Multigenerational analysis of sex-specific phenotypic differences at midgestation caused by abnormal folate metabolism

The exposure to adverse environmental conditions (e.g., poor nutrition) may lead to increased disease risk in an individual and their descendants. In some cases, the results may be sexually dimorphic. A range of phenotypes has been associated with deficiency in or defective metabolism of the vitamin folate. However, the molecular mechanism linking folate metabolism to development is still not well defined nor is it clear whether phenotypes are sex-specific. The enzyme methionine synthase reductase (MTRR) is required for the progression of folate metabolism and the utilization of methyl groups from the folate cycle. Previously, we showed that the hypomorphic Mtrrgt mutation in mice results in metabolic disruption, epigenetic instability, and a wide spectrum of developmental phenotypes (e.g., growth defects, congenital malformations) at midgestation that appear in subsequent wildtype generations. This transgenerational effect only occurs through the maternal lineage. Here, we explore whether the phenotypes that result from either intrinsic or ancestral Mtrr deficiency are sexually dimorphic. We found that no sexual dimorphism is apparent in either situation when the phenotypes were broadly or specifically defined. However, when we focused on the group of phenotypically normal conceptuses derived from maternal grandparental Mtrr deficiency, we observed an apparent increase in placental efficiency in each subsequent generation leading to F4 generation female embryos that weigh more than controls. These data suggest that ancestral abnormal folate metabolism may lead to male grandprogeny that are less able to adapt or female grandprogeny that are programmed to become more sensitive to folate availability in subsequent generations.The following support was given: a Centre for Trophoblast Research (CTR) studentship to N.P., a Royal Society Newton International Fellowship to J.R., a Newnham College (Cambridge) studentship to K.M, and a Wellcome Trust 4-year PhD studentship in Developmental Mechanisms to G.E.T.B. This work was funded by a CTR Next Generation Fellowship, a Lister Research Prize, and an Isaac Newton Trust/Wellcome Trust ISSF/University of Cambridge joint research grant (to E.D.W.)

Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles

This is the final version. Available from BMC via the DOI in this record. Availability of data and materials: All data generated or analysed during this study are included in this published article, its supplementary information files and publicly available repositories. Data generated for this project are available at NCBI Gene Express Omnibus (GEO) under accession number GSE234520 [64]. We also reanalysed data previously made available via GEO (via accession numbers GSE74193 [65], GSE59685 [66], GSE80970 [67], GSE88890 [68], GSE43414 [69]) and the synapse platform (syn7072866 [70], syn8263588 [71]). Code for the analyses presented here can be found on GitHub and Zenodo https://github.com/ejh243/BrainFANS/tree/master/array/DNAm/preprocessing (https://doi.org/https://doi.org/10.5281/zenodo.10402167). Specifically, code for the quality control of the DNAm data can be found at https://github.com/ejh243/BrainFANS/tree/master/array/DNAm/preprocessing and the code for the statistical analyses can be found at https://github.com/ejh243/BrainFANS/tree/master/array/DNAm/analysis/neuralCellComposition. Our new trained deconvolution models for brain are made available to the wider research community via our R package CETYGO available on GitHub (https://github.com/ds420/CETYGO; https://doi.org/10.5281/zenodo.10418430).Background: Due to interindividual variation in the cellular composition of the human cortex, it is essential that covariates that capture these differences are included in epigenome-wide association studies using bulk tissue. As experimentally derived cell counts are often unavailable, computational solutions have been adopted to estimate the proportion of different cell types using DNA methylation data. Here, we validate and profile the use of an expanded reference DNA methylation dataset incorporating two neuronal and three glial cell subtypes for quantifying the cellular composition of the human cortex. Results: We tested eight reference panels containing different combinations of neuronal- and glial cell types and characterised their performance in deconvoluting cell proportions from computationally reconstructed or empirically derived human cortex DNA methylation data. Our analyses demonstrate that while these novel brain deconvolution models produce accurate estimates of cellular proportions from profiles generated on postnatal human cortex samples, they are not appropriate for the use in prenatal cortex or cerebellum tissue samples. Applying our models to an extensive collection of empirical datasets, we show that glial cells are twice as abundant as neuronal cells in the human cortex and identify significant associations between increased Alzheimer’s disease neuropathology and the proportion of specific cell types including a decrease in NeuNNeg/SOX10Neg nuclei and an increase of NeuNNeg/SOX10Pos nuclei. Conclusions: Our novel deconvolution models produce accurate estimates for cell proportions in the human cortex. These models are available as a resource to the community enabling the control of cellular heterogeneity in epigenetic studies of brain disorders performed on bulk cortex tissue.Engineering and Physical Sciences Research CouncilMedical Research CouncilAlzheimer's Research UKMedical Research Counci

Parental breeding age effects on descendants' longevity interact over 2 generations in matrilines and patrilines

Individuals within populations vary enormously in mortality risk and longevity, but the causes of this variation remain poorly understood. A potentially important and phylogenetically widespread source of such variation is maternal age at breeding, which typically has negative effects on offspring longevity. Here, we show that paternal age can affect offspring longevity as strongly as maternal age does and that breeding age effects can interact over 2 generations in both matrilines and patrilines. We manipulated maternal and paternal ages at breeding over 2 generations in the neriid fly Telostylinus angusticollis. To determine whether breeding age effects can be modulated by the environment, we also manipulated larval diet and male competitive environment in the first generation. We found separate and interactive effects of parental and grand-parental ages at breeding on descendants' mortality rate and life span in both matrilines and patrilines. These breeding age effects were not modulated by grand-parental larval diet quality or competitive environment. Our findings suggest that variation in maternal and paternal ages at breeding could contribute substantially to intrapopulation variation in mortality and longevity