P4P: a peptidome-based strain-level genome comparison web tool

Peptidome similarity analysis enables researchers to gain insights into differential peptide profiles, providing a robust tool to discriminate strain-specific peptides, true intra-species differences among biological replicates or even microorganism-phenotype variations. However, no in silico peptide fingerprinting software existed to facilitate such phylogeny inference. Hence, we developed the Peptidomes for Phylogenies (P4P) web tool, which enables the survey of similarities between microbial proteomes and simplifies the process of obtaining new biological insights into their phylogeny. P4P can be used to analyze different peptide datasets, i.e. bacteria, viruses, eukaryotic species or even metaproteomes. Also, it is able to work with whole proteome datasets and experimental mass-to-charge lists originated from mass spectrometers. The ultimate aim is to generate a valid and manageable list of peptides that have phylogenetic signal and are potentially sample-specific. Sample-to-sample comparison is based on a consensus peak set matrix, which can be further submitted to phylogenetic analysis. P4P holds great potential for improving phylogenetic analyses in challenging taxonomic groups, biomarker identification or epidemiologic studies. Notably, P4P can be of interest for applications handling large proteomic datasets, which it is able to reduce to small matrices while maintaining high phylogenetic resolution. The web server is available at http://sing-group.org/p4p.Spanish ‘Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad’ [AGL2013-44039R]; Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020[POCI-01-0145-FEDER-006684];INOU16-05project from the University of Vigo; Fundación AECC. Funding for open access charge: Spanish ‘Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad’ [AGL2013-44039R].info:eu-repo/semantics/publishedVersio

In silico prediction reveals the existence of potential bioactive neuropeptides produced by the human gut microbiota

This work reports on a large-scale potential neuropeptide activity screening in human gut microbiomes deposited in public databases. In our experimental approach, the sequences of the bioactive peptides collected in the MAHMI database, mainly predicted as immunomodulatory or antitumoral, were crossed with those of the neuroactive/digestive peptides. From 91,325,790 potential bioactive peptides, only 581 returned a match when crossed against the 5949 neuroactive peptides from the NeuroPep database and the 15 digestive hormones. Relevant bacterial taxa, such as Ruminococcus sp., Clostridium sp. were found among the main producers of the matching sequences, and many of the matches corresponded to adiponectin and the hormone produced by adipocites, which is involved in glucose homeostasis. These results show, for the first time, the presence of potentially bioactive peptides produced by gut microbiota members over the nervous cells, most notably, peptides with already predicted immunomodulatory or anti-inflammatory activity. Classical (Lactobacillus sp.) and next-generation (Faecalibacterium sp.) probiotics are shown to produce these peptides, which are proposed as a potential mechanism of action of psychobiotics. Our previous experimental results showed that many of these peptides were active when incubated with immune cells, such as dendritic cells, so their effect over the nervous system innervating the gut mucosa holds significant potential and should be explored.Spanish “Programa Estatal de Investigación, Desarrollo e Inovación Orientada a los Retos de la Sociedad” (Grant AGL2016-78311-R); the Asociación Española Contra el Cancer (“Obtención de péptidos bioactivos contra el Cáncer Colo-Rectal a partir de secuencias genéticas de microbiomas intestinales”, grant PS-2016). This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER006684). SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from University of Vigo for hosting its IT infrastructure. This work was partially supported by the Asturias Regional Plan I+D+i for research groups (FYCYT-IDI/2018/000236) and by the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Groupinfo:eu-repo/semantics/publishedVersio

Computational prediction of the bioactivity potential of proteomes based on expert knowledge

Advances in the field of genome sequencing have enabled a comprehensive analysis and annotation of the dynamics of the protein inventory of cells. This has been proven particularly rewarding for microbial cells, for which the majority of proteins are already accessible to analysis through automatic metagenome annotation. The large-scale in silico screening of proteomes and metaproteomes is key to uncover bioactivities of translational, clinical and biotechnological interest, and to help assign functions to certain proteins, such as those predicted as hypothetical. This work introduces a new method for the prediction of the bioactivity potential of proteomes/metaproteomes, supporting the discovery of functionally relevant proteins based on prior knowledge. This methodology complements functional annotation enrichment methods by allowing the assignment of functions to proteins annotated as hypothetical/putative/uncharacterised, as well as and enabling the detection of specific bioactivities and the recovery of proteins from defined taxa. This work shows how the new method can be applied to screen proteome and metaproteome sets to obtain predictions of clinical or biotechnological interest based on reference datasets. Notably, with this methodology, the large information files obtained after DNA sequencing or protein identification experiments can be associated for translational purposes that, in cases such as antibiotic-resistance pathogens or foodborne diseases, may represent changes in how these important and global health burdens are approached in the clinical practice. Finally, the Sequence-based Expert-driven pRoteome bioactivity Prediction EnvironmENT, a public Web service implemented in Scala functional programming style, is introduced as means to ensure broad access to the method as well as to discuss main implementation issues, such as modularity, extensibility and interoperability.This work was supported by the Spanish “Programa Estatal de Investigación, Desarrollo e Inovación Orientada a los Retos de la Sociedad” (grant AGL2013-44039R); the Asociación Española Contra el Cancer (“Obtención de péptidos bioactivos contra el Cáncer Colo-Rectal a partir de secuencias genéticas de microbiomas intestinales”, grant PS2016). This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145- FEDER006684). SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from University of Vigo for hosting its IT infrastructure.info:eu-repo/semantics/publishedVersio

