Enhancing and inhibitory motifs regulate CD4 activity

  1. Mark S Lee
  2. Peter J Tuohy
  3. Caleb Y Kim
  4. Katrina Lichauco
  5. Heather L Parrish
  6. Koenraad Van Doorslaer  Is a corresponding author
  7. Michael S Kuhns  Is a corresponding author
  1. Department of Immunobiology, The University of Arizona College of Medicine, United States
  2. School of Animal and Comparative Biomedical Sciences, University of Arizona, United States
  3. Cancer Biology Graduate Interdisciplinary Program and Genetics Graduate Interdisciplinary Program, The University of Arizona, United States
  4. The BIO-5 Institute, The University of Arizona, United States
  5. The University of Arizona Cancer Center, United States
  6. The Arizona Center on Aging, The University of Arizona College of Medicine, United States

Abstract

CD4+ T cells use T cell receptor (TCR)–CD3 complexes, and CD4, to respond to peptide antigens within MHCII molecules (pMHCII). We report here that, through ~435 million years of evolution in jawed vertebrates, purifying selection has shaped motifs in the extracellular, transmembrane, and intracellular domains of eutherian CD4 that enhance pMHCII responses, and covary with residues in an intracellular motif that inhibits responses. Importantly, while CD4 interactions with the Src kinase, Lck, are viewed as key to pMHCII responses, our data indicate that CD4–Lck interactions derive their importance from the counterbalancing activity of the inhibitory motif, as well as motifs that direct CD4–Lck pairs to specific membrane compartments. These results have implications for the evolution and function of complex transmembrane receptors and for biomimetic engineering.

Editor's evaluation

This paper takes an evolutionary approach to investigate the mechanisms by which CD4 regulates T-cell receptor activation and downstream functional responses. The authors identify conserved and coevolving motifs in the extracellular, transmembrane, and intracellular domains of CD4 that appear to regulate multiple aspects of its function. These findings suggest a recalibration of the perception of CD4 as simply an accessory to the central complex of T-cell receptors and CD3 in pMHC-specific T-cell responses.

https://doi.org/10.7554/eLife.79508.sa0

Introduction

The immunological ‘Big Bang’ that gave rise to RAG-based antigen receptor gene rearrangement in jawed vertebrates produced an adaptive immune system in which each naive B and T cell expresses a clonotypic B or T cell receptor (BCR or TCR) with unique antigen specificity (Bernstein et al., 1996; Flajnik, 2014). The B and T cell repertoires can therefore be thought of as combinatorial libraries from which individual clonotypes, expressing receptors specific to antigen, expand to mount a tailored response. This strategy provides jawed vertebrates with long-lived protection against microbial infection, neoplastic transformation, and is the basis for vaccines; yet it also presents the risk of reactivity against self. As a result, mechanisms have evolved to ensure that the adaptive immune system of jawed vertebrates is on high alert to respond to foreign antigens while maintaining tolerance to self.

For the CD4+ T cell repertoire, discriminating self from foreign begins in the thymus where developmental checkpoints test the strength with which a thymocyte’s clonotypic TCR interacts with composite surfaces of self-peptides embedded within class II MHC (pMHCII) (Huseby et al., 2005). TCRs that interact weakly with self-pMHCII direct positive selection toward the CD4+ lineage, while those that strongly recognize self-pMHCII induce apoptosis to establish central tolerance by removing autoreactive TCR clonotypes from the repertoire by negative selection. For CD4+ T cells that emerge from the thymus, the nature of TCR–pMHCII engagement determines their homeostasis, activation, and differentiation to one of a variety of helper (Th) or regulatory (Treg) phenotypes (Gottschalk et al., 2010; Tubo and Jenkins, 2014). These Th and Treg cells then influence the responses of a variety of other immune cell types.

The conversion of pMHCII-specific information into intracellular signals is an emergent property of five distinct modules: TCR, CD3γε, CD3δε, CD3ζζ, and CD4 (Kobayashi et al., 2020; Kuhns and Davis, 2012). The TCR is the receptor module. It deciphers information encoded within the composite pMHCII surface and relays the information to the immunoreceptor tyrosine-based activation motifs (ITAMs) of three associated signaling modules (CD3γε, CD3δε, and CD3ζζ) (Chen et al., 2022; Gil et al., 2002; Lee et al., 2015). CD4 is the coreceptor module. It binds MHCII on the outside of the CD4+ T cell and interacts with the Src kinase, Lck, via an intracellular CQC clasp motif (Kim et al., 2003; Turner et al., 1990). According to the TCR signaling paradigm, CD4 recruits Lck to phosphorylate the ITAMs of TCR–CD3 complexes when both TCR–CD3 and CD4 coincidently engage pMHCII (Rudd, 2021). Phosphorylation of the ITAMs initiates pMHCII-specific signaling, connecting TCR–CD3 complexes to the broader intracellular signaling machinery (Courtney et al., 2018; Gaud et al., 2018). In this model, the biophysical properties that govern TCR–pMHCII interactions are the key determinants for T cell fate decisions while CD4 plays a supporting role.

Recent evidence suggests however that the role of CD4 within the TCR signaling paradigm requires refinement. CD4’s extracellular domain (ECD) can increase TCR dwell time on pMHCII and position its intracellular domain (ICD) in a defined relationship with the TCR–CD3 complex through coordinated rather than coincident interactions (Glassman et al., 2016; Glassman et al., 2018; Guy and Vignali, 2009). Furthermore, CD4 molecules that are not associated with Lck have been proposed to compete with those that are to limit the number of TCR–CD3 complexes phosphorylated by Lck, thus setting a threshold for the duration of TCR–pMHCII interactions required to initiate signaling (Stepanek et al., 2014). In addition, CD4 molecules that are associated with Lck are reported to play a vital role in pMHCII restriction by sequestering Lck away from TCR–CD3 to prevent off-target signaling by TCR interactions with non-pMHCII molecules (Van Laethem et al., 2013). These models help explain how the stability and composition of TCR–CD3–pMHCII–CD4(+/−Lck) assemblies influence and regulate ITAM phosphorylation. They also suggest that CD4, which has been less-well studied than the TCR, warrants more attention.

Accordingly, we reconstructed the evolutionary history of extant CD4 homologs from boney fish, reptiles, birds, and mammals to evaluate the results of ~435 million years of CD4 evolution in jawed vertebrates. Our analyses identified five putative motifs within the CD4 transmembrane domain (TMD) and ICD that are unique to mammals, or found only in eutherians (placental mammals), and contain residues under purifying selection. Further analyses identified residues within motifs in the ECD, TMD, and ICD that have covaried over evolutionary time. Follow-on structure–function analyses revealed a paradox that cannot be explained by the current TCR signaling paradigm. Specifically, mutating the transmembrane and intracellular motifs increased CD4–Lck association and impaired CD4-driven responses. Conversely, mutating the ICD helix or a motif therein reduced CD4–Lck interactions and enhanced responses. These findings have broad implications for how multisubunit transmembrane receptors relay ligand-specific information across the cell membrane, for our understanding of CD4+ T cell biology, and for biomimetic engineering of synthetic receptors.

Results

Evolutionary analysis of CD4

We performed multiple analyses of available vertebrate CD4 ortholog sequences (n = 99 distinct sequences), representing ~435 million years of evolution, to understand how ancient and ongoing environmental challenges have influenced CD4. The analyzed sequences represent fish, reptiles (including birds), marsupials, and placental mammals. Details related to ortholog selection are outlined in Materials and methods. All sequences and files are available through the DataDryad repository associated with this manuscript. We used mouse CD4 (numbering by UniProt convention) as a reference to facilitate comparisons between evolutionary analyses and experimental studies.

Analysis of sequence conservation between the full set of extant CD4 molecules, or mammalian CD4 molecules only, showed particular conservation in the ICD (Figure 1—figure supplement 1A, B). To investigate the type of evolutionary selection shaping CD4 evolution, we determined nonsynonymous (dN) and synonymous (dS) substitution rates. Codons under diversifying selection have a dN:dS ratio >1 and those under purifying selection have a dN:dS ratio <1 (Figure 1—figure supplement 1C, D). The codon-specific dN:dS ratios were calculated using a fixed effects likelihood (FEL) method on both the full and mammalian only datasets (Kosakovsky Pond and Frost, 2005). Of the 17 codons under diversifying selection, 16 (94.1%) are distributed across the CD4 ectodomain while only one is found in the TMD. Of the 126 residues under purifying selection, 98 are distributed across the CD4 ectodomain (24.8% of all codons in the ECD). In contrast, 45.5% of TMD codons (10 of 22) and 45% (18 of 40) within the ICD are under purifying selection. These data suggest that mutating putative linear motifs within the ICD is selected against, arguing that more than just the CQC clasp is important for CD4 function (Babu et al., 2011; Capra and Singh, 2007; Dyson and Wright, 2005; Gibson, 2009; Kim et al., 2003; Tompa, 2011).

To further characterize the evolution of these motifs, we generated a maximum likelihood phylogenetic tree and predicted most recent common ancestor sequences at each node (Figure 1A and Figure 1—figure supplement 1E; Hochberg and Thornton, 2017). The conservation of specific residues in eutherian CD4 proteins is visualized using logo plot analysis to better consider positional variability of residues within this clade. Additionally, we provide a more complete picture of the pressures shaping CD4 molecules by associating the evolutionary selection signature (i.e., dN:dS ratio) with specific codons using both the mammalian only dataset, as well as all extant CD4 molecules (Figure 1B and Figure 1—figure supplement 1E). Particular attention was given to mouse and human CD4 due to their experimental and human health relevance.

Figure 1 with 1 supplement see all
Evolutionary analysis of the CD4 molecule.

(A) Reduced representation maximum likelihood phylogenetic tree clusters of CD4 sequences are shown with mouse CD4 numbering (uniprot) used as a reference. Residues are colored based on sidechain polarity. Dashes (-) indicate an evolutionary insertion or deletion event. Predicted most recent common ancestor (MRCA) sequences are shown at each node in the tree (Node 1-4). Logo plots of extant eutherian CD4 sequences are aligned at the bottom of the tree. Each stack of letters represents the sequence conservation at that position in the alignment. The height of symbols indicates the relative frequency of each amino acid at that specific position.(B) Synonymous (dS, red bars) and nonsynonymous (dN, blue bars) substitution rates within the CD4 coding sequence are shown as calculated for all CD4 orthologs included in the initial phylogenetic analysis using the Fixed Effects Likelihood (FEL) method. Only bars for which the likelihood ratio test indicated statistical significance (alpha = 0.1) are shown. Black circles show the ratio of both these values. Codons under diversifying selection have a dN:dS ratio >1. Those under purifying selection have a dN:dS ratio <1.(C) A theoretical structural model to show the relative location of the motifs discussed here. The surface rendered ECD of human CD4 (pdb 1WIQ) was joined with a connecting peptide and TMD (built using the PyMol Molecular Graphics system), and ICD (pdb 1Q68). Note here that mouse residue numbering (uniport) is used in this model for consistency with panels A–C.(D) Covarying residues were calculated using MISTIC2. Residues that covary are indicated with a black dot and connected with a solid line. Motifs identified in this study are indicated. The logo plot represents eutherian sequences. The complete MISTIC2 results matrix is available on Dryad (https://doi.org/10.5061/dryad.59zw3r26z). Boxes are used to highlight motifs discussed in this study, while the grey shading indicates the helix-turn region within the ICD. Key: MRCA = Most Recent Common Ancestor; FEL = Fixed Effects Likelihood; dS = Synonymous; dN = nonsynonymous; ECD = Extracellular Domain; TMD = Transmembrane Domain; ICD = Intracellular Domain.

