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A µ-opioid receptor superagonist analgesic with minimal adverse effects.

A correction to this paper has been published: the notice, 42032332, from Europe PMC.

Code ↔ Paper

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  1. [1] § Methods › Fibre photometry › dLight 1.3b ↔ GREEN_ISO_awake_inj.m, lines 83–139 · score 0.71 · pass filtered, biosensor signal, baseline period, awake, Isosbestic, fit
  2. [2] § Methods › Fibre photometry › dLight 1.3b ↔ Spikes_Events_Drug_Script.m, lines 1–48 · score 0.57 · polynomial fit, baseline period, downsampled, Isosbestic, drugs, signal

Paper

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The authors' code

MATLAB · 242 lines · 6.9 KB · no license · 1 match

  1. %% IMPORT AND PROCESS DORIC DATA FROM DORIC FILE
  2. % ([email hidden])
  4. % update: uses ALS calculated baseline to fit isosbestic
  5. % update 20/01/2026: bin peak data
  7. clear
  8. close all
  10. filename = uigetfile({'*.doric'}, 'Pick the FP file'); %select a file (open split down menu and select all file to show the CSV files)
  12. name = input("Enter a name: ", 's');
  13. if isempty(name)
  15. name = strsplit(filename,".");
  16. name = string(name(1));
  17. end
  18. RAWDATA = ExtractDataAcquisition(filename);
  20. GREEN = RAWDATA(3).Data(2).Data;
  21. ISOSBESTIC = RAWDATA(4).Data(2).Data;
  22. % RED = RAWDATA(5).Data(1).Data ;
  23. Times = RAWDATA(3).Data(1).Data ;
  24. sr = round(length(Times)/Times(end)); % update to your sampling rate
  25. %sr= 1205;
  27. Fig1 = figure;
  28. subplot (2,1,1)
  29. plot (Times, GREEN, 'g--');hold on;
  30. %plot( Times, RED, 'r--');hold on;
  31. plot (Times, ISOSBESTIC, 'm--')
  33. % smooth the control trace using the running average
  34. window_size = round(0.5*sr); % in (C*sr), C is the window size in seconds
  35. smooth_ISOS = conv(ISOSBESTIC, ones(1, window_size) / window_size, 'same');
  37. %smooth the GREEN and RED
  38. GREEN_raw = GREEN;
  39. GREEN = smoothdata(GREEN, 'sgolay', 0.1*sr);
  41. %RED_raw = RED;
  42. %RED = smoothdata(RED, 'sgolay', 100);
  44. %% ACTIVATE to IMPORT DIGITAL OR ANALOG SIGNALS if NECESSARY
  45. % data sources will need to be defined
  47. %DIin1pre = RAWDATA(5).Data(1).Data;
  48. %DIin4pre = RAWDATA(4).Data(2).Data;
  49. %AOut1pre = RAWDATA(3).Data(1).Data;
  50. %AOut4pre = RAWDATA(2).Data(3).Data;
  52. % fix the timing/align with locked-in data
  53. %time2 = RAWDATA(2).Data(4).Data;
  55. %AOut4 = interp1(time2, AOut4pre, Times, 'nearest', 'extrap');
  57. %attr = h5readatt(filename, '/DataAcquisition/FPConsole', 'DifferenceMasterStartToFirstData')
  59. %% TRIM DATA TO REMOVE ARTIFACTS
  61. trimON = 1; %seconds to remove at the start
  62. trimOFF = 1; %seconds to remove at the end
  64. GREEN = GREEN(trimON*sr:(length(Times)-trimOFF*sr));
