Delineating the mechanistic principles by which multi-module receptors drive complex biological processes is important both for our broad understanding of receptor biology and for guiding biomimetic engineering of synthetic receptors for therapeutic purposes. For example, the design of conventional single-chain chimeric antigen receptors (CARs) utilized in CAR-T cell therapy was informed by an early 1990s understanding of receptor biology and bears little resemblance to the multi-module receptors that naturally drive T cell responses. These design differences necessitate that conventional CARs integrate with the T cell intracellular signaling machinery differently than their natural counterparts, leading us to posit that fully coopting T cell functions with synthetic receptors requires that they be designed to integrate with the T cell’s intracellular signaling machinery in a way that mimics the native receptors (Harris and Kranz, 2016; Kobayashi et al., 2020; Wu et al., 2020). Achieving this goal requires a more complete understanding of the multi-module receptors that mediate antigen-specific T cell activation. In particular, understanding how CD4⁺ T cells respond to antigen is important for informing biomimetic engineering of CARs because recent data show that redirecting CD4⁺ T cells with CARs is important for long-lived therapeutic efficacy (Melenhorst et al., 2022).

CD4⁺ T cells are driven by five-module receptors that recognize peptide antigens embedded within MHCII (pMHCII). Each naïve CD4⁺ T cell expresses a clonotypic receptor module, called the T cell receptor (TCR), that binds specifically to unique features of composite pMHCII surfaces (Kuhns and Badgandi, 2012; Kuhns and Davis, 2012). The TCR lacks intracellular signaling domains and instead assembles with three signaling modules (CD3γε, δε, and ζζ) that have immunoreceptor tyrosine-based activation motifs (ITAMs), and other motifs, to connect TCR-pMHCII interactions to the intracellular signaling apparatus. CD4 is the coreceptor module. It binds pMHCII in a reciprocal fashion with TCR-CD3 on the outside of CD4⁺ T cells and interacts with the Src kinase, LCK, via an intracellular CQC zinc clasp motif and helix (Huse et al., 1998; Kim et al., 2003). According to the TCR signaling paradigm, co-engagement of pMHCII by TCR-CD3 and CD4 positions LCK and the CD3 ITAMs in the proper spatial proximity to enable LCK phosphorylation of the CD3 ITAMs to initiate signaling (Rudd, 2021). However, when we tested this model directly we found that CD4-LCK interactions are not key determinants of CD3ζ ITAM phosphorylation (pCD3ζ) (Lee et al., 2022). Based on our work and that of other labs, including a study showing that C-terminally truncated CD4 lacking the motifs that mediate CD4-LCK interactions can nevertheless drive CD4⁺ T cell development, proliferation, and T-helper functions, we consider the question of how intracellular pMHCII-specific CD4⁺ T cell signaling is initiated to be unresolved (Glassman et al., 2018; Horkova et al., 2023; Killeen and Littman, 1993; Xu and Littman, 1993).

The answer to this question lies with how the five modules of pMHCII receptors evolved over ~435 million years to refine pMHCII-specific signaling. We have therefore taken a systems biology approach to computationally analyze data from real-world experiments performed in 99 jawed vertebrate species under a variety of conditions over the course of evolution. By computationally reconstructing the evolutionary history of CD4, we identify residues and motifs in the extracellular, transmembrane, and intracellular domains that are of predicted to be functionally important (Lee et al., 2022). We then cross-validated these findings using a well-established T cell hybridoma experimental system (58α^-β^- cells) wherein we performed structure-function analysis of mutants of key motifs to infer their function by evaluating the phenotypes of the mutants relative to the wildtype (WT). When we mutated the intracellular CQC clasp and IKRLL helix motifs that are known to mediate CD4-LCK interactions, we observed significant reductions in CD4-LCK association; yet we did not observe the predicted decrease in CD3ζ ITAM phosphorylation (pCD3ζ). In contrast, when we mutated the transmembrane GGXXG motif and cytoplasmic juxtamembrane (C/F)CV+C palmitoylation motif, we observed an increased frequency of CD4-LCK pairs that were preferentially localized to detergent soluble membrane (DSM) domains. We also found evidence of reduced CD3ζ, ZAP70, and PLCγ1 phosphorylation (pCD3ζ, pZAP70, and pPLCγ1) compared with WT CD4. The reason that constituent residues of these motifs evolved under purifying selection therefore appears to be due to reduced signaling in response to pMHCII when the motifs are altered. We took these data as evidence that the GGXXG and (C/F)CV+C motifs arose in eutherians (placental mammals) to tightly regulate pMHCII-specific signal initiation and postulated that the mutant CD4 molecules might sequester LCK away from TCR-CD3 to prevent CD3ζ phosphorylation. An alternative hypothesis is that the GGXXG and (C/F)CV+C motifs impact pCD3ζ levels independently of CD4-LCK interactions.

In the present study we tested these hypotheses by studying the impact of GGXXG and (C/F)CV+C motif mutants individually, or together, on proximal pMHCII-specific signaling in C-terminally truncated CD4 molecules (T1). Working with T1 eliminated motifs in the intracellular domain, including those that mediate CD4-LCK association. As expected from prior work, truncating CD4 relieved CD4-LCK interactions and reduced IL-2 production. However, there was no impact on the average pCD3ζ levels or the percent of cells with phosphorylated CD3ζ when compared with WT CD4, despite significantly reduced levels of CD4-LCK pairs. These data provide further evidence that the core tenet of the TCR signaling paradigm, wherein CD4 recruits LCK to CD3 ITAMs initiates signaling, needs revising. Importantly, T1 molecules bearing mutations in the GGXXG and (C/F)CV+C motifs individually or together reduced IL-2 production as well as pCD3ζ levels and other TCR-proximal signaling events. The simplest interpretation of these data is that the GGXXG and (C/F)CV+C motifs are key determinants of pMHCII-specific signaling on their own, independent of CD4-LCK interactions.

The goal of this study was to evaluate if the transmembrane GGXXG and juxtamembrane (C/F)CV+C palmitoylation motifs, which are highly conserved in eutherians (placental mammals) and contain constituent residues under purifying selection (Figure 1), influence pMHCII-specific proximal signaling on their own rather than by sequestering CD4-LCK pairs away from membrane rafts. Accordingly, we used 58α^-β^- T cell hybridomas transduced to express the 5c.c7 TCR along with either the WT or mutant CD4 constructs described in Table 1. The well-characterized 5c.c7 TCR recognizes the moth cytochrome c (MCC) peptide 88–103 in the context of mouse MHCII I-E^k. We used this experimental system because 58α^-β^- cells were used in seminal work to link CD4-LCK association to IL-2 production (Glaichenhaus et al., 1991). Specifically, mutating or removing the intracellular CQC clasp motif was shown in that study to reduce both CD4-LCK association and signaling output as measured by IL-2 production. We confirmed those findings in our recent study (Lee et al., 2022).

Figure 1

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WT and T1 equivalently enhance pMHCII-specific proximal signaling

Previously we reported that the C-terminally truncated mouse CD4 T1 mutant (Table 1 and Figure 1 arrow) reduces but does not prevent IL-2 production relative to WT CD4 despite lacking most of the intracellular domain, including the CQC clasp and IKRLL motifs shown to mediate CD4-LCK association (Lee et al., 2022). Because T1 maintains both the transmembrane GGXXG and juxtamembrane (C/F)CV+C motifs, we reasoned that it had utility for studying the contributions these motifs make to pMHCII-specific signaling in the absence of CD4-LCK interactions.

