{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T18:27:56Z","timestamp":1786559276860,"version":"build-2736575974"},"reference-count":24,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"\n Researchers routinely take a gene-regulatory network measured in one species\u2014for example, which transcription factor switches which gene on or off in the mouse\u2014and assume the same wiring holds in another species after matching genes by ancestry (orthology). How often this assumption holds quantitatively has not been measured systematically. Here I test it directly. Using single-cell RNA-sequencing of matched organs, I measure, for each transcription factor, how strongly its expression tracks that of every other gene (a co-expression \u201cedge\u201d), compute these edges independently in human and mouse lung, and ask how well they agree. Edges agree strongly when the organ is the same in both species (rank correlation\n \n \n \n \n \n \u03c1<\/mml:mi>\n =<\/mml:mo>\n 0.74<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ; the direction of an association\u2014activating versus repressing\u2014matches for 100% of the strongest edges), far beyond chance (\n z<\/jats:italic>\n \u2009=\u2009112 against a permutation null). Agreement is, however, highly uneven: edges of identity-defining factors such as NKX2\u22121 (lung), ERG (blood vessel), and RUNX3 (lymphocyte) transfer almost perfectly (\n \n \n \n \n \n \u03c1<\/mml:mi>\n ><\/mml:mo>\n 0.80<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ), whereas edges of signal-activated factors such as HIF1A, STAT1, and CTNNB1 barely transfer at all (median\n \n \n \n \n \n \u03c1<\/mml:mi>\n =<\/mml:mo>\n 0.14<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n for the signal-responsive group). Strikingly, changing the organ within one species disrupts edges\n more<\/jats:italic>\n than changing the species within one organ: 76% of factors are better conserved across species than across tissues. The conserved signal is not merely a by-product of matching cell-type proportions: after statistically removing cell-type identity as a covariate, cross-species agreement remains substantial (\n \n \n \n \n \n \u03c1<\/mml:mi>\n =<\/mml:mo>\n 0.50<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ). Extending the comparison to zebrafish using a real developmental cell atlas (430 million years of divergence), edge agreement falls to weakly positive (\n \n \n \n \n \n \u03c1<\/mml:mi>\n =<\/mml:mo>\n 0.15<\/mml:mn>\n \u2212<\/mml:mo>\n 0.19<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ), and\u2014contrary to a simple expectation\u2014the identity-versus-signaling gap is no longer statistically distinguishable at that distance. Whole-protein sequence identity is a poor predictor of edge transfer at mammalian distances (\n R<\/jats:italic>\n 2<\/jats:sup>\n \u2009<\u20090.01). Finally, benchmarking against a curated database of known regulatory interactions shows that co-expression strength is a weak indicator of direct regulation (area under the ROC curve\n \n \n \n \n \n \u2248<\/mml:mo>\n 0.52<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ), so what transfers across species is conserved co-expression\n structure<\/jats:italic>\n \u2014largely cell-identity programs\u2014rather than validated direct regulatory wiring. Together these results give practical, quantitative guidance on when cross-species transfer of regulatory information is safe and when it is not.\n <\/jats:p>","DOI":"10.1371\/journal.pone.0347366","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T17:34:53Z","timestamp":1786556093000},"page":"e0347366","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ortholog transfer stress test: Regulatory edge conservation across species, tissues, and evolutionary distance"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5342-1499","authenticated-orcid":true,"given":"Ihor","family":"Kendiukhov","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"issue":"9","key":"pone.0347366.ref001","first-page":"559","article-title":"Molecular 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Society Series B: Statistical Methodology"},{"issue":"12","key":"pone.0347366.ref023","first-page":"1000","article-title":"The Cell Ontology version 2023-12-01: Enhanced cross-kingdom knowledge","volume":"3","author":"D Osumi-Sutherland","year":"2023","journal-title":"Nature Computational Science"},{"key":"pone.0347366.ref024","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1146\/annurev.biochem.79.081507.103945","article-title":"Genomic and biochemical insights into the specificity of ETS transcription factors","volume":"80","author":"PC Hollenhorst","year":"2011","journal-title":"Annu Rev Biochem"}],"container-title":["PLOS 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