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Enregistrement W1982461554 · doi:10.1210/en.2008-1586

Do These Genes Make Me Look Fat?

2009· letter· en· W1982461554 sur OpenAlexaffabout
Tara S. Perrot-Sinal

Notice bibliographique

RevueEndocrinology · 2009
Typeletter
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGenetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities
Établissements canadiensDalhousie University
Organismes subventionnairesNational Institutes of HealthNational Institute for Health and Care Research
Mots-clésEndocrinologyInternal medicineGeneBiologyMedicineGenetics

Résumé

récupéré en direct d'OpenAlex

No one would argue that men and women differ, not only in features we consider to be related to reproductive function, but also in ways we do not directly connect to reproduction. Although the results of research designed to examine sex differences are always a hot topic of conversation, there are important reasons to understand as much as we can about each sex, from the level of the genome to behavior. There is strong evidence for sex differences in the incidence and presentation of most disease processes (1) in addition to a long-standing knowledge that men and women differ in the most basic metabolic processes, having serious consequences for pharmacological treatments (2). Despite this, there remains an underrepresentation of female subjects in basic research and clinical trials. For example, although the National Institutes of Health (NIH) has had an official policy to increase the inclusion of women in research since 1993, as recently as 2005, only 3% of all NIH grants have been awarded for work that considers that results may differ according to the sex of the research subjects (3). The excuse for this has always been the cyclical nature of female gonadal steroid levels, which potentially impact on sexually dimorphic measures, making the results from females inherently more variable. Nonetheless, the message that “sex matters” is slowly getting through to the research community, as evidenced by funding agencies, such as the Canadian Institutes of Health Research (http:// www.cihr-irsc.gc.ca/e/25499.html#2.1.2.), mandating that research be performed in both sexes. The study in the current issue of Endocrinology by van Nas et al. (4), in which the authors incorporated the use of powerful molecular techniques (global network and linkage analysis tools) and a solid neuroendocrine approach to understand the role of gonadal hormones and sex chromosomes in sexually dimorphic gene expression, is an excellent example. Sex differences are the result of sexual differentiation, a process that is initiated by the presence/absence of a Y-chromosomeencoded testis-determining factor called SRY in humans (Sry in mice), and then mediated by the presence of Xvs. Y-chromosomes and gonadal steroids (5). The sexual differentiation process, which begins early in development and continues throughout adolescence, results in massive sex differences in phenotype. Most of these differences are the result of natural and/or sexual selection for traits that affect the fitness of each sex differentially (6). Despite the fact that males and females differ so drastically phenotypically, they are almost identical genetically. Thus, the majority of sexually dimorphic traits are the result of sex differences in expression of the same genes. Of relevance, sex-related differences in gene expression have been noted in insects, nematodes, birds, and mammals (6). Mammalian sex differences at the transcriptional level have been known for over 20 yr, with the initial discovery that a number ofcytochromeP450enzyme(Cyp450)genes in rat liverare sexually dimorphic (7). Although not yet documented, it is likely that there are human Cyp450s that are transcribed in a sex-specific manner, thuscontributingtoobserveddifferences inCyp450substrateclearance rates (2). Recently, the application of microarray technologies has allowed for the systematic investigation of sex differences in gene expression in a number of somatic tissues in addition to liver, including kidney, muscle, adipose, whole brain, hypothalamus, myocardium,andheart (reviewed inRef.8).However, themajority of these studies have been hampered by relatively small sample size, resulting in limited power to fully explore tissue specificity in sexually dimorphic gene expression. A previous high-density expression array analysis of 23, 574 transcripts in mice (derived from an intercross between two inbred mouse strains) revealed thousands of genes in liver and adipose tissue showing sexual dimorphism, as well as hundreds in the brain (9). The authors concluded that the extent of sex differences in gene expression is larger than previously realized and that sexually dimorphic genes are highly tissue specific. This report set the stage for the present study by van Nas et al. (4), the goal of which was to investigate sexual dimorphism at the molecular level and explore gene coexpression networks that are preserved between the two sexes or are sex-specific in mice. The authors used a rigorous neuroendocrine experimental design that included intact males and females, gonadectomized (GDX) males and females, administered either placebo, dihydrotestosterone (DHT) or estradiol replacement, and GDX “four core

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)0,002
score de la tête « metaresearch » (Gemma)0,011
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: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,051
Score d'incertitude au seuil0,171

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,011
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,004
Communication savante0,0030,003
Science ouverte0,0010,001
Intégrité de la recherche0,0040,004
Charge utile insuffisante (le modèle a refusé de juger)0,0510,017

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 Opus0,018
Tête enseignante GPT0,271
Écart entre enseignants0,253 · 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
GenreCommentaire

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

En bref

Citations6
Publié2009
Routes d'admission2
Résumé présentoui

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