Accordingly, we generated 5c.c7⁺ 58α^-β^- cells expressing WT CD4, T1, or T1 combined with our previously reported Δbind mutant that disrupts binding to pMHCII in the CD4 D1 domain. This T1^Δbind mutant allowed us to eliminate the contributions of CD4-pMHCII interactions as well as CD4-LCK interactions (Table 1 and Figure 2—figure supplement 1; Glassman et al., 2016; Glassman et al., 2018; Parrish et al., 2015). The goals were to: (1) determine if T1 significantly impairs pMHCII-specific signaling events, relative to WT, as expected because it lacks the CQC and IKRLL motifs that mediate CD4-LCK interactions; (2) use T1^Δbind to establish the baseline above which pMHCII-dependent assembly of CD4 with TCR-CD3 contributes to signaling. The T1 mutant produced measurable IL-2 (response magnitude ~61% of WT, Table 2) that was consistently reduced across a range of MCC peptide concentrations compared to WT, whereas the T1^Δbind mutant did not produce IL-2. We also observed reduced pMHCII-specific endocytosis for T1 and T1^Δbind compared to WT (Figure 2A and Figure 2—figure supplement 2). These data indicate that, in our system, extracellular CD4-pMHCII engagement is essential for IL-2 production while the CD4 intracellular domain C-terminal of R422 contributes to IL-2 production, but is not absolutely necessary in the absence of other elements of the intracellular domain.

Table 2

	IL-2 AUC	IL-241 nMMCC	pCD3ζ MFI	pCD3ζResponse	pZAP70MFI	pZAP70Response	pPLCγ1MFI	pPLCγ1Response	LCK DRM AUC	LCK Total AUC	LCK DSM AUC	CD4MFI	CD4 endocytosis
WT	100	100	100	100	100	100	100	100	100	100	100	100	100
T1	61	14	106	95	106	102	104	113	29	19	16	110	14

1. IL-2 area under the curve (AUC) = response magnitude; IL-2 41 nM moth cytochrome c (MCC) = sensitivity; pCD3ζ, pZAP70, and pPLCγ1 MFI = intensity; pCD3ζ, pZAP70, and pPLCγ1 response = % responders; LCK DRM (detergent resistant membrane), DSM (detergent soluble membrane), and total AUC = sucrose gradient analysis; CD4 MFI = cell surface expression intensity; CD4 endocytosis=activation-induced internalization.

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Next, we evaluated the impact of these mutations on proximal signaling events measured by flow cytometry in response to antigen presenting cells (APCs) expressing agonist MCC:I-E^k after background subtraction of signal levels in response to APCs expressing null Hb:I-E^k (Figure 2—figure supplement 3). Our analysis of five independently generated pairs of WT and T1 cell lines showed variable difference in pCD3ζ levels. For three pairs of lines, the T1 response was higher than the WT and for two it was lower (Figure 2B). The averages of the T1 response normalized to the paired WT controls showed no difference between the population. There were also no differences for the average percent of T1 cells with response levels to MCC that were above background responses to the null Hb peptide compared to their paired WT controls. Similar results were observed for pZAP70 and pPLCγ1 levels (Figure 2—figure supplement 4). These data, summarized in Table 2, provide evidence that CD4-LCK interactions via the CQC clasp and IKRLL motifs are not key determinants of pMHCII-specific early signal events in this experimental system.

In comparison, the T1^Δbind mutant significantly reduced pCD3ζ, pZAP70, or pPLCγ1 levels compared to the T1 control but, interestingly, did not impact the percent of cells responding to agonist MCC:I-E^k (Figure 2C and Figure 2—figure supplement 5). Because the antibodies used to detect pCD3ζ, pZAP70, and pPLCγ1 recognize single phosphorylated tyrosines, these data indicate that CD4 enhances the number of TCR-CD3 complexes per cell with phosphorylated CD3ζ molecules at Y142 (same in human and mouse), as well as the number of phosphorylated ZAP70 and PLCγ1 molecules per cell, in a manner that is dependent on TCR-CD3 and CD4 co-engagement of pMHCII but independent of the intracellular domain C-terminal of R422. A summary of these results can be found in Table 3. A key takeaway is that the average pCD3ζ levels for three independently generated T1^Δbind mutants was 54.86% of the T1 control (p<0.001). CD3ζ phosphorylation therefore occurs upon TCR engagement of cognate pMHCII in the absence of CD4-pMHCII interactions, and CD4 engagement of pMHCII increases the number of phosphorylated TCR-CD3 complexes. Importantly, the pCD3ζ levels observed in the T1^Δbind mutants is subthreshold for IL-2 production at the bulk population level. These results allow us to conclude that pMHCII-specific baseline pCD3ζ levels achieved in our system in the absence of CD4-pMHCII interactions are approximately half of that achieved when CD4 can interact with pMHCII on the outside of the cell. Furthermore, consistent with the kinetic discrimination model of T cell activation, CD4-pMHCII interactions on the outside of the cell appear to be essential to push pCD3ζ levels above the threshold level needed for IL-2 production (Rabinowitz et al., 1996).

Table 3

	IL-2 AUC	IL-241 nMMCC	pCD3ζ MFI	pCD3ζResponse	pZAP70MFI	pZAP70Response	pPLCγ1MFI	pPLCγ1Response	LCK DRM AUC	LCK Total AUC	LCK DSM AUC	CD4MFI	CD4 endocytosis
T1	100	100	100	100	100	100	100	100	100	100	100	100	100
T1 Δbind	2.0	0.00	55	91	72	112	74	107	ND	ND	ND	74	39
TMD	54	17	77	94	88	99	88	107	62	93	111	131	4.0
Palm (2C)	58	117	76	94	86	93	92	98	274	139	131	80	–98
TP (2C)	33	0.2	63	86	80	76	83	83	121	113	115	97	48
Palm (3C)	28	0.2	71	95	86	77	80	89	138	94	92	88	7.0
TP (3C)	32	46	62	85	80	67	71	83	87	101	103	80	–55

1. IL-2 area under the curve (AUC) = response magnitude; IL-2 41 nM moth cytochrome c (MCC) = sensitivity; pCD3ζ, pZAP70, and pPLCγ1 MFI = intensity; pCD3ζ, pZAP70, and pPLCγ1 response = % responders; LCK DRM (detergent resistant membrane), DSM (detergent soluble membrane), and total AUC = sucrose gradient analysis; CD4 MFI = cell surface expression intensity; CD4 endocytosis=activation-induced internalization.

The GGXXG and (C/F)CV+C motifs influence IL-2 production independently of LCK

Having established that T1 contributes to early pMHCII-specific signaling (e.g. pCD3ζ) as well as signaling output (i.e. IL-2), we generated 5c.c7⁺ 58α^-β^- cells expressing WT, T1, or T1 bearing mutants of the GGXXG and/or (C/F)CV+C motifs (Table 1 and Figure 3—figure supplement 1). To study the function of the GGXXG motif alone, we used our previously described TMD mutant (GGXXG to GVXXL) wherein bulky side chains replace the glycines that compose a flat surface on the transmembrane helix that could mediate protein:protein or protein:cholesterol interactions (Fessler, 2016; Parrish et al., 2015; Song et al., 2014; Teese and Langosch, 2015; Wacker et al., 2013). We called these mutants T1-TMD. To test the function of the palmitoylation motif, which contains the core CVRC (418–421) sequence in mouse and humans, we made note of a broader (C/F)CVRC motif in eutherians wherein the majority of ortholog sequences had CCVRC, as found in mouse, and a minority had FCVRC as found in humans. Importantly, position 417 is under purifying selection for all extant CD4 orthologs we analyzed by the fixed effects likelihood (FEL) method (Figure 1), indicating that this residue evolved under purifying selection due to functional importance (Lee et al., 2022). We therefore made T1-Palm (3C) mutants wherein we mutated all three cysteines to serines as well as T1-Palm (2C) mutants where only the core cysteines common to mouse and humans were mutated to serines. Finally, we made mutants containing both the TMD and Palm mutants that we called T1-TP(3C) or T1-TP(2C).

