Proximity labeling reveals ZFP36L1 as a central hub for post-transcriptional regulation networks in T cells

Proximity labeling reveals ZFP36L1 as a central hub for post-transcriptional regulation networks in T cells | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Proximity labeling reveals ZFP36L1 as a central hub for post-transcriptional regulation networks in T cells View ORCID Profile Anouk P. Jurgens , View ORCID Profile Branka Popović , Floris P.J. van Alphen , Leyma Wardak , Antonia Bradarić , View ORCID Profile Sander Engels , Carmen van der Zwaan , Maartje van den Biggelaar , View ORCID Profile Arie J. Hoogendijk , View ORCID Profile Julien Béthune , View ORCID Profile Monika C. Wolkers doi: https://doi.org/10.1101/2025.07.25.666749 Anouk P. Jurgens 1 Sanquin Blood Supply Foundation, Department of Research, T cell differentiation lab , Plesmanlaan 125, 1066 CX Amsterdam, The Netherlands 3 Amsterdam UMC, University of Amsterdam, Landsteiner Laboratory, Amsterdam Institute for Infection & Immunity and Cancer Center Amsterdam , Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands 4 Oncode Institute , Utrecht, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anouk P. Jurgens Branka Popović 1 Sanquin Blood Supply Foundation, Department of Research, T cell differentiation lab , Plesmanlaan 125, 1066 CX Amsterdam, The Netherlands 3 Amsterdam UMC, University of Amsterdam, Landsteiner Laboratory, Amsterdam Institute for Infection & Immunity and Cancer Center Amsterdam , Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands 4 Oncode Institute , Utrecht, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Branka Popović Floris P.J. van Alphen 2 Sanquin Blood Supply Foundation, Department of Research, Bleeding & Hemostasis , Plesmanlaan 125, Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Leyma Wardak 1 Sanquin Blood Supply Foundation, Department of Research, T cell differentiation lab , Plesmanlaan 125, 1066 CX Amsterdam, The Netherlands 3 Amsterdam UMC, University of Amsterdam, Landsteiner Laboratory, Amsterdam Institute for Infection & Immunity and Cancer Center Amsterdam , Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands 4 Oncode Institute , Utrecht, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Antonia Bradarić 1 Sanquin Blood Supply Foundation, Department of Research, T cell differentiation lab , Plesmanlaan 125, 1066 CX Amsterdam, The Netherlands 3 Amsterdam UMC, University of Amsterdam, Landsteiner Laboratory, Amsterdam Institute for Infection & Immunity and Cancer Center Amsterdam , Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands 4 Oncode Institute , Utrecht, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sander Engels 1 Sanquin Blood Supply Foundation, Department of Research, T cell differentiation lab , Plesmanlaan 125, 1066 CX Amsterdam, The Netherlands 3 Amsterdam UMC, University of Amsterdam, Landsteiner Laboratory, Amsterdam Institute for Infection & Immunity and Cancer Center Amsterdam , Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands 4 Oncode Institute , Utrecht, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sander Engels Carmen van der Zwaan 2 Sanquin Blood Supply Foundation, Department of Research, Bleeding & Hemostasis , Plesmanlaan 125, Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maartje van den Biggelaar 2 Sanquin Blood Supply Foundation, Department of Research, Bleeding & Hemostasis , Plesmanlaan 125, Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Arie J. Hoogendijk 2 Sanquin Blood Supply Foundation, Department of Research, Bleeding & Hemostasis , Plesmanlaan 125, Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Arie J. Hoogendijk Julien Béthune 5 Department of Biotechnology , HAW Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Julien Béthune Monika C. Wolkers 1 Sanquin Blood Supply Foundation, Department of Research, T cell differentiation lab , Plesmanlaan 125, 1066 CX Amsterdam, The Netherlands 3 Amsterdam UMC, University of Amsterdam, Landsteiner Laboratory, Amsterdam Institute for Infection & Immunity and Cancer Center Amsterdam , Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands 4 Oncode Institute , Utrecht, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Monika C. Wolkers For correspondence: m.wolkers{at}sanquin.nl Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Effective T cell responses against pathogens require a rapid yet tightly controlled remodeling of the proteome, and RNA binding proteins (RBPs) are key in this process. For instance, the RBP ZFP36L1 prevents excessive protein production and thereby limits immunopathology. ZFP36L1 is primarily known to mediate mRNA decay, but it can also regulate other processes. How its mode of action relates to its interaction partners is, however, not well-understood. Here, we mapped the ZFP36L1 interactome in primary human T cells. Using proximity labeling, we identified known and new interactors that regulate 3’UTR-mediated RNA degradation, deadenylation, stress granule/p-body formation, as well as 5’UTR-mediated translation repression and mRNA decapping. Snapshot analysis uncovered the ZFP36L1 interactome dynamics and RNA (in)dependency throughout T cell activation. Intriguingly, proximity labeling also uncovered regulators of ZFP36L1 protein expression: This included the helicase UPF1, which not only interacts with ZFP36L1 protein but also promotes its protein expression. Altogether, this comprehensive interactome map underlines the versatility of interactions with ZFP36L1 and their possible role in cellular function. INTRODUCTION T cells play a crucial role in clearing infected and malignant cells from our body. To exert their effector function, T cells rapidly remodel their proteome upon antigen recognition 1 – 3 . This includes their ability to produce effector molecules such as granzymes and proinflammatory cytokines, a central feature for their capacity to clear target cells 4 , 5 . Yet, tight regulation of gene expression is key for preventing excessive responses, as it can lead to immunopathology 6 , 7 . To achieve effective yet balanced immune responses, several regulatory layers are in place, and RNA binding proteins (RBPs) are key contributors herein 8 – 11 . Defined by the signals that T cells receive, RBPs control the magnitude and duration of cytokine production 12 – 14 , and they regulate cell cycle and checkpoint molecule production levels 15 – 17 . Notably, mutations in RBPs are linked to autoimmunity in humans 18 , 19 , highlighting the significance of this class of proteins in regulating adequate immune responses. An important RBP for T cell effector function is ZFP36L1. ZFP36L1 belongs to the ZFP36 protein family and mediates mRNA decay of transcripts that contain AU-rich elements (ARE) in their 3’UTR 20 , as exemplified for mRNA coding for cell cycle proteins and cytokines 21 – 25 . ZFP36L1 deletion in T cells results in increased cytokine production, which in turn can restore function in tumor-infiltrating T cells and improve anti-tumor responses in mice 24 . Intriguingly, ZFP36L1 regulates cytokine expression upon T cell activation in a time-dependent manner 17 , 23 , 24 : in the first 4-24h, ZFP36L1 restrains cytokine production, and it does so at least in part by recruiting the CCR4/CNOT complex 24 . However, ZFP36L1 can also exert several other functions: together with its sister protein ZFP36L2, ZFP36L1 binds to ready-to-deploy cytokine mRNAs in memory T cells, thereby conferring a block of translation and preventing aberrant production in the absence of T cell stimuli 26 . Furthermore, ZFP36L1 has been implicated in other cell types in the formation of stress-granules, P-bodies and membraneless structures 20 , 27 , 28 . Provided that most RBPs are highly interactive with RNA and proteins, forming multiple ribonucleoprotein (RNP) 29 complexes, we hypothesized 1) that ZFP36L1 interacts with a broad set of proteins to carry out diverse functions, and 2) that ZFP36L1 may alter its interactome over the course of T cell activation to modulate its activity. However, to date there is only anecdotal evidence of protein interactions with ZFP36L1 in T cells, and a comprehensive map of the ZFP36L1 interactome throughout T cell activation is lacking. To shed light on how ZFP36L1 executes its multiple functions, and which interaction partners it employs to do so, we performed intracellular proximity labeling in human T cells using UltraID, an engineered protein biotin ligase, which rapidly biotinylates proteins within a range of 10 nm 30 . We measured the ZFP36L1 interactome in resting T cells and during T cell activation, and we defined the dependency of RNA for ZFP36L1 interactors. This extensive interactome map uncovered known and previously unreported interactors with ZFP36L1 involved in RNA regulation. In addition, we identified known and novel regulators of ZFP36L1 protein levels. In conclusion, our study yields novel insights into the intricate regulation of gene expression by RBPs, as exemplified here for ZFP36L1. RESULTS Optimizing proximity labeling in primary human T cells To perform proximity labeling for ZFP36L1, we fused the biotin ligase variant UltraID 30 to the N-terminus of ZFP36L1 (UltraID_L1). To identify UltraID_L1 expressing cells, we co-expressed a GFP reporter gene with ZFP36L1 (UltraID_L1_P2A_GFP; Figure 1A ). UltraID fused to the C-terminus of GFP (UltraID_GFP) and UltraID alone (UltraID only) served as controls ( Figure 1A ). As previously reported 30 , clear biotinylation profiles were detected in the human fibrosarcoma cell line FLYRD18 after 10 minutes of incubation with 50 μM biotin ( Supplementary Figure 1A ). This was, however, not the case in human T cells. T cells require biotin-dependent carboxylases for fatty acid synthesis and amino acid metabolism, and proliferating T cells absorb five-fold more biotin than resting T cells 31 – 33 . Therefore, increasing the biotin levels to up to 500 µM was required to obtain detectable biotinylation profiles in human T cells ( Supplementary Figure 1B ). Moreover, as 10 minutes appeared not sufficient to attain proximity labeling in human T cells, we increased the incubation time to 16h, when T cell activation plateaus, therefore requiring less biotin ( Supplementary Figure 1B , second & third lane: UltraID_only & UltraID_L1 ). Because proximity labeling with UltraID_GFP displayed more specific biotinylated protein bands than UltraID protein alone ( Figure 1B , Supplementary Figure 1B ), and because it included a protein of similar size as ZFP36L1 fused to the UltraID enzyme, we continued with UltraID_GFP as control in all downstream analyses. Download figure Open in new tab Figure 1. Proximity labeling in primary human T cells a . Schematic representation of proximity labeling protocol in CD3 + T cells and UltraID plasmids used. Indicated UltraID BirA* fusion genes were retrovirally transduced into CD3 + T cells (n=3 donor pools, 40 donors each). After 6 days, GFP + T cells were FACS-sorted on GFP expression. The next day, T cells were re-activated for 16h with aCD3/aCD28 in the presence or absence of 500 µM biotin. Biotinylated proteins were captured with Streptavidin beads from cell lysates by biotin-streptavidin affinity capture. Biotinylation was confirmed by SDS-PAGE, and biotinylated proteins were identified by Mass Spectrometry. b . Immunoblot for biotinylated proteins of untransduced (UT) T cells, and T cells transduced with UltraID_ZFP36L1_GFP (UltraID_L1) or UltraID_GFP (UltraID_GFP). aRho-GDI was used as loading control. Representative blot of two independently performed experiments, 3 donor pools each. c . Immunoblot of input (10%), flow through and consecutive washes (1-4) of the pulldown, in addition to the elution (10%). Representative blot of two independently performed experiments, 