First, we asked if our analyses would highlight residues that we know to be important for CD4 function by focusing on the evolutionary history of residues in the D3 domain of the CD4 ectodomain that form a solvent-exposed nonpolar patch in 3D space, stabilize TCR–CD3–pMHCII–CD4 assemblies, and increase pMHCII-specific responses (P228, F231, and P281) (Figure 1C; Glassman et al., 2018; Wu et al., 1997). The predicted most recent common ancestor of all amniotes contains a PLXF motif (mouse 228–231) in the D3 domain that is maintained in mammals (Figure 1A, node 1). P281 is not found in the predicted amniote most recent common ancestor but is in the mammalian most recent common ancestor, and extant mammals, suggesting that it arose after mammals diverged from reptiles. Importantly, P228, L229, F231, and P281 have small dN:dS values that are primarily driven by low dN rates, indicating that changing these residues likely affects fitness. Structural analysis indicates that L229 is buried in the hydrophobic core of the D3 domain as is L282 adjacent to P281, while the solvent-exposed P228, F231, and P281 impact CD4 function (Glassman et al., 2018; Wu et al., 1997). These analyses show that our approach identified D3 residues of known functional importance.

We next turned our attention toward the TMD and ICD by focusing on motifs in eutherian CD4 that: (1) deviate from ancestral sequences as well as those of other clades; (2) contain highly conserved residues with evidence of purifying selection. For example, the predicted most recent common ancestor of all amniotes (node 1) contains a GG patch (G402 and G403) in its TMD that is lost in nonavian reptiles but present in extant birds (Figure 1A). In the predicted eutherian most recent common ancestor (node 4), the GG patch expanded into a highly conserved GGXXG motif that is present in the majority of eutherian CD4 proteins (see logo plot) including mouse and human. Importantly, G402 and G403 were found to be under purifying selection using both the full and mammal-only dataset, while G406 was only identified in the full dataset (Figure 1B and Figure 1—figure supplement 1E). Our decision tree therefore identified this motif as potentially important for CD4 function. When considered with our previous work, and work on GXXXG motifs more broadly, the GGXXG motif is likely to mediate heterotypic protein–protein or protein–cholesterol interactions (Parrish et al., 2015; Teese and Langosch, 2015).

Our approach also identified a CV +C motif (‘+’ represents a basic residue) that is highly conserved in eutherian CD4 molecules but not present in the predicted mammalian most recent common ancestor or marsupials (Figure 1A). Palmitoylation of these cysteines is reported to influence membrane raft localization, although this is controversial as association with Lck may also localize CD4 to membrane rafts (Crise and Rose, 1992; Fragoso et al., 2003; Ladygina et al., 2011). Our analyses suggest that the combination of the CV +C and the GGXXG/S motifs are unique to extant eutherians, co-arose during evolution, and may work together to regulate CD4 membrane localization. Of note, we consider it formally possible that what we are considering here as two distinct motifs may be part of one larger functional motif as the TMD and juxtamembrane regions encompassing GGXXG/S and CV +C are heavily conserved in eutherians.

A poly-basic RHRRR motif in the juxtamembrane region of the ICD was also previously reported to impact human CD4 localization to membrane rafts (Popik and Alce, 2004). Our comparative analyses suggest a core HXXR motif (mouse 423–426) with H423 and R426 under purifying selection in the full dataset, and R426 under purifying selection in the mammal-only dataset (Figure 1A, B and Figure 1—figure supplement 1E).

Further downstream, NMR has shown that the ICD of human CD4 contains a helix-turn structure, the sequence of which is highly conserved in mammalian CD4 molecules (gray shaded region, 429–442, Figure 1A, B, D; Kim et al., 2003; Willbold and Rösch, 1996). A conserved IKRLL motif is embedded within the helix. Its origins likely trace back to the predicted amniote most recent common ancestor (node 1) via the presence of a dileucine repeat. Reptiles and birds diverged away from this motif, while the most recent common ancestor of mammals (node 3) evolved a IXRLL motif that is highly conserved. This region includes several residues under purifying selection within the more limited mammalian dataset (436–440 and 442) or within the full extant CD4 dataset (R430, 434–438 that make up the IKRLL motif, S439, and 440–442 that form the turn) (Figure 1B and Figure 1—figure supplement 1E). Many of these residues are important for the helix-turn structure, while I434, L437, and L438 are reported to regulate CD4–Lck interactions and endocytosis, suggesting that they are under purifying selection due to their role in a multifunctional hub (Kim et al., 2003; Sleckman et al., 1992). Given that the helix co-arose with the D3 nonpolar patch that enhances both CD4 and TCR dwell time on pMHCII, as well as pMHCII responses, it is intriguing to speculate that the helix functions in part to counterbalance the function of the nonpolar patch (Glassman et al., 2018).

Finally, C-terminal to the CQC clasp, mammalian CD4 contains a consensus HRΦQK motif (mouse 448–452 in which Φ represents a large hydrophobic residue; Figure 1A). This putative motif is not present in extant fish, reptiles, birds, or even the marsupial CD4 orthologs sequenced to date. Yet, within the mammalian dataset, the codons for H448 and R449 were found to be under purifying selection (Figure 1B). The NMR solution structure of the CD4 ICD indicates that this region is unstructured within human CD4 (Kim et al., 2003). Given the above analyses, we propose that these residues are likely to be of functional importance.

Covariation analyses suggest coevolution of motifs in the ECD and ICD

Because some of the motifs considered above co-arose in mammals or eutherians, we explored if residues in these regions showed evidence of covariation. Constraints on protein function can lead to correlated mutations between residues in a protein that provide further evidence of their functional importance and can highlight networks of functional residues within a protein (Lockless and Ranganathan, 1999). We therefore used MISTIC2 to calculate the covariation between residues of CD4 (Colell et al., 2018; Kowarsch et al., 2010). MISTIC2 quantifies correlations using mutual information as a measure for how much information one random variable provides about another, allowing for detection of covarying relationships between residues that are spatially distant and not just those that are proximal. The exact mechanisms that lead to residue covariation are poorly understood. However, it is widely assumed that the excess of correlated changes in pairs of residues across an evolutionary tree result from molecular coevolution (Brown and Brown, 2010; Capra et al., 2010; Dunn et al., 2008; Hopf et al., 2015; Larson et al., 2000; Marks et al., 2011; Martin et al., 2005; Reynolds et al., 2011).

By analyzing the full dataset we identified five pairs of covarying residues within the ICD helix-turn region (S432–I434; S432–S439; I434–K441; L437–S439; L437–K442), which may be relevant to the structure of this region, its function, or both (Figure 1D). Interestingly, G402 covaries with L438, suggesting covariation between the TMD and ICD. Furthermore, H423 covaries with I434 and S439, and R426 covaries with S439. We also found that P228 and P281 of the nonpolar patch in the D3 domain of the ECD show strong covariation with residues in the ICD helix. Specifically, our data suggest that P228 covaried with S432, I434, S439, and K441, while P281 covaried with S432, L438, and K442. Given that these covarying residues reside in distinct regions that either preclude direct interactions (e.g., ECD, TMD, and ICD), or show no evidence of direct interactions in existing structures (Kim et al., 2003), one interpretation of these results is that the covarying residues represent a network of functional motifs that regulate CD4 activity either through the additive impact of their individual functions and/or through allosteric means.

Functional analysis of motifs

The results above suggest a fitness cost for eutherians if mutations are acquired at residues in the described motifs. Seminal structure–function analyses of CD4 in 58αβ T cell hybridomas established a link between CD4–Lck interactions via the CQC clasp and IL-2 production (Glaichenhaus et al., 1991). We therefore performed similar analyses to ask if there is a functional interplay between the transmembrane GGXXG and juxtamembrane CV +C motifs that co-arose in eutherians and may be part of a larger, more continuous functional unit. We also analyzed the IKRLL motif, S432, and S439 residue of the intracellular helix as prior work and our covariation analysis suggested that the intracellular helix may be a multifunctional hub (Kim et al., 2003; Sleckman et al., 1992).

The goal of our structure function analysis was to change the chemical nature of these CD4 motif and then infer their normal function from the mutant phenotype. We either mutated residues under purifying selection to alanines, reversed charges, changed cysteines to serines, changed serines to alanines to prevent phosphorylation, or changed serines to aspartic acid as a negatively charged phosphomimetic (see Figure 1C and Table 1, mutant names describe the motif targeted).

We evaluated the impact of these mutations in 58αβ cells transduced to express the 5 c.c7 TCR, which recognizes the moth cytochrome c (MCC 88–103) peptide presented in I-Ek (MCC:I-Ek), and WT or mutant CD4 molecules as per our prior work (Glassman et al., 2016; Glassman et al., 2018; Parrish et al., 2016; Parrish et al., 2015). Supplementary file 1 summarizes the impact of the panel of CD4 mutations studied here, relative to WT, for key biochemical and functional properties.

To study the impact of the mutations on membrane localization, Triton X-100 lysates were sucrose gradient fractionated. Proteins that float on the gradient localize to detergent-resistant membrane (DRM) domains rich in membrane raft components, such as cholesterol and sphingolipids including GM1 (Pike, 2006). The remaining proteins localize to detergent-soluble membrane (DSM) domains. We used immunoprecipitation and flow cytometry to quantify the percent of CD4 signal in each fraction, relative to the total, as well as the amount of GM1 or Lck signal in each fraction normalized to the CD4 signal in that fraction (Fragoso et al., 2003; Lee et al., 2021; Parrish et al., 2016). Area under the curve (AUC) was calculated for the DRM (fractions 1–5) and DSM (fractions 6–10) fractions to measure the signal localized to each fraction (Pike, 2006; Sezgin et al., 2017).

Finally, to study the impact of the mutations on signaling we cocultured the 58αβ cells with I-Ek+ M12 cells and a MCC peptide titration to measure IL-2 production as an endpoint readout of signaling. AUC analysis of IL-2 production allowed us to compare response magnitude between samples while responses at the lowest peptide dose (41 nM) reported sensitivity. We also analyzed CD4 and TCR endocytosis which are thought to be linked and serve as measures of pMHCII engagement, although the motifs studied here could impact CD4 endocytosis (Balagopalan et al., 2009; Sleckman et al., 1992). Additionally, we asked if differences in IL-2 production could be linked to differences in proximal pMHCII-specific signaling events by analyzing phosphorylation of key TCR proximal signaling intermediates by flow cytometry (pCD3ζ, pZap70, and pPlcγ1).