  65. %RED = RED(trimON*sr:(length(Times)-trimOFF*sr));
  66. CON = smooth_ISOS(trimON*sr:(length(Times)-trimOFF*sr));
  67. T = Times(trimON*sr:(length(Times)-trimOFF*sr));
  69. % if you have imported signals, activate/rename:
  70. %Pelec = AOut4(trimON*sr:(length(AOut4)-trimOFF*sr));
  71. %Popto = AOut1(trimON*sr:(length(AOut4)-trimOFF*sr));
  72. %IN1 = DIin1(trimON*sr:(length(DIin1)-trimOFF*sr));
  73. %IN2 = DIin2(trimON*sr:(length(DIin2)-trimOFF*sr));
  76. subplot(2,1,1)
  77. plot (T, GREEN, 'g','LineWidth', 2); hold on;
  78. plot(T, CON, 'm','LineWidth', 2)
  79. %plot(T, RED, 'r','LineWidth', 2)
  80. %plot (T, Pelec*0.1)
  83. %% correct using isosbestic
  85. % SCALE ISOSBESTIC CONTROL TO BIOSENSOR SIGNAL LEVELS
  86. % it uses the minima of the signal values
  89. %OLD METHOD
  90. %[~, gminloc] = findpeaks (-GREEN(1:100*sr), 'MinPeakDistance', 3*sr); %find minima locs
  91. %p = polyfit (CON(gminloc), GREEN(gminloc), 1); %fits a a 1st order equation y = a*x + b
  93. % if baseline has no peaks, ingore the minima, take only baseline period (~20s)
  94. %base = 20*60*sr; %the first 20min of data
  95. %p = polyfit (CON(1:base), GREEN(1:base), 1); %fits a a 1st order equation y = a*x + b
  97. % NEW METHOD
  99. % Define parameters for ALS
  100. lambda = sr*1e4; % Smoothing parameter (higher value = smoother baseline)
  101. p = 0.01; % Asymmetry parameter (adjust for how peaks affect baseline)
  104. baseline_G = als_baseline(GREEN, lambda, p);
  106. flatzone = 60*sr; %define the zone in which the signal should be flat
  107. % i.e. before the injection/treatment
  109. p = polyfit (CON(1:flatzone), baseline_G(1:flatzone), 1);
  111. a = p(1); b = p(2);
  113. %scale the control
  114. greenCON = CON*a + b;
  115. %clear p a b
  116. cGREEN = (GREEN - greenCON)./greenCON;
  117. %cGREEN2 = (GREEN - baseline_G)./baselineG; % high pass filter, removes trends
  119. % same for red channel
  120. %[~, rminloc] = findpeaks (-RED, 'MinPeakDistance', 2*sr); %find minima locs
  121. %p = polyfit (CON(rminloc), RED(rminloc), 1); %fits a a 1st order equation y = a*x + b
  122. %a = p(1); b = p(2);
  124. %scale the control
  125. %redCON = CON*a + b;
  126. %clear p a b
  127. %cRED = (RED -redCON)./redCON;
  129. subplot (2,1,1)
  130. plot (T, greenCON, 'm','LineWidth', 2);
  131. plot (T, baseline_G, 'y','LineWidth', 1);
  133. subplot (2,1,2)
  134. %plot(T, cRED, 'r','LineWidth', 2); hold on;
  135. plot (T, cGREEN, 'g','LineWidth', 2, 'DisplayName','Corrected w/ iso'); hold on
  136. %plot (T, cGREEN2, 'b','LineWidth', 1, 'DisplayName','Corrected w/ baseline'); hold on
  138. %plot (T, Pelec*0.02-0.01, 'k');
  139. legend('Corrected w/ iso')
  141. %% trim around injection
  142. % keep 15 pre injection and 30 min post
  143. before = 15 * 60 *sr;
  144. after = 30 * 60 *sr;