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

Tackling probiotic and gut microbiota functionality through proteomics

Abstract Probiotics are live microorganisms which when administered in adequate amounts confer a health benefit on the host. Many strains exert their beneficial effects after transiently colonizing the human gut, where they interact with the rest of the intestinal microorganisms and with the host mucosa. Indeed the human gut harbours a huge number of microorganisms also known as gut microbiota. Imbalances in the relative abundances of the individual components of the gut microbiota may determine the health status of the host and alterations in specific groups have been related to different diseases and metabolic disorders. Proteomics provide a set of high-throughput methodologies for protein identification that are extremely useful for studying probiotic functionality and helping in the assessment of specific health-promoting activities, such as their immunomodulatory activity, the intestinal colonization processes, and the crosstalk mechanisms with the host. Furthermore, proteomics have been used to identify markers of technological performance and stress adaptation, which helps to predict traits such as behaviour into food matrices and ability to survive passage through the gastrointestinal tract. The aim of this review is to compile studies in which proteomics have been used to assess probiotic functionality and to identify molecular players supporting their mechanisms of action. Significance Probiotics are live microorganisms which when administered in adequate amounts confer a health benefit on the host. Molecular basis underlying the functional properties of probiotic bacteria responsible for the health promoting effects have been in the background for many years. Breakthrough of omics technologies in the probiotic and microbiota fields has had a very relevant impact in the elucidation of probiotic mechanisms and in the procedures to select these microorganisms, based on solid scientific evidence. It is unquestionable that, in the near future, the evolution of proteomic techniques will play a pivotal role in the generation of knowledge about the functions of probiotics and gut commensals, still a pending issue in the field of intestinal microbiomics.Research in our lab is funded by Grants AGL2013-44039-R and AGL2013-44761-P from the Spanish “Plan Estatal de I + D + I”. Part of the authors is also partially funded by the [15VI013] Contract-Programme from the University of Vigo and the Agrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/273). Lorena Ruiz has received funding from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007-2013/under REA grant agreement n° 624773. Borja Sánchez was recipient of a Ramón y Cajal postdoctoral contract from the Spanish Ministry of Economy and Competitiveness

BlasterJS: A novel interactive JavaScript visualisation component for BLAST alignment results

Background The wide range of potential applications has made the Basic Local Alignment Search Tool (BLAST) a ubiquitous tool in the field of Molecular Biology. Within this context, it is increasingly appealing to embed BLAST services within larger Web applications. Results This work introduces BlasterJS viewer, a new JavaScript library for the lightweight development of Web-based applications supporting the visualisation of BLAST outputs. BlasterJS detaches from similar data viewers by focusing on the visual and interactive display of sequence similarity results and being completely independent of BLAST services. BlasterJS is compatible with the text outputs generated by the BLAST family of programs, namely BLASTp, BLASTn, BLASTx, tBLASTn, and tBLASTx, and works in all major Web browsers. Furthermore, BlasterJS is available through the EBIs BioJS registry 5, which extends its potential use to a wider scope of bioinformatics applications. Conclusions BlasterJS is new Javascript library that enables easy and seamless integration of visual and interactive representations of BLAST outputs in Web-based applications supporting sequence similarity search. BlasterJS is free accessible at http://sing-group.org/blasterjs/.This work was supported by the Spanish “Programa Estatal de Investigación, Desarrollo e Inovación Orientada a los Retos de la Sociedad” (grant AGL2013-44039R to BS and AL); and the Asociación Española Contra el Cancer ("Obtencio ´n de pe ´pti-dosbioactivos contra el Cáncer ColoRectal a partir de secuencias gene ´ticas de microbiomas intestinales", grant PS-2016 to BS and ABM). This study was also supportedby the PortugueseFoundationfor Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit(AL) and COMPETE2020 (POCI-01-0145-FEDER-006684 to AL). SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from University of Vigo for hosting its IT infrastructure. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Benchmarking biomedical text mining web servers at BioCreative V.5: the technical Interoperability and Performance of annotation Servers - TIPS track

The TIPS track consisted in a novel experimental task under the umbrella of the BioCreative text mining challenges with the aim to, for the first time ever, carry out a text mining challenge with particular focus on the continuous assessment of technical aspects of text annotation web servers, specifically of biomedical online named entity recognition systems. A total of 13 teams registered annotation servers, implemented in various programming languages, supporting up to 12 different general annotation types. The continuous evaluation period took place from February to March 2017. The systematic and continuous evaluation of server responses accounted for testing periods of low activity and moderate to high activity. Moreover three document provider settings were covered, including also NCBI PubMed. For a total of 4,092,502 requests, the median response time for most servers was below 3.74 s with a median of 10 annotations/document. Most of the servers showed great reliability and stability, being able to process 100,000 requests in 5 days.info:eu-repo/semantics/publishedVersio

