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Intergenerational Regulation of Mosaic Supernumerary Chromosomes: Evidence for a Developmental Correction Mechanism from Clinical and Computational Models

ABSTRACT: Objective This study presents a rare case of father-to-offspring mosaic transmission of a supernumerary marker chromosome (SMC) and, through integration of literature analysis and computational modeling, proposes a novel embryonic selection-based regulatory mechanism. Methods Karyotyping and SNP-array were performed on amniotic fluid, cord blood, and parental samples. A systematic review identified 35 additional mosaic SMC transmission cases. Four computational frameworks—agent-based simulation, logistic regression, Bayesian inference, and Markov chain modeling—were used to test the hypothesis of selective regulation during early embryogenesis. Results The father carried a mosaic SMC at 11p11 (57%), transmitted to the fetus (38% in amniotic fluid, 45% in cord blood), with both individuals phenotypically normal and no pathogenic CNVs detected. Literature analysis showed consistent patterns of equal or reduced mosaicism in offspring. Modeling revealed that mild selection pressures could replicate observed retention patterns, supporting a non-random, developmentally regulated mechanism. Conclusion The combined clinical, literature-based, and computational evidence supports a model in which early embryos selectively eliminate SMC+ cells to maintain mosaicism at non-pathogenic levels. This proposed “Shepherd Mechanism” may underlie intergenerational mosaic regulation and provides a theoretical foundation for improved prenatal counseling in mosaic cases.

ORGANISM(S): Homo sapiens

PROVIDER: GSE302224 | GEO | 2025/12/10

REPOSITORIES: GEO

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Project description:Intra-amniotic infection, the invasion of microbes into the amniotic cavity resulting in an inflammatory process, is a clinical condition that can lead to adverse pregnancy outcomes for the mother and fetus as well as severe long-term neonatal morbidities. Despite much research focused on the consequences of intra-amniotic infection, there is still little knowledge about the functional roles of innate immune cells that respond to invading microbes. In the current study, we performed RNA sequencing of sorted neutrophils and monocytes/macrophages from amniotic fluid from women with intra-amniotic infection to determine the transcriptomic differences between these innate immune cells. Further, we sought to identify specific transcriptomic pathways that were significantly altered by the maternal or fetal origin of amniotic fluid neutrophils and monocytes, the presence of a severe fetal inflammatory response, and pregnancy outcome (i.e. preterm or term delivery). We showed that significant transcriptomic differences exist between amniotic fluid neutrophils and monocytes/macrophages from women with intra-amniotic infection that are indicative of the distinct roles these cells play. We also found that amniotic fluid monocytes/macrophages of fetal origin display impaired ability to clear out microbes invading the amniotic cavity compared to those of maternal origin. Notably, we demonstrate that the transcriptomic changes in amniotic fluid monocytes/macrophages are heavily associated with the severity of the fetal inflammatory response, suggesting that the trafficking of fetal neutrophils throughout the umbilical cord is partially modulated by monocytes/macrophages in the amniotic cavity. Finally, we show that amniotic fluid neutrophils and monocytes/macrophages from preterm deliveries display enhanced transcriptomic activity compared to those from term deliveries, highlighting the protective role of these innate immune cells in this vulnerable period. Collectively, these findings demonstrate the underlying complexity of local innate immune responses in women with intra-amniotic infection, and provide new insights into the functions of amniotic fluid neutrophils and monocytes in the amniotic cavity.

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Intergenerational Regulation of Mosaic Supernumerary Chromosomes: Evidence for a Developmental Correction Mechanism from Clinical and Computational Models

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