  145. %
  146. INJstart = 20*60 + 5;
  147. INJend = 20*60 + 15;
  148. %
  149. [~, idxSTART] = min(abs(T - INJstart));
  150. [~, idxEND] = min(abs(T - INJend));
  151. %
  152. cGREENbefore = cGREEN(idxSTART-before:idxSTART);
  153. cGREENafter = cGREEN(idxEND:idxEND+after);
  154. cGREENcut = vertcat(cGREENbefore(2:end), cGREENafter);
  155. %
  156. % %cGREENcut(1357727:1359845)=NaN;
  157. % %cGREENcut(1361776:1366921)=NaN;
  158. %
  159. figure
  160. newT = (-10:1/sr/60:30)';
  161. plot (newT, cGREENcut);hold on;
  162. plot ([0 0], [-0.1 0.2], 'k--')
  163. ylim([-0.1 0.2])
  166. %% quantify peaks
  168. [ampl, loc, width, prom] = findpeaks (cGREENcut, ...
  169. 'MinPeakProminence',0.01,'MinPeakWidth', 0,'MinPeakDistance',0);
  171. width = width/sr; % in s
  172. pk_time = newT(loc);
  173. peak_tbl = table (loc, ampl, prom, width);
  174. plot (pk_time, ampl, 'or')
  175. title('dFF corrected data')
  176. %% zScore
  177. % and its peaks
  179. zScore = (cGREENcut-median(cGREENbefore))./std(cGREENbefore);
  181. [zampl, zloc, zwidth, zprom] = findpeaks (zScore, ...
  182. 'MinPeakProminence',1.6,'MinPeakWidth', 0,'MinPeakDistance',0);
  184. zwidth = zwidth/sr; % in s
  185. zpk_time = newT(zloc);
  186. zscore_peak_tbl = table (zloc, zampl, zprom, zwidth);
  188. figure
  189. plot (newT, zScore); hold on;
  190. plot (zpk_time, zampl, 'or')
  191. title('z-score corrected data')
  193. % bin peak data
  194. timebin = 5; % bin size in minutes
  195. t0 = -10; % STARTING TIME in minutes
  196. nbins = 8; % number of bins
  197. gbins = nan(nbins,4);
  198. for i = 1:nbins
  199. t_start = t0 + timebin*(i-1);
  200. t_end = t0 + timebin*i;
  201. idx = zpk_time >= t_start & zpk_time < t_end;
  202. greenwid = zwidth(idx);
  203. greenamp = zprom(idx);
  204. gbins(i,1)= t_end;
  205. gbins(i,2) = numel(greenwid) / (timebin); % peaks per min
  206. gbins(i,3) = mean(greenwid, 'omitnan');
  207. gbins(i,4) = mean(greenamp, 'omitnan');
  208. end
  210. disp(['Time bin / Pks per min / Width / Prom ...in: ', num2str(timebin), ' minute bins'])
  211. disp(gbins)
  216. %% save figures and selected data
  217. % activate/deatctivate your option
  219. save (name)
  220. %save(name, "T", 'newT', 'cGREENcut', "cGREEN", "peak_tbl","greenCON",'GREEN', 'sr');
  222. %savefig (Fig1, strcat(name,"_Fig1"))
  223. %writetable (Gpeaktbl,strcat("summaryPeaksGREEN_",name,".xls"))
  224. %writetable (Rpeaktbl,strcat("summaryPeaksRED_",name,".xls"))
  228. %%
  229. % ALS function
  230. function baseline = als_baseline(y, lambda, p)
  231. L = length(y);
  232. D = diff(speye(L), 2);
  233. w = ones(L, 1);
  234. for i = 1:10
  235. W = spdiags(w, 0, L, L);
  236. Z = W + lambda * (D' * D);
  237. baseline = Z \ (w .* y(:));
  238. w = p * (y > baseline) + (1 - p) * (y <= baseline);
  239. end
  240. end

