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Background: Obesity is associated with varying degrees of metabolic dysfunction. In this study, we aimed to discover markers of the severity of metabolic impairment in men with obesity via a multiomics approach., Methods: Thirty-two morbidly men with obesity who were candidates for Roux-en-Y gastric bypass (RYGB) surgery were prospectively followed. Nine healthy adults served as controls. Deep phenotyping, including targeted metabolomics, transcriptomics, and brain magnetic resonance imaging (MRI), was performed., Results: Testosterone emerged as a key contributor to phenotypic variability via principal component analysis and was therefore used to further categorize obese patients as having or not having hypogonadotropic hypogonadism (HH). Despite having comparable body mass indices, obese individuals with HH presented with worse metabolic defects than obese individuals without HH, including higher insulin resistance, as well as MRI signs of hypothalamic inflammation and a specific blood transcriptomics signature. The upregulated genes were involved mainly in inflammation, mitochondrial function, and protein translation. Integration of gene expression and clinical data revealed high FGF21 and low cortisol levels as the top markers correlated with the transcriptomic signature of metabolic risk. Following RYGB-induced substantial weight loss, testosterone levels markedly increased in both obese individuals with and without HH, challenging the current definition of hypogonadism. A longitudinal study in a subset of men with obesity following bariatric surgery revealed a unique FGF21 trajectory with a sharp peak at one month post-RYGB that correlated with metabolic and reproductive improvements., Conclusions: Combining clinical, biochemical, and molecular markers allows adequate stratification of metabolic risk in men with obesity and provides novel tools for personalized care., Competing Interests: Declarations. Ethics approval and consent to participate: The research protocol was approved by the Institutional Ethics Committee for Research of the Canton of Vaud, Switzerland (CER-VD), and written informed consent was obtained from all participants. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests., (© 2025. The Author(s).)

Background/objectives: This study aims to elucidate the genetic causes of congenital hypogonadotropic hypogonadism (CHH), a rare genetic disorder resulting in GnRH deficiency, in six families from Pakistan., Methods: Eighteen DNA samples from six families underwent genome sequencing followed by standard evaluation for pathogenic single nucleotide variants (SNVs) and small indels. All families were subsequently analyzed for pathogenic copy number variants (CNVs) using CoverageMaster., Results: Novel pathogenic homozygous SNVs in known CHH genes were identified in four families: two families with variants in GNRHR, and two others harboring KISS1R variants. Subsequent investigation of CNVs in the remaining two families identified novel unique large deletions in ANOS1., Conclusion: A combined, systematic analysis of single nucleotide and CNVs helps to improve the diagnostic yield for variants in patients with CHH., (© 2024. The Author(s).)

Pubertal timing varies considerably and is associated with later health outcomes. We performed multi-ancestry genetic analyses on ~800,000 women, identifying 1,080 signals for age at menarche. Collectively, these explained 11% of trait variance in an independent sample. Women at the top and bottom 1% of polygenic risk exhibited ~11 and ~14-fold higher risks of delayed and precocious puberty, respectively. We identified several genes harboring rare loss-of-function variants in ~200,000 women, including variants in ZNF483, which abolished the impact of polygenic risk. Variant-to-gene mapping approaches and mouse gonadotropin-releasing hormone neuron RNA sequencing implicated 665 genes, including an uncharacterized G-protein-coupled receptor, GPR83, which amplified the signaling of MC3R, a key nutritional sensor. Shared signals with menopause timing at genes involved in DNA damage response suggest that the ovarian reserve might signal centrally to trigger puberty. We also highlight body size-dependent and independent mechanisms that potentially link reproductive timing to later life disease., (© 2024. The Author(s).)

Pubertal timing varies considerably and has been associated with a range of health outcomes in later life. To elucidate the underlying biological mechanisms, we performed multi-ancestry genetic analyses in ~800,000 women, identifying 1,080 independent signals associated with age at menarche. Collectively these loci explained 11% of the trait variance in an independent sample, with women at the top and bottom 1% of polygenic risk exhibiting a ~11 and ~14-fold higher risk of delayed and precocious pubertal development, respectively. These common variant analyses were supported by exome sequence analysis of ~220,000 women, identifying several genes, including rare loss of function variants in ZNF483 which abolished the impact of polygenic risk. Next, we implicated 660 genes in pubertal development using a combination of in silico variant-to-gene mapping approaches and integration with dynamic gene expression data from mouse embryonic GnRH neurons. This included an uncharacterized G-protein coupled receptor GPR83 , which we demonstrate amplifies signaling of MC3R , a key sensor of nutritional status. Finally, we identified several genes, including ovary-expressed genes involved in DNA damage response that co-localize with signals associated with menopause timing, leading us to hypothesize that the ovarian reserve might signal centrally to trigger puberty. Collectively these findings extend our understanding of the biological complexity of puberty timing and highlight body size dependent and independent mechanisms that potentially link reproductive timing to later life disease., Competing Interests: Competing interests J.R.B.P. and E.J.G. are employed by Adrestia Therapeutics. D.J.T. is employed by Genomics PLC. D.L.C. and J.P.B. are employed by GSK. E.T. is employed by Pfizer. D.A.L. has received support from Roche Disgnostics and Medtroic Ltd for work unrelated to the research in this paper. T.D.S. is co-founder and stakeholder of Zoe Global Ltd. P.A.F. conducts research funded by Amgen, Novartis and Pfizer, he received Honoraria from Roche, Novartis and Pfizer. M.W.B. conducts research funded by Amgen, Novartis and Pfizer.

Kisspeptin neurons in the mediobasal hypothalamus (MBH) are critical targets of ovarian estrogen feedback regulating mammalian fertility. To reveal molecular mechanisms underlying this signaling, we thoroughly characterized the estrogen-regulated transcriptome of kisspeptin cells from ovariectomized transgenic mice substituted with 17β-estradiol or vehicle. MBH kisspeptin neurons were harvested using laser-capture microdissection, pooled, and subjected to RNA sequencing. Estrogen treatment significantly ( p.adj . < 0.05) up-regulated 1,190 and down-regulated 1,139 transcripts, including transcription factors, neuropeptides, ribosomal and mitochondrial proteins, ion channels, transporters, receptors, and regulatory RNAs. Reduced expression of the excitatory serotonin receptor-4 transcript ( Htr4 ) diminished kisspeptin neuron responsiveness to serotonergic stimulation. Many estrogen-regulated transcripts have been implicated in puberty/fertility disorders. Patients ( n = 337) with congenital hypogonadotropic hypogonadism (CHH) showed enrichment of rare variants in putative CHH-candidate genes (e.g., LRP1B , CACNA1G , FNDC3A ). Comprehensive characterization of the estrogen-dependent kisspeptin neuron transcriptome sheds light on the molecular mechanisms of ovary-brain communication and informs genetic research on human fertility disorders.

CoverageMaster (CoM) is a copy number variation (CNV) calling algorithm based on depth-of-coverage maps designed to detect CNVs of any size in exome [whole exome sequencing (WES)] and genome [whole genome sequencing (WGS)] data. The core of the algorithm is the compression of sequencing coverage data in a multiscale Wavelet space and the analysis through an iterative Hidden Markov Model. CoM processes WES and WGS data at nucleotide scale resolution and accurately detects and visualizes full size range CNVs, including single or partial exon deletions and duplications. The results obtained with this approach support the possibility for coverage-based CNV callers to replace probe-based methods such as array comparative genomic hybridization and multiplex ligation-dependent probe amplification in the near future., (© The Author(s) 2022. Published by Oxford University Press.)