over 300 hundred million peptide entries, with detailed information about peptide sequence, sources and potential bioactivity. The reference peptide data section is curated manually by domain experts. The in silico peptide data section is populated automatically through the systematic processing of publicly available exoproteomes of the human microbiome. Bioactivity prediction is based on the global alignment of the automatically processed peptides with experimentally validated immunomodulatory and antiproliferative peptides, in the reference section. MAHMI provides researchers with a comparative tool for inspecting the potential immunomodulatory or antiproliferative bioactivity of new amino acidic sequences and identifying promising peptides to be further investigated. Moreover, researchers are welcome to submit new experimental evidence on peptide bioactivity, namely, empiric and structural data, as a proactive, expert means to keep the database updated and improve the implemented bioactivity prediction method. Bioactive peptides identified by MAHMI have a huge biotechnological potential, including the manipulation of aberrant immune responses and the design of new functional ingredients/foods based on the genetic sequences of the human microbiome. Hopefully, the resources provided by MAHMI will be useful to those researching gastrointestinal disorders of autoimmune and inflammatory nature, such as Inflammatory Bowel Diseases. MAHMI database is routinely updated and is available free of charge.This work was funded by the Grant AGL2013-44039-R from the Spanish “Plan Estatal de IþDþI”, and the Grant EM2014/046 from the “Plan Galego de investigaci on, innovaci on e crecemento 2011-2015”. Borja S anchez was recipient of a Ram on y Cajal postdoctoral contract from the Spanish Ministry of Economy and Competitiveness. This work was also partially funded by the [14VI05] ContractProgramme from the University of Vigo and the Agrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/273) and the European Union’s Seventh Framework Programme FP7/REGPOT-2012-2013.1 under grant agreement n 316265, BIOCAPS. This document reflects only the author’s views and the European Union is not liable for any use that may be made of the information contained herein

Improving phylogeny reconstruction at the strain level using peptidome datasets

Typical bacterial strain differentiation methods are often challenged by high genetic similarity between strains. To address this problem, we introduce a novel in silico peptide fingerprinting method based on conventional wet-lab protocols that enables the identification of potential strain-specific peptides. These can be further investigated using in vitro approaches, laying a foundation for the development of biomarker detection and application-specific methods. This novel method aims at reducing large amounts of comparative peptide data to binary matrices while maintaining a high phylogenetic resolution. The underlying case study concerns the Bacillus cereus group, namely the differentiation of Bacillus thuringiensis, Bacillus anthracis and Bacillus cereus strains. Results show that trees based on cytoplasmic and extracellular peptidomes are only marginally in conflict with those based on whole proteomes, as inferred by the established Genome-BLAST Distance Phylogeny (GBDP) method. Hence, these results indicate that the two approaches can most likely be used complementarily even in other organismal groups. The obtained results confirm previous reports about the misclassification of many strains within the B. cereus group. Moreover, our method was able to separate the B. anthracis strains with high resolution, similarly to the GBDP results as benchmarked via Bayesian inference and both Maximum Likelihood and Maximum Parsimony. In addition to the presented phylogenomic applications, whole-peptide fingerprinting might also become a valuable complementary technique to digital DNA-DNA hybridization, notably for bacterial classification at the species and subspecies level in the future.This research was funded by Grant AGL2013-44039-R from the Spanish “Plan Estatal de I+D+I”, and by Grant EM2014/046 from the “Plan Galego de investigación, innovación e crecemento 2011-2015”. BS was recipient of a Ramón y Cajal postdoctoral contractfrom the Spanish Ministry of Economyand Competitiveness. This work was also partially funded by the [14VI05] Contract-Programme from the University of Vigo and the Agrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/273).The research leading to these results has also received funding from the European Union’s Seventh Framework Programme FP7/REGPOT-2012-2013.1 under grant agreement n˚ 316265, BIOCAPS. This document reflects only the authors’ views and the European Union is not liable for any use that may be made of the information contained herein. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

MAHMI database: a comprehensive MetaHit-based resource for the study of the mechanism of action of the human microbiota

The Mechanism of Action of the Human Microbiome (MAHMI) database is a unique resource that provides comprehensive information about the sequence of potential immunomodulatory and antiproliferative peptides encrypted in the proteins produced by the human gut microbiota. Currently, MAHMI database contains over 300 hundred million peptide entries, with detailed information about peptide sequence, sources and potential bioactivity. The reference peptide data section is curated manually by domain experts. The in silico peptide data section is populated automatically through the systematic processing of publicly available exoproteomes of the human microbiome. Bioactivity prediction is based on the global alignment of the automatically processed peptides with experimentally validated immunomodulatory and antiproliferative peptides, in the reference section. MAHMI provides researchers with a comparative tool for inspecting the potential immunomodulatory or antiproliferative bioactivity of new amino acidic sequences and identifying promising peptides to be further investigated. Moreover, researchers are welcome to submit new experimental evidence on peptide bioactivity, namely, empiric and structural data, as a proactive, expert means to keep the database updated and improve the implemented bioactivity prediction method. Bioactive peptides identified by MAHMI have a huge biotechnological potential, including the manipulation of aberrant immune responses and the design of new functional ingredients/foods based on the genetic sequences of the human microbiome. Hopefully, the resources provided by MAHMI will be useful to those researching gastrointestinal disorders of autoimmune and inflammatory nature, such as Inflammatory Bowel Diseases. MAHMI database is routinely updated and is available free of charge.Ministerio de Economía y Competitividad | Ref. AGL2013-44039-RXunta de Galicia | Ref. EM2014/04

Bayesian tree based on [Cyto_PI_60-more] dataset.

<p>It contains peptides with a length higher than 60 amino acids obtained from cytoplasmic proteins with an isoelectric point between 4.5 and 5.5. Bayesian analysis was performed in two independent runs using four Markov chains and 1,000,000 generations [[<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005271#pcbi.1005271.ref058" target="_blank">58</a>]. A majority-rule consensus tree (50%) was obtained after discarding the initial 25% of the trees and only support above 60% is shown. The leaf labels refer to the current NCBI nomenclature, whereas the BCG groups represent the recently revised names [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005271#pcbi.1005271.ref012" target="_blank">12</a>].</p

Summary data on the NEXUS generation process for Bacillus cereus benchmarking.

<p>Running time is displayed using hours:minutes:seconds format.</p

The Bacillus strains used in this study. Genome and protein data were retrieved from the BioProject collection of the NCBI in July, 2015.

<p>The Bacillus strains used in this study. Genome and protein data were retrieved from the BioProject collection of the NCBI in July, 2015.</p

Bayesian tree based on [Cyto_PI_51–60] dataset.

<p>It contains peptides with 51–60 amino acids obtained from cytoplasmic proteins with an isoelectric point between 4.5 and 5.5. Bayesian analysis was performed in two independent runs using four Markov chains and 1,000,000 generations [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005271#pcbi.1005271.ref058" target="_blank">58</a>]. A majority-rule consensus tree (50%) was obtained after discarding the initial 25% of the trees and only support above 60% is shown. The leaf labels refer to the current NCBI nomenclature, whereas the BCG groups represents the recently revised names [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005271#pcbi.1005271.ref012" target="_blank">12</a>].</p

Summary data on the different phylogenetic trees.

<p>Specifically, the average support obtained by the different methods and the size of the character matrices used.</p