Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.017 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".