Evidence map›Paper›PMID 33317634›Full record

ArticleClinical epigenetics2020

High-resolution analyses of human sperm dynamic methylome reveal thousands of novel age-related epigenetic alterations.

Mingju Cao, Xiaojian Shao, Peter Chan, Warren Cheung, Tony Kwan, Tomi Pastinen, Bernard Robaire

Open access · goldAbstract read
In one paragraph

Article in Clinical epigenetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
3.7field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 70 citations in OpenAlex.

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  8. Male aging in germ cells: What are we inheriting?Genetics and molecular biology · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 2 countries.

Mingju CaoDepartment of Pharmacology and Therapeutics, McGill University, 3655 Promenade Sir William Osler, Montreal, QC, H3G 1Y6, Canada.
Xiaojian ShaoDepartment of Human Genetics, McGill University, 740 Docteur-Penfield Avenue, Montreal, QC, H3A 0G1, Canada.
Peter ChanDepartment of Urology, McGill University Health Centre, 1001 Boulevard Decarie, Montreal, QC, H4A 3J1, Canada.
Warren CheungCenter for Pediatric Genomic Medicine, Children's Mercy Kansas City, 2401 Gilham Road, Kansas City, MO, 64108, USA.
Tony KwanDepartment of Human Genetics, McGill University, 740 Docteur-Penfield Avenue, Montreal, QC, H3A 0G1, Canada.
Tomi PastinenDepartment of Human Genetics, McGill University, 740 Docteur-Penfield Avenue, Montreal, QC, H3A 0G1, Canada.
Bernard RobaireDepartment of Pharmacology and Therapeutics, McGill University, 3655 Promenade Sir William Osler, Montreal, QC, H3G 1Y6, Canada. bernard.robaire@mcgill.ca.ORCID 0000-0001-6631-2400
Children's Mercy Hospital · USMcGill University and Génome Québec Innovation Centre · CAMcGill University Health Centre · CAMcGill University · CA

Funding

CIHR TE1-138298
6 · The paper itself

Abstract

backgroundChildren of aged fathers are at a higher risk of developing mental disorders. Alterations in sperm DNA methylation have been implicated as a potential cause. However, age-dependent modifications of the germ cells' epigenome remain poorly understood. Our objective was to assess the DNA methylation profile of human spermatozoa during aging.

resultsWe used a high throughput, customized methylC-capture sequencing (MCC-seq) approach to characterize the dynamic DNA methylation in spermatozoa from 94 fertile and infertile men, who were categorized as young, 48 men between 18-38 years or old 46 men between 46-71 years. We identified more than 150,000 age-related CpG sites that are significantly differentially methylated among 2.65 million CpG sites covered. We conducted machine learning using our dataset to predict the methylation age of subjects; the age prediction accuracy based on our assay provided a more accurate prediction than that using the 450 K chip approach. In addition, we found that there are more hypermethylated (62%) than hypomethylated (38%) CpG sites in sperm of aged men, corresponding to 798 of total differential methylated regions (DMRs), of which 483 are hypermethylated regions (HyperDMR), and 315 hypomethylated regions (HypoDMR). Moreover, the distribution of age-related hyper- and hypomethylated CpGs in sperm is not random; the CpG sites that were hypermethylated with advanced age were frequently located in the distal region to genes, whereas hypomethylated sites were near to gene transcription start sites (TSS). We identified a high density of age-associated CpG changes in chromosomes 4 and 16, particularly HyperDMRs with localized clusters, the chr4 DMR cluster overlaps PGC1α locus, a protein involved in metabolic aging and the chr16 DMR cluster overlaps RBFOX1 locus, a gene implicated in neurodevelopmental disease. Gene ontology analysis revealed that the most affected genes by age were associated with development, neuron projection, differentiation and recognition, and behaviour, suggesting a potential link to the higher risk of neurodevelopmental disorders in children of aged fathers.

conclusionWe identified thousands of age-related and sperm-specific epigenetic alterations. These findings provide novel insight in understanding human sperm DNA methylation dynamics during paternal aging, and the subsequently affected genes potentially related to diseases in offspring.

Indexed as

AdultAgedAgingCase-Control StudiesChildCpG IslandsDNA MethylationEpigenomeEpigenomicsFathersFertilityGene OntologyGerm CellsHumansMachine LearningMalePeroxisome Proliferator-Activated Receptor Gamma Coactivator 1-alphaPPARGC1A protein, humanRBFOX1 protein, humanRNA Splicing FactorsAdvanced paternal ageDNA methylationFertilityMCC-seqSpermatozoa

Identifiers

PMID33317634
PMCPMC7735420
OpenAlexW3112369087

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.