3 donor pools each. d . Top: Principal component analysis (PCA) of total cell lysates. Color indicates constructs expressed in T cells, circle/triangle indicates whether or not biotin was added. Bottom: LFQ intensity of ZFP36L1 in the presence or absence of aCD3/aCD28 activation and biotin. Dotted line indicates endogenous ZFP36L1 levels in untransduced activated T cells. e . Top: PCA for biotinylated proteins identified by MS upon streptavidin pulldown in T cell screen 1 of indicated conditions. Bottom: LFQ intensity of ZFP36L1 in the presence or absence of aCD3/aCD28activation and biotin. Dotted line indicates endogenous ZFP36L1 levels in untransduced activated T cells. See Supplementary Figure 1 Proximity labeling identifies the ZFP36L1 interactome in primary human T cells Having established proximity labeling in human T cells, we questioned which proteins interact with ZFP36L1 upon T cell activation. To avoid alterations in T cell function by exogenous ZFL36L1 expression, we aimed for limited (∼20%) transduction efficiency ( Supplementary Figure 1C ) . This expression level did not influence T cell viability, the expression levels of the activation marker CD69, or the production of TNF, a reported target of ZFP36L1 24 , 34 ( Supplementary Figure 1D, E ). Furthermore, to limit inter-donor variability, we used 3 T cell pools of 40 healthy donors each. FACS-sorted UltraID_L1 and UltraID_GFP expressing T cells were expanded for 7 days and then activated for 16 hours with αCD3/αCD28 in the presence or absence of biotin, a timeframe when ZFP36L1 displays its highest activity in T cells 17 . Immunoblots revealed distinct biotinylation patterns between UltraID_GFP and UltraID_L1 expressing T cells ( Figure 1C , Supplementary Figure 1F ), indicating differential protein interactions. Mass spectrometry (MS) analysis of the total cell lysates identified in total 5250 proteins (range 4500-5000 proteins/sample; Supplementary Figure 2A , Supplementary Table 1) . Principal component analysis (PCA) confirmed that biotinylation per se did not affect the total proteome ( Figure 1D ). Rather, T cell activation was the prime driver of differential protein expression ( Figure 1D , Supplementary Figure 2B ). As expected, differentially expressed proteins between resting and activated T cells included ZFP36L1, which was induced upon T cell activation ( Figure 1D ). MS analysis of isolated biotinylated proteins identified a total of 2439 proteins (range: 1700-2500 proteins/sample; Supplementary Figure 2C , Supplementary Table 2) . As expected from its fusion to the proximity labeling enzyme, ZFP36L1 was primarily detected in the UltraID_L1 pull down ( Figure 1E ) . Again, PCA was mainly driven by T cell activation status, together with the expression of UltraID_GFP versus UltraID_L1 ( Figure 1E , Supplementary Figure 2D ) . To uncover the ZFP36L1 interactome, we utilized supervised classification 35 . In short, we generated theoretical profiles in which the conditions of interest (interacting with ZFP36L1 in resting T cells (shape 1) or in resting and activated T cells (shape 2)) were set to an arbitrary high and low in all other samples, after which differentially expressed proteins in the ‘high’ sample were correlated with all statistically significant proteins ( for more details see methods section ) ( Figure 2A ) . Using a cut-off of log fold change (LFC) >1, adjusted p-value (p-adj) <0.05, and Pearson correlation ≥0.6, we identified 156 proteins in close proximity to ZFP36L1. 27 ZFP36L1 interactions depended on the presence of UltraID_L1 and were found with T cell activation only (shape 1), and 129 irrespective of the T cell activation status (shape 2; Figure 2A, B , Supplementary Figure 2E , Supplementary Table 3 ). As expected, gene Ontology (GO) analysis from shape 2 identified RNA-related processes, such as mRNA decay, RNP granules, and mRNA binding ( Figure 2C ). Identified proteins included previously reported ZFP36L1 interactors, such as the deadenylating CCR4-CNOT complex members CNOT1, CNOT2, CNOT3, CNOT7, CNOT6L, CNOT10 and CNOT11 ( 36 – 39 ; Figure 2B, C ) . Other known ZFP36L1 interactors were the 14-3-3 protein family members YWHAB and YWHAZ 40 , 41 and the exocyst complex components EXOC1, EXOC3, EXOC4 24 ( Figure 2B ) . Download figure Open in new tab Figure 2. Proximity labeling identifies the ZFP36L1 interactome in primary human T cells a . Supervised classification of biotinylated proteins identified as ZFP36L1 interactors. Top panel indicates which sample(s) were set to an arbitrary high. Shape 1: biotinylated proteins enriched (LFC>1, p.adj <0.05) in activated T cells expressing UltraID_L1 compared to all other conditions. Shape 2: biotinylated proteins enriched in T cells expressing UltraID_L1 irrespective of T cell activation status. Correlation between hits and disorderspecific theoretical protein profiles using a Pearson correlation coefficient of >0.6 (dot-ted line) and Benjamini-Hochberg adjusted P value (p.adj) <0.05 was used as threshold. b . Heatmap of supervised classification displaying biotinylated proteins identified in a). Color scale represents Z-scored log2 median-centered averaged intensities. c . GO analysis of identified ZFP36L1 interactors from shape 2. Protein interactors were devided according to biological process, cellular component and molecular function. d . Venn diagram depicting the overlap of identified ZFP36L1 interactors (Pearson correlation coefficient >0.5) from shape 1 and 2 for Jurkat cells and two independently performed pulldowns in primary T cells (T cell screen 1 and 2). See Supplementary Figure 2 & 3 Repeating the UltraID proximity labeling in primary human T cells confirmed 31 proteins in close proximity to ZFP36L1. Thus, even with the lower coverage of the second screen, UltraID proximity labeling provides robust outcomes ( Figure 2D , Supplementary Figure 3A , Supplementary Table 4, 5) . Furthermore, an UltraID-L1 pulldown in Jurkat cells yielded similar - yet not identical - proteins in proximity to ZFP36L1 ( Figure 2D , Supplementary Figure 3B-D , Supplementary Table 6, 7 ), highlighting the added value of performing proximity labeling in primary cells. Similarly, 32 proteins (6.8%) found in either one of both T cell screens overlapped with a recently published ZFP36L1-APEX proximity labelling in HEK293T cell line, and only 8 proteins were shared between the two T cell screens and HEK293T cells 42 . Yet, 256 and 183 proteins were specifically enriched in HEK293T cells or T cells, respectively ( Supplementary Figure 3E ) , suggesting that either expression of certain proteins is cell-type specific, or ZFP36L1 has cell-type specific protein interactions, in addition to its core interactome. Combined, we here showcased the potential of UltraID proximity labeling in primary human T cells to measure protein-protein interactions, as exemplified for ZFP36L1. ZFP36L1 interactome covers a multitude of RNA-related processes as well as protein turnover We next zoomed in on the cellular processes annotated for known and candidate interactors of ZFP36L1. Specifically, we examined proteins that were either found in close proximity to ZFP36L1 in at least two out of three screens, or that showed strong correlation with ZFP36L1 proximity in our ‘shapes’ in T cells screen 1 and that were part of a complex with another candidate interactor. As expected, most identified proteins contribute to mRNA decay processes such as mRNA stabilization, de-adenylation, de-capping and degradation ( Figure 3A ) . In addition to the above-mentioned CCR4-NOT complex proteins ( Figure 3A, B ), we identified novel interactions from the de-adenylation complex. This included the PAN2/3 complex proteins ( Figure 3A ) , which trim Poly (A) tails prior to mRNA degradation through the CCR4-Not complex, a rate limiting step of mRNA degradation 43 , 44 . We also detected PUM1, which directly binds to the CCR4-NOT complex and supports its function in mRNA de-adenylation 45 and TNRC6C, which directly associates with the PAN2/3 and CCR4-NOT complexes 46 , in addition to its contribution to microRNA degradation ( Figure 3A ). These ubiquitous interactions of ZFP36L1 with most - if not all - proteins involved in mRNA de-adenlyation pathway thus evidence ZFP36L1’s central place in this process. Of note, and in line with previous reports of the cell-cycle dependent nuclear localization of ZFP36L1 42 , 47 , we detected interactions with the nuclear alternative poly-adenylation regulating proteins CSTF2 and CSTF2T 48 and with Fam120A, which associates with the splicing protein SRFS1 49 ( Figure 3A ). Download figure Open in new tab Figure 3. ZFP36L1 interactome correlates with a multitude of RNA control mechanisms a . Network of the ZFP36L1 interactome identified in at least 1/2 proximity labeling T cell screens and/or the Jurkat screen. Cut off for T cell screen 1 was Pearson correlation of 0.6, and for T cell screen 2 and Jurkat screen 0.5, and LFC > 1 and p.adj < 0.05 for all three. Proteins annotated according to molecular function or cellular location. Black: previously reported interactions, Orange: newly identified proteins interacting with ZFP36L1. b. -e . LFQ intensity plots of identified ZFP36L1 interactors. Black circles: significantly enriched proteins (p.adj<0.05) in Biotin over no Biotin samples. Grey circles: no significant enrichment. Fold-change (log2FC) enrichment is indicated by color code. n= 3 donor pools. In the mRNA degradation pathway, we identified the RNA helicase UPF1 as being in close proximity to ZFP36L1, which regulates nonsense-mediated mRNA decay (NMD), Staufen-mediated mRNA decay (SMD), and Regnase-1-mediated mRNA decay 50 – 53 ( Figure 3C ) . ZFP36L1 also interacted with MOV10 ( Figure 3A ), a 5’ to 3’ RNA helicase that supports UPF1 in target mRNA degradation 54 . Intriguingly, UPF1 deficiency in early murine B cell development resembles the phenotype of ZFP36L1/ZFP36L2 deficiency, which leads to perturbed thymic and B cell development in mice 52 , 55 . UPF1 and MOV10 interactions with ZFP36, the sister protein of ZFP36L1, were recently reported in HEK293T cells (Bestehorn et al. 2025) ( Supplementary Figure 2E ), indicating that interactions with UPF1 and MOV10 are conserved in different cell types and between ZFP36 family members. This also holds true for the poly(A) RNase PARN and the RISC complex member AGO2 ( Figure 3A ) , confirming that ZFP36 proteins play a central role in regulating mRNA stability of their target genes. In line with the described location of ZFP36L1 in stress granules (SG) and P-bodies 27 , ZFP36L1 interacted with the SG and P-body associated proteins PATL1, UBAP2L, and MOV10 together with G3BP1 (formation of stress granules) and DDX6 (formation of p-bodies) 56 – 59 ( Figure 3A, B, D ). UltraID proximity labeling extended this known list of SG and P-body associated ZFP36L1 candidate interactors to FXR1/2, ATXN2L, PUM1, UBAP2, YBX1, and PAN3 43 , 60 , 61 . We also identified proteins in close proximity to ZFP36L1 that are involved in RNA processes regulated through the 5’UTR. This included the decapping proteins DCP2 and DCP1B 62 and PATL1, one of the strongest ZFP36L1 candidate interactors ( Figure 3A, D , Supplementary Figure 2E ), which also interacts with DCP1 20 , 43 . DDX6 could also possibly contribute to this 5’UTR interaction, as it can bind to the EDC complex to assist mRNA decapping by DCP1/2 and PATL1 63 . Finally, the RBP scaffolding proteins GIGYF1, GIGYF2 and ZNF598, were consistently identified in close proximity to ZFP36L1 ( Figure 3A, E ). GIGYF1 and GIGYF2 interact with DDX6 and with the translation repressor 4EHP (Tollenaere et al. 2019; Weber et al. 2020), both of which we also found interacting with ZFP36L1 ( Figure 3A , 2B ) . Interestingly, ZFP36L1 also interacts with factors