In silico and functional analyses of immunomodulatory peptides encrypted in the human gut metaproteome

Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jff.2020.103969.
This work supports the massive presence of potential immunomodulatory peptides in the human gut metaproteome. These peptides were identified through the MAHMI database as potentially anti-inflammatory, and sixteen of them synthesized for characterize their mechanism of action. From them, peptide HM14 was encrypted in an extracellular protein produced by Bifidobacterium longum, a common member of the human microbiota, and displayed the highest anti-inflammatory capability. Molecular mechanism of action of HM14 pointed to a specific interaction between this immunomodulatory peptide and antigen presenting cells, which resulted in a higher formation of iTreg cells. Moreover, HM14 was effective in decreasing pro-inflammatory parameters in PBMCs isolated from a cohort of Crohns patients. Finally, non-targeted metabolomics confirmed the ability of HM14 to modulate the metabolic activity of PBMCs to fulfil its energy and biosynthetic requirements. Overall, our combined in silico/multiomics approach supports the human gut metaproteome as a source for immunomodulatory peptides.
Our work is supported by the Spanish “Programa Estatal de Investigación. Desarrollo e Innovación Orientada a los Retos de la Sociedad” (grants AGL2013-44761-P and 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), by the Asturias Regional Plan I + D + i for research groups (FYCYT-IDI/2018/000236) and by the Autonomic “Investigadores Emerxentes do Sistema Universitario de Galicia” (Grant EM2014/046). This work was partially supported 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 Group. Finally, the authors wish to thank Jaume Morales and Rubén García form Agilent Technologies for technical support.
info:eu-repo/semantics/publishedVersion