OSCR

Covalent pan-TEAD inhibitors block YAP activity and demonstrate brain penetrance in a Hippo-dependent cancer model.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches
  1. [1] § Methods › RNA analyses ↔ R/linear_association.R, lines 155–251 · score 0.58 · precision weighted, limma, log, genes
  2. [2] § Methods › Sample preparation for proteomic analysis › Statistical analysis of mass spectrometry data ↔ R/linear_association.R, lines 155–251 · score 0.52 · empirical Bayes, sum, linear, fitted, model

Paper

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The authors' code

R · 252 lines · 8.8 KB · no license · 2 matches

  1. require(magrittr)
  2. require(ashr)
  3. require(limma)
  4. require(corpcor)
  5. require(WGCNA)
  6. require(plyr)
  7. require(tidyverse)
  9. #' Calculates the linear association between a matrix of features A and a vector y.
  10. #'
  11. #' @param X n x m numerical matrix of features (missing values are allowed).
  12. #' @param Y n x p numerical matrix of responses (missing values are allowed)
  13. #' @param W n x p numerical matrix of confounders (missing values are not allowed).
  14. #' @param n.min Numeric value controlling the minimum degrees of freedom required for p-values.
  15. #' @param shrinkage Boolean to control whether adaptive shrinkage should be applied.
  16. #' @param alpha Numeric value controling the alpha value for adaptive shrinkage.
  17. #' @param MHC_direction String ("x" or "y") indicating which variable is independent, defaults to matrix with more columns.
  18. #'
  19. #' @return A list with components:
  20. #' \describe{
  21. #' \item{N}{Vector of degrees of freedom for each feature.}
  22. #' \item{rho}{Vector of uncorrected beta estimates controlled for W.}
  23. #' \item{beta}{Vector of corrected beta estimates controlled for W.}
  24. #' \item{beta.se}{Vector of standard errors of the beta estimates.}
  25. #' \item{p.val}{Vector of p-values (without adaptive shrinkage).}
  26. #' \item{q.val}{Vector of q-values (without adaptive shrinkage).}
  27. #' \item{res.table}{A table with the following columns (values calculated with adaptive shrinkage): \cr
  28. #' betahat: estimated linear coefficient controlled for W. \cr
  29. #' sebetahat: standard error for the estimate of beta. \cr
  30. #' NegativeProb: posterior probabilities that beta is negative. \cr
  31. #' PositiveProb: posterior probabilities that beta is positive \cr
  32. #' lfsr: local and global FSR values. \cr
  33. #' svalue: s-values.\cr
  34. #' lfdr: local and global FDR values. \cr
  35. #' qvalue: q-values for the effect size estimates. \cr
  36. #' PosteriorMean: moderated effect size estimates. \cr
  37. #' PosteriorSD: standard deviations of moderated effect size estimates. \cr
  38. #' dep.var: dependent variables (column names of Y). \cr
  39. #' ind.var: independent variables (column names of X).
  40. #' }
  41. #'}
  42. #'
  43. #' @export
  44. #'
  45. lin_associations = function (X,
  46. Y,
  47. W = NULL,
  48. n.min = 4,
  49. shrinkage = T,
  50. alpha = 0,
  51. MHC_direction = NULL) {
  52. if (is.null(MHC_direction)) {
  53. MHC_direction = ifelse(length(Y) >= length(X), "x", "y")
  54. }
  55. X.NA <- !is.finite(X); X[X.NA] = NA