that can promote translation, such as UBAP2 and UBAP2L proteins 64 , as well as the m6A reader PPRC2A, which is reported to regulate mRNA metabolism and facilitate translation of uORF-containing transcripts by promoting leaky scanning 65 ( Figure 3A ). In addition to RBP-ZFP36L1 interactions, we detected interactions between ZFP36L1 and the phosphoserine-containing protein binders YWHAB/Z ( Figure 3A ). These 14-3-3 proteins were previously described to regulate the protein turnover of ZFP36L1 66 , 67 . In addition, ZFP36L1 interacted with the protein quality control protein SQSTM1 (p62), which relocates ubiquitinylated proteins to the lysosome for degradation ( Figure 3A ). Lastly, ZFP36L1 interacts with SIRT2 ( Figure 3A ), which de-acetylates lysines and thereby alters protein expression and function 68 . In conclusion, UltraID proximity labeling uncovered a multitude of ZFP36L1 interactions with proteins that contribute to a broad range of regulatory pathways, each defining different aspects of regulating the fate of target mRNA and protein expression. Time-dependent protein-protein interactions with ZFP36L1 Having identified a broad scala of proteins in close proximity with ZFP36L1 ( Figure 3 ), we next questioned whether these interactions were constant, or whether they were subject to change over the course of T cell activation. To answer this question, we performed a time-lapse proximity labeling, by activating T cells for 2h, 5h, and 16h, and by adding biotin between these timepoints for 2h, for 3h (from 2-5h of activation) or for 9h (from 5-16h of activation) ( Figure 4A , Supplementary Figure 4A ) . These time points were chosen based on the effects on previously measured target mRNAs of ZFP36L1, which can occur early on (2h), at 5h of T cell activation, or rather later at 16h of T cell activation (Popovic 2023, Petkau 2022, Zandhuis 2025). Download figure Open in new tab Figure 4. Time-resolved proximity labeling reveals dynamic interactions with ZFP36L1 during T cell activation a . Graphical representation of UltraID time course experiment. T cells were re-activated with aCD3/aCD28 for indicated time points (magenta dotted arrow) in the presence or absence of biotin. Biotin was added at indicated time points (black line). b . PCA for indicated conditions. Outlined circles indicate different timepoints of T cell activation. Color indicates UltraID_L1 (Blue) and UltraID_GFP (Green). Circle indicates the addition of biotin, triangle: no biotin added. c . Protein abundance in full cell lysates of proteins that were significantly enriched for biotinylation (LFC>1, p.adj <0.05) in the time course of UltraID_L1 samples (panel d). Grey indicates not detected. Fold-change (log2FC) enrichment is indicated by color code. n= 3 donor pools. d . Scaled intensity profiles of UltraID_L1-enriched proteins across activation time points, as determined by supervised classification of 6 shapes ( Supplementary Figure 4B ) identified to contain interaction partners with a Pearson correlation of >0.5, and p.adj 1. n= 3 donor pools. See Supplementary Figure 4 PCA of biotinylated proteins revealed that the time of T cell activation rather than the biotinylation time was the prime driver of differential protein abundances, and in addition that the UltraID_L1 pulldowns slightly clustered away from UltraID_GFP control pulldowns ( Figure 4B ; Supplementary Table 8 ). Supervised classification of binding kinetics of proteins in proximity to ZFP36L1 (LFC>1, adjusted p<0.05, Pearson correlation ≥0.5) resulted in 11 shapes, of which 6 shapes contained enriched proteins ( Supplementary Figure 4B, C ; Supplementary Table 8, 9) . MS analysis of total cell lysates revealed limited changes throughout T cell activation of protein abundance of ZFP36L1 interactors, thereby excluding the possibility that alterations of interactions are a mere consequence of differential protein levels ( Figure 4C , Supplementary Table 10) . Many proteins in proximity to ZFP36L1 were already detected in resting T cells (0h; Supplementary Figure 4C ) , which points to robust, activation-independent interactions with ZFP36L1. Alternatively, some interactions may also result from the exogenous ZFP36L1 expression in UltraID_L1-containing T cells, which is slightly higher than that of endogenous ZFP36L1 ( Figure 1D ). Nevertheless, we could clearly identify differential protein interaction profiles with ZFP36L1 throughout the 16h time course of T cell activation. Some proteins such as the SG/PB-associated proteins UBAP2 and UBAP2L, the decapping protein DCP1B, as well as GIGYF2 and CSTF2T interacted with ZFP36L1 throughout T cell activation (shape 1; Figure 4D ). Other proteins reduced their interaction with ZFP36L1 at 2h before they gradually increased their interactions again (shapes 2+3, Figure 4D ). This class included proteins involved in mRNA degradation, and translation repression, such as including UPF1, GIGYF1, and CSTF2, but also the deadenylation proteins TNRC6A-C and the SG/PB-associated proteins ATXN2L, PUM1, PATL1, and YBX1 ( Figure 4D ) . We also identified proteins that increased their interaction with ZFP36L1 upon T cell activation (shapes 4+5; Figure 4D ) . Intriguingly, the majority of these were mitochondrial small and large ribosomal proteins that are translated in the cytoplasm, after which they are transported to the mitochondria where they assemble into complexes 69 (MRPS & MRPL, Figure 4D ). T cell activation triggers mitochondrial biogenesis, which is essential for translating cytotoxic molecules and for maintaining sustained cytotoxicity 70 – 72 . Because the total protein levels of mitochondrial proteins did not change upon T cell activation ( Figure 4C , Supplementary Table 10) , ZFP36L1 may thus potentially regulate their localization and/or transport of translated proteins. We also identified proteins that only briefly increased their interaction with ZFP36L1 at 2h of activation (shape 6, Figure 4D ). These included the ribosomal proteins RPL4, RPL28 and RPL30, and the mitochondrial proteins MRPS14 and NDUFA9, the latter being a component of Complex I of the respiratory chain 73 ( Figure 4D ). In conclusion, ZFP36L1 displays both constant and time-dependent interactions, and these altered interactions belong to different RNA regulatory pathways, which could define different emphasis of ZFP36L1 on its divergent PTR functions over the course of T cell activation. RNA-dependent and -independent interactions with ZFP36L1 Having deciphered the ZFP36L1 interactome and its kinetics, we next asked whether the interactions with ZFP36L1 required the presence of RNA. To study this, we performed a co-immuno-precipitation (Co-IP) of ZFP36L1 with untreated T cell lysates or with RNAse pre-treated lysates ( Supplementary Figure 5A ) . MS analysis revealed that in addition to ZFP36L1 enrichment versus IgG control, treatment with RNAse defined the distribution of samples in the PCA ( Figure 5A , Supplementary 5B, C ; Supplementary Table 11, 12 ; LFC≥1; p.adj<0.05). Download figure Open in new tab Figure 5. RNA-dependency of interactions with ZFP36L1. Co-IP with ZFP36L1 antibodies and IgG control with T cells lysates generated at indicated time points that were treated or not with 200 U/mL RNAse A during the entire protocol (lysis, incubation & washes). a. PCA of indicated conditions. Green dots: RNA present, Orange dots: RNAse A treated. Different shapes represent different timepoints of activation. b . Upset plot of proteins identified as ZFP36L1 interactors in the Co-IP or indicated proximity labeling essay. Numbers refer to numbers of proteins found interacting with ZFP36L1 in indicated conditions. LFC>1, p.adj0.5. Proteins indicated in bold were also identified in HEK293T cells 42 . c . LFQ protein intensities from Co-IP time course of 44 proteins co-identified by the Co-IP and at least 1 proximity labeling (panel b). Green dots: RNA present, Orange dots: RNAse A treated. Proteins were categorized into known RNA-binding proteins (RBPs) and non-RBPs that displayed RNA-dependent, RNA-independent or RNA-repelled interactions with ZFP36L1 (as defined in Supplementary Figure 5F ). n= 3 donor pools. See Supplementary Figure 5 The Co-IP executed in the presence of RNA identified several prototypic ZFP36L1 interactors, including the CCR4-NOT and EXOCS proteins, together with its sister proteins ZFP36 and ZFP36L2 ( Supplementary Figure 5C ) . In line with the proximity labeling time course ( Figure 4 ) , the Co-IP also detected ZFP36L1 interactome dynamics throughout T cell activation, with most differential interactions measured at 5h of T cell activation ( Supplementary Figure 5D ) . The majority of protein interactions with ZPF36L1 did not change with pre-treatment and were thus RNA independent, as exemplified for 0h and 5h of T cell activation ( Supplementary Figure 5E ). Yet, some interactions were reduced upon RNAse treatment (RNA-dependent) or even increased (RNA-repelled; Supplementary Figure 5E ). Because the overlap of ZFP36L1 interaction partners was limited between the Co-IP and proximity labeling experiments ( Figure 5B ) , we focused for downstream analysis on the 44 proteins that were co-identified in at least 1 UltraID screen, and that we therefore considered high-confidence ZFP36L1 interactors ( Supplementary Figure 5F ) . RBPs that displayed reduced interactions with ZFP36L1 in the absence of RNA included UPF1 50 , 51 , YBX1, MOV10, the exon junction complex protein CASC3 74 , LSM6, a component of the RNA degradation and spliceosome complex 75 , 76 , and the poly-A binding proteins PABPC1 and PABPC4 77 , 78 ( Figure 5C : RNA-dependent ; Supplementary Table 11) . The preferred interaction with ZFP36L1 in the presence of RNA was constant throughout time for all proteins, except for MOV10, which showed the highest RNA-mediated ZFP36L1 interaction at 5h of T cell activation ( Figure 5C ) . Non-RBPs preferentially interacting with ZFP36L1 in the presence of RNA were AIMP2, a scaffold for the tRNA synthetase complex 79 , 80 , in addition to WDR11 and FAM91A1, which belong to the complex that tethers vesicles to the Golgi apparatus 81 . We also identified HAUS3 and HAUS5 involved in microtubule generation 82 , 83 and STRIP1 (Fam40a), which regulates the cell shape by modulating actin filament dynamics 84 , 85 , the latter with strongest effects of RNA depletion at 0h and 2h of T cell activation ( Figure 5C – RNA-dependent) . Interestingly, CNOT proteins showed increased binding to ZFP36L1 when RNA was depleted ( Figure 5C RNA-repelled) , suggesting that the interaction to the c-terminal CNOT interacting motif of ZFP36 proteins 36 , 38 does not require the presence of RNA to maintain its interaction with ZFP36L1. Some RBPs were oblivious to RNA depletion for their interaction with ZFP36L1 (AGO2, AGO3, APOBEC3G, Stau2, FXR1, FXR2, LARP1, PRRC2A, TNCRB6B, RBM27, DISL3 and ZFP36L1) ( Figure 5C ; RNA-independent) . As expected, interactions with SQSTM1 (p62) and proteins involved in protein turnover and post-translational modifications did not require RNA (FAM120A, LSP1, NFKBIE, PEX14, PGAM5, YPEL5, the scaffolding protein ZNF598), Figure 5C ). Fam98A, which stimulates PRMT1-induced arginine methylation showed even increased interaction with ZFP36L1 when RNA was depleted 86 ( Figure 5C ). In conclusion, this analysis uncovers the context-dependent nature of interactions with ZFP36L1. UPF1 KO and GIGYF1/2 KO T cells display cellular stress through increased protein translation Lastly, we aimed to decipher the role of ZFP36L1 candidate interactors in T cells. We focused on 4 proteins that exert divergent post-transcriptional functions: the nonsense-mediated decay factor UPF1, the de-adenylation proteins/translational repressors GIGYF1 and GIGYF2, and the nuclear alternative poly-adenylation