To confirm that truncating CD4 relieved CD4-LCK association, and evaluate if the mutations impacted any residual CD4-LCK interactions that may occur due to colocalization in particular membrane domains, we performed sucrose gradient analysis of detergent lysates of WT, T1, T1-TMD, T1-Palm(2C), T1-Palm(3C), T1-TP(2C), and T1-TP(3C). We observed significantly reduced CD4-LCK association in detergent resistant membranes (DRMs, aka membrane rafts) and DSMs for T1 compared to WT (Figure 3—figure supplement 2; Pike, 2006). The mutants did not impact residual CD4-LCK association in the DRMs or DSMs. Furthermore, the total LCK signal associated with CD4 was significantly lower for T1 compared with the WT, and the residual association was not impacted by the mutants (Table 3 and Figure 3—figure supplement 3). We therefore conclude that, in our experimental system, any functional differences between the T1-TMD, T1-Palm(2C/3C), or T1-TP(2C/3C) mutants and the T1 control is a function of the motif we are studying in the absence of direct CD4-LCK interactions.

Next, we measured IL-2 production by WT, T1, and CD4 mutant cells to evaluate the impact of the mutations on signaling output in response to MCC. Here again, T1 was slightly lower than WT, while the T1-TMD reduced the IL-2 response magnitude and sensitivity relative to the T1 control (Figure 3A). This was expected given our prior work showing that the TMD mutant reduced IL-2 production driven by a shorter CD4 truncation mutant (CD4T, mutated at R420) (Parrish et al., 2015). Mutating the GGXXG or (C/F)CV+C motifs individually or together did not impact pMHCII-induced TCR endocytosis relative to the T1 control, and activation-induced CD4 endocytosis was inhibited for all truncated CD4 molecules (Figure 3—figure supplement 4A and B). The T1-Palm(2C), T1-TP(2C), T1-Palm(3C), and T1-TP(3C) all reduced IL-2 production relative to T1, indicating that they also influence the LCK-independent contribution of CD4 to pMHCII-specific signaling (Figure 3B). Of note, the T1-TP(2C) IL-2 response magnitude was lower than that of T1-Palm(2C) alone, consistent with what we previously observed with the full-length versions of these CD4 mutants (Figure 3B; Lee et al., 2022). Furthermore, the T1-Palm(3C) response magnitude was lower than that of T1-Palm(2C), suggesting that the additional cysteine contributes to signaling. Together, these data indicate that the GGXXG and (C/F)CV+C motifs contribute to the signaling cascade that leads to pMHCII-specific IL-2 production when CD4 does not directly interact with LCK via the intracellular CQC clasp and IKRLL motifs. These data do not tell us where in the signaling pathway the contributions are made.

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The CD4 GGXXG and (C/F)CV+C motifs are key determinants of early signaling

To evaluate if the GGXXG and (C/F)CV+C motifs influence early signaling events, we measured pCD3ζ, pZAP70, and pPLCγ1 levels for four independently generated T1-TMD lines, three independently generated T1-Palm(2C) lines, and three independently generated T1-TP(2C) lines relative to their matched T1 controls in response to APCs expressing MCC:I-E^k (Figure 4 and Figure 4—figure supplements 1 and 2, and Table 3). For the T1-TMD, T1-Palm(2C), and T1-TP(2C) mutant lines we observed lower average levels of pCD3ζ, pZAP70, and pPLCγ1 relative to the T1 controls. These data indicate that the GGXXG and (C/F)CV+C motifs contribute to CD4’s ability to enhance proximal signaling in the absence of CD4-LCK interactions.

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Of note, when we considered the average fold change of the means of the independently generated mutant cell lines relative to their paired T1 controls, we included the mean of the fold change of the T1^Δbind mutant cells from Figure 2C relative to their paired T1 controls for comparison (dotted line, Figure 4B and Figure 4—figure supplements 1B and 2B). Doing so provides visual boundaries within which to consider the contribution that CD4 T1, which can bind pMHCII, makes to proximal signaling in relationship to T1^Δbind (dotted line) that cannot bind pMHCII. These boundaries then provide an additional guide for evaluating the impact of the mutations on proximal signaling. For example, the T1-TMD and T1-Palm(2C) showed reduced pCD3ζ levels midway between T1 and T1^Δbind, which equated to a reduced contribution to pCD3ζ levels made by these mutants to ~48% of the contribution to pCD3ζ levels made by T1, while the T1-TP(2C) contributed ~16% of the pCD3ζ levels contributed by T1. The double mutant therefore nearly eliminated the contribution made by T1 to pCD3ζ levels. Although these are relative comparisons, the data further suggest a role for the GGXXG and (C/F)CV+C motifs in contributing to CD4-mediated pMHCII-specific signaling in the absence of CD4-LCK interactions.

Because we had found that T1^Δbind cells failed to produce IL-2 in response to high peptide concentrations in our dose-response experiments (Figure 1A), whereas the T1-TP(2C) mutant cells made IL-2 in response to high peptide concentrations (Figure 3B), we found it interesting that the early signaling responses we measured to APCs expressing high ligand densities of tethered pMHCII were similar between the T1^Δbind and T1-TP(2C) cells. We therefore evaluated IL-2 production of the T1, T1^Δbind, and T1-TP(2C) in response to the APCs used in these signaling studies to ask if they followed the same pattern of responses to high peptide doses in our prior experiments. We found that the T1-TP(2C) cells made approximately half as much IL-2 as the T1 controls, whereas IL-2 production by the T1^Δbind cells was negligible (Figure 4—figure supplement 3). These data indicate that the ability of CD4 to interact with pMHCII can partially overcome the loss of function of the GGXXG and (C/F)CV+C motifs at high ligand densities with respect to a downstream signaling output, such as IL-2 production, even if the early signaling events measured at 2 min after pMHCII engagement were similar.

Finally, we considered the percent of responders (i.e. those cells that signaled above background) for the mutant cell lines. We observed small decreases in responding T1-TP(2C) cells for pCD3ζ and pZAP70 relative to the control, while pPLCγ1 trended lower for this mutant. These data indicate that mutating the GGXXG and (C/F)CV+C motifs together in the absence of the intracellular domain can reduce the number of cells that initiate pMHCII-specific signaling (Figure 4C and Figure 4—figure supplements 1C and 2C). We did not observe this phenotype in our prior study when the intracellular domain was present, suggesting this is unique to the absence of the intracellular domain (Lee et al., 2022).

We also measured early signaling for three independently generated T1-Palm(3C) and T1-TP(3C) mutant cell lines compared to their paired T1 controls. For all lines, pCD3ζ, pZAP70, and pPLCγ1 levels were significantly lower than the controls, which was reflected in the normalized fold change (Figure 5A and B and Figure 5—figure supplement 1A and B, 2A and B). The fold change for the T1-TP(3C) levels relative to the T1 was similar to T1-TP(2C) (Figure 5B and Figure 5—figure supplements 1B and 2B and Table 3). These data provide further evidence that the GGXXG and (C/F)CV+C motifs together are key determinants of the LCK-independent contributions that CD4 makes to amplifying pMHCII-specific proximal signaling.