The GGXXG and CV +C motifs influence CD4 membrane localization and function

First, we asked if the GGXXG and CV +C motifs together influence membrane domain localization and function. We included the CQC clasp motif in this analysis because Lck has myristylation and palmitoylation sites that could influence membrane domain localization of CD4 when associated via the clasp (Ladygina et al., 2011). Accordingly, we generated 5 c.c7+ 58αβ cells expressing either WT CD4 or the following mutants: TMD, Palm, Clasp, TMD + Palm (TP), TMD + Palm + Clasp (TPC) (Table 1 and Figure 2—figure supplement 1).

Table 1
Motifs and mutants analyzed in this study.
Motif location/known functionMutant namesMutated motifResidue mutations
TMD/protein or cholesterol interactionsTMDGGxxGG403V, G406L
Juxtamembrane/palmitoylationPalmCV +CC418S, C421S
TMD + palm/raft localizationTPGGxxG, CV +CG403V, G406L, C418S, C421S
ICD clasp/interact with Lck, LatClaspCQCC444S, C446S
TMD + palm + clasp/raft, Lck, Lat interactionTPCGGxxG, CV +C, CQCSee TMD + palm + clasp above
Total ICD helixHTotal helix mutationaa430–442 (to NGPGGNPGGNAGG)
Total helix + claspHCTotal helix + CQCaa430–442, C444S, C446S
Helix IKRLL onlyLLIKRLLL437A, L438L
Helix serines onlySSRMSQIKRLLSEKKS432A, S439A
Phosphomimetic helix serinespSSRMSQIKRLLSEKKS432D, S439D
Helix IKRLL + serines (does not express)LL +SSSee LL and SSL437A, L438L, S432A, S439A
Helix IKRLL + phosphomimetic serinesLL + pSSSee LL and pSSL437A, L438L, S432D, S439D
C-terminally truncated CD4CD4-T1Ends at R422R422 is the last residue
Extracellular D3 domain nonpolar patchD3PatchPXLFP228E, F231E
Extracellular D1 C″-strand (binds pMHCII)ΔbindGKGVLIRK68D, V70D, L71S, I72D, R73S
IKRLL + D3 nonpolar patchLL + D3PatchIKRLL +PXLFL437A, L438L + P228E, F231E
IKRLL + ΔbindLL+ ΔbindSee LL + ΔbindL437A, L438L + K68D, V70D, L71S, I72D, R73S

To analyze membrane domain localization, we first focused on the percent of CD4 signal in each sucrose gradient fraction, relative to the total, to account for any differences in the amount of CD4 between samples and independent experiments (Figure 2A and Figure 2—figure supplement 2A). AUC analysis showed that the Palm and Clasp mutants trended lower than WT for DRM localization in our sample size, consistent with prior work (Fragoso et al., 2003), while the TP and TPC mutants were significantly reduced. The TP and TPC mutants trended slightly higher in DSMs. These data indicate that the GGXXG plus CV +C motifs together mediate CD4 localization to DRMs.

Figure 2 with 4 supplements see all
The GGXXG + CV+C motifs influence CD4 membrane domain localization and function.

(A) CD4 signal for each sucrose gradient fraction is shown as a percent of the total CD4 signal detected in all fractions (left). The area under the curve (AUC) is presented for the normalized CD4 signal in the detergent resistant membrane (DRM) fractions (center) and detergent soluble membrane (DSM) fractions (right). (B) Cholera toxin subunit B (CTxB) signal is shown for each sucrose fraction normalized to the CD4 signal detected in the corresponding fraction (left). The AUC is shown for the normalized CTxB signal in the DRM (center) and DSM (right) fractions. (C) Lck signal is shown for each sucrose fraction normalized to the CD4 signal detected in the corresponding fraction (left). The AUC is shown for the normalized Lck signal in the DRM (center) and DSM (right) fractions. (D) IL-2 production is shown in response to a titration of MCC peptide (left). AUC analysis for the dose response is shown as a measure of the response magnitude (center). The average response to a low dose (41nM) of peptide is shown as a measure of sensitivity (right).For (A-C) each data point represents the mean ± SEM for the same three independent experiments (biological replicates). For (D), the dose response represents one of three experiments showing the mean ± SEM of triplicate wells (technical replicates). the magnitude and sensitivity data represents the mean ± of three independent experiments (biological replicates). One-way ANOVA with a Dunnet's posttest for comparisons with WT samples, or a Sidak's posttest for comparisons between selected samples, were performed. Key: AUC = Area Under the Curve; DRM = Detergent Resistant Membrane; DSM = Detergent Soluble Membrane; CTxB = Cholera Toxin subunit B.

We next normalized the cholera toxin subunit B (CTxB) signal in each fraction to the CD4 signal in that fraction to assess the amount of GM1 that co-IP’d with CD4 per fraction (Figure 2B and Figure 2—figure supplement 2B). We did this because membrane rafts are heterogenous in protein and lipid composition, and reasoned that CTxB staining would help us evaluate if our mutations allowed CD4 to remain in membrane rafts, as defined by the DRM fraction, but inhabit different subdomains with different compositions within the DRM fraction (Pike, 2006). AUC analysis revealed that the Clasp and TPC mutants within DRMs had reduced CTxB staining, and the TP mutant had lower CTxB staining than the Palm mutant. There were no noteworthy differences in the DSM fractions. For CD4 molecules within DRMs, the clasp therefore influences CD4 association with GM1-containing membrane subdomains while the GGXXG and CV +C motifs together have a greater influence on subdomain localization than the CV +C motif alone.

We also normalized the Lck signal in each fraction to the CD4 signal detected in that fraction to analyze the amount of Lck that co-IP’d with CD4 per fraction (Figure 2C and Figure 2—figure supplement 2C). The Palm, Clasp, TP, and TPC signals were all greatly reduced for AUC analysis of the DRM, indicating that both the CV +C and clasp motifs influence association with Lck in DRMs. AUC analysis also showed that only the Clasp and TPC mutants had reduced Lck association in DSMs, whereas the Palm mutant trended higher, and the TP mutant had significantly increased association with Lck relative to the WT. The CQC clasp motif therefore influences CD4–Lck interactions whereas the GGXXG and CV +C motifs together influence the type of membrane domain in which Lck-associated CD4 molecules localize.

To determine how these motifs influence pMHCII responses we measured IL-2 production in response to a titration of MCC peptide. If the frequency of CD4–Lck interactions is the chief determinant for pMHCII responses, then only the Clasp and TPC mutants should reduce IL-2 production as the Palm and TP mutants interacted with Lck in the DSM (Glaichenhaus et al., 1991; Stepanek et al., 2014). But, if CD4 association with Lck in the DRMs is important, then the Palm and TP mutants would be expected to have reduced IL-2 production. We observed a hierarchy of IL-2 production of WT > TMD > Palm > Clasp ≥ TP ≥ TPC in response to a titration of MCC (Figure 2D) that was reflected in AUC analysis. Also, the TP mutant produced less IL-2 than the Palm mutant. The same hierarchy of IL-2 production was observed in response to the lowest dose of MCC tested (41 nM). Of note, only the TPC mutant impacted TCR endocytosis, which is typically a measure of triggered TCRs, while CD4 endocytosis inversely mirrored the normalized CD4–Lck signal in DRMs which either suggests that the CQC motif and GGXXG together with CV +C motif directly impact CD4 endocytosis upon triggering, or that positioning of CD4 in DRMs is important for cointernalization with the TCR (Figure 2—figure supplement 3). Overall, the data suggest that the CV +C and GGXXG motifs together enhance pMHCII responses by impacting CD4 membrane domain localization rather than CD4–Lck association. Indeed, we found higher overall CD4–Lck association in the TP cells than the WT (Figure 2—figure supplement 4 and Supplementary file 1), supporting the conclusion that the frequency of CD4–Lck pairs is not the chief determinant of IL-2 responses to agonist pMHCII in this system.

The intracellular helix interacts with Lck and attenuates pMHCII responses

To study the intracellular helix-turn structure we first replaced residues 430–442 with NGPGGNPGGNAGG to disrupt the chemical and structural nature of the helix-turn region but maintain its length (Table 1). We also combined this helix (H) mutant with the clasp mutant (HC) to explore how they work together (Figure 3—figure supplement 1). Both mutants localized in DRMs and DSMs similar to the WT, both reduced CD4–Lck interactions as expected from prior work, and yet, unexpectedly, both showed a higher magnitude and sensitivity of IL-2 responses to agonist pMHCII than the WT (Figure 3 and Figure 3—figure supplements 2 and 3; Kim et al., 2003; Sleckman et al., 1992). We also observed more TCR endocytosis for the H mutant than the WT or HC mutant, indicating that the increased IL-2 output by the H mutant might reflect more triggered TCRs over the course of 16 hr. Finally, because CD4 can increase TCR dwell time on pMHCII, the failure of the H and HC mutant CD4 molecules to endocytose over the course of 16 hr of 58αβ cell co-culture with antigen-presenting cells (APCs) could result in more sustained signaling in that time period and partially explain the increased IL-2 (Figure 3—figure supplement 4; Glassman et al., 2018; Sleckman et al., 1992).

Figure 3 with 4 supplements see all
The intracellular helix attenuates response magnitude and sensitivity.

(A) CD4 signal for each sucrose gradient fraction is shown as a percent of the total CD4 signal detected in all fractions (left). The area under the curve (AUC) is presented for the normalized CD4 signal in the detergent resistant membrane (DRM) fractions (center) and detergent soluble membrane (DSM) fractions (right). (B) Lck signal is shown for each sucrose fraction normalized to the CD4 signal detected in the corresponding fraction (left). The AUC is shown for the normalized Lck signal in the DRM (center) and DSM (right) fractions. (C) IL-2 production is shown in response to a titration of MCC peptide (left). AUC analysis for the dose response is shown as a measure of the response magnitude (center). The average response to a low dose (41nM) of peptide is shown as a measure of sensitivity (right). For (A–C) The data are presented as in Figure 2. Key: AUC = Area Under the Curve; DRM = Detergent Resistant Membrane; DSM = Detergent Soluble Membrane.

The IKRLL motif and flanking serines regulate pMHCII responses

To determine if mutating specific residues within the helix would mimic the loss of the helix (H mutant) we mutated the dileucine repeat (L437A + L438A = LL mutant) within the IKRLL motif of the ICD helix (Table 1 and Figure 1C). I/LXXLL motifs and dileucine repeats are known protein interaction mediators, and structural data indicate that they contribute to CD4 ICD helix interaction with Lck and AP-2 (Kelly et al., 2008; Kim et al., 2003). We also mutated the intracellular helix serines because (de)phosphorylation at one or both residues may regulate function (Sleckman et al., 1992). Our SS mutant (S432A.S439A) was designed to prevent phosphorylation or any interactions involving the hydroxyl groups, while the negatively charged pSS mutant (S432D.S439D) was used to mimic phosphorylation at these residues. We also combined mutations (LL + SS and LL + pSS) to infer how these residues may work together within the helix given their covariation over evolutionary time (Figure 1D). Of note, the LL + SS mutant did not express on the cell surface and thus was not analyzed further. The SS mutant had lower surface expression than the WT and the LL + pSS expression was slightly reduced (Figure 4—figure supplement 1).