  56. Y.NA <- !is.finite(Y); Y[Y.NA] = NA
  57. N <- (t(!X.NA) %*% (!Y.NA)) - 2
  58. if (!is.null(W)) {
  59. P <- W %*% corpcor::pseudoinverse(t(W) %*% W) %*% t(W)
  60. X[X.NA] <- 0
  61. X <- X - (P %*% X)
  62. X[X.NA] <- NA
  63. Y[Y.NA] <- 0
  64. Y <- Y - (P %*% Y)
  65. Y[Y.NA] <- NA
  66. N = N - dim(W)[2]
  67. }
  69. f = function(A) {
  70. if (is.matrix(A)) {
  71. res = apply(A, 2, function(x)
  72. sd(x, na.rm = T))
  73. } else{
  74. res = sd(A, na.rm = T)
  75. }
  76. return(res)
  77. }
  78. sx <- f(X)
  79. sy <- f(Y)
  81. rho <- WGCNA::cor(X, Y, use = "pairwise.complete.obs")
  82. beta <- t(t(rho / sx) * sy)
  83. beta.se <- t(t(sqrt(1 - rho ^ 2) / sx) * sy) / sqrt(N)
  84. p.val <- 2 * pt(-abs(beta / beta.se), N)
  85. p.val[N < n.min] <- NA
  86. p.val[(sx == 0) | !is.finite(sx), ] <- NA
  87. p.val[, (sy == 0) | !is.finite(sy)] <- NA
  89. if (MHC_direction == "y") {
  90. q.val = apply(p.val, 2, function(x)
  91. p.adjust(x, method = "BH"))
  92. } else{
  93. q.val = t(apply(p.val, 1, function(x)
  94. p.adjust(x, method = "BH")))
  95. }
  96. if (shrinkage) {
  97. res.table <- list()
  98. if (MHC_direction == "y") {
  99. for (ix in 1:dim(p.val)[2]) {
  100. fin <- is.finite(p.val[, ix])
  101. res <- ashr::ash(beta[fin, ix],
  102. pmax(beta.se[fin, ix], 1e-10),
  103. mixcompdist = "halfuniform",
  104. alpha = alpha)$result
  105. if (!is.null(res)) {
  106. res$dep.var <- colnames(Y)[ix]
  107. res$ind.var <- rownames(res)
  108. res$p.val <- p.val[fin, ix]
  109. res.table[[ix]] <- res
  110. }
  111. }
  112. }
  113. else {
  114. for (ix in 1:dim(p.val)[1]) {
  115. fin <- is.finite(p.val[ix, ])
  116. res <- tryCatch(
  117. ashr::ash(
  118. beta[ix, fin],
  119. pmax(beta.se[ix, fin], 1e-10),
  120. mixcompdist = "halfuniform",
  121. alpha = alpha
  122. )$result,
  123. error = function(e)
  124. NULL
  125. )
  126. if (!is.null(res)) {
  127. res$dep.var <- rownames(res)
  128. res$ind.var <- colnames(Y)[ix]
  129. res$p.val <- p.val[ix, fin]
  130. res.table[[ix]] <- res
  131. }
  132. }
  133. }
  134. res.table <- dplyr::bind_rows(res.table)
  135. }
  136. else {
  137. res.table <- NULL
  138. }
  139. return(
  140. list(
  141. N = N,
  142. rho = rho,
  143. beta = beta,
  144. beta.se = beta.se,
  145. p.val = p.val,
  146. q.val = q.val,
  147. res.table = res.table
  148. )
  149. )
  150. }
  155. #' Estimate linear-model stats for a matrix of data using limma with empirical Bayes moderated t-stats for p-values
  156. #'
  157. #' @param mat: Nxp data matrix with N cell lines and p genes
  158. #' @param vec: N vector of independent variables. Can be two-group labels as factors, bools, or can be numeric
  159. #' @param covars: Optional Nxk matrix of covariates
  160. #' @param weights: Optional N vector of precision weights for each data point
  161. #' @param target_type: Name of the column variable in the data (default 'Gene')
  162. #' @param limma_trend: Whether to fit an intensity trend with the empirical Bayes variance model
  163. #'
  164. #' @return: data frame of stats
  165. #' @export
  166. #'
  167. #' @examples
  168. #' CRISPR = load.from.taiga(data.name='avana-2-0-1-d98f',
  169. #' data.version=1,
