A Taxonomically-informed Mass Spectrometry Search Tool for Microbial Metabolomics Data

Zuffa, Simone; Schmid, Robin; Bauermeister, Anelize; Gomes, Paulo Wender Portal; Caraballo‐Rodríguez, Andrés Mauricio; Abiead, Yasin El; Aron, Allegra T.; Gentry, Emily C.; Zemlin, Jasmine; Meehan, Michael J.; Avalon, Nicole E.; Cichewicz, Robert H.; Buzun, Ekaterina; Terrazas, Marvic Carrillo; Hsu, Chia-Yun; Oles, Renee; Ayala, Adriana Vasquez; Zhao, Jiaqi; Chu, Hiutung; Jackrel, Sara L.; Tugizimana, Fidele; Nephali, Lerato; Dubery, Ian A.; Madala, Ntakadzeni E.; Moreira, Eduarda Antunes; Costa-Lotufo, Letı́cia V.; Lopes, Norberto Peporine; Rezende‐Teixeira, Paula; Jimenez, Paula Christine; Rimal, Bipin; Patterson, Andrew D.; Traxler, Matthew F.; Pessotti, Rita de Cássia; Alvarado-Villalobos, Daniel; Tamayo‐Castillo, Giselle; Chaverri, Priscila; Escudero-Leyva, Efraín; Quirós-Guerrero, Luis; Bory, Alexandre Jean; Joubert, Juliette; Rutz, Adriano; Wolfender, Jean‐Luc; Allard, Pierre‐Marie; Sichert, Andreas; Pontrelli, Sammy; Pullman, Benjamin; Bandeira, Nuno; Gerwick, William H.; Gindro, Katia; Massana-Codina, Josep; Wagner, Berenike; Forchhammer, Karl; Petras, Daniel; Aiosa, Nicole; Garg, Neha; Liebeke, Manuel; Bourceau, Patric; Kang, Kyo Bin; Gadhavi, Henna; Carvalho, Luiz Pedro S. de; Santos, Mariana Silva dos; Pérez-Lorente, Alicia Isabel; Molina-Santiago, Carlos; Romero, Diego; Franke, Raimo; Brönstrup, Mark; Leon, Arturo Vera Ponce de; Pope, Phillip B.; Rosa, Sabina Leanti La; Barbera, Giorgia La; Roager, Henrik Munch; Laursen, Martin Frederik; Hammerle, Fabian; Siewert, Bianka; Peintner, Ursula; Licona-Cassani, Cuauhtémoc; Licona-Cassani, Cuauhtémoc; Rampler, Evelyn; Hildebrand, Felina; Koellensperger, Gunda; Schoeny, Harald; Hohenwallner, Katharina; Panzenboeck, Lisa; Gregor, Rachel; O’Neill, Ellis C.; Roxborough, Eve Tallulah; Odoi, Jane; Bale, Nicole J.; Sheng, Ding; Damsté, Jaap S. Sinninghe; Guan, Xue Li; Cui, Jerry; Ju, Kou San; Silva, Denise Brentan; Silva, Fernanda Motta Ribeiro da; Silva, Gilvan Ferreira da; Koolen, Héctor H. F.; Grundmann, Carlismari O.; Clement, Jason A.; Mohimani, Hosein; Broders, Kirk; McPhail, Kerry L.; Rath, Christopher M.; McDonald, Daniel; Knight, Rob; Wang, Mingxun; Dorrestein, Pieter C.

doi:10.1101/2023.07.20.549584

Cited by 3 publications

(2 citation statements)

References 57 publications

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“…To add further evidence for the bacterial origin of the endolides, an extended repository-scale MassQL search of all data sets deposited on MASSIVE from the same mass analyzer (Q-Exactive Orbitrap) was carried out by targeting the accurate masses for the iminium ion as well as the neutral loss of NMefAla. Additionally, we also applied a taxonomically informed mass spectrometry search tool for microbial metabolomics data (microbeMASST) with several different endolides to find putatively cultivable endolide producers that might allow for more detailed biosynthetic studies of the endolides in the future. Unfortunately, both attempts to detect other producer organisms of NMefAla-containing natural products in the repositories were unsuccessful and thus highlight the rarity of the NMefAla building block.…”

Section: Resultsmentioning

confidence: 99%

A MassQL-Integrated Molecular Networking Approach for the Discovery and Substructure Annotation of Bioactive Cyclic Peptides

Berger,

Alenfelder,

Steinmüller

et al. 2024

J. Nat. Prod.

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The marine sponge-derived fungus Stachylidium bicolor 293 K04 is a prolific producer of specialized metabolites, including certain cyclic tetrapeptides called endolides, which are characterized by the presence of the unusual amino acid N-methyl-3-(3-furyl)-alanine. This rare feature can be used as bait to detect new endolide-like analogs through customized fragment pattern searches of tandem mass spectrometry data using the Mass Spec Query Language (MassQL). Here, we integrate endolide-specific MassQL queries with molecular networking to obtain substructural information guiding the targeted isolation and structure elucidation of the new proline-containing endolides E (1) and F (2). We showed that endolide F (but not E) is a moderate antagonist of the arginine vasopressin V 1A receptor, a member of the G proteincoupled receptor superfamily.