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Finally, the percent responders were lower for pCD3ζ and pZAP70 for T1-TP(3C) compared to WT (Figure 5 and Figure 5—figure supplements 1C and 2C). These data are consistent with the idea that, in the absence of the intracellular domain, mutating these motifs influences initiation of key signaling events. See Table 3 for a summary of the data.

Our computational reconstruction of CD4 evolution provided access to a subset of results from experiments that Nature performed over ~435 million years in a greater variety of jawed vertebrates, in the wild, than could be achieved with mouse or human samples in the lab (Lee et al., 2022). It allowed us to identify residues that were selected under purifying selection, and are thus predicted to be functionally significant, because acquiring a mutation at these residues would affect fitness and results in a failure to propagate the change to future progeny. Our structure-function analysis of these residues in 58α^-β^- cells, which we continued in this study, provided a second system to cross-validate the functional significance of particular residues and allowed us to start providing mechanistic insights into why these residues are important.

The data presented here are not consistent with predictions of the prevailing model in which CD4 recruits LCK to CD3 ITAMs to initiate signaling. Importantly, this study builds upon our prior work by showing that while reducing CD4-LCK interactions reduces pMHCII-specific IL-2 production in our system, either by mutating the CQC clasp or removing the CD4 intracellular domain entirely, this cannot be attributed to reductions in pCD3ζ levels as predicted by the TCR signaling paradigm (Lee et al., 2022). It is notable that, in the absence of CD4-pMHCII interactions (T1^Δbind mutant), pCD3ζ levels were roughly half of those measured when CD4-pMHCII interactions occurred (T1 mutant). We take these data together as evidence that there is a threshold for the number of phosphorylated CD3ζ molecules, and/or the duration of signaling, that is required to drive IL-2 production in our system. We also conclude that CD4 binding to pMHCII functions to increase the number of TCR-CD3 complexes per cell that experience CD3ζ phosphorylation to reach that threshold and/or sustain signaling. Furthermore, CD4-pMHCII interactions similarly impact pZAP70 and pPLCγ1 levels, albeit to a lesser extent for the latter.

A logical extension of these conclusions is that there is a density of agonist pMHCII below which CD4 binding to pMHCII is essential for signal initiation. This idea is supported by findings that CD4 is necessary for signal propagation and amplification to the calcium mobilization step in response to fewer than 25 agonist pMHCII (Irvine et al., 2002). Attempting to accurately measure the contributions of CD4 to ITAM phosphorylation, with or without mutant motifs, at very low densities of pMHCII would be challenging and is technically beyond the scope of this study. Nevertheless, in this and our prior publication we did not observe the expected decreases in pCD3ζ levels when CD4-LCK interactions were reduced by mutating the CQC clasp motif, the IKRLL motif, or by eliminating the intracellular domain. Furthermore, we found in our prior study that IL-2 production increased significantly when we mutated the full intracellular helix, the helix and CQC clasp together, or the IKRLL motif alone. These direct tests of the TCR signaling paradigm are inconsistent with predictions of the model. As such, they suggest that the contributions of CD4 to signal initiation and amplification at the early timepoint we measured are independent of direct CD4-LCK interactions.

Because the combined impact of mutating the GGXXG and (C/F)CV+C motifs reduced pCD3ζ levels to ~16% of the contribution of CD4-pMHCII engagement, even when direct CD4-LCK interactions were absent, we can conclude that the GGXXG and (C/F)CV+C motifs are key determinants that help CD4 drive signaling above the threshold required for IL-2 production. Our data also provide evidence that mutating the GGXXG and (C/F)CV+C motifs together can reduce the frequency of cells experiencing CD3ζ and ZAP70 phosphorylation in the absence of the intracellular domain. Given that palmitoylation is rapidly reversible, these data suggest that the switch-like function of the palmitoylation motif may influence signal initiation. Interestingly, unlike CD4-pMHCII interactions, these motifs are not essential for signaling output at high ligand densities as we observed low levels of IL-2 produced in cells bearing mutants of these motifs with increasing doses of agonist pMHCII. Binding of CD4 and TCR-CD3 to pMHCII can therefore amplify pMHCII-specific signaling without the contributions of the GGXXG and (C/F)CV+C motifs, or motifs in the intracellular domain, albeit at lower levels than if these motifs are intact. This could be due to the contributions of additional unidentified motifs in the transmembrane domain, or to the increase in TCR-CD3 dwell time on pMHCII that is mediated by the CD4 extracellular domain (Glassman et al., 2018). Given that these motifs were essential for IL-2 production at low ligand densities, a logical extension of these data is that there is an agonist pMHCII density below which these motifs are essential for the initiation of early signaling events.

The data from our current study indicate that the GGXXG and (C/F)CV+C motifs do not influence early signaling events by regulating the ability of CD4 to either recruit LCK to, or sequester it away from, the TCR-CD3 ITAMs as we previously postulated (Lee et al., 2022). Instead, they work independently of CD4-LCK interactions to enhance pMHCII-specific signaling. It is tempting to speculate that because these motifs can regulate CD4 membrane domain localization and, because in other proteins palmitate moieties can sandwich cholesterol against the flat surface of a GG patch on a transmembrane domain helix, these motifs may allow CD4 to regulate local concentration of cholesterol or cholesterol sulfate around TCR-CD3, perhaps by taking such molecules away from TCR-CD3 to stabilize allosteric changes associated with signaling (Chen et al., 2022; Fessler, 2016; Gil et al., 2002; Lee et al., 2015; Song et al., 2014; Swamy et al., 2016; Teese and Langosch, 2015; Wacker et al., 2013; Wang et al., 2016). These CD4 motifs may also impact the accessibility of ITAMs for LCK phosphorylation by changing the local membrane environment around TCR-CD3 upon reciprocal engagement of pMHCII (Aivazian and Stern, 2000; Xu et al., 2008). Finally, it is worth considering that, in our prior study, we found G402 of the GGXXG motif has covaried over evolutionary time with L438 of the intracellular helix IKRLL motif that has inhibitory function, suggesting that the function of these motifs are co-evolving (Lee et al., 2022). In support of this idea, we reported that the signal enhancing activity of the GGXXG and (C/F)CV+C motifs together counterbalanced the inhibitory activity of the IKRLL motif with regard to IL-2 production. It is unclear at this point if this counterbalancing is simply additive of individual motif functions, or if there is a functional interplay of the motifs when the intracellular domain of CD4 is present. Delineating the mechanistic details by which the GGXXG and (C/F)CV+C motifs increase signaling on their own, or when combined, and how their function integrates with those of other motifs will be the subject of future investigations.

Altogether, the data in this and our preceding study provide direct evidence that complement indirect results indicating that pMHCII-specific signaling is not initiated, or dependent on, CD4 recruitment of LCK kinase activity to the CD3 ITAMs (Glassman et al., 2018; Horkova et al., 2023; Killeen and Littman, 1993; Lee et al., 2022; Xu and Littman, 1993). Recent work on CD8 suggest that this coreceptor also does not recruit LCK to phosphorylate CD3 ITAMs (Casas et al., 2014; Wei et al., 2020). In total, these findings suggest that the TCR signaling paradigm needs revising as it pertains to coreceptor-LCK interactions. For CD4, the existing evidence suggest that reciprocal binding of CD4 and TCR-CD3 enables free LCK and FYN to initiate signaling by phosphorylating the ITAMs and ZAP70 (Glassman et al., 2016; Glassman et al., 2018; Horkova et al., 2023; Lee et al., 2022; Salmond et al., 2009; van Oers et al., 1996). The contribution of the current study is in showing the GGXXG and (C/F)CV+C motifs work together to enhance these early signaling events independently of direct CD4-LCK interactions.