For these lines, the SS and LL + pSS showed a decreased percent of CD4 localized in DRMs compared with the WT, yet none of the mutations significantly changed the percent of CD4 signal localized to DSMs. Furthermore, none of the mutations impacted CD4-associated CTxB signal in DRMs, although the LL + pSS trended lower, and only the pSS and LL + pSS mutants reduced the amount of CTxB signal associated with CD4 in DSMs (Figure 4A, B and Figure 4—figure supplement 2A and B). These data suggest that the IKRLL motif alone does not influence membrane domain localization, but that the hydroxyl group on the serine residues and a negative charge at these positions can influence membrane domain localization.

Figure 4 with 6 supplements see all
The IKRLL motif mediates the inhibitory function of the helix.

(A) CD4 signal normalized as a percent of the total is shown for each sucrose gradient fraction (left) along with the AUC analysis for the DRM (center) and DSM (right) fractions. (B) Cholera toxin subunit B (CTxB) signal normalized to CD4 signal detected is shown for each sucrose fraction (left) along with the AUC analysis for the DRM (center) and DSM fractions (right). (C) Lck signal normalized to CD4 signal is shown for each sucrose fractions (left) along with the AUC analysis for the DRM (center) and DSM (right) fractions. (D) IL-2 dose response to MCC peptide (left). AUC analysis as a measure of the response magnitude (center), and the average response to a low dose (41nM) of MCC as a measure of sensitivity (right) are shown. For (A–D), the data are presented as in Figure 2. Key: AUC = Area Under the Curve; DRM = Detergent Resistant Membrane; DSM = Detergent Soluble Membrane; CTxB = Cholera Toxin subunit B.

Regarding CD4–Lck association, we found that the Lck signal associated with the LL mutant was reduced in the DRM fraction (Figure 4C and Figure 4—figure supplement 2C). Interestingly, CD4–Lck association trended lower for the pSS mutant than the WT and was lower than the SS mutant in DRMs. Within DSMs, the LL trended lower than the WT, the SS mutant was greatly increased over the WT, and the pSS mutant was equivalent to the WT. These data extend prior work indicating that the IKRLL motif of the ICD helix mediates CD4–Lck interactions while S432 and/or S439, which do not contact Lck directly in the NMR structure, play a role in regulating CD4–Lck association at the helix (Kim et al., 2003; Sleckman et al., 1992).

For IL-2 we found that both the responses magnitude and sensitivity to agonist pMHCII were greatly increased for the LL mutant compared with the WT even though the total amount of CD4-associated Lck was lower (Figure 4D and Figure 4—figure supplement 3). In contrast, the SS line had higher total CD4-associated Lck than the WT yet the magnitude and sensitivity of IL-2 responses were lower. These data further support the idea that CD4–Lck interactions are not the chief determinant of IL-2 responses to agonist pMHCII in this system. Also of note, both the pSS and LL + pSS mutants had equivalent IL-2 responses to the WT (Figure 4D). Together, the data suggest that the IKRLL motif functions to inhibit the magnitude and sensitivity of pMHCII responses and that phosphorylation of the flanking serines regulate this activity.

We also evaluated if differences in IL-2 output by these mutant cells could be correlated with changes in TCR or CD4 surface levels during stimulation with APCs. While there was a trend toward more TCR internalization with the LL mutant, and less with the SS mutant, we could not attribute differences in IL-2 production to TCR triggering (Figure 4—figure supplement 4A). The LL, SS, pSS, and LL + pSS mutants all failed to endocytose CD4 to the same extent as the WT, with no internalization being observed for the LL and LL + pSS mutants after 16 hr. The lack of a correlation between IL-2 responses and TCR or CD4 endocytosis for these helix mutants pointed to the CD4 ICDs as being responsible for directing different IL-2 outputs between these mutants. To test this further we generated 58αβ cells expressing either CD4 WT, a C-terminally truncated mutant that ends at R422 (CD4-T1), or the LL mutant (Figure 1 and Table 1). We reasoned that CD4-T1, which was previously reported to relieve CD4−Lck interactions and diminish IL-2, should not be endocytosed during co-culture with APCs because it lacks the ICD helix (Glaichenhaus et al., 1991) therefore, if the CD4-T1 mutant fails to endocytose upon stimulation yet directs reduced IL-2 responses relative to the LL mutant, then the IL-2 responses directed by the mutants in Figure 4D can be directly attributed to the ICD and not CD4 levels. We found that CD4-T1 expressed at similar levels to LL, failed to internalize upon stimulation, and directed reduced IL-2 responses relative to the LL mutant (Figure 4—figure supplement 5A–C). These data further support the conclusion that differences in IL-2 production in Figure 4D are directed by differences in the CD4 ICDs more than cell surface levels.

Finally, we performed ELISpot to ask if the difference in IL-2 production between the WT and LL mutant cells was due to an increased frequency of responders making IL-2. We observed more responders for the LL mutant cells than the WT for two independently generated lines (Figure 4—figure supplement 6). The average spot intensity was also higher for the LL mutant in one of the two lines tested, and trended higher for the other, which suggest each cell made more IL-2 within the assay period. The simplest interpretation of these data, when considered with the increased sensitivity and response magnitude measured by ELISA, is that the LL mutation lowers the signaling threshold that must be overcome for IL-2 production.

Evidence for counterbalancing functions between CD4 motifs

The data in Figure 4D corroborated the functional link between S432 and/or S439 and the IKRLL motif predicted by our covariation analysis (Figure 1C), which also predicted a link between the intracellular helix and the ectodomain D3 nonpolar patch that arose in the predicted mammalian most recent common ancestor (Figure 1—figure supplement 1E). We hypothesized that the advantage gained from the ability of CD4 to stabilize TCR–CD3–pMHCII interactions and increase signal strength necessitated the coevolution of elements with the ability to regulate the enhanced signaling capacity. Alternatively, the inhibitory function of the helix allowed for the evolution of the nonpolar patch. Regardless, these data suggest a functional counterbalancing action between both motifs. Accordingly, we combined an ELXE mutant (P228E + F231E) of the PLXF motif in the D3 domain, which reduces 58αβ− IL-2 responses (Glassman et al., 2018), with the LL mutation (Figure 5—figure supplement 1) to ask if the ICD helix regulates the increased signaling afforded by the nonpolar patch. As a control we combined a GKGVLIR to GDGDSDS mutant in the D1 domain (CD4Δbind, Figure 1C and Table 1), which kills CD4 binding to pMHCII (Glassman et al., 2016; Parrish et al., 2015), to confirm that the LL mutant phenotype is dependent on CD4–pMHCII interactions. We found that the LL + ELXE double mutant drove similar IL-2 response magnitude and sensitivity to agonist pMHCII as the WT, while the LL + Δbind double mutant completely impaired responses (Figure 5A, B). Therefore, the intracellular helix and IKRLL motif therein do not regulate pMHCII-independent activity of CD4 in our system; rather, they counterbalance the formation of a stable TCR–CD3–pMHCII–CD4 assembly mediated by the ectodomain nonpolar patch.

Figure 5 with 1 supplement see all
Coevolving motifs in the extracellular and intracellular domains functionally counterbalance each other.

(A) AUC analysis of IL-2 dose response to MCC peptide are shown as a measure of the response magnitude for the indicated samples. (B) The average IL-2 response to a low dose (41nM) of MCC is shown as a measure of sensitivity for the indicated samples. For (A and B) the magnitude and sensitivity data represent the mean ± SEM of three independent experiments (biological replicates) for which triplicate measurements were performed (technical replicates). One-way ANOVA was performed with a Dunnett's posttest for comparisons with WT samples, and a Sidak's posttest for comparisons between selected samples. Individual graphs indicate experiments that were performed with cell lines generated at the same time. Key: AUC = Area Under the Curve.

Our covariation analysis also suggested a functional link between the GGXXG motif in the TMD with the intracellular helix. Because the TP mutant severely reduced the magnitude and sensitivity of IL-2, we combined it with the LL mutation (LL + TP) to ask if these mutations counterbalance each other. We found that cells expressing the double mutant had similar IL-2 responses to the WT, which were lower than the LL cells only, indicating that motifs within the TMD and ectodomain can exert counterbalancing activities on pMHCII responses (Figure 5A, B). As the GGXXG and CV +C motifs co-arose in eutherians after the intracellular helix and nonpolar patch, these data points to additional pressure to evolve motifs with the capacity to regulate CD4 function by regulating its contribution to pMHCII-specific signaling.

Distinct CD4 motifs differentially impact TCR–CD3 signal transduction

Because IL-2 production is an endpoint readout for signaling, we also asked if the IL-2 phenotypes of the Clasp, TP, and LL mutants could be attributed to defects in proximal signaling events. Accordingly, we analyzed phosphorylation of CD3ζ and Zap70, both Lck substrates, as well as Plcγ1 which is phosphorylated by ITK after it is activated by Lck (Figure 2—figure supplement 4 and Figure 4—figure supplement 3; Courtney et al., 2018; Gaud et al., 2018). If the abundance of CD4–Lck pairs is directly related to the magnitude of these signaling steps, then the Clasp and the LL mutants should have lower pCD3ζ, pZap70, and pPlcγ1 levels compared to the WT because the mutations reduced total CD4–Lck abundance by ~31% and ~49% of WT levels, respectively, while the TP mutant should have increased levels of pCD3ζ, pZap70, and pPlcγ1 because this mutant increased total CD4–Lck abundance to 123% of the WT (Supplementary file 1; Glaichenhaus et al., 1991; Rudd, 2021; Stepanek et al., 2014). Alternatively, if CD4 sequesters Lck away from TCR–CD3 until pMHCII engagement to prevent signal initiation by free Lck, and free Lck is more active than CD4-associated Lck, then the Clasp and LL mutants should have equivalent or higher pCD3ζ, pZap70, and pPlcγ1 levels than the WT due to free Lck while the TP mutant should have either equivalent or reduced levels due to higher CD4–Lck interactions and sequestration (Van Laethem et al., 2007; Wei et al., 2020).

