Attribute Network-based Ranking (ANeR) identifies conserved translational fingerprints of M-phase
Abstract
Entry into M-phase requires mRNA translation, but whether mitotic and meiotic divisions share a conserved translational programme and which newly synthesized proteins promote Cdk1 activation remain unclear. We developed Attribute Network-based Ranking (ANeR), a multi-objective network-based method that ranks mRNAs according to their proximity to a training group across heterogeneous two-condition omics datasets. Applied to 30 datasets covering mitotic and meiotic divisions, ANeR identified a conserved M-phase translational fingerprint. Benchmarking ANeR predictions against independent mitotic datasets, together with experimental validation in Xenopus oocytes, confirmed that ANeR prioritizes proteins whose accumulation increases during both mitotic and meiotic M-phases. Application of ANeR to the meiotic translational programme identified Fam76b and Cdca3 as previously unrecognized translationally activated regulators required for timely Cdk1 activation, demonstrating that ANeR can uncover new regulators of meiotic M-phase. Analysis of the oocyte translational programme established how broadly CPE/PAS regulatory rules apply, identified transcripts that do not conform to these rules, and experimentally validated their translational activation. Together, these findings establish that vertebrate mitotic and meiotic transitions share a conserved programme of translationally regulated transcripts and demonstrate that ANeR can identify new regulators of cell division. Broadly, ANeR provides a flexible framework for investigating diverse biological questions using a collection of two-condition datasets.
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BibTeX
@article{eivers2026attribute,
title = {{Attribute Network-based Ranking (ANeR) identifies conserved translational fingerprints of M-phase}},
author = {Sarah B Eivers and Axelle Baumgartner and Marcio Dorn and Catherine Jessus and Juliana Silva-Bernardes and Enrico Maria Daldello},
journal = {Nucleic Acids Research},
year = {2026},
month = oct,
doi = {10.1093/nar/gkag970},
url = {https://hal.science/hal-05828741v1},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Attribute Network-based Ranking (ANeR) identifies conserved translational fingerprints of M-phase
AU - Sarah B Eivers
AU - Axelle Baumgartner
AU - Marcio Dorn
AU - Catherine Jessus
AU - Juliana Silva-Bernardes
AU - Enrico Maria Daldello
PY - 2026
DA - 2026/10/14
JO - Nucleic Acids Research
DO - 10.1093/nar/gkag970
UR - https://hal.science/hal-05828741v1
AB - Entry into M-phase requires mRNA translation, but whether mitotic and meiotic divisions share a conserved translational programme and which newly synthesized proteins promote Cdk1 activation remain unclear. We developed Attribute Network-based Ranking (ANeR), a multi-objective network-based method that ranks mRNAs according to their proximity to a training group across heterogeneous two-condition omics datasets. Applied to 30 datasets covering mitotic and meiotic divisions, ANeR identified a conserved M-phase translational fingerprint. Benchmarking ANeR predictions against independent mitotic datasets, together with experimental validation in Xenopus oocytes, confirmed that ANeR prioritizes proteins whose accumulation increases during both mitotic and meiotic M-phases. Application of ANeR to the meiotic translational programme identified Fam76b and Cdca3 as previously unrecognized translationally activated regulators required for timely Cdk1 activation, demonstrating that ANeR can uncover new regulators of meiotic M-phase. Analysis of the oocyte translational programme established how broadly CPE/PAS regulatory rules apply, identified transcripts that do not conform to these rules, and experimentally validated their translational activation. Together, these findings establish that vertebrate mitotic and meiotic transitions share a conserved programme of translationally regulated transcripts and demonstrate that ANeR can identify new regulators of cell division. Broadly, ANeR provides a flexible framework for investigating diverse biological questions using a collection of two-condition datasets.
SN - 0305-1048
SN - 1362-4962
ER -