Finally, it is interesting to consider what the primary purpose of CD4-LCK interactions via the CQC clasp and IKRLL motifs are if not to recruit LCK kinase activity to the CD3 ITAMs. Data from two studies suggest that LCK makes a kinase-independent contribution to pMHCII-specific responses; it is therefore plausible that the phenotype reported for CD4 when the CQC clasp is mutated is due to relieving the kinase-independent scaffolding function of LCK (Horkova et al., 2023; Xu and Littman, 1993). In addition, CD4-LCK interaction via the CQC clasp motif and the intracellular helix will prevent the helix from interacting with other partners, thus mutating the CQC clasp would favor helix interactions with other putative partners (Lee et al., 2022). Finally, the CQC clasp is thought to play a role in positioning CD4 in proximity to LAT, either through direct or indirect interactions, which could contribute to the phenotype of CQC clasp mutants (Bosselut et al., 1999; Lo et al., 2018; Lo et al., 2019). If CD4-LCK and CD4-LAT interactions are mutually exclusive, and both require the CQC clasp, then mutating this motif would have a greater impact than just relieving CD4-LCK interactions (Bosselut et al., 1999). These questions remain outstanding, as do questions concerning how the intracellular helix regulates pMHCII-specific signaling.

In closing, the broader implications of the data presented in our current and prior evolution-structure-function studies provide fertile ground for future directions beyond those mentioned above. Our approach allows us to identify functionally important residues/motif and then interrogate basic mechanistic principles of their function that should hold true across CD4⁺ T cell subsets since the biochemical and biophysical bases for interactions between CD4 and its interacting partners (i.e. other proteins or membrane components) should be the same in 58α^-β^- cells as in thymocytes or in different CD4⁺ T cells. However, different CD4⁺ T cell populations will have different levels of expression of relevant proteins (e.g. interacting partners or modifying enzymes), and may even have differences in lipid composition, such that the impact of the motifs we have identified and studied to date may lead to different outcomes in different CD4⁺ T cell subsets (http://immpres.co.uk/) (Tuosto and Xu, 2018). For example, differences in expression of enzymes with switch-like functions, such as those that add or remove palmitate to the (C/F)CV+C motif, or those that phosphorylate or dephosphorylate the serines in the intracellular helix that regulate CD4-LCK interactions and inhibitory activity, may vary between naïve CD4⁺ T cells, different Th subsets, or Tregs to differentially tune the activity of these motifs and their impact on pMHCII-specific signaling. Now that we have identified residues and broader motifs that have proven to be functionally important in vivo over 435 million years of evolution, while also providing mechanistic insights as to why the residues are important, there is utility in evaluating the impact of mutant CD4 mice on thymocyte development, naïve CD4⁺ T cell activation and differentiation, and the execution of Th and Treg effector functions. In so doing, we will gain additional insights into why there is a fitness cost if a mutation is acquired in these residues. Such knowledge will increase our fundamental understanding of CD4⁺ T cell biology and will also be critical for biomimetic engineering of synthetic receptors that can redirect CD4⁺ T cell activity for therapeutic purposes.

Raw data, include alignments and phylogenetic trees associated with Figure 1 as well as the source data associated statistical values shown in remaining figures, are available on Dryad (see Dryad: https://doi.org/10.5061/dryad.p8cz8w9vw).

1. 1. Lee M
   2. Tuohy P
   3. Kim C
   4. Yost P
   5. Lichauco K
   6. Parrish H
   7. Van Doorslaer K
   8. Kuhns M

   (2024) Dryad Digital Repository

   Data for: The CD4 transmembrane GGXXG and juxtamembrane (C/F)CV+C motifs mediate pMHCII-specific signaling independently of CD4-Lck interactions.

   https://doi.org/10.5061/dryad.p8cz8w9vw

1. 1. Aivazian D
   2. Stern LJ

   (2000) Phosphorylation of T cell receptor zeta is regulated by a lipid dependent folding transition

   Nature Structural Biology 7:1023–1026.

   https://doi.org/10.1038/80930

   • PubMed
   • Google Scholar
2. 1. Ashkenazy H
   2. Erez E
   3. Martz E
   4. Pupko T
   5. Ben-Tal N

   (2010) ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids

   Nucleic Acids Research 38:W529–W533.

   https://doi.org/10.1093/nar/gkq399

   • PubMed
   • Google Scholar
3. 1. Ashkenazy H
   2. Abadi S
   3. Martz E
   4. Chay O
   5. Mayrose I
   6. Pupko T
   7. Ben-Tal N

   (2016) ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules

   Nucleic Acids Research 44:W344–W350.

   https://doi.org/10.1093/nar/gkw408

   • PubMed
   • Google Scholar
4. 1. Bosselut R
   2. Zhang W
   3. Ashe JM
   4. Kopacz JL
   5. Samelson LE
   6. Singer A

   (1999) Association of the adaptor molecule LAT with CD4 and CD8 coreceptors identifies a new coreceptor function in T cell receptor signal transduction

   The Journal of Experimental Medicine 190:1517–1526.

   https://doi.org/10.1084/jem.190.10.1517

   • PubMed
   • Google Scholar
5. 1. Buslje CM
   2. Santos J
   3. Delfino JM
   4. Nielsen M

   (2009) Correction for phylogeny, small number of observations and data redundancy improves the identification of coevolving amino acid pairs using mutual information

   Bioinformatics 25:1125–1131.

   https://doi.org/10.1093/bioinformatics/btp135

   • PubMed
   • Google Scholar
6. 1. Casas J
   2. Brzostek J
   3. Zarnitsyna VI
   4. Hong J
   5. Wei Q
   6. Hoerter JAH
   7. Fu G
   8. Ampudia J
   9. Zamoyska R
   10. Zhu C
   11. Gascoigne NRJ

   (2014) Ligand-engaged TCR is triggered by LCK not associated with CD8 coreceptor

   Nature Communications 5:5624.

   https://doi.org/10.1038/ncomms6624

   • PubMed
   • Google Scholar
7. 1. Chen Y
   2. Zhu Y
   3. Li X
   4. Gao W
   5. Zhen Z
   6. Dong D
   7. Huang B
   8. Ma Z
   9. Zhang A
   10. Song X
   11. Ma Y
   12. Guo C
   13. Zhang F
   14. Huang Z

   (2022) Cholesterol inhibits TCR signaling by directly restricting TCR-CD3 core tunnel motility

   Molecular Cell 82:1278–1287.

   https://doi.org/10.1016/j.molcel.2022.02.017

   • PubMed
   • Google Scholar
8. 1. Colell EA
   2. Iserte JA
   3. Simonetti FL
   4. Marino-Buslje C

   (2018) MISTIC2: comprehensive server to study coevolution in protein families

   Nucleic Acids Research 46:W323–W328.

   https://doi.org/10.1093/nar/gky419

   • PubMed
   • Google Scholar
9. 1. Fessler MB

   (2016) The Intracellular Cholesterol Landscape: dynamic Integrator of the Immune Response

   Trends in Immunology 37:819–830.