To test these predictions, we analyzed pCD3ζ, pZap70, and pPlcγ1 levels by flow cytometry for TCR+ CD4+ 58αβ− cells coupled to APCs expressing either the null peptide hemoglobin 64–76 (Hb) tethered to I-Ek (Hb:I-Ek) or the agonist MCC peptide tethered to I-Ek (MCC:I-Ek) (Figure 6—figure supplement 1A–C). This approach allowed us to evaluate the impact of the CD4 motifs studied here on proximal signaling initiated by engagement of cognate ligand, which cannot be achieved with conventional anti-CD3 antibody crosslinking approaches, while the high ligand density of tethered MCC:I-Ek allowed for rapid synchronous engagement of TCRs to monitor proximal signaling events similar to conventional antibody-induced signaling (i.e., the TCRs did not have to find agonist peptide among irrelevant pMHCII on peptide-pulsed APCs). One caveat to this approach is that the high density of MCC:I-Ek might mask CD4 contributions that we and others have reported to be more apparent for responses to low densities of agonist pMHCII (Glassman et al., 2018; Irvine et al., 2002). However, this concern is somewhat mitigated by prior work showing that IL-2 production with this experimental setup is CD4 dependent (Parrish et al., 2016). Moreover, we found that TCR+ CD4+ 58αβ cells bearing the Clasp and TP mutants made less IL-2 than those bearing the WT in response to APCs expressing tethered MCC:I-Ek, while cells bearing the LL mutant made more IL-2 than the WT (Figure 6—figure supplement 2). These data suggest that the Clasp, TP, and LL mutations similarly impact the signaling pathways that lead to IL-2 production, be it in response to low or high densities of agonist pMHCII.

For the paired WT and Clasp, WT and TP, and WT and LL cell lines in Figure 6 marked by solid symbols we performed three independent experiments, collecting 10,000 coupled cells per experiment, and concatenated the flow cytometry data prior to further analysis. For those paired WT and LL mutant cells marked by open symbols, we performed the experiment once each. For data processing, we subtracted the MCC:I-Ek phospho-protein intensity from the Hb:I-Ek intensity to determine the percent of coupled cells that responded to agonist pMHCII. We then compared the mean fluorescence intensity (MFI) of the WT responders to the mutants to evaluate differences in the intensity of the response. We also compared the frequency of couples, which was unaffected by the mutations (Figure 6—figure supplement 3).

Figure 6 with 4 supplements see all
The CQC, GGXXG + CV+C, and IKRLL motifs differentially impact proximal TCR-CD3 signaling.

(A) Phosphorylation intensity for CD3ζ (left), Zap70 (center), and Plcγ1 (right) are shown for paired (connecting line) WT and Clasp mutant cell lines. Four independently generated cell lines were tested. (B) Phosphorylation intensity for CD3ζ (left), Zap70 (center), and Plcγ1 (right) are shown for paired (connecting line) WT and TP mutant cell lines. Three independently generated cell lines were tested. (C) Phosphorylation of CD3ζ (left), Zap70 (center), and Plcγ1 (right) are shown for paired (connecting line) WT and LL mutant cell lines. Five independently generated cell lines were tested. For (A–C), filled symbols represents the mean ± SEM of concatenated data for coupled cells from three independent experiments. 10,000 coupled cells were collected per experiment (technical replicates), resulting in the concatenation of 30,000 coupled cells total from the 3 independent biological replicates. For (C), the open symbols represent the mean and ± SEM for one single experiment (10,000 coupled cells analyzed). One-way ANOVA was performed with a Dunnett's posttest when the experiments involved multiple comparisons. Student's t-test were performed for when only WT and mutant pairs were analyzed in an experiment. The derived p values for each independent cell line comparing the mutant CD4 to its paired WT is shown. Next to each symbol the number of cells determined to have responded to stimuli are shown with the percentage of responding cells.

For the Clasp mutation, we found no difference in the pCD3ζ MFI compared to the WT for two independently generated cell lines, for a third line the pCD3ζ MFI was reduced to ~83% of WT, and for a fourth line we saw a small but statistically significant reduction to ~92% of WT (Figure 6A). There was no obvious impact on the percent of responders. Interestingly, pZap70 and pPlcγ1 MFI were significantly lower for all four Clasp lines compared to their respective WTs, despite no clear difference in percent responders. The most parsimonious interpretation of these data is that, as tested, reducing CD4–Lck interactions by mutating the CQC clasp does not prevent, or consistently reduce, pCD3ζ phosphorylation but does reduce the phosphorylation of other Lck substrates. The Clasp mutation therefore did not impact pCD3ζ levels in response to agonist pMHCII as predicted by the TCR signaling paradigm.

For the TP mutant, pCD3ζ MFI levels were significantly lower than WT for two of three independently generated cell lines tested in response to the agonist pMHCII, MCC:I-Ek, while all three TP lines had slightly higher percent responders for pCD3ζ than their paired WTs (Figure 6B). For pZap70 and pPlcγ1, all sets of lines showed reduced MFI for the TP mutant compared with the WT without impacting the percent responders. The simplest interpretation of these data is that regulation of membrane domain localization of CD4–Lck pairs by the GGXXG and CV +C motifs can influence pCD3ζ, pZap70, and pPlcγ1 levels in our system. We therefore take these data as evidence that when the CQC clasp is intact, CD4–Lck pairs must be localized in the appropriate membrane compartment for efficient phosphorylation of CD3ζ.

For the LL mutant, we observed no consistent difference in pCD3ζ, pZap70, or PLCγ1 MFI or percent responders that would allow us to attribute the increase in IL-2 for the LL mutant to proximal signaling differences in response to the agonist pMHCII, MCC:I-Ek, at least not within the 2 min at which these events were evaluated (Figure 6C). These data suggest the interactions regulated by the intracellular helix, including CD4–Lck interactions, do not consistently impact early signaling events as measured here.

Finally, because the Clasp mutation did not reduce pCD3ζ levels in response to agonist pMHCII as predicted by the dominant paradigm, we asked if we would see differences in proximal signaling in response to weak stimulus. Specifically, we and others have shown that IL-2 production by 5 c.c7 CD4+ T cells in response to low doses of agonist MCC peptide is CD4 dependent, as are 5 c.c7 CD4+ T cell and 5 c.c.7+ CD4+ 58αβ cell IL-2 responses to the low affinity, weak agonist altered peptide ligand T102S (Glassman et al., 2018; Irvine et al., 2002; Parrish et al., 2016). We therefore compared proximal signaling of our Clasp mutant and WT cells using APCs expressing a tethered T102S:I-Ek (Figure 6—figure supplement 4A). Two of the lines showed no difference in pCD3ζ MFI levels between the Clasp mutant and paired WT, another had a pCD3ζ MFI for the Clasp mutant that was ~117% of the WT, and the last line had pCD3ζ MFI for the Clasp mutant that was ~80% of WT. There were also no clear differences in the percent of responders. Further, we saw no consistent difference in pZap70 MFI or percent responders between the WT and Clasp mutants in response to the weak stimuli. Finally, for pPlcγ1 MFI, three of the four cell lines had reduced MFI for the Clasp mutants compared with their WT controls. However, for the fourth line the Clasp mutant had higher pPlcγ1 levels than the WT control. The percent responders also trended higher in three of the four Clasp lines. Importantly, the IL-2 responses to APCs expressing the tethered T102S:I-Ek were lower for the Clasp mutants in all four sets of lines relative to their paired WT controls (Figure 6—figure supplement 4A). These data are not consistent with predictions of the TCR signaling paradigm.

Discussion

The goal of this study was to gain novel insights into how ~435 million years of natural selection shaped eutherian CD4 function. We therefore used evolutionary and covariation analyses to guide structure–function studies of mouse CD4 in 58αβ T cell hybridomas. We note that while these cells may lack some elements of signal transduction found in real T cells, their IL-2 responses to pMHCII are CD4 dependent so relevant signaling pathways are intact (Glassman et al., 2018). Furthermore, protein–protein interactions between the CD4 motifs studied here with their interacting partners should be the same at a biochemical level as in real T cells provided the interacting partners are expressed. Indeed, the link between the CQC clasp motif, CD4–Lck interactions, and IL-2 production were established in seminal work using 58αβ cells (Glaichenhaus et al., 1991). Finally, because expression levels of CD4, Lck, adaptor proteins, or other molecules that might interact with CD4 vary between phenotypically different T cell populations (http://immpres.co.uk/), the motifs studied here may affect different outcomes in different T cell subsets. Our data are therefore likely to reflect general principles concerning the function of the motifs we identified, even if they may not reflect exactly how the motifs uniquely influence thymocyte, naive, Th, Treg, or Tm cell behavior.

A central tenet of the TCR signaling paradigm, and related models, is that CD4−Lck interactions via the CQC clasp allow CD4 to recruit Lck to phosphorylate the CD3 ITAM upon pMHCII engagement (Glaichenhaus et al., 1991; Rudd, 2021; Stepanek et al., 2014). However, when we tested this model directly with our Clasp mutant, we did not observe consistent reductions in CD3ζ phosphorylation as expected in response to agonist or weak agonist pMHCII. We did observe reduced Zap70 and Plcγ1 phosphorylation in response to agonist pMHCII, which helps explain the reduced IL-2 responses reported here and elsewhere for CQC clasp mutants (Glaichenhaus et al., 1991). We interpret these data as evidence that, in our system, Lck can efficiently phosphorylate CD3ζ ITAMs, but not other substrates (e.g., Zap70), even if CD4−Lck abundance is reduced by mutating the CQC clasp. We cannot, however, rule out that mutating the CQC clasp would impact CD3ζ phosphorylation in response to low densities of agonist pMHCII where CD4 is known to be critical for downstream signaling responses such as calcium mobilization or IL-2 production (Glassman et al., 2018; Irvine et al., 2002); however, on this point it is worth noting that our previous work points to an essential role for the CD4 ectodomain in mediating sensitivity to weak agonist pMHCII, as well as low doses of agonist pMHCII (Glassman et al., 2018). Overall, a key takeaway from the results with our Clasp mutant is that focusing solely on CD4−Lck interactions via the CQC clasp fails to convey a full understanding of the functional significance of CD4−Lck pairs.

Indeed, the Clasp mutant is better understood when considered with our TP mutant, which was nearly identical to the Clasp mutant regarding IL-2 responses even though it had ~123% more total CD4−Lck pairs than WT CD4. Enrichment of CD4−Lck pairs in the DSMs of TP mutants corresponded with lower CD3ζ, Zap70, and Plcγ1 phosphorylation, providing evidence that the Clasp and TP mutant IL-2 phenotypes are similar for different reasons. We favor the following interpretation: (1) Lck freed by our Clasp mutant can phosphorylate CD3ζ ITAMs but cannot as efficiently phosphorylate other substrates; (2) CD4−Lck pairs sequestered to the wrong membrane compartment by our CD4 TP mutation does not efficiently phosphorylate CD3ζ ITAMs and other substrates. Taken together, we think the simplest interpretation of these data is that CD4 association with Lck regulates the phosphorylation of Lck targets, including ITAMs, because membrane domain localization of CD4−Lck pairs is regulated. This interpretation supports a variant of the TCR signaling paradigm in which CD4 sequesters Lck away from TCR–CD3 to prevent spurious signaling until reciprocal engagement of pMHCII by TCR–CD3 and CD4 allows Lck recruitment to the CD3 ITAMs (Glassman et al., 2018; Van Laethem et al., 2007). Varying CD4 palmitoylation would then allow for tuning of CD4 function.

