The ortholog of the Human T2D Risk Gene STEAP2 is Required for Glucose Stimulated Insulin Secretion in Mice

Hawthorne, A.; Kelly, K.; Tigue, M.; El-Mekkoussi, H.; Waite, E.; Erhan, Y.; Manduchi, E.; Dashti, S.; Rozzo, A.; Roman, J.; Doliba, N.; Stoffers, D.; Kaestner, K. H.

doi:10.64898/2026.09.26.754731

bioRxiv · 10.64898/2026.09.26.754731

The ortholog of the Human T2D Risk Gene STEAP2 is Required for Glucose Stimulated Insulin Secretion in Mice

Hawthorne, A.·Kelly, K.·Tigue, M.·El-Mekkoussi, H.·Waite, E.·Erhan, Y.·Manduchi, E.·Dashti, S.·Rozzo, A.·Roman, J.·Doliba, N.·Stoffers, D.·Kaestner, K. H.

View original source ↗Export metadata ↓

Abstract

Type 2 diabetes is a complex metabolic disease with a strong genetic component, yet this genetic risk is driven by common variants in up to 600 loci with small effect sizes. Variants in the STEAP2 locus, encoding Six-transmembrane epithelial antigen of the prostate-2, are highly associated with altered glycemic traits and type 2 diabetes risk. To elucidate its function in pancreatic beta-cells, we utilized Crisp-Cas9 assisted homologous recombination to derive a conditional null allele for Steap2 (Steap2fl/fl) in mice and crossed this to the rat insulin promoter (Rip)-Cre transgene. Ablation of Steap2 induced glucose intolerance, blunted glucose-stimulated Ca2+ mobilization and stunted first phase glucose-simulated insulin secretion. In contrast, the incretin effect and second phase insulin secretion were not affected. Islet respirometry and transmission electron microscopy (TEM) identified disrupted cristate morphology and impaired glucose-stimulated oxygen consumption as causally responsible for the observed, impaired triggering pathway of insulin secretion. These findings suggest that at least part of the genetic risk for T2D conferred by Steap2 variants is likely due to its function in maintaining optimal mitochondrial respiration in pancreatic beta-cells.

Explore related subjects

genetics Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Hawthorne, A., Kelly, K., Tigue, M., El-Mekkoussi, H., Waite, E., Erhan, Y., Manduchi, E., Dashti, S., Rozzo, A., Roman, J., Doliba, N., Stoffers, D., Kaestner, K. H.. 2026-10-02. The ortholog of the Human T2D Risk Gene STEAP2 is Required for Glucose Stimulated Insulin Secretion in Mice. https://doi.org/10.64898/2026.09.26.754731

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Cellular context shapes the transcriptomic impact of somatic variants

Somatic SNVs are commonly prioritized by recurrence or sequence annotation, leaving the molecular consequences of private variants unresolved. We developed PrismSNV and Ligo-Seq, a single-cell full-length transcriptome sequencing platform, to quantify transcriptomic displacement associated with within-cell SNV-state toggling. Across 26 samples from three cancer types, more than 60% of candidate SNVs showed significant cell-type-specific displacement with over 30% variation in magnitude. Sequence-based deleteriousness was poorly aligned with cellular displacement, and fewer than 3% of top-displacement SNVs across six tumor types were catalogued in ClinVar. Displacement was strongest in transcriptionally plastic, less differentiated states. Analysis of 655,349 CD4+ T-cell transcriptomes further showed that SNV-gene associations changed in magnitude and sometimes direction during activation. Lack of recurrence or clinical annotation does not imply transcriptomic inertness; variant impact is shaped by cellular context rather than sequence alone, establishing the variant-cell pair as a critical unit for interpreting somatic variation.

genetics↗

bioRxiv2026

Genetic and Epigenetic Landscape of Self-Identified Hispanics/Latino in All Of Us

Human genetic studies still underrepresent many populations, limiting discovery and interpretation of medically relevant variation. Extensive admixture in self-identified Hispanic/Latino populations increases the need for representative reference data and ancestry-aware interpretation of medically relevant variation. We used long-read sequencing to analyze genetic variation, ancestry, and DNA methylation in 1,490 self-identified Hispanic/Latino participants from the All of Us Research Program. We identified 10 million novel variants and more than 100 candidate phenotype associations, with fine-mapping prioritizing structural variants over nearby single-nucleotide variants in more than half. We linked 3,329 tandem repeats to DNA methylation, resolved widespread allele-specific methylation, and identified ancestry-linked regulation across paralogous genes. These results reveal previously inaccessible genetic and regulatory variation and show the value of long-read sequencing for diverse populations.

genetics↗

bioRxiv2026

The genetic architecture of human DNA methylome variation

Population DNA methylome analyses remain largely confined to reference-defined genomic space despite extensive human genetic diversity. A central challenge is therefore to define comparable methylation phenotypes across diverse genomes and map the genetic architecture of methylome variation. Here, using 460 haplotypes from the Human Pangenome Reference Consortium Release 2, we develop two complementary pangenome-based frameworks: one compares methylation within homologous regions while retaining haplotype-specific structural configuration and CpG content; the other represents CpG island (CGI) variation at homologous loci as sequence-resolved alleles. We identified 47,039 CGI alleles, including 8,040 absent from CHM13; 62% were attributable to structural variants and 31% to small variants. Through local genealogies, we reconstructed CGI gain, loss and turnover, revealing how present-day methylation differences align with CGI evolutionary trajectories. We found that genome-wide, methylation variability was buffered against genetic diversity across more than 90% of the genome, with exceptions at extreme nucleotide diversity and locally around structural variants. Our methylation framework enabled population-scale allele-aware methylation quantitative trait locus (mQTL) mapping into non-reference and structurally complex sequence, where mQTL-associated intervals were enriched. mQTLs linked methylation to transcriptomic phenotypes, population differentiation and complex traits, supporting 8% of population differentially methylated regions and colocalizing with more than half of genome-wide association study loci. Together, our study redefines human methylome analysis and our findings reveal a genome-wide architecture of genetic-epigenetic coupling, reshaping how methylation variation is interpreted across human development and disease.

genetics↗