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

Viral Hormones: Do They Impact Human Endocrinology?

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

Bibliographic record

VenueEndocrinology · 2019
Typeletter
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsDiabetes CanadaUniversity of Toronto
Fundersnot available
KeywordsInternal medicineEndocrinologyHormoneBiologyMedicine

Abstract

fetched live from 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0480.032
Insufficient payload (model declined to judge)0.0100.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.358
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2019
Admission routes1
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