  170. #' data.file='ceres_gene_effects',
  171. #' transpose = T)
  172. #' is_panc <- load.from.taiga(data.name = 'ccle-lines-lineages') %>% .[, 'pancreas']
  173. #' ulines <- intersect(rownames(CRISPR), names(is_panc))
  174. #' lim_res <- run_lm_stats_limma(CRISPR[ulines,], is_panc[ulines])
  175. #' @export run_lm_stats_limma
  176. run_lm_stats_limma <- function(mat, vec, covars = NULL, weights = NULL,
  177. target_type = 'Gene', limma_trend = FALSE) {
  179. udata <- which(!is.na(vec))
  180. if (!is.numeric(vec)) {
  181. pred <- factor(vec[udata])
  182. stopifnot(length(levels(pred)) == 2) #only two group comparisons implemented so far
  183. n_out <- colSums(!is.na(mat[udata[pred == levels(pred)[1]],,drop=F]))
  184. n_in <- colSums(!is.na(mat[udata[pred == levels(pred)[2]],,drop=F]))
  185. min_samples <- pmin(n_out, n_in) %>% set_names(colnames(mat))
  186. } else {
  187. pred <- vec[udata]
  188. min_samples <- colSums(!is.na(mat[udata,]))
  189. }
  190. #there must be more than one unique value of the independent variable
  191. if (length(unique(pred)) <= 1) {
  192. return(NULL)
  193. }
  194. #if using covariates add them as additional predictors to the model
  195. if (!is.null(covars)) {
  196. if (!is.data.frame(covars)) {
  197. covars <- data.frame(covars)
  198. }
  199. combined <- covars[udata,, drop = FALSE]
  200. combined[['pred']] <- pred
  201. form <- as.formula(paste('~', paste0(colnames(combined), collapse = ' + ')))
  202. design <- model.matrix(form, combined)
  203. design <- design[, colSums(design) != 0, drop = FALSE]
  204. } else {
  205. design <- model.matrix(~pred)
  206. }
  207. if (!is.null(weights)) {
  208. if (is.matrix(weights)) {
  209. weights <- t(weights[udata,])
  210. } else{
  211. weights <- weights[udata]
  212. }
  213. }
  215. fit <- limma::lmFit(t(mat[udata,]), design, weights = weights)
  216. fit <- limma::eBayes(fit, trend = limma_trend)
  217. targ_coef <- grep('pred', colnames(design), value = TRUE)
  218. results <- limma::topTable(fit, coef = targ_coef, number = Inf)
  220. if (colnames(results)[1] == 'ID') {
  221. colnames(results)[1] <- target_type
  222. } else {
  223. results %<>% rownames_to_column(var = target_type)
  224. }
  226. results$min_samples <- min_samples[results[[target_type]]]
  228. two_to_one_sided <- function(two_sided_p, stat, test_dir) {
  229. #helper function for converting two-sided p-values to one-sided p-values
  230. one_sided_p <- two_sided_p / 2
  231. if (test_dir == 'right') {
  232. one_sided_p[stat < 0] <- 1 - one_sided_p[stat < 0]
  233. } else {
  234. one_sided_p[stat > 0] <- 1 - one_sided_p[stat > 0]
  235. }
  236. return(one_sided_p)
  237. }
  239. results %<>%
  240. set_colnames(plyr::revalue(colnames(.), c('logFC' = 'EffectSize', 'AveExpr' = 'Avg',
  241. 't' = 't_stat', 'B' = 'log_odds',
  242. 'P.Value' = 'p.value', 'adj.P.Val' = 'q.value',
  243. 'min_samples' = 'min_samples'))) %>%
  244. na.omit()
  245. results %<>%
  246. dplyr::mutate(p.left = two_to_one_sided(p.value, EffectSize, 'left'),
  247. p.right = two_to_one_sided(p.value, EffectSize, 'right'),
  248. q.left = p.adjust(p.left, method = 'BH'),
  249. q.right = p.adjust(p.right, method = 'BH'))
  250. return(results)
  251. }