GREEN_ISO_awake_inj.m at commit 0fcc79d, no license · at the source

Overview

Authors: Juan L. Gomez1, Emilya N. Ventriglia1, Zachary J. Frangos1, Agnieszka Sulima2, Michael J. Robertson3,4, Michael D. Sacco3,4, Reece C. Budinich1, Ilinca M. Giosan5, Tongzhen Xie5, Oscar Solis1, Anna E. Tischer1, Jennifer M. Bossert6, Kiera E. Caldwell6, Hannah Bonbrest6, Amelie Essmann7,8, Zelai M. Garçon-Poca7,8, Shinbe Choi9, Michael R. Noya9, Feonil Limiac9, Ali Arce9
and 24 other authorsGrant C. Glatfelter10, Margaret Robinson11, Li Chen11, Angelina A. Mullarkey3, Dain R. Brademan3, Garrett Enten12, William Dunne1, César Quiroz12, Ingrid Schoenborn1, Chae Bin Lee13,14, Rana Rais13,14, Daniel P. Holt15, Robert F. Dannals15, Lei Shi11, Ruth Hüttenhain3, Sergi Ferré12, Eugene Kiyatkin9, Jordi Bonaventura7,8, Yavin Shaham6, Venetia Zachariou5, Michael H. Baumann10, Georgios Skiniotis3,4,16,17, Kenner C. Rice2, Michael Michaelides1
ORCID iDs: Zachary J. Frangos, Michael J. Robertson, Reece C. Budinich, Tongzhen Xie, Oscar Solis, Anna E. Tischer, Amelie Essmann, Zelai M. Garçon-Poca, Shinbe Choi, Feonil Limiac, Garrett Enten, William Dunne, Ingrid Schoenborn, Lei Shi, Ruth Hüttenhain, Sergi Ferré, Jordi Bonaventura, Yavin Shaham, Georgios Skiniotis, Kenner C. Rice, Michael Michaelides
17 affiliations
  1. Biobehavioral Imaging and Molecular Neuropsychopharmacology Section, National Institute on Drug Abuse Intramural Research Program,Baltimore, MD USA
  2. Drug Design and Synthesis Section, Molecular Targets and Medication Discovery Branch, National Institute on Drug Abuse Intramural Research Program,Baltimore, MD USA
  3. Department of Molecular and Cellular Physiology, Stanford University School of Medicine,Stanford, CA USA
  4. Department of Structural Biology, Stanford University School of Medicine,Stanford, CA USA
  5. Department of Pharmacology, Physiology and Biophysics, Boston University Chobanian and Avedisian School of Medicine,Boston, MA USA
  6. Neurobiology of Relapse Section, National Institute on Drug Abuse Intramural Research Program,Baltimore, MD USA
  7. Departament de Patologia i Terapèutica Experimental, Institut de Neurociències, Universitat de Barcelona,L’Hospitalet de Llobregat, Spain
  8. Neuropharmacology and Pain Group, Neuroscience Program, Bellvitge Institute for Biomedical Research (IDIBELL),L’Hospitalet de Llobregat, Spain
  9. Behavioral Neuroscience Research Branch, National Institute on Drug Abuse Intramural Research Program,Baltimore, MD USA
  10. Designer Drug Research Unit, National Institute on Drug Abuse Intramural Research Program,Baltimore, MD USA
  11. Computational Chemistry and Molecular Biophysics Section, National Institute on Drug Abuse Intramural Research Program,Baltimore, MD USA
  12. Integrative Neurobiology Section, National Institute on Drug Abuse Intramural Research Program,Baltimore, MD USA
  13. Johns Hopkins Drug Discovery, Johns Hopkins School of Medicine,Baltimore, MD USA
  14. Department of Neurology, Johns Hopkins School of Medicine,Baltimore, MD USA
  15. Department of Radiology, Johns Hopkins School of Medicine,Baltimore, MD USA
  16. Department of Structural Biology, St Jude Children’s Research Hospital,Memphis, TN USA
  17. Center of Excellence for Structural Cell Biology, St Jude Children’s Research Hospital,Memphis, TN USA
Institutions: National Institute on Drug Abuse (United States); Stanford University (United States); Boston University (United States); Universitat de Barcelona (Spain); Institut d'Investigació Biomédica de Bellvitge (Spain); Johns Hopkins University (United States); St. Jude Children's Research Hospital (United States)
Journal: Nature, volume 652, issue 8112, pages 1393-1404
Dates: received 20 April 2025; accepted 19 February 2026; published online 1 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41586-026-10299-9 · PMID 41922775 · PMCID PMC13128446 · OpenAlex W7147426389
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), rat (organism), other condition (population), pain (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Small molecules, Pharmacology, Addiction, Neuropathic pain
MeSH: Analgesics*, Analgesics, Opioid*, Receptors, Opioid, mu*, Animals, Benzimidazoles, Dopamine, Drug Tolerance, Humans, Male, Mice, Mice, Inbred C57BL, Nucleus Accumbens, Pain, Rats, Rats, Sprague-Dawley, Signal Transduction, Synaptic Transmission (* major topic)
Topic: Receptor Mechanisms and Signaling (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIDA NIH HHS (R01 DA056354); Intramural NIH HHS (ZIA DA000069, ZIA DA000606)
Citations: cited by 7 papers (Europe PMC); 99 references in the paper
Research resources: tested for mycoplasma contamination RRID:CVCL_0063
Notices: A correction to this paper has been published (42032332, from Europe PMC)