   https://doi.org/10.1016/j.it.2016.09.001

   • PubMed
   • Google Scholar
10. Preprint

    1. Foley G
    2. Mora A
    3. Ross CM
    4. Bottoms S
    5. Sützl L
    6. Lamprecht ML
    7. Zaugg J
    8. Essebier A
    9. Balderson B
    10. Newell R

    (2020) Identifying and Engineering Ancient Variants of Enzymes Using Graphical Representation of Ancestral Sequence Predictions (GRASP)

    bioRxiv.

    https://doi.org/10.1101/2019.12.30.891457

    • Google Scholar
11. 1. Gil D
    2. Schamel WWA
    3. Montoya M
    4. Sánchez-Madrid F
    5. Alarcón B

    (2002) Recruitment of Nck by CD3 epsilon reveals a ligand-induced conformational change essential for T cell receptor signaling and synapse formation

    Cell 109:901–912.

    https://doi.org/10.1016/s0092-8674(02)00799-7

    • PubMed
    • Google Scholar
12. 1. Glaichenhaus N
    2. Shastri N
    3. Littman DR
    4. Turner JM

    (1991) Requirement for association of p56lck with CD4 in antigen-specific signal transduction in T cells

    Cell 64:511–520.

    https://doi.org/10.1016/0092-8674(91)90235-q

    • PubMed
    • Google Scholar
13. 1. Glassman CR
    2. Parrish HL
    3. Deshpande NR
    4. Kuhns MS

    (2016) The CD4 and CD3δε cytosolic juxtamembrane regions are proximal within a compact TCR-CD3-pMHC-CD4 macrocomplex

    Journal of Immunology 196:4713–4722.

    https://doi.org/10.4049/jimmunol.1502110

    • PubMed
    • Google Scholar
14. 1. Glassman CR
    2. Parrish HL
    3. Lee MS
    4. Kuhns MS

    (2018) Reciprocal TCR-CD3 and CD4 engagement of a nucleating pmhcii stabilizes a functional receptor macrocomplex

    Cell Reports 22:1263–1275.

    https://doi.org/10.1016/j.celrep.2017.12.104

    • PubMed
    • Google Scholar
15. 1. Harris DT
    2. Kranz DM

    (2016) Adoptive T cell therapies: a comparison of t cell receptors and chimeric antigen receptors

    Trends in Pharmacological Sciences 37:220–230.

    https://doi.org/10.1016/j.tips.2015.11.004

    • PubMed
    • Google Scholar
16. 1. Holst J
    2. Wang H
    3. Eder KD
    4. Workman CJ
    5. Boyd KL
    6. Baquet Z
    7. Singh H
    8. Forbes K
    9. Chruscinski A
    10. Smeyne R
    11. van Oers NSC
    12. Utz PJ
    13. Vignali DAA

    (2008) Scalable signaling mediated by T cell antigen receptor-CD3 ITAMs ensures effective negative selection and prevents autoimmunity

    Nature Immunology 9:658–666.

    https://doi.org/10.1038/ni.1611

    • PubMed
    • Google Scholar
17. 1. Horkova V
    2. Drobek A
    3. Paprckova D
    4. Niederlova V
    5. Prasai A
    6. Uleri V
    7. Glatzova D
    8. Kraller M
    9. Cesnekova M
    10. Janusova S
    11. Salyova E
    12. Tsyklauri O
    13. Kadlecek TA
    14. Krizova K
    15. Platzer R
    16. Schober K
    17. Busch DH
    18. Weiss A
    19. Huppa JB
    20. Stepanek O

    (2023) Unique roles of co-receptor-bound LCK in helper and cytotoxic T cells

    Nature Immunology 24:174–185.

    https://doi.org/10.1038/s41590-022-01366-0

    • PubMed
    • Google Scholar
18. 1. Hur EM
    2. Son M
    3. Lee OH
    4. Choi YB
    5. Park C
    6. Lee H
    7. Yun Y

    (2003) LIME, a novel transmembrane adaptor protein, associates with p56lck and mediates T cell activation

    The Journal of Experimental Medicine 198:1463–1473.

    https://doi.org/10.1084/jem.20030232

    • PubMed
    • Google Scholar
19. 1. Huse M
    2. Eck MJ
    3. Harrison SC

    (1998) A Zn2+ ion links the cytoplasmic tail of CD4 and the N-terminal region of LCK

    The Journal of Biological Chemistry 273:18729–18733.

    https://doi.org/10.1074/jbc.273.30.18729

    • PubMed
    • Google Scholar
20. 1. Irvine DJ
    2. Purbhoo MA
    3. Krogsgaard M
    4. Davis MM

    (2002) Direct observation of ligand recognition by T cells

    Nature 419:845–849.

    https://doi.org/10.1038/nature01076

    • PubMed
    • Google Scholar
21. 1. Jones DT
    2. Taylor WR
    3. Thornton JM

    (1992) The rapid generation of mutation data matrices from protein sequences

    Computer Applications in the Biosciences 8:275–282.

    https://doi.org/10.1093/bioinformatics/8.3.275

    • PubMed
    • Google Scholar
22. 1. Katoh K
    2. Misawa K
    3. Kuma K
    4. Miyata T

    (2002) MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform

    Nucleic Acids Research 30:3059–3066.

    https://doi.org/10.1093/nar/gkf436

    • PubMed
    • Google Scholar
23. 1. Killeen N
    2. Littman DR

    (1993) Helper T-cell development in the absence of CD4-p56lck association

    Nature 364:729–732.

    https://doi.org/10.1038/364729a0

    • PubMed
    • Google Scholar
24. 1. Kim KJ
    2. Kanellopoulos-Langevin C
    3. Merwin RM
    4. Sachs DH
    5. Asofsky R

    (1979)

    Establishment and characterization of BALB/c lymphoma lines with B cell properties

    Journal of Immunology 122:549–554.

    • PubMed
    • Google Scholar
25. 1. Kim PW
    2. Sun ZYJ
    3. Blacklow SC
    4. Wagner G
    5. Eck MJ

    (2003) A zinc clasp structure tethers Lck to T cell coreceptors CD4 and CD8

    Science 301:1725–1728.

    https://doi.org/10.1126/science.1085643

    • PubMed
    • Google Scholar
26. 1. Kobayashi S
    2. Thelin MA
    3. Parrish HL
    4. Deshpande NR
    5. Lee MS
    6. Karimzadeh A
    7. Niewczas MA
    8. Serwold T
    9. Kuhns MS

    (2020) A biomimetic five-module chimeric antigen receptor (^5MCAR) designed to target and eliminate antigen-specific T cells

    PNAS 117:28950–28959.

    https://doi.org/10.1073/pnas.2012495117

    • PubMed
    • Google Scholar
27. 1. Kosakovsky Pond SL
    2. Frost SDW

    (2005) Not So Different After All: A Comparison of Methods for Detecting Amino Acid Sites Under Selection

    Molecular Biology and Evolution 22:1208–1222.

    https://doi.org/10.1093/molbev/msi105

    • Google Scholar
28. 1. Kosakovsky Pond SL
    2. Poon AFY
    3. Velazquez R
    4. Weaver S
    5. Hepler NL
    6. Murrell B
    7. Shank SD
    8. Magalis BR
    9. Bouvier D
    10. Nekrutenko A
    11. Wisotsky S
    12. Spielman SJ
    13. Frost SDW
    14. Muse SV

    (2020) HyPhy 2.5-A customizable platform for evolutionary hypothesis testing using phylogenies

    Molecular Biology and Evolution 37:295–299.