linear_association.R at commit 1986dc8, no license · at the source

Overview

Authors: Thijs J. Hagenbeek1, Jason Zbieg2, Russell Smith2, Sayantanee Paul1, Luca Gerosa3, Consuelo Torrini4, Alissa D. Guarnaccia1,5, Christy Ong1, Jennifer A. Lacap6, Miaoran Ning7, Nicole M. Sodir6, Marc Hafner3, Julien Tremblay3, James Hawley3, Bryan Chan2, Vishal A. Verma2, Ramsay E. Beveridge8, Peter L. Hsu9, Gözde Ulas10, Lisha Wang10
and 34 other authorsJim Nonomiya10, Shu Chen7, Victoria Pham5, Joshua Webster11, Jessica Preston11, Jeff Hung11, Jeff Eastham11, Debra Dunlap11, Wendy Lee2, Paul Beroza2, Naema Nayyar4, Scott Martin1, Eva Lin1, Julie Weng1, Scott A. Foster1, Frances Shanahan1, Rina Fong9, Gladys Boenig9, Paola Di Lello9, Marta H. Kubala9, Thomas Hunsaker6, Mirunalini Ravichandran6, Pablo Saenz-Lopez Larrocha6, Jeffrey Lau6, Le An12, Elizabeth Levy12, Maria N. Lorenzo13, Jennie R. Lill5, Zora D. Modrusan5, Yi-Chen Chen7, Xiaosai Yao3, Priscilla K. Brastianos4, Danilo Maddalo6, Anwesha Dey1
ORCID iDs: Consuelo Torrini, Alissa D. Guarnaccia, Julien Tremblay, James Hawley, Vishal A. Verma, Peter L. Hsu, Joshua Webster, Wendy Lee, Paul Beroza, Gladys Boenig, Paola Di Lello, Priscilla K. Brastianos, Danilo Maddalo
13 affiliations
  1. Department of Discovery Oncology, Genentech,South San Francisco, CA USA
  2. Department of Discovery Chemistry, Genentech,South San Francisco, CA USA
  3. Department of gRED Computational Sciences, Genentech,South San Francisco, CA USA
  4. Department of Medicine, Massachusetts General Hospital Cancer Center, Harvard Medical School,Boston, MA USA
  5. Department of Proteomic and Genomic Technologies, Genentech,South San Francisco, CA USA
  6. Department of Translational Oncology, Genentech,South San Francisco, CA USA
  7. Department of Drug Metabolism and Pharmacokinetics, Genentech,South San Francisco, CA USA
  8. Paraza Pharma Inc,Montreal, QC Canada
  9. Department of Structural Biology, Genentech,South San Francisco, CA USA
  10. Department of Biochemical and Cellular Pharmacology, Genentech,South San Francisco, CA USA
  11. Department of Research Pathology, Genentech,South San Francisco, CA USA
  12. Department of Small Molecule Pharmaceutical Sciences, Genentech,South San Francisco, CA USA
  13. Department of Protein Chemistry, Genentech,South San Francisco, CA USA
Institutions: Department of Discovery Oncology, Genentech,South San Francisco, CA USA; Massachusetts General Hospital (United States); Paraza Pharma (Canada) (Canada)
Journal: Nature communications, volume 17, issue 1, article 8049
Dates: received 24 April 2025; accepted 5 June 2026; published online 27 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74722-5 · PMID 42365001 · PMCID PMC13454592 · OpenAlex W7166327128
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), other condition (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Machine learning, Smoothing, state filtering, decompositions
Keywords: Drug development, Targeted therapies
MeSH: Adaptor Proteins, Signal Transducing*, Antineoplastic Agents*, Brain*, Brain Neoplasms*, DNA-Binding Proteins*, Protein Serine-Threonine Kinases*, Transcription Factors*, Animals, Cell Line, Tumor, Cell Proliferation, Female, Hippo Signaling Pathway, Humans, Mice, Signal Transduction, TEA Domain Transcription Factors, Xenograft Model Antitumor Assays, YAP-Signaling Proteins (* major topic)
Topic: Hippo pathway signaling and YAP/TAZ (Cell Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: U.S. Department of Energy (76SF00515, DE–AC02–76SF00515, AC02‐76SF00515, P30-GM133894, DE-AC02); Massachusetts General Hospital; National Institutes of Health (DE-AC02-76SF00515); Office of Science (AC02-76SF00515, DE-AC02-76SF0051 5, DE-AC02, P30GM133894, 76SF00515); National Institute of General Medical Sciences (P30GM133894, DE-AC02-76SF00515); Basic Energy Sciences (DE-AC02, P30GM133894, 76SF00515, AC02-76SF00515, DE-AC-02-76SF00515); Biological and Environmental Research (DE- AC0276SF00515, P30GM133894); SLAC National Accelerator Laboratory (DEAC02−76SF00515, AC02‐76SF00515)
Citations: cited by 2 papers (Europe PMC); 80 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

broadinstitute/cdsr_models

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1986dc81e10171419080d4875662398f6d035020, 3 August 2022
Languages: R (4)
Size: 18 files, 4 scripts
Software Heritage: archived
Found in: the text, “PRISM analysis”
Holds: README, environment (DESCRIPTION), documentation, 1 notebook
Not found: license file, CITATION.cff, tests, continuous integration
Tools: tidyverse (4 files), limma (2 files), reshape2 (1 file), WGCNA (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/broadinstitute/cdsr_models

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 4 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41467-026-74722-5.