Abstract

Developing safe and effective pain medications is an ongoing challenge for human health. Agonists for the µ-opioid receptor (MOR) are essential pain medications, but their high intrinsic efficacy also induces adverse side effects, including respiratory depression, constipation, tolerance, dependence, withdrawal and addiction1–7. Strategies to limit adverse effects traditionally include developing MOR agonists that have low intrinsic efficacy or that preferentially activate G-protein signalling over β-arrestin signalling8. Here we identify a novel MOR agonist with supramaximal intrinsic efficacy and a unique pharmacological profile that produced effective analgesia in rodents with minimal adverse effects. N-desethyl-fluornitrazene (DFNZ) was derived from a class of synthetic benzimidazole opioids called nitazenes. DFNZ has impaired brain penetrance, a unique spatiotemporal MOR cellular signalling profile, and diminished efficacy at the MOR–galanin 1 receptor (GAL1) heteromer. DFNZ does not induce respiratory depression, tolerance or MOR downregulation after repeated exposure. Compared with other MOR agonists, DFNZ has limited effects on dopamine neurotransmission in nucleus accumbens and weaker reinforcing effects in the drug self-administration procedure. These results provide novel insights about MOR and nitazene pharmacology, have important implications for pain and addiction treatment, and challenge the prevailing dogma that high-efficacy MOR agonists cannot constitute safe and effective therapeutic agents.

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wdunne3/Calcium-Fiber-Photometry-Analysis

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Commit: 5692b79b36d2d110988111070b5728c0d2a7e50a, 21 November 2025
Languages: MATLAB (8)
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BonaventuraLab/fiber-photometry

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Tools: Signal Processing Toolbox (6 files)
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At the source: github.com/BonaventuraLab/fiber-photometry

Code availability

Analysis code is available at GitHub (https://github.com/wdunne3/Calcium-Fiber-Photometry-Analysis/ and https://github.com/BonaventuraLab/fiber-photometry).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

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Data availability

The data supporting the findings of this study are available within the paper and its supplementary information files. The cryo-EM density map has been deposited in the Electron Microscopy Data Bank under accession codes EMD-70069 (http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-70069) (global refinement), EMD-70070 (http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-70070) (local refinement, receptor) and EMD-70071 (http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-70071) (composite map) and the coordinates have been deposited in the Protein Data Bank under accession number 9O36 (https://doi.org/10.2210/pdb9O36/pdb). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (https://www.ebi.ac.uk/pride/) partner repository99 with the dataset identifier PXD062863 (http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD062863). Reviewers can access these data using the project accession PXD062863 (http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD062863) and reviewer token 62GfWQNbF6w3. Alternatively, these data may also be accessed by logging into the PRIDE website using the reviewer username and password mmgadjWUcRls. Should any data that are not included in the paper be needed, they are available from the corresponding author upon reasonable request. Source data are provided with this paper.