regulating protein CSTF2T. UPF1 interacts with ZFP36L1 preferentially at 0h and 16h, and it does so in an RNA-dependent manner ( Figure 4D / 5C ). GIGYF1 shows similar interaction kinetics with ZFP36L1 as UPF1 ( Figure 4D ). Conversely, GIGYF2 and CSTF2T maintain their interaction with ZFP36L1 throughout T cell activation ( Figure 4D ). Of note, UPF1, GIGYF1 and GIGYF2 belong to the core ZFP36L1 interactome, being identified also in HEK293T cells 42 ( Supplementary Figure 2 , 5 & 7 ). We first gene-edited T cells with CRISPR-Cas9 to delete UPF1, CSTF2T, GIGYF1 and GIGYF2 ( Supplementary Figure 6A ) . Because GIGYF1 and GIGYF2 cooperate with each other 87 and single deletions had no effect (not shown), we generated GIGYF1/GIGYF2 double deficient T cells (GIGYF1/2 KO). The viability of GIGYF1/2 KO and CSTF2T KO T cells was comparable to that of control T cells ( Supplementary Figure 6B ) . Only UPF1 KO T cells were less viable ( Supplementary Figure 6B ) . We next measured the global translation rate with puromycin incorporation for 10 min. CSTF2T deficiency showed very limited effects in resting and in activated T cells ( Figure 6A ) . In contrast, UPF1 KO and GIGFYF1/2 KO T cells displayed substantially higher global translation rates in resting T cells compared to control T cells ( Figure 6A ). T cell activation for 2h led to a substantial drop in puromycin incorporation in both UPF1 KO and to some extent GIGFYF1/2 KO T cells, to levels that were lower than those of control T cells ( Figure 6 ) . This could not merely be attributed to cell viability for GIGYF1/2 KO T cells ( Supplementary Figure 6B ); although UPF1 KO displayed high cell death upon activation. Because ZFP36L1 KO T cells showed very limited changes in puromycin incorporation ( Supplementary Figure 6C ) , we conclude that UPF1 and GIGYF1/2 are the driving force in changing the global translation rates. Download figure Open in new tab Figure 6. UPF1 and GIGYF1/2 deletion alters translation and stress responses in human CD8 + T cells. a . Puromycin incorporation in indicated CRISPR-Cas9 gene-edited T cells or non-targeting crRNA (ctrl)-edited T cells at indicated time points of activation with aCD3/aCD28. Left panel: Representative plot of puromycin incorporation as quantified by flow cytometry. Geometric Mean Fluorescent Intensity (gMFI) value indicated on the right. Right panel: puromycin gMFI during rest or indicated time of stimulation with aCD3/aCD28. n=2-5 donors. b . Detection of unfolded proteins with tetraphenyl ethene maleimide (TMI) dye. Left panel: Representative plot of TMI as quantified by flow cytometry. Geometric Mean Fluorescent Intensity (gMFI) value indicated on the right. Right panel: TMI gMFI during rest or indicated time of stimulation with aCD3/aCD28. n= 3 donors. c . Left panel: representative CD69 expression in resting control and indicated KO T cells measured by flow cytometry. Right panel: percentage of CD69 + T cells. Data shown as mean ± SD. n=3 donors. d . Left panel: Representative Granzyme B (GZMB) expression measured by flow cytometry. Right panel: percentage of GZMB + T cells in resting conditions and after 2h of stimulation with aCD3/aCD28. n=6 donors, data shown as mean ± SD (right). A& B) Two-way ANOVA with Dunnett’s correction C& D) One-way ANOVA with Dunnett’s correction. *p < 0.05, ** p < 0.01, *** p<0.001. See Supplementary Figure 6 Higher protein production can lead to unfolded protein response and consequentially, induce cell stress 88 . We therefore measured the level of unfolded proteins with tetraphenyl ethene maleimide (TMI), which becomes fluorescent when engaging with free thiol side chains that are exposed in unfolded proteins 89 . Concomitant with lower cell viability, UPF1 KO T cells showed increased levels of unfolded proteins ( Figure 6B ), Also GIGYF1/2 KO cells had higher levels of TMI staining at resting state, and particularly at 2h of T cell activation, although not statistically significant ( Figure 6B ) . After 5h of activation, however, the levels of unfolded proteins normalized for all control and KO T cells ( Figure 6B ). T cell activation substantially increases the translation rate (Wolf et al. 2020; Marchingo et al. 2020; Sinclair and Cantrell). We therefore questioned whether the observed higher puromycin incorporation in resting UPF1 KO and GIGYF1/2 KO T cells indicated an altered T cell activation status. Indeed, the percentage of resting T cells expressing the activation marker CD69 more than doubled in UPF1 KO and GIGYF1/2 KO T cells compared to control T cells ( Figure 6C ) . Granzyme B expression was also substantially higher in resting UPF1 KO and GIGYF1/2 KO T cells, and T cell activation resulted in stronger release of Granzyme B (=loss of signal), most prominently in UPF1 KO T cells, again pointing to a high T cell activation state in the absence of T cell triggers ( Figure 6D ) . Combined, these data suggest that UPF1 and GIGYF1/2 contribute to T cell fitness by limiting protein translation and activation status in resting T cells. UPF1 promotes ZFP36L1 protein production We next sought to decipher the interplay of ZFP36L1 with UPF1 and GIGYF1/2. Re-analysis of the comparative interactome study of wild type ZFP36L1 with a tandem zinc finger (TZF) mutant failing to interact with RNA by Bestehorn et al. 42 revealed that only GIGYF1, but not UPF1 and GIGYF2, lost its interaction with ZFP36L1 when the TZF was mutated ( Figure 7A ), indicating divergent requirements of these three proteins for interacting with ZFP36L1. Download figure Open in new tab Figure 7. UPF1 interacts with ZFP36L1 mRNA and modulates its expression at the transcript level. a . Quantification of ZFP36L1 interactors identified by immunoprecipitation–mass spectrometry (IP-MS) in HEK293T cells expressing either wild-type or zinc-finger mutant (TZFmut) ZFP36L1. Data extracted from Bestehorn et al . 42 b . Immunoblot of ZFP36L1 and UPF1 in non-targeting crRNA (Ctrl) treated, UPF1 KO (UPF1) and GIGYF1+2 KO (G1+2) from resting or aCD3/aCD28 activated T cells (2h). aRho-GDI was used as loading control. Representative of at least 6 donors. c . WT and ZFP36L1 KO cells were analysed using MS. LFQ intensity of GIGYF2, UPF1 and ZFP36L1 from human control and ZFP36L1 gene edited T cells, extracted from MS data depicted at either resting state or 3h PMA/Iono activations. n= 6 independent donors. n.d = not detected. d . Immunoblot of ZFP36L1, ZFP36L2 and UPF1 in control and UPF1 KO (KO) T cells treated with MG132 or vehicle control for 2h, executed in resting T cells (left) or 2h aCD3/aCD28 re-activated T cells (right). e . ZFP36L1 mRNA levels in control, UPF1 KO and GIGYF1+2 KO cells at resting state (left) and 2h of activation (right), measured by RT-qPCR. Data normalized to 18S . f . RNA stability assay of ZFP36L1 using actinomycin D treatment started after 2h of activation. mRNA decay measurements of ZFP36L1 mRNA. g . Native RNA immunoprecipitation (RIP) with UPF1 antibody or IgG isotype control from lysates of resting T cells. qRT-PCR of endogenous mRNAs from RIP of UPF1 relative to IgG control. n=3 donor pools, mean ± SD. E-G) Two-way ANOVA with Dunnett’s correction. *p < 0.05. n.s = non-significant. Strikingly, when we measured the effect of UPF1 and GIGYF1/2 depletion on ZFP36L1 protein expression, we found that UPF1 deletion substantially reduced the ZFP36L1 protein levels in both resting and activated T cells (2h; Figure 7B ) . Because neither UPF1 nor GIGYF2 protein expression was altered in ZFP36L1 KO T cells ( Figure 7C ) , we conclude that UPF1 is an upstream regulator of ZFP36L1 protein expression. UPF1 does not modulate the protein stability of ZFP36L1, because blocking the proteasomal degradation with MG132 did not recover ZFP36L1 protein expression in UPF1 KO T cells ( Figure 7D ). In contrast, proteasomal block restored the protein expression of its sister protein ZFP36L2, irrespective of UPF1 deletion ( Figure 7D ) . Thus, we concluded that UPF1 regulates the generation of ZFP36L1 protein. Protein output is in part defined by the number of mRNA templates. Interestingly, ZFP36L1 mRNA expression was reduced in UPF1 KO T cells, but not in GIGYF1/2 KO T cells, yet without overt effects on mRNA stability during the 2h treatment with the transcription inhibitor Actinomycin D ( Figure 7E, F ) . A native RNA-immunoprecipitation followed by qRT-PCR (RIP-qRT-PCR) on unmodified T cells confirmed that UPF1 interacts with ZFP36L1 mRNA ( Figure 7G ) , further supporting the hypothesis that UPF1 is an upstream regulator of ZFP36L1 expression. Thus, UPF1 not only interacts with ZFP36L1 protein as uncovered by proximity labeling, but UPF1 also supports the protein expression of ZFP36L1, at least in part by regulating its transcript levels, and we hypothesize that the helicase activity of UPF1 could potentially contribute to this. Combined, this finding showcases how proximity labeling can uncover novel protein-protein interactions and help identify novel regulatory nodes of proteins, as exemplified here for ZFP36L1. DISCUSSION In this study, we uncovered the multifaceted nature of the ZFP36L1 interactome in human T cells. We report versatile interaction kinetics with ZFP36L1 during T cell activation, and we defined how RNA mediates these interactions. This comprehensive interactome analysis positions ZFP36L1 at the center of multiple post-transcriptional regulatory pathways in T cells. Beyond its established role in recruiting the CCR4-NOT deadenylation complex 36 – 39 , we detected proximity of ZFP36L1 to a multitude of novel interactions with proteins involved in mRNA decapping (PATL1 and DDX6) and mRNA degradation pathways, as well as translation repressors (GIGYF1, GIGYF2, ZNF598 and 4EHP) and other regulatory pathways. Notably, many of the newly identified candidate interactors are part of complexes of previously annotated ZFP36L1 interactors. Therefore, it would be interesting to decipher how ZFP36L1 comes in proximity with the different subunits, and to determine whether ZFP36L1 plays an active role in the formation of these RBP complexes. One way we aimed to decipher this was by defining the requirement of RNA for interacting with ZFP36L1. These data clearly depicted the RNA (in)dependency of several candidates. However, it should also be considered that some protein-protein interactions indicated as RNA-independent may initially be mediated by RNA and persist due to high affinity interactions or conformational locking. A key finding of our study is that the ZFP36L1 interactome undergoes remodeling during T cell activation. For instance, proteins involved in mRNA degradation and translation repression processes are reduced during early T cell activation. We hypothesize that this temporary suspension of these interactions may be required to allow for efficient protein synthesis to occur, a key feature to drive rapid remodeling of the T cell proteome upon T cell activation 1 – 3 . The subsequent re-establishment of these interactions at 5h of T cell activation coincides with the time point when ZFP36L1 commences to dampen the T cell activation program, including for instance the cytokine production 17 , 23 , 24 . How the landscape of interaction partners is altered remains to be established. Because we find very limited alterations in RBP expression levels upon T cell activation, it is conceivable that post-translational modifications (PTMs) define the alterations in the RBP interactome of ZFP36L1 to allow for the rapid shift in the proteome and production of effector molecules. Supporting this hypothesis, we identified several post-translational modifiers and PTM-binders interacting with ZFP36L1, including the 14-3-3 proteins YWHAB/YWHAZ interacting with post-translational modifications, the protein deadenlyase SIRT2, and the CTLH E3 ubiquitin