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Section: Resultsmentioning

confidence: 99%

A MassQL-Integrated Molecular Networking Approach for the Discovery and Substructure Annotation of Bioactive Cyclic Peptides

Berger,

Alenfelder,

Steinmüller

et al. 2024

J. Nat. Prod.

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“…Similarly, machine learning has been applied to develop de‐novo antimicrobial peptides (AMPs) with broad‐spectrum antibiotic activity against MDR bacteria (Pandi et al., 2023). Metabolic modelling has also proven to be useful for identifying new antibiotic drug targets (Gwynne et al., 2023), as the possibility to identify microbe‐derived metabolites and relative producers without a priori knowledge recently implemented by the microbeMASST, a taxonomically informed mass spectrometry search tool (Zuffa et al., 2024).…”

Section: How Can We Improve Novel Antibiotic Discovery?mentioning

confidence: 99%

The antibiotic crisis: On the search for novel antibiotics and resistance mechanisms

Van Goethem,

Marasco,

Hong

et al. 2024

Microbial Biotechnology

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In the relentless battle for human health, the proliferation of antibiotic‐resistant bacteria has emerged as an impending catastrophe of unprecedented magnitude, potentially driving humanity towards the brink of an unparalleled healthcare crisis. The unyielding advance of antibiotic resistance looms as the foremost threat of the 21st century in clinical, agricultural and environmental arenas. Antibiotic resistance is projected to be the genesis of the next global pandemic, with grim estimations of tens of millions of lives lost annually by 2050. Amidst this impending calamity, our capacity to unearth novel antibiotics has languished, with the past four decades marred by a disheartening ‘antibiotic discovery void’. With nearly 80% of our current antibiotics originating from natural or semi‐synthetic sources, our responsibility is to cast our investigative nets into uncharted ecological niches teeming with microbial strife, the so‐called ‘microbial oases of interactions’. Within these oases of interactions, where microorganisms intensively compete for space and nutrients, a dynamic and ever‐evolving microbial ‘arms race’ is constantly in place. Such a continuous cycle of adaptation and counter‐adaptation is a fundamental aspect of microbial ecology and evolution, as well as the secrets to unique, undiscovered antibiotics, our last bastion against the relentless tide of resistance. In this context, it is imperative to invest in research to explore the competitive realms, like the plant rhizosphere, biological soil crusts, deep sea hydrothermal vents, marine snow and the most modern plastisphere, in which competitive interactions are at the base of the microorganisms' struggle for survival and dominance in their ecosystems: identify novel antibiotic by targeting microbial oases of interactions could represent a ‘missing piece of the puzzle’ in our fight against antibiotic resistance.

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Use of proteomic-based MALDI-TOF mass spectra for identification of bacterial pathogens in aquaculture: a review

Çağatay

2024

Aquacult Int

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The fisheries and aquaculture sectors are growing rapidly, reflecting their importance in meeting the ever-increasing human population’s demands for animal protein. Production progress in this sector, however, is challenging as a result of increased deaths from epidemics caused by bacterial infectious diseases in aquaculturally important species. In order to minimize the impact of such diseases, quick and reliable diagnosis of pathogens, timely intervention, and control of the disease are essential to ensure the health of aquaculture and fisheries stocks. Thus, high-throughput proteomics-based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been used as a rapid, accurate, and species-specific tool for the identification of bacterial fish pathogens and antibiotic resistance. The aim of this article is to review and evaluate the results of nearly three hundred papers published from 2010 to 2023 on the application of MALDI-TOF MS in aquaculture, showing that this method has been increasingly used over the years for the species-level identification and antibiotic resistance of fifty different gram-positive and negative bacterial aquatic pathogens encountered in wild or cultured fish, shellfish, invertebrates, and crustaceans. In addition, the history and general principles of the MALDI-TOF MS method are also mentioned in the article so that fish disease researchers interested in the use of this technology can see all aspects of the topic.

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A Taxonomically-informed Mass Spectrometry Search Tool for Microbial Metabolomics Data

Cited by 3 publications

References 57 publications

A MassQL-Integrated Molecular Networking Approach for the Discovery and Substructure Annotation of Bioactive Cyclic Peptides

A MassQL-Integrated Molecular Networking Approach for the Discovery and Substructure Annotation of Bioactive Cyclic Peptides

The antibiotic crisis: On the search for novel antibiotics and resistance mechanisms

Use of proteomic-based MALDI-TOF mass spectra for identification of bacterial pathogens in aquaculture: a review

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