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Enregistrement W4386950971 · doi:10.1093/infdis/jiad405

Thinking Small, Stinking Big: The World of Microbial Odors

2023· article· en· W4386950971 sur OpenAlexaboutno aff
Tzvi Pollock, Audrey R. Odom John

Notice bibliographique

RevueThe Journal of Infectious Diseases · 2023
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvanced Chemical Sensor Technologies
Établissements canadiensnon disponible
Organismes subventionnairesEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthBurroughs Wellcome FundU.S. Department of Defense
Mots-clésBiology

Résumé

récupéré en direct d'OpenAlex

Humans live embedded in an ecosystem of microbial life. Nowhere is this ecosystem more apparent to the naked eye (or, rather, nose) than in the world of microbially derived odors. From the fungal fragrances that sing of environmental mold to the bacterial bouquet that warns us off week-old leftovers, our air comprises a microbial miasma of information, ripe for research and exploration. Microbially derived odors, especially those produced by pathogens, can prove useful in the toolkit of both the microbe and the diagnostician. A rapidly increasing number of studies highlight the need for further understanding of these odors—their origins, their functions, and their utility in our hands. Microbial odors are mediated by volatile organic compounds (VOCs), small organic molecules with low boiling points, generally synthesized during the microbe's metabolism. Multicellular organisms typically detect VOCs via dedicated odor receptors, such as those in the vertebrate nose or on arthropod antennae. In the laboratory, individual VOCs produced by microbes can be characterized using gas chromatography/mass spectrometry, while the global fingerprint of microbial odors can be recognized through use of electronic nose technology. Which microbial VOCs mediate what odors—and to what purpose—are topics of ongoing investigation in the field. Why even care about the little smells of our microscopic neighbors? Specific odors have long been associated with human disease and have served well as rudimentary and noninvasive diagnostics. As early as 400 Bce, Hippocrates advised that students smell their patients’ breath to diagnose illness [1]. While our understanding of the underlying pathophysiology may have advanced in the millennia hence, conditions such as portal hypertension and diabetic ketoacidosis are readily recognized by their associated characteristic odors. Microbial pathogens tend to demonstrate more subtle odor profiles than these noninfectious conditions, as the odoriferous insult operates on far smaller orders of magnitude. This has not stopped us from seeking out new methods of detecting microbial infection by way of produced VOCs. Compared to other testing mechanisms, testing the odor profile of patients is minimally invasive. Additionally, as the testing relies on key metabolic products of the infection rather than easily mutated antigens, the prospect of microbial mutation away from diagnostic efficacy is far more remote. Detection of infection by way of scent has already proven possible thanks to both human technology and mammalian cooperation. In partnership with our four-legged friends, human clinicians have been able to pinpoint infections with startling accuracy on the basis of scent alone. African giant pouched rats (Cricetomys gambianus) in Tanzania and Mozambique have been successfully trained to sniff out and differentiate sputum samples from patients infected with tuberculosis, with greater accuracy than even trained human microscopists [2]. In Canada and the Netherlands, meanwhile, odor-sniffing dogs (Canis lupus familiaris) have been trained to identify Clostridium difficile in stool [3]. Preliminary studies additionally abound investigating the potential of canine assistance in the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection [4]. Manmade attempts are likewise promising, with breath tests for infections such as tuberculosis, SARS-CoV-2, and even malaria showing high sensitivity and accuracy [5]. Our ability to accurately and noninvasively pinpoint infection looks to be steadily improving, in part by detecting pungent pathogens. In the push and pull of host-pathogen competition, any evolutionary advantage over the adversary is liable to be maintained, and any disadvantage quickly discarded. Why, then, might pathogenic microbes release VOCs at all, if they might facilitate host detection and avoidance of infection? Microbial odors are not simply an inevitable byproduct of host metabolism during infection; pathogens such as Mycobacterium tuberculosis or Plasmodium falciparum, the causative agent of severe human malaria, are known to directly produce similar volatiles in vitro as they do in patients [6]. While many microbial VOCs are likely unavoidable derivatives of normal pathogen metabolism, evidence suggests some microbes have evolved to use odors to influence their hosts and disseminate. The sweet, earthy scent of petrichor that follows a light rain is mediated in part by the VOC geosmin, which is produced by soil-dwelling bacteria such as the spore-forming Streptomyces coelicolor. S. coelicolor actively synthesizes geosmin when sporulating, and the resulting scent can be detected far and wide. One study demonstrated that geosmin production rendered S. coelicolor especially attractive to soil-resident arthropods called springtails (Folsomia candida) [7]. This attraction was shown to enhance the dispersion of S. coelicolor, as the springtails consume and spread the spores that proved so alluring. In a sinister parallel, similar dynamics have been observed in human infection. As mentioned earlier, the malaria parasite P. falciparum is known to produce detectable changes in human breath metabolites during infection [8]. Unfortunately, we are not the only ones to have noticed. Studies have found that the vectors of malaria, Anopheles mosquitoes, are suckers for the scent of Plasmodium spp. infection [9]. Patients infected with the parasite, specifically during the life stage in which the parasite requires vector uptake, are twice as attractive to the mosquito as uninfected patients. In this way, the parasite manages to enhance its own transmission with just an inviting aroma. We float every day through a vast ocean of smells that we are only now learning to pick out of the ambient air. Pathogens have clearly wasted no time in utilizing scent to bolster their own fitness. Now that we are aware of these microbial odors, which opportunities to leverage them for human health will bear fruit? Only the nose knows. Financial support. A. O. J. is supported by the National Institutes of Health (grant numbers R01AI103280, R01HD109963, R01AI171514, and R33HD105594); the US Department of Defense; and is an Investigator in the Pathogenesis of Infectious Diseases of the Burroughs Wellcome Fund.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,480
Score d'incertitude au seuil0,272

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,012
Tête enseignante GPT0,220
Écart entre enseignants0,208 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2023
Routes d'admission1
Résumé présentoui

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