Comparing the Clasp mutant with our intracellular helix mutants provides additional insights into the relationship between CD4−Lck interactions and CD4 function. First, although our LL mutation reduced CD4−Lck interactions to half of WT levels, we again did not observe clear evidence of reduced CD3ζ phosphorylation. Second, if we consider that only a small fraction of WT CD4 molecules are naturally paired with Lck (~6% in 58αβ cells) then we can intuit that the intracellular helices of those CD4 molecules that are not paired with Lck are free to mediate other function (Parrish et al., 2016; Stepanek et al., 2014). If we further consider that our Clasp mutant has an intact IKRLL motif within the intracellular helix that is generally not occupied by Lck, and is thus free to mediate those other functions, then we can infer from the increased IL-2 production driven by our H, HC, and LL mutants (which have ~25%, ~14%, and ~49% of WT levels of CD4−Lck association, respectively, but disrupted IKRLL motifs) that a key function of the IKRLL motif is to prevent CD4 molecules that are not paired with Lck from driving pMHCII-specific signaling. Indeed, comparing our Clasp and LL mutant signaling phenotypes implies that one potential function of the IKRLL motif is to inhibit free Lck from phosphorylating substrates other than ITAMs (e.g., Zap70). This would explain the conundrum of why our H, HC, and LL mutants relieve CD4−Lck interactions and yet increase IL-2 responses. Together, we consider the simplest interpretation of our data to be that the IKRLL motif of the intracellular helix is under purifying selection because it mediates CD4−Lck interactions, CD4 endocytosis, and performs a previously unreported inhibitory function. Our SS, pSS, and LL + pSS mutants all suggest that the phosphorylation states of the helix serines regulates these functions. We take these data as evidence that the multifunctional intracellular helix is a key regulator of pMHCII responses.

This conclusion is further supported by our finding of evolutionary covariation between proximal as well as distant residues and motifs. One example is the covariation of proximal residues within the intracellular helix, such as the serines and IKRLL motif, that regulate CD4−Lck interactions and CD4 inhibitory activity. For more distant residues, the GGXXG and CV +C co-arose in the predicted most recent common ancestor of eutherians, have been maintained under purifying selection, and together regulate CD4−Lck membrane domain localization. They also counterbalance the inhibitory activity of the ICD helix, suggesting a functional link that is supported by residue covariation between the GGXXG motif of the TMD and ICD helix. Moreover, our analyses provide evidence that interactions mediated by the ectodomain nonpolar patch, which enhances signaling by stabilizing TCR–CD3–pMHCII–CD4 assemblies on the outside of the cell, are coupled with the intracellular helix that regulates signaling (Glassman et al., 2018). Key residues in these motifs covaried over evolutionary time, the motifs co-arose in the predicted most recent common ancestors of mammals, and they have been conserved in marsupials and eutherians under purifying selection. Additionally, our data show that the intracellular helix and nonpolar patch counterbalance each other with regard to IL-2 production. The broader conclusion from these results is that the emergence of the CD4 intracellular helix, with its ability to counterbalance the signal-enhancing activity of other motifs, was a key moment in the regulation of mammalian CD4 function.

Understanding how these motifs work individually to influence eutherian CD4 function and CD4+ T cell biology in fine molecular detail represents fertile ground for future directions. Because GXXXG and GG motifs can interact with cholesterol, and palmitate can interact with protein-bound cholesterol (PDB 4IB4), coupling of rapidly reversible palmitoylation of the CV +C motif with a cholesterol-bound state of the GGXXG motif may allow finer regulation of CD4 membrane domain localization and function than can be achieved with a palmitoylation motif alone (Fessler, 2016; Song et al., 2014; Teese and Langosch, 2015; Wacker et al., 2013). Structural analysis will help test this hypothesis. For the intracellular helix, the IKRLL motif clearly has multiple interacting partners that dictate its activity. Some are known, such as Lck and AP2 (Kelly et al., 2008; Kim et al., 2003; Sleckman et al., 1992); yet our data lead us to hypothesize that the inhibitory activity of the helix is the result of one or more additional partners. The data here and elsewhere suggest that the preference of partners is likely to be regulated both by their relative abundance, membrane domain localization, and the phosphorylation state of the helix serines. In addition, given that T443 just outside the helix has covaried over evolution with L438 in the IKRLL motif as well as Q445 of the CQC clasp motif, it is reasonable to speculate that the phosphorylation state of this residue might further regulate interactions between the helix and/or clasp and their binding partners. Moreover, given that the CQC clasp links CD4 to Lck by coordinating a zinc, it is reasonable to consider if and how changes in zinc concentration regulate CD4−Lck interacts for their own function. Importantly, zinc-regulated changes in Lck association would impact occupation of the IKRLL motif by Lck, which would impact the ability of the intracellular helix to interact with other partners to exert other activity. In sum, CD4 function is likely to be regulated by the switch-like activity of multiple motifs in its ICD.

Future studies aimed at understanding how these motifs work together will allow us to better understand why some of them have covaried over evolutionary time. Two plausible, nonmutually exclusive modes can be envisioned for how individual motifs may work together within the network of counterbalancing activities described here. First, given the switch-like activity of the intracellular helix serines, palmitoylation sites in the CV +C motif, and zinc coordination of the CQC clasp, CD4 activity might be finetuned by the sum of the switch states of each motif in the way that the sum of digital states determines a computational output. Second, allosteric effects may also be at play. For example, extracellular interaction mediated by the D3 nonpolar patch might induce an allosteric change in the transmembrane GGXXG motif or intracellular helix that might impact binding partner preferences. These possibilities, which are not mutually exclusive, suggest just how complex the regulation of CD4 may be and give insights into the work that will be required to describe exactly how CD4 functions.

In closing, our multidisciplinary results highlight a network of function-regulating motifs within eutherian CD4 that would not otherwise be obvious. In so doing we extend a theme of counterposing activities that regulate eutherian pMHCII responses at the population level (e.g., helper and regulatory CD4+ T cells) and cellular level (e.g., costimulatory and coinhibitory molecules) to that of a single molecule (e.g., CD4 nonpolar patch and ICD helix). Furthermore, our data concerning the residue covariation and functional coordination of the ectodomain nonpolar patch and intracellular helix provide evidence that a multidomain transmembrane protein, which serves as one component of a multimodule receptor complex, can coevolve binding activity on the outside of a cell with regulatory activity on the inside of the cell to dictate the molecule’s function within the multimodule receptor complex. We expect this to emerge as a common theme for other complex transmembrane receptors tasked with relaying information across the membrane to the intracellular signaling machinery. Collectively, our results advance our understanding of T cell biology and have translational value given efforts to engineer synthetic receptors for therapeutic purposes, such as chimeric antigen receptors (CARs). While CAR-T cell therapy has shown considerable promise, problems with sensitivity and side effects now suggest that the absence of mechanisms to mediate or regulate the relay of information across the membrane have a fitness cost for this form of CAR (Labanieh et al., 2018). We therefore think that biomimetic designs, incorporating strategies refined over ~435 million years of iterative testing in a variety of vertebrates, will ultimately lead to more sensitive and reliable synthetic receptors (Kobayashi et al., 2020). Such biomimetic engineering will require a doubling down on basic research efforts to elucidate the evolutionary blueprint for key immune receptors.

Materials and methods

Evolutionary analyses

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Available CD4 orthologs were identified through reciprocal blast-based searches and downloaded from GenBank. BLAST may not only identify orthologs, so additional criteria were used to include putative orthologous CD4 sequences in our analyses: the presence of a domain structure consisting of four extracellular Ig domains followed by a TMD and a C-terminal ICD, including the presence of the Lck binding clasp (CxC). Sequences that were shorter, contained frameshift mutations, or displayed high sequence variability were excluded from the analysis. For the current study, teleost fish were considered to be the oldest living species that contain a CD4 molecule given that a CD4 ortholog was not reported in the elephant shark (Callorhinchus milii), although future analyses of other cartilaginous fishes might yield more distant orthologs (Venkatesh et al., 2014). The final dataset contained 99 unique CD4 orthologs, ranging from teleost fish to human. These (putative) coding sequences were translated to amino acids and aligned using MAFFT (Katoh et al., 2002). For codon-based analyses, the aligned amino acid sequences were back translated to nucleotides to maintain codons. The multiple sequence alignments were further processed to remove all insertions (indels) relative to the mouse CD4 sequence (NM_013488.3) to maintain consistent numbering of sites. The 5′ and 3′ regions of the CD4 molecules were not consistently aligned due to different start codon usage or extensions of the ICD, respectively. The alignment was edited to start at the codon (AAG) coding for K48 within mouse CD4. The alignment that includes all 99 CD4 sequences ends at the last cysteine residue that makes up the CQC clasp. For the mammalian only dataset, the alignment terminates at the mouse CD4 stop codon.

FastTree was used to estimate maximum likelihood trees (Price et al., 2010). For amino acid-based trees, the Jones–Taylor–Thornton (JTT) model of evolution was selected, while the general time-reversible model was used for nucleotide trees (Jones et al., 1992; Waddell and Steel, 1997). In all conditions, a discrete gamma model with 20 rate categories was used. A reduced representation tree (based on the amino acid alignment) is shown in Figure 1A. We used the same JTT model to estimate the marginal reconstructions of nodes indicated in Figure 1A. Phylogenetic trees and logo plots were visualized in Geneious and further edited using Adobe Illustrator.

Ancestral sequences were estimated using GRASP (Foley et al., 2020).

Codon-based analysis of selection was performed using the hypothesis testing FEL model as implemented within the phylogenies (HyPhy) package (version 2.5.14) (MP) (Kosakovsky Pond et al., 2020; Pond et al., 2005; Weaver et al., 2018). The back-translated codon-based alignments described above were used for these analyses. FEL uses likelihood ratio tests to assess a better fit of codons that allowed selection (p < 0.1). When calculating values for all CD4 orthologs included in the initial phylogenetic analysis we analyzed and identified the sequences within the mammalian clade as the foreground branches on which to test for evolutionary selection in order to maximize statistical power.

Covariation between protein residues was calculated using the MISTIC2 server. We calculated four different covariation methods (MIp, mFDCA, plmDCA, and gaussianDCA) (Colell et al., 2018). Protein conservation scores were calculated based on the protein alignment using the ConSurf Server (Ashkenazy et al., 2016; Ashkenazy et al., 2010). ConSurf conservation scores are normalized, so that the average score for all residues is zero, with a standard deviation of one. The lower the score, the more conserved the protein position. For the purpose of this study, residues were considered to covary if the MI was larger than 4 and both residues had a ConSurf conservation score lower than −0.5. Also, pairs with an MI larger than 8 were considered to covary if the conservation score was below −0.3. Using these criteria, we selected 0.5% of all possible pairs as recommended (Buslje et al., 2009; Colell et al., 2018).

Raw data, including alignments and phylogenetic trees, associated with Figure 1, Supplementary file 1 are available on Dryad (https://doi.org/10.5061/dryad.59zw3r26z).