Versions

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Version 2, 28 September 2026

  • Funding: added U.S. Department of Energy: 76SF00515, DE–AC02–76SF00515, AC02‐76SF00515, P30-GM133894, DE-AC02; Massachusetts General Hospital; National Institutes of Health: DE-AC02-76SF00515; Office of Science: AC02-76SF00515, DE-AC02-76SF0051 5, DE-AC02, P30GM133894, 76SF00515; National Institute of General Medical Sciences: P30GM133894, DE-AC02-76SF00515; Basic Energy Sciences: DE-AC02, P30GM133894, 76SF00515, AC02-76SF00515, DE-AC-02-76SF00515; Biological and Environmental Research: DE- AC0276SF00515, P30GM133894; SLAC National Accelerator Laboratory: DEAC02−76SF00515, AC02‐76SF00515

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 54 authors, 2 keywords, 18 MeSH terms, 80 references.

Cite

This paper

Hagenbeek, T. J., Zbieg, J., Smith, R., Paul, S., Gerosa, L., Torrini, C., Guarnaccia, A. D., Ong, C., Lacap, J. A., Ning, M., Sodir, N. M., Hafner, M., Tremblay, J., Hawley, J., Chan, B., Verma, V. A., Beveridge, R. E., Hsu, P. L., Ulas, G., . . . Dey, A. (2026). Covalent pan-TEAD inhibitors block YAP activity and demonstrate brain penetrance in a Hippo-dependent cancer model. Nature communications, 17(1), 8049. https://doi.org/10.1038/s41467-026-74722-5

BibTeX

@article{hagenbeek2026covalent,
author = {Hagenbeek, Thijs J. and Zbieg, Jason and Smith, Russell and Paul, Sayantanee and Gerosa, Luca and Torrini, Consuelo and Guarnaccia, Alissa D. and Ong, Christy and Lacap, Jennifer A. and Ning, Miaoran and Sodir, Nicole M. and Hafner, Marc and Tremblay, Julien and Hawley, James and Chan, Bryan and Verma, Vishal A. and Beveridge, Ramsay E. and Hsu, Peter L. and Ulas, Gözde and Wang, Lisha and Nonomiya, Jim and Chen, Shu and Pham, Victoria and Webster, Joshua and Preston, Jessica and Hung, Jeff and Eastham, Jeff and Dunlap, Debra and Lee, Wendy and Beroza, Paul and Nayyar, Naema and Martin, Scott and Lin, Eva and Weng, Julie and Foster, Scott A. and Shanahan, Frances and Fong, Rina and Boenig, Gladys and Di Lello, Paola and Kubala, Marta H. and Hunsaker, Thomas and Ravichandran, Mirunalini and Saenz-Lopez Larrocha, Pablo and Lau, Jeffrey and An, Le and Levy, Elizabeth and Lorenzo, Maria N. and Lill, Jennie R. and Modrusan, Zora D. and Chen, Yi-Chen and Yao, Xiaosai and Brastianos, Priscilla K. and Maddalo, Danilo and Dey, Anwesha},
title = {{Covalent pan-TEAD inhibitors block YAP activity and demonstrate brain penetrance in a Hippo-dependent cancer model}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {8049},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74722-5},
url = {https://doi.org/10.1038/s41467-026-74722-5},
pmid = {42365001},
pmcid = {PMC13454592}
}