protein ligase complex component YELP5 ( Figure 3 ). Future studies should address which signaling pathways downstream of the T cell receptor and costimulatory molecules drive these modifications and thus alter ZFP36L1’s interactions and its activity. In conclusion, the time-lapse proximity labeling during T cell activation demonstrates that the ZFP36L1 interactome is context dependent and versatile. The advantage of proximity labeling is that it can detect interactions in living cells, capturing physiologically relevant interactions in their native cellular context, including weak and transient interactions 91 , 92 . In particular transient interactions are typically lost during cell lysis procedures required for e.g. Co-IP. For instance, whereas GIGYF1, GIGYF2, and CSTF2T interactions were clearly measured with proximity labeling, we failed to detect these interactions over background in the ZFP36L1 Co-IP. Furthermore, proximity labeling identified interactions of ZFP36L1 with nuclear factors such as the polyadenylation factors CSTF2 and CSTF2T, and the RNA decapping protein PATL1, which can shuttle between nucleus and cytoplasm 93 . Moreover, we also identified proteins in proximity to ZFP36L1 that are associated with granules (G3BP1 FXR1/2, ATXN2L, PUM1, YBX1, UBAP2/L, MOV10) 61 , structures, or membrane-bound organelles (NUPL1; nuclear pore complex 94 ), and these cellular compartments are generally disrupted during cell lysis. This included also interactions with non-RBPs, like SIRT2, YELP5 and SQSTM1. Our investigation of functional interactions also revealed a previously uncharacterized relationship between UPF1 and ZFP36L1. UPF1, a central component of the nonsense-mediated mRNA decay pathway 50 , 51 , 95 , 96 , interacts with ZFP36L1 protein primarily in resting T cells and at later time points of T cell activation when translation activity is returning back towards levels of resting T cells. UPF1 orchestrates co-translational mRNA surveillance by recruiting the endonuclease SMG6 and UPF2 97 . These two proteins were not identified by proximity labeling as ZFP36L1 interactors with SMG6 or UPF2. Therefore, it remains to be determined whether the helicase activity of UPF1 supports the entry of ZFP36L1 to its target mRNA together with SMG6 and UPF2, or whether it exerts a fundamentally different activity with ZFP36L1. UPF1, however, also appears to directly regulate the protein expression of ZFP36L1. ZFP36L1 protein expression is rapidly induced upon T cell activation 17 , 23 , 24 . In the absence of UPF1, ZFP36L1 protein expression in resting and activated T cells is substantially impaired. UPF1 binds to ZFP36L1 mRNA, and its depletion reduces the overall mRNA levels of ZFP36L1 mRNA. These findings combined identify UPF1 as an upstream regulator of ZFP36L1 protein expression, and they suggest that UPF1 possibly monitors and thereby safeguards the quality of ZFP36L1 transcripts, allowing for its protein production to occur. This, in combination with the evidence for ZFP36L1:UPF1 interaction, could implicate that ZFP36L1 can indirectly regulate its own levels through UPF1. In conclusion, our findings position ZFP36L1 as a central coordinator in a complex network of post-transcriptional regulatory mechanisms in T cells. Through its dynamic interactions with components of mRNA decay, translation repression, and potentially mitochondrial translation machineries, ZFP36L1 appears equipped to orchestrate the temporal sequence of gene/protein expression events required for proper T cell activation. The regulatory relationship between UPF1 and ZFP36L1 highlights the intricate interdependence of post-transcriptional control systems and creates new opportunities for understanding how these mechanisms shape T cell function in immunity and disease. MATERIALS AND METHODS Cell culture Peripheral blood mononuclear cells (PBMCs) from anonymized healthy donors were used in accordance with the Declaration of Helsinki (Seventh Revision, 2013) after written informed consent (Sanquin). PBMCs were isolated by Lymphoprep density gradient separation (Stemcell Technologies) and cryopreserved until further use. Human T cells and Jurkat cells were cultured in Culture Medium (Iscove’s Modified Dubecco’s Medium (IMDM) supplemented with 10% fetal bovine serum (FBS), 100 U/mL penicillin, 100 µg/mL streptomycin, and 2 mM L-glutamine supplemented with 100 IU/mL recombinant human rhIL2 (Protech) and 10 ng/mL rhIL-15 (Protech). Of note, because biotin deprivation induces stress and inflammatory response in T cells (Elahi, Sabui, and Said 2018), T cells were cultured in biotin-containing media (53nM). Human fibrosarcoma FLYRD18 cells (ADCC) were cultured in IMDM supplemented with 10% FBS, 1% L-glutamine, 1% Penicillin-Streptomycin antibiotic solution at 37°C, 5% CO2 and split every 2 days at 1:10. All cells were cultured at 37°C with 5% CO 2 . T cell activation T cells from defrosted PBMCs were activated for 48h as previously described 98 . Briefly, 24-well plates were pre-coated overnight at 4°C with 4µg/mL rat a-mouse IgG2a (MW1483, Sanquin) in phosphate-buffered saline (PBS). Plates were washed with PBS and coated for >3 h with 1 µg/mL αCD3 (HIT3a, Biolegend) at 37°C. 1.3×10 6 PBMCs/well were seeded with 1 µg/mL soluble αCD28 (CD28.2, Biolegend) in 1 mL Iscove’s Modified Dubecco’s Medium (IMDM) supplemented with 10% fetal bovine serum (FBS), 100 U/mL penicillin, 100 µg/mL streptomycin, and 2 mM L-glutamine. After 48h of activation at 37°C, 5% CO2, cells were harvested and cultured in standing T25/T75 tissue culture flasks (Thermo Scientific) at a density of 0.8×10 6 /mL in culture medium. Media was refreshed every 2-3 days. Upon nucleofection, T cells were cultured in T cell mixed media (Miltenyi) supplemented with 5% heat-inactivated human serum, 5% FBS, 100 U/mL Penicillin, 100 µg/mL streptomycin, 2 mM L-glutamine, 100 IU/mL rhIL-2 (Peprotech), and 10 ng/mL rhIL-15 (Peprotech) for 4 days after switching back to Culture Medium for another 3 days. T cells were re-activated in Culture Medium in 96-wells plates with 10 ng/mL Phorbol Myristate Acetate (PMA) and 1μM Ionomycin, or with αCD3 (Pelicluster; Sanquin) and αCD28 (Biolegend), 1µg/mL each. For proteasomal inhibition, 2µM of MG-132 (Sigma-Aldrich) was used. Generation UltraID constructs UltraID_ZFP36L1_GFP or UltraID_GFP fusion proteins were generated as gBlock DNA by IDT. These were then transferred into the pMX_Puro backbone (Addgene) into ClaI and EcoRI restriction sites according to the manufacturer’s protocol (T4; NEB) (sequence upon request). pMX_UltraID was generated by digesting pMX_UltarID_ZFP36L1_P2A_GFP vector with NotI and ligating it back together to remove ZFP36L1_P2A_GFP. Plasmid DNA was amplified in DH5 α super-competent bacteria (NEB) and isolated with NucleoSpin Plasmid Transfection-grade Mini-Prep isolation kit (Machery-Nagel). Sequences were confirmed with Sanger sequencing. Virus production and retroviral transduction 1×10 5 FLYRD18 cells/well were pre-seeded in 6-well culture plates and cultured overnight at 37°C. Transfection was carried out with GeneJammer (Agilent) according to the manufacturer’s protocol. Cells were cultured for 48h at 32°C. αCD3/αCD28-activated T cells (48h) were retrovirally transduced as previously described 24 . Briefly, non-tissue culture treated 24-well plates were pre-coated overnight with 50 µg/mL Retronectin (Takara). Plates were washed once with PBS, 800 µL viral supernatant/well was added and spun at 4°C at 2820g. Supernatant was removed and 1×10 6 T cells/well were added, plates spun for 5 min at 180g and incubated overnight at 37°C. Supernatant was replaced with fresh Culture Medium. After 48h, T cells were harvested and cultured in T25/T75 flasks for 6–8 days as described above. Flow cytometry To analyze GFP protein expression, T cells were washed with FACS buffer (PBS, containing 1% FBS and 2 mM EDTA) and labeled for 20 min at 4°C with indicated antibodies (see Appendix A). Dead cells were excluded with Near-IR (Life Technologies). For intracellular cytokine staining, cells were cultured with 1 µg/mL brefeldin A for indicated time points, fixed, and permeabilized with Cytofix/Cytoperm kit according to the manufacturer’s protocol (BD Biosciences) prior to acquisition using FACSymphony. Data were analyzed with FlowJo (BD Biosciences, version 10). Immunoblotting Cell lysates (1×10 6 cells) were prepared by standard procedures using RIPA lysis buffer (Thermo) supplemented with protease and phosphatase inhibitors (Thermo). Lysates were run on 4–12% SDS-PAGE (Thermo). SDS-PAGE gels were directly transferred onto nitrocellulose membranes (iBlot2, Thermo). Membranes were blocked with 5% BSA TBST solution (Fraction V, Sigma) and incubated overnight with α-Streptavidin-HRP, α-UPF1 (Proteintech), α-CSTF2T (Proteintech), ZFP36L1 (abcam) or α-RhoGDI (MAB9959, Abnova), followed by α-Rabbit (4050-05, Southern Biotech) or α-Mouse (1031-05, Southern Biotech) HRP-conjugated secondary antibodies. UltraID Proximity-dependent biotin identification assay was performed according to Kubitz et al. 30 , with the following modifications. Retrovirally transduced Jurkat cells and human T cells were FACS-sorted based on GFP expression 3 days post transduction and expanded for another 7 days. Cells were activated with αCD3/αCD28 for 2-16 h or left unactivated (rested) in the presence or absence of (50-) 500µM biotin dissolved in DMSO and incubated in the dark at 37°C. Cells were harvested, washed twice with ice-cold PBS, and lysed in 1 ml lysis buffer (10 mM Tris/HCl pH 7.5, 150 mM NaCl, 0.5 mM EDTA + HALT Protease/Phosphatase Inhibitor cocktail (Thermo, 1861281) + 0.5% NP40 (v/v)) for 5 min in at 4 °C. Lysates were sonicated 3 x 30 sec using a Branson Sonifier 450 device, keeping cells on ice for 1 min in between sessions. Cell lysates were cleared by centrifugation 14,000xg at 4°C for 20 min. Cleared lysates were added to pre-washed 200 μl Dynabeads MyOne Streptavidin C1 (65002, Invitrogen) beads (2x wash with PBS, equilibrated in lysis buffer for 5 min at 4°C and washed with lysis buffer). Mix was incubated overnight rotating at 4°C. The following day, beads were washed 3x using wash buffer 1 (10 mM Tris/HCl pH 7.5, 150 mM NaCl, 0.5 mM EDTA + HALT Protease/Phosphatase Inhibitor cocktail (Thermo, 1861281) and wash buffer 2 (10 mM Tris/HCl pH 7.5, 150 mM NaCl). Proteins were digested on-bead and prepared for Mass Spectrometry analysis as described below. Co-immunoprecipitation Cytoplasmic lysates of 50×10 6 αCD3/αCD28-activated human CD3+ T cells were prepared (simultaneously prepared from same donor pools cells of the UltraID timecourse experiment of Figure 4 ) using lysis buffer (140 mM NaCl, 5 mM MgCl2, 20 mM Tris/HCl pH7.6, 1% Digitonin) freshly supplemented with 1% protease inhibitor cocktail (Sigma). Protein A Dynabeads (Invitrogen) were prepared according to the manufacturer’s protocol. Cell lysate was pretreated with 200 U/mL RNAse A (Thermo Scientific). immunoprecipitated for 4h at 4ºC with 10 µg polyclonal rabbit a-ZFP36L1 (ABN192, Sigma-Aldrich) or with isotype control (12–370, Sigma-Aldrich) ± 200 U/mL RNAse A (Thermo Scientific). Beads were washed twice with wash buffer (150 mM NaCl, 10 mM Tris/HCl pH7.6, 2 mM EDTA, protease/phosphatase inhibitor cocktail) and twice with 10 mM Tris/HCl pH7.6. Immunoprecipitated proteins were reduced and on-bead alkylated. Proteins were detached with 250 ng trypsin for 2 h at 20ºC. Beads were removed and proteins were further digested into peptides with 350 ng trypsin for 16 h. Peptides were prepared for MS analysis, as described below. Mass spectrometry data acquisition For the UltraID T cell screen 1 and Jurkat screen: tryptic peptides were separated by nanoscale C18 reverse phase chromatography coupled online to an Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Scientific) via