    https://doi.org/10.1093/molbev/msz197

    • PubMed
    • Google Scholar
29. 1. Kuhns MS
    2. Davis MM

    (2007) Disruption of extracellular interactions impairs T cell receptor-CD3 complex stability and signaling

    Immunity 26:357–369.

    https://doi.org/10.1016/j.immuni.2007.01.015

    • PubMed
    • Google Scholar
30. 1. Kuhns MS
    2. Badgandi HB

    (2012) Piecing together the family portrait of TCR-CD3 complexes

    Immunological Reviews 250:120–143.

    https://doi.org/10.1111/imr.12000

    • PubMed
    • Google Scholar
31. 1. Kuhns MS
    2. Davis MM

    (2012) TCR signaling emerges from the sum of many parts

    Frontiers in Immunology 3:159.

    https://doi.org/10.3389/fimmu.2012.00159

    • PubMed
    • Google Scholar
32. 1. Lee MS
    2. Glassman CR
    3. Deshpande NR
    4. Badgandi HB
    5. Parrish HL
    6. Uttamapinant C
    7. Stawski PS
    8. Ting AY
    9. Kuhns MS

    (2015) A mechanical switch couples T cell receptor triggering to the cytoplasmic Juxtamembrane Regions of CD3ζζ

    Immunity 43:227–239.

    https://doi.org/10.1016/j.immuni.2015.06.018

    • PubMed
    • Google Scholar
33. 1. Lee MS
    2. Tuohy PJ
    3. Kim CY
    4. Lichauco K
    5. Parrish HL
    6. Van Doorslaer K
    7. Kuhns MS

    (2022) Enhancing and inhibitory motifs regulate CD4 activity

    eLife 11:e79508.

    https://doi.org/10.7554/eLife.79508

    • PubMed
    • Google Scholar
34. 1. Letourneur F
    2. Malissen B

    (1989) Derivation of a T cell hybridoma variant deprived of functional T cell receptor alpha and beta chain transcripts reveals a nonfunctional alpha-mRNA of BW5147 origin

    European Journal of Immunology 19:2269–2274.

    https://doi.org/10.1002/eji.1830191214

    • PubMed
    • Google Scholar
35. 1. Lo WL
    2. Shah NH
    3. Ahsan N
    4. Horkova V
    5. Stepanek O
    6. Salomon AR
    7. Kuriyan J
    8. Weiss A

    (2018) LCK promotes ZAP70-dependent LAT phosphorylation by bridging ZAP70 to LAT

    Nature Immunology 19:733–741.

    https://doi.org/10.1038/s41590-018-0131-1

    • PubMed
    • Google Scholar
36. 1. Lo WL
    2. Shah NH
    3. Rubin SA
    4. Zhang W
    5. Horkova V
    6. Fallahee IR
    7. Stepanek O
    8. Zon LI
    9. Kuriyan J
    10. Weiss A

    (2019) Slow phosphorylation of a tyrosine residue in LAT optimizes T cell ligand discrimination

    Nature Immunology 20:1481–1493.

    https://doi.org/10.1038/s41590-019-0502-2

    • PubMed
    • Google Scholar
37. 1. Melenhorst JJ
    2. Chen GM
    3. Wang M
    4. Porter DL
    5. Chen C
    6. Collins MA
    7. Gao P
    8. Bandyopadhyay S
    9. Sun H
    10. Zhao Z
    11. Lundh S
    12. Pruteanu-Malinici I
    13. Nobles CL
    14. Maji S
    15. Frey NV
    16. Gill SI
    17. Loren AW
    18. Tian L
    19. Kulikovskaya I
    20. Gupta M
    21. Ambrose DE
    22. Davis MM
    23. Fraietta JA
    24. Brogdon JL
    25. Young RM
    26. Chew A
    27. Levine BL
    28. Siegel DL
    29. Alanio C
    30. Wherry EJ
    31. Bushman FD
    32. Lacey SF
    33. Tan K
    34. June CH

    (2022) Decade-long leukaemia remissions with persistence of CD4+ CAR T cells

    Nature 602:503–509.

    https://doi.org/10.1038/s41586-021-04390-6

    • Google Scholar
38. 1. Parrish HL
    2. Glassman CR
    3. Keenen MM
    4. Deshpande NR
    5. Bronnimann MP
    6. Kuhns MS

    (2015) A Transmembrane domain GGxxG Motif in CD4 Contributes to Its LCK-independent function but does not mediate CD4 dimerization

    PLOS ONE 10:e0132333.

    https://doi.org/10.1371/journal.pone.0132333

    • PubMed
    • Google Scholar
39. 1. Parrish HL
    2. Deshpande NR
    3. Vasic J
    4. Kuhns MS

    (2016) Functional evidence for TCR-intrinsic specificity for MHCII

    PNAS 113:3000–3005.

    https://doi.org/10.1073/pnas.1518499113

    • PubMed
    • Google Scholar
40. 1. Pike LJ

    (2006) Rafts defined: a report on the Keystone Symposium on Lipid Rafts and Cell Function

    Journal of Lipid Research 47:1597–1598.

    https://doi.org/10.1194/jlr.E600002-JLR200

    • PubMed
    • Google Scholar
41. 1. Pond SLK
    2. Frost SDW
    3. Muse SV

    (2005) HyPhy: hypothesis testing using phylogenies

    Bioinformatics 21:676–679.

    https://doi.org/10.1093/bioinformatics/bti079

    • PubMed
    • Google Scholar
42. 1. Price MN
    2. Dehal PS
    3. Arkin AP

    (2010) FastTree 2--approximately maximum-likelihood trees for large alignments

    PLOS ONE 5:e9490.

    https://doi.org/10.1371/journal.pone.0009490

    • PubMed
    • Google Scholar
43. 1. Rabinowitz JD
    2. Beeson C
    3. Lyons DS
    4. Davis MM
    5. McConnell HM

    (1996) Kinetic discrimination in T-cell activation

    PNAS 93:1401–1405.

    https://doi.org/10.1073/pnas.93.4.1401

    • PubMed
    • Google Scholar
44. 1. Rudd CE

    (2021) How the Discovery of the CD4/CD8-p56^LCK complexes changed immunology and immunotherapy

    Frontiers in Cell and Developmental Biology 9:626095.

    https://doi.org/10.3389/fcell.2021.626095

    • PubMed
    • Google Scholar
45. 1. Salmond RJ
    2. Filby A
    3. Qureshi I
    4. Caserta S
    5. Zamoyska R

    (2009) T-cell receptor proximal signaling via the Src-family kinases, LCK and Fyn, influences T-cell activation, differentiation, and tolerance

    Immunological Reviews 228:9–22.

    https://doi.org/10.1111/j.1600-065X.2008.00745.x

    • PubMed
    • Google Scholar
46. 1. Song Y
    2. Kenworthy AK
    3. Sanders CR

    (2014) Cholesterol as a co-solvent and a ligand for membrane proteins

    Protein Science 23:1–22.

    https://doi.org/10.1002/pro.2385

    • PubMed
    • Google Scholar
47. 1. Swamy M
    2. Beck-Garcia K
    3. Beck-Garcia E
    4. Hartl FA
    5. Morath A
    6. Yousefi OS
    7. Dopfer EP
    8. Molnár E
    9. Schulze AK
    10. Blanco R
    11. Borroto A
    12. Martín-Blanco N
    13. Alarcon B
    14. Höfer T
    15. Minguet S
    16. Schamel WWA