RIS

TY - JOUR
AU - Hagenbeek, Thijs J.
AU - Zbieg, Jason
AU - Smith, Russell
AU - Paul, Sayantanee
AU - Gerosa, Luca
AU - Torrini, Consuelo
AU - Guarnaccia, Alissa D.
AU - Ong, Christy
AU - Lacap, Jennifer A.
AU - Ning, Miaoran
AU - Sodir, Nicole M.
AU - Hafner, Marc
AU - Tremblay, Julien
AU - Hawley, James
AU - Chan, Bryan
AU - Verma, Vishal A.
AU - Beveridge, Ramsay E.
AU - Hsu, Peter L.
AU - Ulas, Gözde
AU - Wang, Lisha
AU - Nonomiya, Jim
AU - Chen, Shu
AU - Pham, Victoria
AU - Webster, Joshua
AU - Preston, Jessica
AU - Hung, Jeff
AU - Eastham, Jeff
AU - Dunlap, Debra
AU - Lee, Wendy
AU - Beroza, Paul
AU - Nayyar, Naema
AU - Martin, Scott
AU - Lin, Eva
AU - Weng, Julie
AU - Foster, Scott A.
AU - Shanahan, Frances
AU - Fong, Rina
AU - Boenig, Gladys
AU - Di Lello, Paola
AU - Kubala, Marta H.
AU - Hunsaker, Thomas
AU - Ravichandran, Mirunalini
AU - Saenz-Lopez Larrocha, Pablo
AU - Lau, Jeffrey
AU - An, Le
AU - Levy, Elizabeth
AU - Lorenzo, Maria N.
AU - Lill, Jennie R.
AU - Modrusan, Zora D.
AU - Chen, Yi-Chen
AU - Yao, Xiaosai
AU - Brastianos, Priscilla K.
AU - Maddalo, Danilo
AU - Dey, Anwesha
TI - Covalent pan-TEAD inhibitors block YAP activity and demonstrate brain penetrance in a Hippo-dependent cancer model
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/27
VL - 17
IS - 1
SP - 8049
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74722-5
UR - https://doi.org/10.1038/s41467-026-74722-5
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-74722-5",
"type": "article-journal",
"title": "Covalent pan-TEAD inhibitors block YAP activity and demonstrate brain penetrance in a Hippo-dependent cancer model",
"container-title": "Nature communications",
"author": [
{
"family": "Hagenbeek",
"given": "Thijs J."
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{
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{
"family": "Paul",
"given": "Sayantanee"
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{
"family": "Gerosa",
"given": "Luca"
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{
"family": "Torrini",
"given": "Consuelo"
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{
"family": "Guarnaccia",
"given": "Alissa D."
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{
"family": "Ong",
"given": "Christy"
},
{
"family": "Lacap",
"given": "Jennifer A."
},
{
"family": "Ning",
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{
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{
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{
"family": "Verma",
"given": "Vishal A."
},
{
"family": "Beveridge",
"given": "Ramsay E."
},
{
"family": "Hsu",
"given": "Peter L."
},
{
"family": "Ulas",
"given": "Gözde"
},
{
"family": "Wang",
"given": "Lisha"
},
{
"family": "Nonomiya",
"given": "Jim"
},
{
"family": "Chen",
"given": "Shu"
},
{
"family": "Pham",
"given": "Victoria"
},
{
"family": "Webster",
"given": "Joshua"
},
{
"family": "Preston",
"given": "Jessica"
},
{
"family": "Hung",
"given": "Jeff"
},
{
"family": "Eastham",
"given": "Jeff"
},
{
"family": "Dunlap",
"given": "Debra"
},
{
"family": "Lee",
"given": "Wendy"
},
{
"family": "Beroza",
"given": "Paul"
},
{
"family": "Nayyar",
"given": "Naema"
},
{
"family": "Martin",
"given": "Scott"
},
{
"family": "Lin",
"given": "Eva"
},
{
"family": "Weng",
"given": "Julie"
},
{
"family": "Foster",
"given": "Scott A."
},
{
"family": "Shanahan",
"given": "Frances"
},
{
"family": "Fong",
"given": "Rina"
},
{
"family": "Boenig",
"given": "Gladys"
},
{
"family": "Di Lello",
"given": "Paola"
},
{
"family": "Kubala",
"given": "Marta H."
},
{
"family": "Hunsaker",
"given": "Thomas"
},
{
"family": "Ravichandran",
"given": "Mirunalini"
},
{
"family": "Saenz-Lopez Larrocha",
"given": "Pablo"
},
{
"family": "Lau",
"given": "Jeffrey"
},
{
"family": "An",
"given": "Le"
},
{
"family": "Levy",
"given": "Elizabeth"
},
{
"family": "Lorenzo",
"given": "Maria N."
},
{
"family": "Lill",
"given": "Jennie R."
},
{
"family": "Modrusan",
"given": "Zora D."
},
{
"family": "Chen",
"given": "Yi-Chen"
},
{
"family": "Yao",
"given": "Xiaosai"
},
{
"family": "Brastianos",
"given": "Priscilla K."
},
{
"family": "Maddalo",
"given": "Danilo"
},
{
"family": "Dey",
"given": "Anwesha"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "8049",
"DOI": "10.1038/s41467-026-74722-5",
"PMID": "42365001",
"PMCID": "PMC13454592",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-74722-5",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
27
]
]
}
}

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