a nanoelectrospray ion source (Nanospray Flex Ion Source, Thermo Scientific). Peptides were loaded on a 20cm 75–360µm inner-outer diameter fused silica emitter (New Objective) packed in-house with ReproSil-Pur C18-AQ, 1.9μm resin (Dr Maisch GmbH). The column was installed on a Dionex Ultimate3000 RSLC nanoSystem (Thermo Scientific) using a MicroTee union formatted for 360μm outer diameter columns (IDEX) and a liquid junction. The spray voltage was set to 2.15kV. Buffer A was composed of 0.1% formic acid and buffer B of 0.1% formic acid, 80% acetonitrile. Peptides were loaded for 17 min at 300nl/min at 5% buffer B, equilibrated for 5 min at 5% buffer B (17-22 min) and eluted by increasing buffer B from 5-28% (22-80 min) and 28-40%(80-85 min), followed by a 5 min wash to 95 % and a 5 min regeneration to 5%. Survey scans of peptide precursors from 375 to 1500 m/z were performed at 120K resolution (at 200 m/z) with a 4×10 5 ion count target. Tandem mass spectrometry was performed by isolation with the quadrupole with isolation window 0.7, HCD fragmentation with normalized collision energy of 30, and rapid scan mass spectrometry analysis in the ion trap. The MS2 ion count target was set to 3×10 4 , and the max injection time was 20ms. Only those precursors with charge state 2–7 were sampled for MS2. The dynamic exclusion duration was set to 20s with a 10ppm tolerance around the selected precursor and its isotopes. Monoisotopic precursor selection was turned on. The instrument was run in top speed mode with 1s cycles. All data were acquired with Xcalibur SII software 99 . For the UltraID T cell sreen 2, UltraID timecourse screen, and ZFP36L1 Co-IP, samples were measured using TimsTOF-DIA as previously described 100 . One amendment was made to the protocol, which encompassed that 30 samples per day were measured instead of 60. For the total cell lysates: cells were lysed and aquired as previously described 101 , using the Orbitrap Fusion Lumos Tribbrid mass spectrometer. Mass Spectrometry data Analysis Raw mass spectrometry files were processed with the MaxQuant computational platform, version 1.6.2.10. Proteins and peptides were identified using the Andromeda search engine by querying the reviewed human Uniprot database (downloaded 09-22-2023 20426 entries). Standard settings with the additional options match between runs, and unique peptides for quantification were selected. The generated ‘proteingroups.txt’ data were imported in R4.30/Rstudio 2024.04.1. ‘reverse’, ‘potential contaminants’ and ‘only identified by site’ peptides were filtered out and label free quantification values were log2 transformed. Missing values were imputed by a normal distribution (width=0.3, shift = 1.8), assuming these proteins were close to the detection limit. Statistical analyses were performed using moderated t-tests in the LIMMA package. A Benjamini-Hochberg adjusted P value 1 was considered statistically significant and relevant. For supervised classification, we generated theoretical profiles in which conditions of interest intensities were set to an arbitrary high and low in all other samples (Kreft et al. 2023). These profiles were correlated with all statistically significant proteins. Correlation between data and theoretical protein profiles was performed using Pearson correlations, a ρ > 0.5 or 0.6 and Benjamini-Hochberg adjusted P value <0.05 was used as threshold. Gene ontology overrepresentation analyses were performed using the clusterprofiler package 102 . The R package ggplot2 was used for graphical representations. Genetic modification of T cells with Cas9 RNPs crRNAs were designed in Benchling ( https://benchling.com ; Appendix B). Cas9 RNP production and T cell nucleofection was performed as previously described (Freen-van Heeren et al. 2020). Briefly, Alt-R crRNA and tracrRNA were reconstituted to 100 µM in Nuclease Free Duplex buffer (all IDT). As a negative control, nontargeting negative control crRNA #1 was used (IDT). Oligos were mixed at equimolar ratios (i.e. 4.5 mL total crRNA + 4.5 mL tracrRNA) in nuclease-free PCR tubes and denatured by heating at 95°C for 5 min. Nucleic acids were cooled down to room temperature prior to mixing them with 30 µg Alt-R™ S.p. Cas9 Nuclease V3 (IDT) to produce Cas9 ribonuclear proteins (RNPs). Mixture was incubated at room temperature for at least 10 min prior to nucleofection. For nucleofection, human CD3 + T cells activated for 48 h with αCD3/αCD28 were washed twice with PBS and resuspended in P2 buffer (Lonza). Cells were electroporated in 16-well strips in a 4D Nucleofector X unit (Lonza) with program EH100 for human T cells &P2 buffer. Knockout efficiency was determined 5 d after electroporation by Western blot, or by PCR. RNA immunoprecipitation Cytoplasmic lysates of 25×10 6 human CD3 + T cells activated for 3h with αCD3/αCD28 were prepared using lysis buffer (10 mM HEPES, pH 7.0, 100 mM KCl, 5 mM MgCl2, 0.5% NP40) freshly supplemented with 1 mM DTT, 100 U/ml RNase OUT (both Invitrogen), 0.4 mM Ribonucleoside Vanadyl Complex (NEB) and 1% EDTA-free protease/phosphatase inhibitor cocktail (Thermo Scientific). Protein G Dynabeads (Invitrogen) were prepared according to manufacturer’s protocol. The lysate was immunoprecipitated for 4h at 4ºC with 10 µg polyclonal α-UPF1 (ProteinTech) or a polyclonal rabbit IgG isotype control (12–370, Sigma-Aldrich). RNA was extracted directly from beads by using Zymo Quick-RNA Miniprep Kit, and mRNA expression was measured by RT-PCR as described above. Quantitative PCR analysis T cells were activated for indicated time point with αCD3/αCD28. For mRNA half-life measurements, cells were treated for an additional 1h or 2h with 5 µg/mL actinomycin D (ActD) (Sigma-Aldrich). Total RNA was extracted using Quick-RNA MiniPrep plus kip (Zymo). cDNA was synthesized with SuperScript III (Invitrogen), RT-PCR was performed using SYBR Green on a StepOne Plus (Applied Biosystems). Ct values were normalized to RPS18 levels. Quantification and statistical analysis Results are shown as mean ± SD. Statistical analysis was performed with GraphPad Prism 10 with Wilcoxon two-tailed ratio paired or unpaired Student’s t test when comparing two groups, or with one-way ANOVA test with Dunnett correction when comparing more than two groups. p values < 0.05 were considered statistically significant. SUPPLEMENTARY FIGURE LEGENDS Download figure Open in new tab Supplemental Figure 1. Optimizing Proximity labeling in primary human T cells a . Immunoblot of biotinylated proteins with aStreptavidin-HPR of FLYRD18 cells left untransduced (UT) or transduced with indicated constructs (UltraID_L1 with two different DNA preparations and UltraID_only) in the presence or absence of ±50µM biotin for 10 min. b . Immunoblot of T cells transduced with UT, UltraID_L1 or UltraID_only in the presence or absence of aCD3/aCD28 and with indicated concentrations of Biotin for 16h. Rho-GDI was used as loading control. Representative of at 6 donors in two independently performed experiments. c . Flow cytometry plot of transduction efficiency based on GFP expression. d . CD69 expression of untransduced (UT), UltraID_L1 (Blue) or UltraID_GFP (Red) T cells that were rested for 7 days, or that were re-activated with aCD3/aCD28 for 16h in the presence or absence of 500µM Biotin. e . UT (Green), UltraID_GFP (Red), and UltraID_L1 (Blue) transduced T cells were reactivated with aCD3/aCD28 for 5h. Top: gating strategy. Bottom: percentage of TNF expressing T cells. f . Enrichment of biotinylated proteins as visualized by Streptavidin blot of 10% input and 10% of the final elution of T cells transduced with UltraID_L1 or UltraID_GFP, and that were re-activated with aCD3/aCD28 for 16h in the presence of biotin. Representative of at least 2 independent experiments with 3 donor pools. See Figure 1 Download figure Open in new tab Supplemental Figure 2. Proximity labeling identifies the ZFP36L1 interactome in primary human T cells a . Number of identified proteins of the total Cell Lysis (TCL) by Mass Spectrometry (MS). Each bar represents one donor pool of 40 donors. b . Heatmap of all proteins that were significantly enriched in one of the 12 conditions in Total Cell Lysate (5250 proteins, LFC >1, p.adj<0.05). Color scale represents Z-scored log2 median-centered averaged intensities. c . Number of identified proteins after UltraID_L1 streptavidin pulldown (IP) by MS. Each bar represents one donor pool of 40 donors. d . Heatmap of proteins that were significantly enriched in the UltraID-L1 IP: T cell screen 1 (2439 proteins, LFC >1, p.adj<0.05). Color scale represents Z-scored log2 mediancentered averaged intensities. e . LFC mean intensities of protein expression in the total cell lysates that were found to interact with ZFP36L1 as depicted in Figure 2B . Proteins with LFC >1, p.adj<0.05 are considered significantly enriched. Scale represents LFC average intensities. Mean of 3 donor pools. See Figure 2 Download figure Open in new tab Supplemental Figure 3. Proximity labeling identifies the ZFP36L1 interactome in Jurkat cells a . Repeat experiment of UltraID Pull down for ZFP36L1 (T cell screen 2). Heatmap of supervised classification displaying biotinylated proteins that were significantly enriched. Correlation between hits and disorder-specific theoretical protein profiles had a Pearson correlation of >0.5, p.adj 1. Color scale represents Z-scored log2 mediancentered averaged intensities. b . Flow cytometry analysis of GFP-expressing Jurkat cells transduced with indicated constructs. Representative of 3 technical replicates. c . Streptavidin immunoblot of input (10%), flow through and consecutive washes (1-4) of the pulldown, in addition to the elution (10%). Representative of at least 2 independent experiments with 3 technical replicates. d . Heatmap of supervised classification displaying biotinylated proteins that were enriched in Jurkat UltraID_L1 16h screen. Correlation between hits and disorder-specific theoretical protein profiles had a Pearson correlation of >0.5 and p.adj 1. Color scale represents Z-scored log2 median-centered averaged intensities. e . Venn diagram of ZFP36L1 protein interactions from the UltraID T cell screen 1 and 2 identified in FIg xxx, compared with the ZFP36L1 interactome screen in HEK 293T cells from 42 See Figure 2 Download figure Open in new tab Supplemental Figure 4. Time-resolved proximity labeling reveals dynamic interactions with ZFP36L1 a . Immunoblot of biotinylated proteins of indicated T cells after 2h and 5h of activation with aCD3/aCD28. Indicated concentration of biotin was added during the entire time of T cell activation. Rho-GDI served as loading control. Blot representative of 3 independent donors. b . Shape analysis of biotinylated proteins from the T cell activation time course ( Figure 5B ) using supervised classification, identified to contain interaction partners with a Pearson correlation of >0.5, and p.adj 1. Mean of 3 donor pools. c . Heatmap of supervised classification of biotinylated proteins enriched in the shape analysis from panel b. Proteins marked in red were also identified in T cell screen 1 and/or 2. Color scale represents Z-scored log2 median-centered averaged intensities. See Figure 4 Download figure Open in new tab Supplemental Figure 5. Co-IP reveals RNA-dependent and -independent interactions of ZFP36L1. a . RNA profile of T cell lysates measured by Bioanalyser trace with or without treatment throughout entire protocol with RNAse A (200 U/mL) that were used for the ZFP36L1 Co-IP. (L) = ladder. N= 3 donor pools. b . Number of proteins that were significantly enriched in the ZFP36L1 Co-IP (Red) vs IgG controls (Blue) (LFC >1, p.adj <0.05) at different time points of T cell activation. c . Volcano plot of proteins identified in the ZFP36L1 Co-IP at 