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Enregistrement W2964998394 · doi:10.1210/en.2019-00541

Viral Hormones: Do They Impact Human Endocrinology?

2019· letter· en· W2964998394 sur OpenAlexaff
David M. Irwin

Notice bibliographique

RevueEndocrinology · 2019
Typeletter
Langueen
DomaineMedicine
ThématiqueViral gastroenteritis research and epidemiology
Établissements canadiensDiabetes CanadaUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésInternal medicineEndocrinologyHormoneBiologyMedicine

Résumé

récupéré en direct d'OpenAlex

Viruses continue to surprise. Although most known viruses contain only a handful of genes that are necessary for replication, packaging, and infection of host cells, some have genomes as large as those of bacteria and have many genes of unknown function. Even viruses with smaller genomes have novel genes with surprising functions. A recent review by Huang et al. (1) in Endocrinology focuses on the discovery of viral genes that mimic mammalian hormones. Most viruses that infect mammals, and other species, are poorly understood yet pose risks to human health (2, 3). Huang et al. (1) show that they might have underappreciated effects on our endocrinology. Viruses have evolved numerous methods, including antigenic variation and host factor mimicry, to counter recognition and responses by host immune systems. Antigenic variation allows faster-evolving viruses to display novel mutations on their protein coats to escape antibody surveillance, and mimicry of host factors allow them to modulate the immune response. Both approaches give viruses a better chance to survive and replicate in a host and spread within populations. Whereas mimicry of host immune factors has long received considerable attention (2, 4), mimicry of other physiological systems has not. The review by Huang et al. (1) presents evidence that the endocrinology of hosts is likely also being manipulated by viruses. The review expands on work published by Altindis et al. (5), who showed that searches of the small number of characterized viral genomes revealed a surprising number of viruses containing gene sequences that predict proteins with similarity to human hormones. Altindis et al. (5) searched for all known (at that time) viral genomes within the National Center for Biotechnology Information database for 62 human metabolic-relevant peptides, which included hormones, metabolism-related cytokines, and growth factors. These searches identified many viral genomes that had genomic sequences that predict proteins with similarity to 16 of the tested peptides (5). Considering that sequences of viruses are underrepresented in these databases and that the authors searched with only human peptide sequences, it is clear that viruses have the potential to make mimics of a large fraction of regulatory peptides. Just because viruses have sequences that potentially make peptides with similarity to human regulatory peptides does not mean that these peptides mimic the actions of the regulatory peptides. Divergent sequence evolution occurs in genes over time, with greater divergence occurring with increased time or higher mutation rates. Viruses typically have higher mutation rates; thus, their sequences can diverge rapidly. Some genes, despite the passage of considerable periods of time and divergence in sequence, retain a shared function. A well-known example is the master gene Pax6, where mouse Pax6 can functionally replace the Drosophila melanogaster (insect) ortholog in the development of functional eyes (6). However, divergent evolution can also quickly result in sequences that have different functions; indeed, many hormones share common ancestors yet have divergently evolved to acquire distinct functions. Examples include insulin and IGF-1 or glucagon and glucagon-like peptide-1, pairs of peptide hormones that share common ancestors and sequence similarity but have distinct, although some overlapping, functions. To show that a peptide acts as a mimic, functional experimental work is required. Huang et al. (1) illustrate the importance of these functional experiments, and review the demonstration that sequences in viruses encoding peptides similar to some hormones potentially act as mimics. In addition to identifying sequences in viruses with similarity to 16 different human hormones and regulatory peptides, Altindis et al. (5) functionally characterized one set of them, the viral insulin-like peptides (VILPs). A potential criticism of Altindis et al. (5) is that they used only mammalian model systems (although these are the best developed) to characterize the insulin-like and IGF-1–like functions of the VILPs, even though these peptides are found only in viruses that infect fish. Fish models might have been better; however, given the conservation of the physiological functions of these hormones across vertebrates, it is unlikely that different results would have been seen. Indeed, these experiments actually emphasize the potential human impact of these viral hormones—not only might they work in the normal host species, but also might act in humans if they became exposed. There is evidence that humans do encounter these viruses (1, 4). Presence of these hormone-like sequences in viral genomes raises the question of how viruses might benefit from making hormone mimics. Many hormones promote cellular growth, and cell division is necessary for viral replication. But as Huang et al. (1) point out, there are other possible reasons. They could simply be used for viral entry into cells, by expressing hormone-like sequences on the surface of the virus, with cell-surface hormone receptors used for attachment and endocytosis of the virus into the cells. Alternatively, viruses might take advantage of the roles hormones have in modulating the immune system. Clearly, much more work is needed to determine what roles these hormone-like sequences play and whether they are harmful or beneficial. In addition to viruses, a long history suggests that bacteria have genes encoding mimics of hormones (7). However, with the completion of the Escherichia coli genome, no genes similar to mammalian hormones were found. More recently, a peptide called melanocortin-like peptide of E. coli (MECO-1) was found that mimics the function of melanocortin (8). Unlike VILPs, MECO-1 does not share sequence similarity with the hormone it is mimicking, but it still mimics its function. As Huang et al. (1) point out, this is a limitation of similarity-based searches. Mimics are not required to have primary sequence similarity with the molecules that they mimic; they may just need to fold into structures, or surfaces, that have molecular similarity, allowing these molecules to mimic interactions. In summary, Huang et al. (1) review the importance of the complete environment in understanding biology. We are only beginning to appreciate the value and importance of the bacterial microbiome in health and disease, and our understanding of the role of viruses, including their role in endocrinology, is also just beginning. Disclosure Summary: The author has nothing to disclose. Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study. melanocortin-like peptide of Escherichia coli viral insulin-like peptide

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,031
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,048
Score d'incertitude au seuil0,035

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,031
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0040,004
Communication savante0,0050,006
Science ouverte0,0010,003
Intégrité de la recherche0,0480,032
Charge utile insuffisante (le modèle a refusé de juger)0,0100,006

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 Opus0,036
Tête enseignante GPT0,358
Écart entre enseignants0,322 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

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
Publié2019
Routes d'admission1
Résumé présentnon

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