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Recorded: type, language, journal, volume, issue, pages, dates, 44 authors, 4 keywords, 17 MeSH terms, 2 funders, 95 references, 1 RRID, 1 integrity notice.

Cite

This paper

Gomez, J. L., Ventriglia, E. N., Frangos, Z. J., Sulima, A., Robertson, M. J., Sacco, M. D., Budinich, R. C., Giosan, I. M., Xie, T., Solis, O., Tischer, A. E., Bossert, J. M., Caldwell, K. E., Bonbrest, H., Essmann, A., Garçon-Poca, Z. M., Choi, S., Noya, M. R., Limiac, F., . . . Michaelides, M. (2026). A µ-opioid receptor superagonist analgesic with minimal adverse effects. Nature, 652(8112), 1393-1404. https://doi.org/10.1038/s41586-026-10299-9

BibTeX

@article{gomez2026opioid,
author = {Gomez, Juan L. and Ventriglia, Emilya N. and Frangos, Zachary J. and Sulima, Agnieszka and Robertson, Michael J. and Sacco, Michael D. and Budinich, Reece C. and Giosan, Ilinca M. and Xie, Tongzhen and Solis, Oscar and Tischer, Anna E. and Bossert, Jennifer M. and Caldwell, Kiera E. and Bonbrest, Hannah and Essmann, Amelie and Garçon-Poca, Zelai M. and Choi, Shinbe and Noya, Michael R. and Limiac, Feonil and Arce, Ali and Glatfelter, Grant C. and Robinson, Margaret and Chen, Li and Mullarkey, Angelina A. and Brademan, Dain R. and Enten, Garrett and Dunne, William and Quiroz, César and Schoenborn, Ingrid and Lee, Chae Bin and Rais, Rana and Holt, Daniel P. and Dannals, Robert F. and Shi, Lei and Hüttenhain, Ruth and Ferré, Sergi and Kiyatkin, Eugene and Bonaventura, Jordi and Shaham, Yavin and Zachariou, Venetia and Baumann, Michael H. and Skiniotis, Georgios and Rice, Kenner C. and Michaelides, Michael},
title = {{A µ-opioid receptor superagonist analgesic with minimal adverse effects}},
journal = {Nature},
year = {2026},
month = apr,
volume = {652},
number = {8112},
pages = {1393--1404},
publisher = {Nature Portfolio},
issn = {0028-0836},
doi = {10.1038/s41586-026-10299-9},
url = {https://doi.org/10.1038/s41586-026-10299-9},
pmid = {41922775},
pmcid = {PMC13128446}
}