16h of T cell activation. Only proteins identified in all three replicates were considered putative interactors. Yellow dots indicate CNOT and EXOSC proteins. Purple dots indicate proteins that were among the top ZFP36L1 interactors in the UltraID_L1 interactome screens. d . umber of proteins identified as gained (red) or lost (blue) in ZFP36L1 Co-IP upon RNAse A treatment at indicated time points, compared to the IgG control. Proteins with p.adj 1 or LFC >-1 were considered significant. e . Scatter plot analysis of protein responses to RNase treatment between 0h and 5h of αCD3/αCD28 stimulation. Proteins are classified as RNA-repelled (red), RNA-independent (black), or RNA-dependent (blue). Significance threshold of >1 LFC is indicated by dashed lines. f . Dot plot depicting protein identification across multiple datasets (T cell 1& 2, Jurkat, Timecourse, Co_IP). Each row represents a different protein, with colored dots indicating presence/detection in each dataset. Proteins are only shown when identified in >2 screens. See Figure 5 Download figure Open in new tab Supplemental Figure 6. UPF1 and GIGYF1/2 deletion alters T cell viability. a . Validation of CRISPR-Cas9 gene-editing for indicated RBPs. 6 days after gene editing, efficiency was determined by immunoblot (left panel: UPF1 and CSFT2T, with Rho-GDI as loading control), by PCR on genomic DNA (middle panel: GIGYF2; arrows indicate PCR product of WT and KO product), or by TIDE analysis (right panel: GIGYF1 KO cells). Representative of n=9 independent donors. b . Cell viability of indicated T cells as defined by IR-dye through flow cytometry at indicated time points of activation post αCD3/αCD28 re-stimulation. n= 2 donors. c . Puromycin incorporation assay in ZFP36L1 KO versus WT T cells activated with PMA/Iono for indicated time points, as quantified by flow cytometry. Numbers indicate gMFI values. Representative of 3 donors. See Figure 6 AUTHOR CONTRIBUTIONS Conceptualization, A.P.J. and M.C.W.; Methodology, A.P.J., B.P., M.v.B., J.B. and M.C.W.; Investigation, A.P.J., L.W., A.B., F.P.J.v.A., C.v.d.Z.; Formal Analysis, A.P.J., and A.J.H..; Validation, A.P.J., S.E., and A.B.; Writing-Original Draft, A.P.J and M.C.W.; Writing – Review & Editing, A.P.J., B.P., A.J.H., F.P.J.v.A., C.v.d.Z., M.v.B., J.B., and M.C.W.; Supervision, M.C.W.; Funding Acquisition, M.C.W. COMPETING INTERESTS The authors declare no competing interests. MATERIALS & CORRESPONDENCE M.C. Wolkers m.wolkers{at}sanquin.nl MATERIALS AVAILABILITY This study did not generate new unique reagents. DATA CODE AND AVAILABILITY The mass spectrometry proteomics data of the RNA Aptamer Pulldown have been deposited to the ProteomeXchange Consortium via the PRIDE 104 partner repository with the dataset identifier PXD061869. This paper does not report original custom code. All codes used in this paper are available from the lead contact upon request. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. Acknowledgements We thank A. Guislain and S. Toll for technical. help and N. Servaas and K. Rooijers for critical reading of the manuscript. This research was supported by Oncode Institute, the European Research Council consolidator award PRINTERS 817533, and Landsteiner Foundation for Blood Transfusion Research grant 2202, all to M.C.W.; and the Landsteiner Foundation for Blood Transfusion Research grant 2103 to B.P. and M.C.W. Appendix Appendix: Key resources Appendix A View this table: View inline View popup Appendix Appendix B View this table: View inline View popup Download powerpoint REFERENCES 1. ↵ Wolf , T. et al. Dynamics in protein translation sustaining T cell preparedness . Nat. Immunol . 21 , 927 – 937 ( 2020 ). OpenUrl CrossRef PubMed 2. Sinclair , L. V. & Cantrell , D. A. Protein Synthesis and Metabolism in T Cells . Annu. Rev. Immunol . 43 , 343 – 66 ( 2025 ). OpenUrl PubMed 3. ↵ Howden , A. J. M. et al. Quantitative analysis of T cell proteomes and environmental sensors during T cell differentiation . Nat. Immunol . 20 , 1542 – 1554 ( 2019 ). OpenUrl CrossRef PubMed 4. ↵ Zhang , B. , Karrison , T. , Rowley , D. A. & Schreiber , H. IFN-γ– and TNF-dependent bystander eradication of antigen-loss variants in established mouse cancers . J. Clin. Invest . 118 , 1398 – 1404 ( 2008 ). OpenUrl CrossRef PubMed Web of Science 5. ↵ Cullen , S. P. , Brunet , M. & Martin , S. J. Granzymes in cancer and immunity . Cell Death Differ . 17 , 616 – 623 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 6. ↵ Morgan , R. A. et al. Case Report of a Serious Adverse Event Following the Administration of T Cells Transduced With a Chimeric Antigen Receptor Recognizing ERBB2 . Mol. Ther . 18 , 843 – 851 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 7. ↵ Fara , A. , Mitrev , Z. , Rosalia , R. A. & Assas , B. M. Cytokine storm and COVID-19: a chronicle of pro-inflammatory cytokines . Open Biol . 10 , ( 2020 ). 8. ↵ Turner , M. & DÍaz-Muñoz , M. D. RNA-binding proteins control gene expression and cell fate in the immune system review-article . Nat. Immunol . 19 , 120 – 129 ( 2018 ). OpenUrl CrossRef PubMed 9. Hentze , M. W. , Castello , A. , Schwarzl , T. & Preiss , T. A brave new world of RNA-binding proteins . Nat. Rev. Mol. Cell Biol . 19 , 327 – 341 ( 2018 ). OpenUrl CrossRef PubMed 10. Salerno , F. , Turner , M. & Wolkers , M. C. Dynamic post-transcriptional events governing CD8+ T cell homeostasis and effector function . Trends Immunol . 41 , 240 – 254 ( 2020 ). OpenUrl CrossRef PubMed 11. ↵ Gebauer , F. , Schwarzl , T. , Valcárcel , J. & Hentze , M. W. RNA-binding proteins in human genetic disease . Nat. Rev. Genet . 22 , 185 – 198 ( 2020 ). OpenUrl CrossRef PubMed 12. ↵ Jeltsch , K. M. & Heissmeyer , V. Regulation of T cell signaling and autoimmunity by RNA-binding proteins . Curr. Opin. Immunol . 39 , 127 – 135 ( 2016 ). OpenUrl CrossRef PubMed 13. Behrens , G. et al. Disrupting Roquin-1 interaction with Regnase-1 induces autoimmunity and enhances antitumor responses . Nat. Immunol . 22 , 1563 – 1576 ( 2021 ). OpenUrl CrossRef PubMed 14. ↵ Li , Y. et al. The RNA binding protein Arid5a drives IL-17–dependent autoantibody-induced glomerulonephritis . J. Exp. Med . 221 , ( 2024 ). 15. ↵ Coelho , M. A. et al. Oncogenic RAS Signaling Promotes Tumor Immunoresistance by Stabilizing PD-L1 mRNA . Immunity 47 , 1083 – 1099 ( 2017 ). OpenUrl CrossRef PubMed 16. Petkau , G. et al. Zfp36l1 establishes the high-affinity CD8 T-cell response by directly linking TCR affinity to cytokine sensing . Eur. J. Immunol . 54 , ( 2024 ). 17. ↵ Zandhuis , N. D. et al. Combined Deletion of ZFP36L1 and ZFP36L2 Drives Superior Cytokine Production in T Cells at the Cost of Cell Fitness . Eur. J. Immunol . 55 , ( 2025 ). 18. ↵ Tavernier , S. J. et al. A human immune dysregulation syndrome characterized by severe hyperinflammation with a homozygous nonsense Roquin-1 mutation . Nat. Commun . 10 , ( 2019 ). 19. ↵ Cuchet-Lourenço , D. et al. VCAM1 -expressing T cells and systemic autoimmunity in Regnase-1 deficiency . Preprint at doi: 10.1101/2025.01.21.25320127 ( 2025 ). OpenUrl Abstract / FREE Full Text 20. ↵ Lykke-Andersen , J. & Wagner , E. Recruitment and activation of mRNA decay enzymes by two ARE-mediated decay activation domains in the proteins TTP and BRF-1 . Genes Dev . 19 , 351 – 361 ( 2005 ). OpenUrl Abstract / FREE Full Text 21. ↵ Moore , M. J. et al. ZFP36 RNA-binding proteins restrain T cell activation and anti-viral immunity . eLife 7 , ( 2018 ). 22. Cook , M. E. et al. The ZFP36 family of RNA binding proteins regulates homeostatic and autoreactive T cell responses . Sci. Immunol . 7 , eabo0981 ( 2022 ). OpenUrl CrossRef PubMed 23. ↵ Petkau , G. et al. The timing of differentiation and potency of CD8 effector function is set by RNA binding proteins . Nat. Commun . 13 , ( 2022 ). 24. ↵ Popović , B. et al. Time-dependent regulation of cytokine production by RNA binding proteins defines T cell effector function . Cell Rep . 42 , ( 2023 ). 25. ↵ Zandhuis , N. D. et al. Regulation of IFN-γ production by ZFP36L2 in T cells is time-dependent . Eur. J. Immunol . ( 2024 ). 26. ↵ Salerno , F. et al. Translational repression of pre-formed cytokine-encoding mRNA prevents chronic activation of memory T cells . Nat. Immunol . 19 , 828 – 837 ( 2018 ). OpenUrl CrossRef PubMed 27. ↵ Kedersha , N. et al. Stress granules and processing bodies are dynamically linked sites of mRNP remodeling . J. Cell Biol . 169 , 871 – 884 ( 2005 ). OpenUrl Abstract / FREE Full Text 28. ↵ Ma , W. & Mayr , C. A membraneless organelle associated with the endoplasmic reticulum enables 3’UTR-mediated protein-protein interactions . Cell 175 , 1492 – 1506 ( 2018 ). OpenUrl CrossRef PubMed 29. ↵ Street , L. A. et al. Large-scale map of RNA-binding protein interactomes across the mRNA life cycle . Mol. Cell 84 , 3790 – 3809 . ( 2024 ). OpenUrl CrossRef PubMed 30. ↵ Kubitz , L. et al. Engineering of ultraID, a compact and hyperactive enzyme for proximity-dependent biotinylation in living cells . Commun. Biol . 5 , ( 2022 ). 31. ↵ Zempleni , J. & Mock , D. M. Uptake and metabolism of biotin by human peripheral blood mononuclear cells . Am. J. Physiol.-Cell Physiol . 275 , C382 – C388 ( 1998 ). OpenUrl PubMed Web of Science 32. Zempleni , J. & Mock , D. M. Mitogen-induced proliferation increases biotin uptake into human peripheral blood mononuclear cells . Am. J. Physiol.-Cell Physiol . 276 , C1079 – C1084 ( 1999 ). OpenUrl PubMed 33. ↵ Zempleni , J. & Mock , D. M. Utilization of Biotin in Proliferating Human Lymphocytes . J. Nutr . 130 , 335S – 337S ( 2000 ). OpenUrl 34. ↵ Kontoyiannis , D. , Pasparakis , M. , Pizarro , T. T. , Cominelli , F. & Kollias , G. Impaired On/Off Regulation of TNF Biosynthesis in Mice Lacking TNF AU-Rich Elements: Implications for Joint and Gut-Associated Immunopathologies . Immunity 10 , 287 – 398 ( 1999 ). OpenUrl 35. ↵ Kreft , I. C. et al. Proteomic landscapes of inherited platelet disorders with different etiologies . J. Thromb. Haemost . 21 , 359 – 372 . ( 2023 ). OpenUrl CrossRef PubMed 36. ↵ Fabian , M. R. et al. Structural basis for the recruitment of the human CCR4–NOT deadenylase complex by tristetraprolin . Nat. Struct. Mol. Biol . 20 , 735 – 739 ( 2013 ). OpenUrl CrossRef PubMed 37. Ito-Kureha , T. et al. The CCR4–NOT deadenylase complex safeguards thymic positive selection by down-regulating aberrant pro-apoptotic gene expression . Nat. Commun . 11 , ( 2020 ). 38. ↵ Otsuka , H. et al. ARE-binding protein ZFP36L1 interacts with CNOT1 to directly repress translation via a deadenylation-independent mechanism . Biochimie 174 , 49 – 56 ( 2020 ). OpenUrl PubMed 39. ↵ Akiyama , T. & Yamamoto , T. Regulation of Early Lymphocyte Development via mRNA Decay Catalyzed by the CCR4-NOT Complex . Front. Immunol . 12 , ( 2021 ). 40. ↵ Bustin , S. A. & Mckay , I. A. The Product of the Primary Response Gene BRF1 Inhibits the Interaction between 14-3-3 Proteins and cRaf-1 in the Yeast Trihybrid System . DNA Cell Biol . 18 , 653 – 661 ( 1999 ). OpenUrl CrossRef PubMed 41. ↵ Johnson , B. A. , Stehn , J. R. , Yaffe , M. B. & Blackwell , T. K. Cytoplasmic Localization of Tristetraprolin Involves 14-3-3-dependent and -independent Mechanisms . J. Biol. Chem . 277 , 18029 – 18036 ( 2002 ). OpenUrl Abstract / FREE Full Text 42. ↵ Bestehorn , A. et al. Cytoplasmic mRNA decay controlling inflammatory gene expression is determined by pre-mRNA fate decision . Mol. Cell 85 , ( 2025 ). 43. ↵ Scheller , N. et al. Identification of PatL1, a human homolog to yeast P body component Pat1 . Biochim. Biophys. Acta BBA - Mol. Cell Res . 1773 , 1786 – 1792 ( 2007 ). OpenUrl 44. ↵ Yamashita , A. et al. Concerted action of poly(A) nucleases and decapping enzyme in mammalian mRNA turnover . Nat. Struct. Mol. Biol . 