Cell ReportsMolecular BiologyPeer-reviewed journal article
Sex-biased islet β cell dysfunction is caused by the MODY MAFA S64F variant by inducing premature aging and senescence in males
Emily M. WalkeriD, Jeeyeon ChaiD, Xin TongiD, Min Amy GuoiD, Jin-Hua Liu, Sophia YuiD, Donato IacovazzoiD, Franck Mauvais‐JarvisiD, Sarah Elizabeth FlanaganiD, Márta KorbonitsiD, John M. StaffordiD, David Aaron JacobsoniD, Roland W. SteiniD
Abstract
A heterozygous missense mutation producing a variant of the islet {beta}-cell-enriched MAFA transcription factor (Ser(S)64Phe(F) MAFA) was identified in humans who developed adult-onset, {beta}-cell dysfunction (diabetes or insulinomatosis), with men more prone to diabetes. This mutation engenders increased stability to the normally unstable MAFA protein. To obtain insight into how this variant impacts {beta} cell function, we developed a mouse model expressing S64F MafA and found sex-dependent phenotypes, with heterozygous mutant males displaying impaired glucose tolerance while females were slightly hypoglycemic with improved blood glucose clearance. Only heterozygous males showed transiently higher MafA protein levels preceding the onset of glucose intolerance and sex-dependent, differential expression of genes involved in calcium signaling, DNA damage, aging, and senescence. Functional changes in islet calcium handling and signs of islet aging and senescence processes were uniquely observed in male animals. In addition, S64F MAFA expression in human, male EndoC-{beta}H2 {beta} cells accelerated cellular senescence and increased production of senescence-associated secretory proteins compared to cells expressing wild-type MAFA. Together, these results implicate a conserved mechanism of accelerated islet aging and senescence in promoting diabetes in S64F MAFA carriers in a sex-dependent manner.
- organism: mouse
- organism: human
- assay: bulk RNA-seq
- assay: protein structure
- research focus: differential expression
- research focus: cell-cell communication
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
- Citations
- 48
- FWCI
- 7.74
- Field percentile
- top 1%
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