RIS

TY - JOUR
AU - Gomez, Juan L.
AU - Ventriglia, Emilya N.
AU - Frangos, Zachary J.
AU - Sulima, Agnieszka
AU - Robertson, Michael J.
AU - Sacco, Michael D.
AU - Budinich, Reece C.
AU - Giosan, Ilinca M.
AU - Xie, Tongzhen
AU - Solis, Oscar
AU - Tischer, Anna E.
AU - Bossert, Jennifer M.
AU - Caldwell, Kiera E.
AU - Bonbrest, Hannah
AU - Essmann, Amelie
AU - Garçon-Poca, Zelai M.
AU - Choi, Shinbe
AU - Noya, Michael R.
AU - Limiac, Feonil
AU - Arce, Ali
AU - Glatfelter, Grant C.
AU - Robinson, Margaret
AU - Chen, Li
AU - Mullarkey, Angelina A.
AU - Brademan, Dain R.
AU - Enten, Garrett
AU - Dunne, William
AU - Quiroz, César
AU - Schoenborn, Ingrid
AU - Lee, Chae Bin
AU - Rais, Rana
AU - Holt, Daniel P.
AU - Dannals, Robert F.
AU - Shi, Lei
AU - Hüttenhain, Ruth
AU - Ferré, Sergi
AU - Kiyatkin, Eugene
AU - Bonaventura, Jordi
AU - Shaham, Yavin
AU - Zachariou, Venetia
AU - Baumann, Michael H.
AU - Skiniotis, Georgios
AU - Rice, Kenner C.
AU - Michaelides, Michael
TI - A µ-opioid receptor superagonist analgesic with minimal adverse effects
T2 - Nature
J2 - Nature
PY - 2026
DA - 2026/04/01
VL - 652
IS - 8112
SP - 1393
EP - 1404
SN - 0028-0836
PB - Nature Portfolio
DO - 10.1038/s41586-026-10299-9
UR - https://doi.org/10.1038/s41586-026-10299-9
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41586-026-10299-9",
"type": "article-journal",
"title": "A µ-opioid receptor superagonist analgesic with minimal adverse effects",
"container-title": "Nature",
"author": [
{
"family": "Gomez",
"given": "Juan L."
},
{
"family": "Ventriglia",
"given": "Emilya N."
},
{
"family": "Frangos",
"given": "Zachary J."
},
{
"family": "Sulima",
"given": "Agnieszka"
},
{
"family": "Robertson",
"given": "Michael J."
},
{
"family": "Sacco",
"given": "Michael D."
},
{
"family": "Budinich",
"given": "Reece C."
},
{
"family": "Giosan",
"given": "Ilinca M."
},
{
"family": "Xie",
"given": "Tongzhen"
},
{
"family": "Solis",
"given": "Oscar"
},
{
"family": "Tischer",
"given": "Anna E."
},
{
"family": "Bossert",
"given": "Jennifer M."
},
{
"family": "Caldwell",
"given": "Kiera E."
},
{
"family": "Bonbrest",
"given": "Hannah"
},
{
"family": "Essmann",
"given": "Amelie"
},
{
"family": "Garçon-Poca",
"given": "Zelai M."
},
{
"family": "Choi",
"given": "Shinbe"
},
{
"family": "Noya",
"given": "Michael R."
},
{
"family": "Limiac",
"given": "Feonil"
},
{
"family": "Arce",
"given": "Ali"
},
{
"family": "Glatfelter",
"given": "Grant C."
},
{
"family": "Robinson",
"given": "Margaret"
},
{
"family": "Chen",
"given": "Li"
},
{
"family": "Mullarkey",
"given": "Angelina A."
},
{
"family": "Brademan",
"given": "Dain R."
},
{
"family": "Enten",
"given": "Garrett"
},
{
"family": "Dunne",
"given": "William"
},
{
"family": "Quiroz",
"given": "César"
},
{
"family": "Schoenborn",
"given": "Ingrid"
},
{
"family": "Lee",
"given": "Chae Bin"
},
{
"family": "Rais",
"given": "Rana"
},
{
"family": "Holt",
"given": "Daniel P."
},
{
"family": "Dannals",
"given": "Robert F."
},
{
"family": "Shi",
"given": "Lei"
},
{
"family": "Hüttenhain",
"given": "Ruth"
},
{
"family": "Ferré",
"given": "Sergi"
},
{
"family": "Kiyatkin",
"given": "Eugene"
},
{
"family": "Bonaventura",
"given": "Jordi"
},
{
"family": "Shaham",
"given": "Yavin"
},
{
"family": "Zachariou",
"given": "Venetia"
},
{
"family": "Baumann",
"given": "Michael H."
},
{
"family": "Skiniotis",
"given": "Georgios"
},
{
"family": "Rice",
"given": "Kenner C."
},
{
"family": "Michaelides",
"given": "Michael"
}
],
"container-title-short": "Nature",
"volume": "652",
"issue": "8112",
"page": "1393-1404",
"DOI": "10.1038/s41586-026-10299-9",
"PMID": "41922775",
"PMCID": "PMC13128446",
"ISSN": "0028-0836",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41586-026-10299-9",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
1
]
]
}
}

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