12 , 1054 – 1063 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 45. ↵ Enwerem , I. I. I. et al. Human Pumilio proteins directly bind the CCR4-NOT deadenylase complex to regulate the transcriptome . RNA 27 , 445 – 464 ( 2021 ). OpenUrl Abstract / FREE Full Text 46. ↵ Braun , J. E. , Huntzinger , E. , Fauser , M. & Izaurralde , E. GW182 Proteins Directly Recruit Cytoplasmic Deadenylase Complexes to miRNA Targets . Mol. Cell 44 , 120 – 133 ( 2011 ). OpenUrl CrossRef PubMed Web of Science 47. ↵ Matsuura , Y. , Noguchi , A. , Sakai , S. , Yokota , N. & Kawahara , H. Nuclear accumulation of ZFP36L1 is cell cycle-dependent and determined by a C-terminal serine-rich cluster . J. Biochem. (Tokyo) 168 , 477 – 489 ( 2020 ). OpenUrl CrossRef PubMed 48. ↵ Chuvpilo , S. et al. Alternative polyadenylation events contribute to the induction of NF-ATc in effector T cells . Immunity 10 , 261 – 269 ( 1999 ). OpenUrl CrossRef PubMed Web of Science 49. ↵ Cho , S. et al. FAM120A couples SREBP-dependent transcription and splicing of lipogenesis enzymes downstream of mTORC1 . Mol. Cell 83 , 3010 – 3026 ( 2023 ). OpenUrl CrossRef PubMed 50. ↵ Fiorini , F. , Bagchi , D. , Le Hir , H. & Croquette , V. Human Upf1 is a highly processive RNA helicase and translocase with RNP remodelling activities . Nat. Commun . 6 , ( 2015 ). 51. ↵ Musaev , D. et al. UPF1 regulates mRNA stability by sensing poorly translated coding sequences . Cell Rep . 43 , ( 2024 ). 52. ↵ Iwai , N. et al. UPF1 plays critical roles in early B cell development . Nat. Commun . 15 , ( 2024 ). 53. ↵ Fiorini , F. et al. HTLV-1 Tax plugs and freezes UPF1 helicase leading to nonsense-mediated mRNA decay inhibition . Nat. Commun . 9 , ( 2018 ). 54. ↵ Gregersen , L. H. et al. MOV10 Is a 511 to 311 RNA Helicase Contributing to UPF1 mRNA Target Degradation by Translocation along 311 UTRs . Mol. Cell 54 , 573 – 585 ( 2014 ). OpenUrl CrossRef PubMed 55. ↵ Hodson , D. J. et al. Deletion of the RNA-binding proteins ZFP36L1 and ZFP36L2 leads to perturbed thymic development and T lymphoblastic leukemia . Nat. Immunol . 11 , 717 – 724 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 56. ↵ Teixeira , D. , Sheth , U. , Valencia-Sanchez , M. A. , Brengues , M. & Parker , R. Processing bodies require RNA for assembly and contain nontranslating mRNAs . RNA 11 , 371 – 382 ( 2005 ). OpenUrl Abstract / FREE Full Text 57. Bounedjah , O. et al. Free mRNA in excess upon polysome dissociation is a scaffold for protein multimerization to form stress granules . Nucleic Acids Res . 42 , 8678 – 8691 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 58. Ayache , J. et al. P-body assembly requires DDX6 repression complexes rather than decay or Ataxin2/2L complexes . Mol. Biol. Cell 26 , 2579 – 2595 ( 2015 ). OpenUrl Abstract / FREE Full Text 59. ↵ Tourrière , H. et al. The RasGAP-associated endoribonuclease G3BP mediates stress granule assembly . J. Cell Biol . 222 , ( 2023 ). 60. ↵ Fenger-Grøn , M. , Fillman , C. , Norrild , B. & Lykke-Andersen , J. Multiple Processing Body Factors and the ARE Binding Protein TTP Activate mRNA Decapping . Mol. Cell 20 , 905 – 915 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 61. ↵ Curdy , N. et al. The proteome and transcriptome of stress granules and P bodies during human T lymphocyte activation . Cell Rep . 42 , ( 2023 ). 62. ↵ Chen , T.-W. et al. Human DCP1 is crucial for mRNA decapping and possesses paralog-specific gene regulating functions . eLife 13 , ( 2024 ). 63. ↵ Sharif , H. et al. Structural analysis of the yeast Dhh1–Pat1 complex reveals how Dhh1 engages Pat1, Edc3 and RNA in mutually exclusive interactions . Nucleic Acids Res . 41 , 8377 – 8390 ( 2013 ). OpenUrl CrossRef PubMed 64. ↵ Luo , E.-C. et al. Large-scale tethered function assays identify factors that regulate mRNA stability and translation . Nat. Struct. Mol. Biol . 27 , 989 – 1000 ( 2020 ). OpenUrl CrossRef PubMed 65. ↵ Bohlen , J. , Roiuk , M. , Neff , M. & Teleman , A. A. PRRC2 proteins impact translation initiation by promoting leaky scanning . Nucleic Acids Res . 51 , 3391 – 3409 ( 2023 ). OpenUrl CrossRef PubMed 66. ↵ Stoecklin , G. et al. MK2-induced tristetraprolin:14-3-3 complexes prevent stress granule association and ARE-mRNA decay . EMBO J . 23 , 1313 – 1324 ( 2004 ). OpenUrl Abstract / FREE Full Text 67. ↵ Benjamin , D. , Schmidlin , M. , Min , L. , Gross , B. & Moroni , C. BRF1 Protein Turnover and mRNA Decay Activity Are Regulated by Protein Kinase B at the Same Phosphorylation Sites . Mol. Cell. Biol . 26 , 9497 – 9507 ( 2006 ). OpenUrl Abstract / FREE Full Text 68. ↵ Knyphausen , P. et al. Insights into Lysine Deacetylation of Natively Folded Substrate Proteins by Sirtuins . J. Biol. Chem . 291 , 14677 – 14694 ( 2016 ). OpenUrl Abstract / FREE Full Text 69. ↵ Christian , B. E. & Spremulli , L. L. Mechanism of protein biosynthesis in mammalian mitochondria . Biochim. Biophys. Acta BBA - Gene Regul. Mech . 1819 , 1035 – 1054 ( 2012 ). OpenUrl 70. ↵ Scharping , N. E. et al. The Tumor Microenvironment Represses T Cell Mitochondrial Biogenesis to Drive Intratumoral T Cell Metabolic Insufficiency and Dysfunction . Immunity 45 , 374 – 388 ( 2016 ). OpenUrl CrossRef PubMed 71. Lisci , M. et al. Mitochondrial translation is required for sustained killing by cytotoxic T cells . Science 374 , ( 2021 ). 72. ↵ Lisci , M. & Griffiths , G. M. Arming a killer: mitochondrial regulation of CD8+ T cell cytotoxicity . Trends Cell Biol . 33 , 138 – 147 ( 2023 ). OpenUrl PubMed 73. ↵ Baertling , F. et al. NDUFA9 point mutations cause a variable mitochondrial complex I assembly defect . Clin. Genet . 93 , 111 – 118 ( 2018 ). OpenUrl CrossRef PubMed 74. ↵ Zhang , X. et al. An Atomic Structure of the Human Spliceosome . Cell 169 , 918 – 929 ( 2017 ). OpenUrl CrossRef PubMed 75. ↵ He , W. & Parker , R. Functions of Lsm proteins in mRNA degradation and splicing . Curr. Opin. Cell Biol . 12 , 346 – 350 ( 2000 ). OpenUrl CrossRef PubMed Web of Science 76. ↵ Khusial , P. , Plaag , R. & Zieve , G. W. LSm proteins form heptameric rings that bind to RNA via repeating motifs . Trends Biochem. Sci . 30 , 522 – 528 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 77. ↵ Qi , Y. , Wang , M. & Jiang , Q. PABPC1——mRNA stability, protein translation and tumorigenesis . Front. Oncol . 12 , ( 2022 ). 78. ↵ Oliveira , A. G. et al. Interaction between poly(A)–binding protein PABPC4 and nuclear receptor corepressor NCoR1 modulates a metabolic stress response . J. Biol. Chem . 299 , ( 2023 ). 79. ↵ Hahn , H. , Park , S. H. , Kim , H.-J. , Kim , S. & Han , B. W. The DRS–AIMP2–EPRS subcomplex acts as a pivot in the multi-tRNA synthetase complex . IUCrJ 6 , 958 – 967 ( 2019 ). OpenUrl PubMed 80. ↵ Zhou , Z. , Sun , B. , Huang , S. , Yu , D. & Zhang , X. Roles of aminoacyl-tRNA synthetase-interacting multi-functional proteins in physiology and cancer . Cell Death Dis . 11 , 579 ( 2020 ). OpenUrl PubMed 81. ↵ Navarro Negredo , P. , Edgar , J. R. , Manna , P. T. , Antrobus , R. & Robinson , M. S. The WDR11 complex facilitates the tethering of AP-1-derived vesicles . Nat. Commun . 9 , 596 ( 2018 ). OpenUrl CrossRef PubMed 82. ↵ Lawo , S. et al. HAUS, the 8-Subunit Human Augmin Complex, Regulates Centrosome and Spindle Integrity . Curr. Biol . 19 , 816 – 826 ( 2009 ). OpenUrl CrossRef PubMed Web of Science 83. ↵ Uehara , R. et al. The augmin complex plays a critical role in spindle microtubule generation for mitotic progression and cytokinesis in human cells . Proc. Natl. Acad. Sci . 106 , 6998 – 7003 ( 2009 ). OpenUrl Abstract / FREE Full Text 84. ↵ Bai , S. W. et al. Identification and characterization of a set of conserved and new regulators of cytoskeletal organization, cell morphology and migration . BMC Biol . 9 , ( 2011 ). 85. ↵ Suryavanshi , N. , Furmston , J. & Ridley , A. J. The STRIPAK complex components FAM40A and FAM40B regulate endothelial cell contractility via ROCKs . BMC Cell Biol . 19 , ( 2018 ). 86. ↵ Akter , K. A. , Mansour , M. A. , Hyodo , T. & Senga , T. FAM98A associates with DDX1-C14orf166-FAM98B in a novel complex involved in colorectal cancer progression . Int. J. Biochem. Cell Biol . 84 , 1 – 13 ( 2017 ). OpenUrl CrossRef PubMed 87. ↵ Giovannone , B. et al. Two Novel Proteins That Are Linked to Insulin-like Growth Factor (IGF-I) Receptors by the Grb10 Adapter and Modulate IGF-I Signaling . J. Biol. Chem . 278 , 31564 – 31573 ( 2003 ). OpenUrl Abstract / FREE Full Text 88. ↵ Cao , Y. et al. ER stress-induced mediator C/EBP homologous protein thwarts effector T cell activity in tumors through T-bet repression . Nat. Commun . 10 , ( 2019 ). 89. ↵ Chen , M. Z. et al. A thiol probe for measuring unfolded protein load and proteostasis in cells . Nat. Commun . 8 , 474 ( 2017 ). OpenUrl CrossRef PubMed 90. Marchingo , J. M. , Sinclair , L. V. , Howden , A. J. & Cantrell , D. A. Quantitative analysis of how myc controls t cell proteomes and metabolic pathways during t cell activation . eLife 9 , 1 – 23 ( 2020 ). OpenUrl CrossRef PubMed 91. ↵ Roux , K. J. , Kim , D. I. , Raida , M. & Burke , B. A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells . J. Cell Biol . 196 , 801 – 810 ( 2012 ). OpenUrl Abstract / FREE Full Text 92. ↵ Branon , T. C. et al. Efficient proximity labeling in living cells and organisms with TurboID . Nat. Biotechnol . 36 , 880 – 898 ( 2018 ). OpenUrl CrossRef PubMed 93. ↵ Marnef , A. , Weil , D. & Standart , N. RNA-related nuclear functions of human Pat1b, the P-body mRNA decay factor . Mol. Biol. Cell 23 , 213 – 224 ( 2012 ). OpenUrl Abstract / FREE Full Text 94. ↵ Ishikawa , K. -i. Prediction of the Coding Sequences of Unidentified Human Genes. VIII. 78 New cDNA Clones from Brain Which Code for Large Proteins in vitro . DNA Res . 4 , 307 – 313 ( 1997 ). OpenUrl CrossRef PubMed 95. ↵ Sun , X. , Perlick , H. A. , Dietz , H. C. & Maquat , L. E. A mutated human homologue to yeast Upf1 protein has a dominant-negative effect on the decay of nonsensecontaining mRNAs in mammalian cells . Proc. Natl. Acad. Sci . 95 , 10009 – 10014 ( 1998 ). OpenUrl Abstract / FREE Full Text 96. ↵ Kim , Y. K. & Maquat , L. E. UPFront and center in RNA decay: UPF1 in nonsense-mediated mRNA decay and beyond . RNA 25 , 407 – 422 ( 2019 ). OpenUrl Abstract / FREE Full Text 97. ↵ Langer , L. M. et al. UPF1 helicase orchestrates mutually exclusive interactions with the SMG6 endonuclease and UPF2 . Nucleic Acids Res . 52 , 6036 – 6048 ( 2024 ). OpenUrl CrossRef PubMed 98. ↵ Nicolet , B. P. & Wolkers , M. C. The relationship of mRNA with protein expression in CD8+ T cells associates with gene class and gene characteristics . PLOS ONE 17 , e0276294 ( 2022 ). OpenUrl CrossRef PubMed 99. ↵ Kreft , I. C. et al. Variant mapping using mass spectrometry–based proteotyping as a diagnostic tool in von Willebrand disease . J. Thromb. Haemost . 22 , 1894 – 1908 ( 2024 ). OpenUrl PubMed 100. ↵ Laan , S. N. J. et al. von Willebrand disease-specific defects and proteomic signatures in endothelial colony–forming cells . J. Thromb. Haemost . ( 2025 ) doi: 10.1016/j.jtha.2025.04.024 . OpenUrl CrossRef 101. ↵ Groten , S. A. , Smit , E. R. , Van Den Biggelaar , M. & Hoogendijk , A. J. The proteomic landscape of in vitro cultured endothelial cells across vascular beds . Commun. Biol . 7 , ( 2024 ). 102. ↵ Yu , G. , Wang , L.-G. , Han , Y. & He , Q.-Y. clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters . OMICS J. Integr. Biol . 16 , 284 – 287 ( 2012 ). OpenUrl CrossRef PubMed 103. Cox , J. et al. Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment . J. Proteome Res . 10 , 1794 – 1805 ( 2011 ). OpenUrl CrossRef PubMed Web of Science 104. ↵ Perez-Riverol , Y. et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences . Nucleic Acids Res . 50 , D543 – D552 ( 2022 ). OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted July 27, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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