Next generation community assessment of biomedical entity recognition web servers: metrics, performance, interoperability aspects of BeCalm

Background: Shared tasks and community challenges represent key instruments to promote research, collaboration and determine the state of the art of biomedical and chemical text mining technologies. Traditionally, such tasks relied on the comparison of automatically generated results against a so-called Gold Standard dataset of manually labelled textual data, regardless of efficiency and robustness of the underlying implementations. Due to the rapid growth of unstructured data collections, including patent databases and particularly the scientific literature, there is a pressing need to generate, assess and expose robust big data text mining solutions to semantically enrich documents in real time. To address this pressing need, a novel track called “Technical interoperability and performance of annotation servers” was launched under the umbrella of the BioCreative text mining evaluation effort. The aim of this track was to enable the continuous assessment of technical aspects of text annotation web servers, specifically of online biomedical named entity recognition systems of interest for medicinal chemistry applications. Results: A total of 15 out of 26 registered teams successfully implemented online annotation servers. They returned predictions during a two-month period in predefined formats and were evaluated through the BeCalm evaluation platform, specifically developed for this track. The track encompassed three levels of evaluation, i.e. data format considerations, technical metrics and functional specifications. Participating annotation servers were implemented in seven different programming languages and covered 12 general entity types. The continuous evaluation of server responses accounted for testing periods of low activity and moderate to high activity, encompassing overall 4,092,502 requests from three different document provider settings. The median response time was below 3.74 s, with a median of 10 annotations/document. Most of the servers showed great reliability and stability, being able to process over 100,000 requests in a 5-day period. Conclusions: The presented track was a novel experimental task that systematically evaluated the technical performance aspects of online entity recognition systems. It raised the interest of a significant number of participants. Future editions of the competition will address the ability to process documents in bulk as well as to annotate full-text documents.Portuguese Foundation for Science and Technology | Ref. UID/BIO/04469/2013Portuguese Foundation for Science and Technology | Ref. COMPETE 2020 (POCI-01-0145-FEDER-006684)Xunta de Galicia | Ref. ED431C2018/55-GRCEuropean Commission | Ref. H2020, n. 65402

Targeted depletion of pks+ bacteria from a fecal microbiota using specific antibodies

The pks island is one of the most prevalent pathogenicity islands among the Escherichia coli strains that colonize the colon of colorectal carcinoma (CRC) patients. This pathogenic island encodes the production of a nonribosomal polyketide-peptide named colibactin, which induces double-strand breaks in DNA molecules. Detection or even depletion of this pks-producing bacteria could help to understand the role of these strains in the context of CRC. In this work, we performed a large-scale in silico screening of the pks cluster in more than 6,000 isolates of E. coli. The results obtained reveal that not all the pks-detected strains could produce a functional genotoxin and, using antibodies against pks-specific peptides from surface cell proteins, a methodology for detection and depletion of pks+ bacteria in gut microbiotas was proposed. With our method, we were able to deplete a human gut microbiota of this pks+ strains, opening the door to strain-directed microbiota modification and intervention studies that allow us to understand the relation between these genotoxic strains and some gastrointestinal diseases. The human gut microbiome has also been hypothesized to play a crucial role in the development and progression of colorectal carcinoma (CRC). Between the microorganisms of this community, the Escherichia coli strains carrying the pks genomic island were shown to be capable of promoting colon tumorigenesis in a colorectal cancer mouse model, and their presence seems to be directly related to a distinct mutational signature in patients suffering CRC. This work proposes a novel method for the detection and depletion of pks-carrying bacteria in human gut microbiotas. In contrast to methods based on probes, this methodology allows the depletion of low-abundance bacterial strains maintaining the viability of both targeted and non-targeted fractions of the microbiota, allowing the study of the contribution of these pks-carrying strains to different diseases, such as CRC, and their role in other physiological, metabolic or immune processes.This work was supported by the Spanish “Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad” (Grant AGL2016-78311-R and contract BES-2017-080978, funded by AEI/FEDER, UEAGL2016-78311-R); the Asociación Española Contra el Cáncer (“Obtención de péptidos bioactivos contra el Cáncer Colo-Rectal a partir de secuencias genéticas de microbiomas intestinales”, Grant PS-2016 and by the Asturias Regional Plan I+D+i for research groups (FICYT-IDI/2018/000236, funded by PCTI Gobierno del Principado de Asturias/FEDER, UE). This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER006684). SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from the University of Vigo for hosting its IT infrastructure. A.B.M. was supported by a predoctoral contract from the AECC. Borja Sánchez and Abelardo Margolles are on the scientific board and are co-founders of Microviable Therapeutics SL. The other authors have no competing interests. Results presented in this paper are protected under European Patent EP19383077 (WO2021110833A1 and US20230029322A1; Tools and methods to detect and isolate colibactin producing bacteria).info:eu-repo/semantics/publishedVersio

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