    (2016) A cholesterol-based allostery model of T cell receptor phosphorylation

    Immunity 44:1091–1101.

    https://doi.org/10.1016/j.immuni.2016.04.011

    • PubMed
    • Google Scholar
48. 1. Teese MG
    2. Langosch D

    (2015) Role of GxxxG Motifs in Transmembrane Domain Interactions

    Biochemistry 54:5125–5135.

    https://doi.org/10.1021/acs.biochem.5b00495

    • PubMed
    • Google Scholar
49. 1. Tuosto L
    2. Xu C

    (2018) Editorial: membrane lipids in T cell functions

    Frontiers in Immunology 9:1608.

    https://doi.org/10.3389/fimmu.2018.01608

    • PubMed
    • Google Scholar
50. 1. van Oers NS
    2. Lowin-Kropf B
    3. Finlay D
    4. Connolly K
    5. Weiss A

    (1996) alpha beta T cell development is abolished in mice lacking both LCK and Fyn protein tyrosine kinases

    Immunity 5:429–436.

    https://doi.org/10.1016/s1074-7613(00)80499-9

    • PubMed
    • Google Scholar
51. 1. Venkatesh B
    2. Lee AP
    3. Ravi V
    4. Maurya AK
    5. Lian MM
    6. Swann JB
    7. Ohta Y
    8. Flajnik MF
    9. Sutoh Y
    10. Kasahara M
    11. Hoon S
    12. Gangu V
    13. Roy SW
    14. Irimia M
    15. Korzh V
    16. Kondrychyn I
    17. Lim ZW
    18. Tay B-H
    19. Tohari S
    20. Kong KW
    21. Ho S
    22. Lorente-Galdos B
    23. Quilez J
    24. Marques-Bonet T
    25. Raney BJ
    26. Ingham PW
    27. Tay A
    28. Hillier LW
    29. Minx P
    30. Boehm T
    31. Wilson RK
    32. Brenner S
    33. Warren WC

    (2014) Elephant shark genome provides unique insights into gnathostome evolution

    Nature 505:174–179.

    https://doi.org/10.1038/nature12826

    • PubMed
    • Google Scholar
52. 1. Wacker D
    2. Wang C
    3. Katritch V
    4. Han GW
    5. Huang XP
    6. Vardy E
    7. McCorvy JD
    8. Jiang Y
    9. Chu M
    10. Siu FY
    11. Liu W
    12. Xu HE
    13. Cherezov V
    14. Roth BL
    15. Stevens RC

    (2013) Structural features for functional selectivity at serotonin receptors

    Science 340:615–619.

    https://doi.org/10.1126/science.1232808

    • PubMed
    • Google Scholar
53. 1. Waddell PJ
    2. Steel MA

    (1997) General time-reversible distances with unequal rates across sites: mixing gamma and inverse Gaussian distributions with invariant sites

    Molecular Phylogenetics and Evolution 8:398–414.

    https://doi.org/10.1006/mpev.1997.0452

    • PubMed
    • Google Scholar
54. 1. Wang F
    2. Beck-García K
    3. Zorzin C
    4. Schamel WWA
    5. Davis MM

    (2016) Inhibition of T cell receptor signaling by cholesterol sulfate, a naturally occurring derivative of membrane cholesterol

    Nature Immunology 17:844–850.

    https://doi.org/10.1038/ni.3462

    • PubMed
    • Google Scholar
55. 1. Weaver S
    2. Shank SD
    3. Spielman SJ
    4. Li M
    5. Muse SV
    6. Kosakovsky Pond SL

    (2018) Datamonkey 2.0: a modern web application for characterizing selective and other evolutionary processes

    Molecular Biology and Evolution 35:773–777.

    https://doi.org/10.1093/molbev/msx335

    • PubMed
    • Google Scholar
56. 1. Wei Q
    2. Brzostek J
    3. Sankaran S
    4. Casas J
    5. Hew LS-Q
    6. Yap J
    7. Zhao X
    8. Wojciech L
    9. Gascoigne NRJ

    (2020) LCK bound to coreceptor is less active than free LCK

    PNAS 117:15809–15817.

    https://doi.org/10.1073/pnas.1913334117

    • PubMed
    • Google Scholar
57. 1. Wu L
    2. Wei Q
    3. Brzostek J
    4. Gascoigne NRJ

    (2020) Signaling from T cell receptors (TCRs) and chimeric antigen receptors (CARs) on T cells

    Cellular & Molecular Immunology 17:600–612.

    https://doi.org/10.1038/s41423-020-0470-3

    • PubMed
    • Google Scholar
58. 1. Xu H
    2. Littman DR

    (1993) A kinase-independent function of LCK in potentiating antigen-specific T cell activation

    Cell 74:633–643.

    https://doi.org/10.1016/0092-8674(93)90511-n

    • PubMed
    • Google Scholar
59. 1. Xu C
    2. Gagnon E
    3. Call ME
    4. Schnell JR
    5. Schwieters CD
    6. Carman CV
    7. Chou JJ
    8. Wucherpfennig KW

    (2008) Regulation of T cell receptor activation by dynamic membrane binding of the CD3epsilon cytoplasmic tyrosine-based motif

    Cell 135:702–713.

    https://doi.org/10.1016/j.cell.2008.09.044

    • PubMed
    • Google Scholar

Author details

1. Mark S Lee

   Department of Immunobiology, The University of Arizona College of Medicine, Tucson, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

2. Peter J Tuohy

   Department of Immunobiology, The University of Arizona College of Medicine, Tucson, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Caleb Y Kim

   Department of Immunobiology, The University of Arizona College of Medicine, Tucson, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0093-3049

4. Philip P Yost

   Department of Immunobiology, The University of Arizona College of Medicine, Tucson, United States

   Contribution

   Data curation, Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Katrina Lichauco

   Department of Immunobiology, The University of Arizona College of Medicine, Tucson, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9480-2893

6. Heather L Parrish

   Department of Immunobiology, The University of Arizona College of Medicine, Tucson, United States

   Contribution

   Conceptualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Koenraad Van Doorslaer

   1. Department of Immunobiology, The University of Arizona College of Medicine, Tucson, United States
   2. School of Animal and Comparative Biomedical Sciences, The University of Arizona, Tucson, United States
   3. Cancer Biology Graduate Interdisciplinary Program and Genetics Graduate Interdisciplinary Program, The University of Arizona, Tucson, United States
   4. The BIO-5 Institute, The University of Arizona, Tucson, United States
   5. The University of Arizona Cancer Center, Tucson, United States
   6. The Arizona Center on Aging, The University of Arizona College of Medicine, Tucson, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   vandoorslaer@arizona.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2985-0733

8. Michael S Kuhns

   1. Department of Immunobiology, The University of Arizona College of Medicine, Tucson, United States
   2. Cancer Biology Graduate Interdisciplinary Program and Genetics Graduate Interdisciplinary Program, The University of Arizona, Tucson, United States
   3. The BIO-5 Institute, The University of Arizona, Tucson, United States
   4. The University of Arizona Cancer Center, Tucson, United States
   5. The Arizona Center on Aging, The University of Arizona College of Medicine, Tucson, United States

   Contribution

   Conceptualization, Data curation, Supervision, Funding acquisition, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   mkuhns@arizona.edu

   Competing interests

   Has disclosed an outside interest in Module Therapeutics to the University of Arizona. Conflicts of interest resulting from this interest are being managed by the University of Arizona in accordance with their policies

   "This ORCID iD identifies the author of this article:" 0000-0002-0403-6313

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