Cell lines

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58αβ T cell hybridoma lines were generated from Kuhns Lab stocks of parental 58αβ T cell hybridoma cells (obtained from Y.H. Chien at Stanford University) by retroviral transduction and maintained in culture by standard techniques as previously described (Glassman et al., 2018; Letourneur and Malissen, 1989). 58αβ T cell hybridomas lack expression of endogenous TCRα and TCRβ chains, are CD4 negative, make IL-2 in response to TCR signaling, and are variant of the DO-11.10.7 mouse T cell hybridoma (Balb/c T cell fused to BW5147 thymoma) (Letourneur and Malissen, 1989). We validate the cells lines by these characteristics as well as expression of H2-Dd to validate Balb/c origin. In brief, 1 day after transduction the cells were cultured in 5 μg/ml puromycin(Invivogen) and 5 μg/ml zeocin (Thermo Fisher Scientific) in RPMI 1640 (Gibco) supplemented with 5% fetal bovine serum (FBS) (Atlanta Biologicals or Omega Scientific), penicillin–streptomycin–glutamine (Cytiva), 10 µg/ml Ciprofloxacin (Sigma), and 50 µM beta-2-mercaptoethanol (Thermo Fisher Scientific). The next day drug concentrations were increased to 10 μg/ml puromycin (Invivogen) and 100 μg/ml zeocin (Thermo Fisher Scientific) in 10 ml in a T25 flask. Aliquot of 1 × 107 cells were frozen at days 5, 7, and 9. Cells were thawed from the day 5 freeze and cultured for 3 days in 10 μg/ml puromycin and 100 μg/ml zeocin, and maintained below 1 × 106 cells/ml to use in the functional assays. Cells used in the functional assays were grown to 0.8 × 106 cells/ml density and replicates of three functional assays were performed every other day. If cells exceeded 1 × 106 cells/ml at any point in the process they were discarded as they lose reactivity at high cell densities and a new set of vials was thawed. Typically, two independent WT and mutant pairs were generated for any given mutant and tested for IL-2 to gain further confidence in a response phenotype. When cells lines are presented together in a graph, that indicates that the cell lines (WT and mutants sets) were generated at the same time from the same parental cell stock.

Given the number of mutant CD4 cell lines generated and handled in this study, the identity of the transduced CD4 gene was verified by PCR sequencing at the conclusion of three independent functional assays. 58αβ T cell hybridomas were lysed using DirectPCR Tail Lysis Buffer (Viagen Biotech) with proteinase K (Sigma) for 2 hr at 65°C. Cells were then heated at 95°C for 10 min. Cell debris was pelleted, and the supernatants were saved. CD4 was amplified by PCR using Q5 DNA Hot Start Polymerase (New England BioLabs) in 0.2 μM dNTP, 0.2 μM primer concentration, Q5 reaction buffer, and water. CD4 was amplified using the following primers:

  • 5′ primer: acggaattccgctcgagcgccaccatggtgcgagccatctctctcttagg

  • 3′ primer: ctagcaagcttgtcgactcaagatcttcattagatgagattatggctcttctgc

Product were purified using SpinSmart Nucleic Acid Purification Columns (Thomas Scientific) and sent to Eton Bioscience for sequencing with the following 5′ CD4 primer: gtctctgaggagcagaag.

The I-Ek+ M12 cells used as APCs were previously reported (Glassman et al., 2018). M12 cells are a murine B cell lymphoma from Balb/c mice (H2-Dd validated) (Kim et al., 1979). Cells were cultured in RPMI 1640 (Gibco) supplemented with 5% FBS (Atlanta Biologicals or Omega Scientific), pencillin–streptomycin–glutamine (Cytiva), 10 µg/ml ciprofloxacin (Sigma), 50 μM beta-2-mercaptoethanol (Thermo Fisher Scientific), and 5 μg/ml puromycin (Invivogen) and 50 μg/ml Zeocin (Thermo Fisher). The parental cells are maintained in Kuhns Lab stocks and were originally obtained from MM Davis stocks (Stanford University).

Parental 58αβ T cell hybridoma and M12 cells are periodically treated with Plasmocyn and tested for mycoplasma contamination by PCR using primer sequences available from American Type Culture Collection (ATCC) (5′ primer sequence: TGCACCATCTGTCACTCTGTTAACCTC; 3′ primer sequence: GGGAGCAAACAGGATTAGATACCCT). All transduced cell lines used here were grown in ciprofloxacin and generated from parental cells that had tested negative for mycoplasma.

For retroviral production we used Phoenix-eco cells from the Nolan Lab (ATCC CRL-3214).

Antibodies

AntibodiesVendorCatalog numberRRID
anti-mouse CD4 eFlour 450, clone GK1.5Thermo Fisher Scientific48-0041-82AB_10718983
anti-mouse TCRα APC, clone RR8-1Thermo Fisher Scientific17-5800-82AB_19853170
anti-mouse CD3ε PE-Cy7, clone 145–2 C11Thermo Fisher Scientific25-0031-82AB_469572
anti-mouse IL-2, clone JES6-1A12BioLegend503,702AB_315292
biotin anti-mouse IL-2, clone JES6-5H4BioLegend503,804AB_315298
Streptavidin HRPBioLegend405,210
anti-mouse TCRβ PE, clone KJ25BD Pharmingen553,209AB_394709
biotin anti-mouse CD4 (Clone RM4-4)BioLegend116,010AB_2561504
anti-mouse CD4 APC, clone GK1.5BioLegend100,412AB_312697
anti-mouse Lck PE, clone 3A5Santa Cruzsc-433
Cholera Toxin Subunit B Alexa Fluor 488Thermo Fisher ScientificC22841
anti-mouse pCD3ζ Alexa Flour 647, clone K25-407.69BD Phosflow558,489AB_647152
anti-mouse pZap70 APC, clone n3kobu5Thermo Fisher Scientific17-9006-42AB_2573268
anti-mouse pPlcγ1 PE, clone A17025ABioLegend612,404AB_2801120

Flow cytometry

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Cell surface expression of CD4 and TCR–CD3 complexes was measured by flow cytometry. In brief, cells were stained for 30 min at 4°C in Fluorescence-Activated Cell Sorting (FACS) buffer (phosphate-buffered saline [PBS], 2% FBS, and 0.02% sodium azide) using anti-CD4 (clone GK1.5, eFluor 450 conjugate, Thermo Fisher Scientific), anti-TCRα (anti-Vα11, clone RR8-1, APC conjugate, Thermo Fisher Scientific), anti-CD3ε (145-2C11, Thermo Fisher Scientific), and GFP was detected as a measure of the TCRβ-GFP subunit. Analysis was performed on a Canto II or LSRII (BD Biosciences) at the Flow Cytometry Shared Resource at the University of Arizona. Flow cytometry data were analyzed with FlowJo Version 9 software (Becton, Dickinson & Company).

Functional assays

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IL-2 production was measured to quantify pMHCII responses. 5 × 104 transduced 58αβ T cell hybridomas were cocultured with 1 × 105 transduced I-Ek+ M12 cells in triplicate in a 96 well round bottom plate in RPMI with 5% FBS (Omega Scientific), Pen-Strep + L-glutamine (Cytiva), 10 ng/ml ciprofloxacin (Sigma), and 50 μM beta-2-mercaptoethanol (Fisher) in the presence of titrating amounts of MCC 88–103 peptide (purchased from 21st Century Biochemicals at >95% purity) starting at 30 μM MCC and a 1:3 titration (Glassman et al., 2018). For experiments with APCs expressing tethered pMHCII, 5 × 104 58αβ T cell hybridomas were cultured with 1 × 105 MCC:I-Ek+ or T102S:I-Ek+ M12 cells in triplicate in a 96-well round bottom plate using the same culture conditions as above. The supernatants were collected and assayed for IL-2 concentration by ELISA after 16 hr of co-culture at 37°C. Anti-mouse IL-2 (clone JES6-1A12, BioLegend) antibody was used to capture IL-2 from the supernatants, and biotin anti-mouse IL-2 (clone JES6-5H4, BioLegend) antibody was used as the secondary antibody. Streptavidin–Horse Radish Peroxidase (HRP) (BioLegend) and 3,3′,5,5′-Tetramethylbenzidine (TMB) substrate (BioLegend) were also used.

To assess engagement-induced endocytosis, CD4 surface levels were measured by flow cytometry 16 hr after coculture with APCs and peptide as described above for IL-2 quantification. 96-Well plates containing cells were washed with ice cold FACS buffer (PBS, 2%FBS, 0.02% sodium azide), transferred to ice, and Fc receptors were blocked with Fc block mAb clone 2.4G2 for 15 min at 4°C prior to surface staining for 30 min at 4°C with anti-CD4 (clone GK1.5 EF450, Invitrogen) and anti-Vβ3 TCR clone (clone KJ25, BD Pharmingen) antibodies. Cells were washed with FACS buffer prior to analysis on a LSRII (BD Biosciences) at the Flow Cytometry Shared Resource at the University of Arizona. Flow cytometry data were analyzed with FlowJo Version 10 software (Becton, Dickinson & Company). The average of the geometric mean of the TCR or CD4 signal was taken for the triplicate of the post 58αβ cells cocultured with M12 I-Ek+ cells at 0 μM MCC concentration. Each value of the raw gMFI of TCR or CD4 for cells cultured at 10 μM MCC was subtracted from the average gMFI at 0 μM. The values show the change of gMFI from 0 to 10μM.

For ELISpot analysis, 1.25 × 103 transduced 58αβ T cell hybridomas were mixed with 1.5 × 105 M12 cells that expressed MCC peptide tethered to I-Ek in triplicate wells on a mixed cellulose ester membrane plate (Merck Millipore) coated with 10 μg/ml anti-mouse IL-2 (clone JES6-1A12, BioLegend) antibody. Cells were co-cultured for 16 hr at 37°C in culture media as listed above. Plates were washed, probed with biotin anti-mouse IL-2 (clone JES6-5H4, BioLegend), washed, and probed with streptavidin–HRP (BioLegend). KPL TrueBlue Peroxidase Substrate (Sera Care) were used to identify spots according to the manufacturer’s instructions. Spots and spot intensity were enumerated from triplicate wells on a ImmunoSpot counter from Cellular Technologies Limited using the ImmunoSpot 7.0.13.0 software.

Sucrose gradient analysis

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Membrane fractionation by sucrose gradient was performed similar to previously described methods (Hur et al., 2003; Parrish et al., 2016). For cell lysis, 6 × 107 58αβ T cell hybridomas were harvested and washed 2× using TNE buffer (25 mM Tris, 150 mM NaCl, 5 mM Ethylenediaminetetraacetic acid (EDTA)). Cells were lysed on ice in 1% Triton-X detergent in TNE in a total volume of 1 ml for 10 min and then dounce homogenized 10×. The homogenized lysate was transferred to 14 × 95 mm Ultraclear Ultra Centrifuge tubes (Beckman). The dounce homogenizer was rinsed with 1.6 ml of the 1% lysis buffer, which was then was added to the Ultracentrifuge tube. 2.5 ml of 80% sucrose was added to the centrifuge tube with lysate and mixed well. Gently, 5 ml of 30% sucrose was added to the centrifuge tubes, creating a 30% sucrose layer above the ~40% sucrose/lysate mixture. Then, 3 ml of 5% sucrose was added gently to the centrifuge tube, creating another layer. The centrifuge tubes were spun 18 hr at 4°C in a SW40Ti rotor at 36,000 rpm.

Analysis of membrane fractions was performed via flow-based fluorophore-linked immunosorbent assay (FFLISA) as previously described (Parrish et al., 2016). In brief, 88 μl of Streptavidin Microspheres 6.0 μm (Polysciences) were coated overnight at 4°C with 8 μg of biotinylated anti-CD4 antibody (clone RM4-4, BioLegend). Prior to immunoprecipitation, beads were washed with 10 ml of FACS buffer (1× PBS, 2%FBS, 0.02% sodium azide) and resuspended in 3.5 ml of 0.1% Triton X-100 lysis wash buffer in TNE. For each cell line lysed, 10 FACS tubes were prepared with 50 μl of the washed RM4-4 coated beads. Upon completion of the spin, 500 μl was carefully taken off the top of the centrifuge tubes and discarded. Following this, 1 ml was extracted from the top of the tube, carefully as to not disrupt the gradient, and added to a FACS tube with coated beads and capped. This was repeated for 10 individual fractions in separate FACS tubes. Following the extraction, lysates were incubated with the beads for 90 min, inverting the tubes to mix every 15 min.

Following the immunoprecipitation, FACS tubes were washed 3× using 0.1% Triton-X lysis wash buffer in TNE. Tubes were then stained using 1 μl anti-CD4 (APC conjugate; clone GK1.5, BioLegend), 1.5 μl anti-Lck (PE conjugate, clone 3A5, Santa Cruz Biotechnology), and 1 μl CTxB (AF 488 conjugate, Thermo Fisher Scientific, resuspended as per manufacturer’s instructions) for 45 min at 4°C. Following the stain, tubes were washed using 0.1% Triton-X lysis wash buffer in TNE. Analysis of beads was performed on a LSRII (BD Biosciences) at the Flow Cytometry Shared Resource at the University of Arizona. 104 events were collected per sample. Flow cytometry data were analyzed with FlowJo Version 10 software (Becton, Dickinson & Company).

For FFLISA analysis, raw gMFI values for fraction 1 were subtracted from the rest of the fractions to account for background, such that the gMFI of fraction 1 is 0. To normalize the data, the percentage of CD4 within any given fraction (fx) relative to the total CD4 gMFI (CD4 signal % of total) was calculated by dividing the gMFI signal in a given fraction (fx) by the sum of the total CD4 gMFI signal [sum(f1:f10)CD4 gMFI] and multiplying by 100 [e.g., fx % of total = fxCD4 gMFI/Sum(f1:f10)CD4 gMFI × 100]. To normalize the CTxB and Lck signal in any given fraction relative to the CD4 signal in that same fraction (CTxB or Lck normalized to CD4) the gMFI of CTxB or Lck in fx was divided by the CD4 gMFI of that fx and then multiplied by the percentage of CD4 within fx (e.g., Normalized fx Lck = fx Lck gMFI/fx CD4 gMFI × fx CD4% of total CD4 gMFI). AUC analysis was performed with GraphPad Prism 9 for fractions 1–6 to determine the AUC for the DRM domains due to their floating phenotypes, and for fractions 6–10 to determine the AUC for the DSM domains.

Intracellular signaling analysis

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M12 cells expressing Hb:I-Ek (null) or MCC:I-Ek (cognate) tethered pMHCII complexes were labeled with Tag-it Violet according to the manufacturer’s instructions (BioLegend). M12 cells and 58αβ cells were then chilled on ice for 30 min, 5 × 105 of each cell type were mixed together in 1.5 ml snap cap tubes, and the cells were pelleted at 2000 rpm for 30 s at 4°C to force interactions. The supernatant was removed and the tubes were transferred to a 37°C water bath for 2 min to enable signaling. Fixation Buffer (BioLegend Inc) was then added for 15 min at 37°C. Cells were washed twice with FACs buffer, pelleted at 350 × g for 5 min at room temperature, resuspended in 1 ml True-Phos Perm Buffer (BioLegend Inc), and incubated at −20°C for 16 hr.

Cells were blocked with anti-mouse FcRII mAb clone 2.4G2 hybridoma supernatants (ATCC) for 30 min, pelleted, and stained on ice for 60 min with anti-pCD3ζ (clone K25-407.69, Alexa Fluor 647 conjugate, BD Biosciences) in one sample tube, or with anti-pZap70 (clone n3kobu5, APC conjugate, Invitrogen) and anti-pPlcγ1 (clone A17025A, PE conjugate, BioLegend) in a separate sample tube at the vendor-recommended concentrations. Finally, cells were washed 2× with FACS buffer at 1000 × g for 5 min at room temperature and analyzed on a Canto II (BD Biosciences) at the Flow Cytometry Shared Resource at the University of Arizona or on a BD Fortessa. 1 × 104 58αβ cell:M12 cell couples were collected per sample.

Flow cytometry data were analyzed with FlowJo Verison 10 software (Becton, Dickinson & Company) by gating on 58αβ and M12 cell couples, as described previously (Glassman et al., 2018). Histograms of the pCD3ζ, pZap70, or pPlcγ1 intensity for the gated population were then generated and data expressing the gated populations as numbers of cells within intensity bins was exported from FlowJo into Microsoft Excel where the number of cells for each bin intensity value for MCC:I-Ek stimulated cells was subtracted from Hb:I-Ek stimulated cells on a bin-by-bin basis. This allowed us to enumerate the intensity differences per bin upon stimulation with the agonist pMHCII over background. Mean intensity and standard error of the mean were calculated based on the background subtracted (MCC:I-Ek-Hb:I-Ek) data. The data were then transferred to Prism 9 where we performed smoothing analysis with 500 nearest neighbors to smooth the line profile for graphing purposes. Those intensity bins with positive values were considered to contain cells that had responded to the MCC:I-Ek stimuli above background.

Statistical analysis

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Statistical analyses of sucrose gradient and functional assays were performed with GraphPad Prism 9 software as indicated in the figure legends. For each functional assay (IL-2 production and CD4 endocytosis), each individual experiment (biological replicate) was performed with triplicate analysis (technical replicates) and each experiment was repeated three times (three biological replicates). For sucrose gradient analysis, 104 beads were collected by flow cytometry in each experiment (technical replicates) and each experiment was performed three times (biological replicates). Three biological replicates were chosen for each analysis as per convention, and no power calculations were determined. One-way analysis of variance was performed with a Dunnett’s posttest when all mutants tested in an experiment were compared to a control sample (e.g., WT). Sidak’s posttest were applied when comparing between two specific samples. These posttests were chosen based on Prism recommendations. Student’s unpaired t-tests (two-tailed) were performed when comparing WT and LL mutant samples only for phosphorylation analysis.

Materials availability statement

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Raw data, including alignments and phylogenetic trees, associated with Figure 1, Supplementary file 1 are available on Dryad (https://doi.org/10.5061/dryad.59zw3r26z). Cell lines and constructs are available upon request.

Data availability

Raw data, including alignments and phylogenetic trees, associated with figures 1 and S1 as well as source data and statistics for remaining figures are available on Dryad (https://doi.org/10.5061/dryad.59zw3r26z).

The following data sets were generated
    1. Lee M
    2. Tuohy P
    3. Kim C
    4. Lichauco K
    5. Parrish H
    6. Van Doorslaer K
    7. Kuhns M
    (2022) Dryad Digital Repository
    Data associated with "Enhancing and inhibitory motifs have coevolved to regulate CD4 activity".
    https://doi.org/10.5061/dryad.59zw3r26z

References

    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.

Decision letter

  1. Richard N McLaughlin
    Reviewing Editor; Pacific Northwest Research Institute, United States
  2. Carla V Rothlin
    Senior Editor; Yale School of Medicine, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting the paper "Enhancing and inhibitory motifs have coevolved to regulate CD4 activity" for consideration at eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Senior Editor. Although the work is of interest, we regret to inform you that the findings at this stage are too preliminary for further consideration at eLife.

All three reviewers agreed that the comparative genomics/evolution-motivated approach to identify new CD4 functions is intriguing and likely successfully identified 'novel' regions of CD4 that contribute to its function. However, there was also agreement that the major findings of the manuscript, while intriguing, fail to meet the significance bar for eLife due largely to a lack of mechanistic details of how distal regions of CD4 interact (thermodynamically or functionally) and control downstream TCR activation. In addition, the reviewers commented that the manuscript is written in a way that is largely inaccessible to non-experts that comprise much of the eLife readership and, as such, the broad and generalizable impact of the work was not apparent.

Reviewer #1 (Recommendations for the authors):

Lee and colleagues use evolutionary genomics to identify regions of CD4 that appear to have evolved under functional constraint and which were not previously appreciated to contribute to CD4 function. They proceed to test the function of these regions using in vitro assays to measure how mutations in these regions affect several aspects of CD4 biology including changes in CD4 localization on the membrane, interaction of CD4 with the downstream kinase Lck, and ultimately IL-2 production. The authors find effects on IL-2 production from mutations in all of identified motifs. Of interest, mutations in three of the regions tend to decrease IL-2 production, but mutations in one motif of the intracellular domain (termed "helix" or "IKRLL") increase IL-2 production. From this data, the authors also find evidence that interaction with the Lck kinase may not be necessary to stimulate IL-2 production, counter to the established model of CD4 signaling. Next, an analysis of statistical covariation in CD4 protein sequences showed significant covariation between the intracellular helix and most of the newly identified functional motifs in the intracellular, transmembrane, and extracellular domains. Finally, the authors combine mutations in the intracellular helix (which increase IL-2; "LL") with mutations in evolutionarily covarying regions (which decrease IL-2; "R426A, Clasp, TP") to measure the effects of the double mutants relative to each single mutant. All double mutants appear to behave as the additive effects of the individual mutations, without clear non-additive or epistatic effects.

Overall, the authors use a powerful combination of evolutionary analyses and biochemistry/molecular biology to identify novel functional regions of CD4. However, the author's conclusion that these motifs have coevolved to "finetune" the MHCII response are overstated and not supported by the functional data. Specifically, it is unclear what evidence the authors provide to support regulation of the ECD by intracellular or transmembrane regions. In figure 7, R426A, clasp, and TM mutants all decrease IL-2 on their own. The effects of these mutations in the background of the LL mutation seem to be simply additive, providing no evidence of interaction between these motifs (a la a network of coevolving residues). The covariation could reflect a 'rheostat' wherein mutations in a stimulatory domain are compensated by mutations in an enhancing domain and vice versa, but there need be no regulation in the sense of a perturbation/input at one motif affects the function of another/allostery.

To be suitable for a broad readership journal like eLife, the manuscript would benefit from a figure which shows the biological context (all the relevant molecules and the regions of interest in CD4). In addition, the diagrams included in the supplementary showing the mutations and their location within CD4 could be included in this or should be moved to the main figures. Similarly, some summary figure showing the mutations and their effects in a simple manner (up/down/wt) would be immensely useful to process the proposed interaction of mutations.

The relevance of the functions measured is not clear – what do we know about the function of raft vs non-raft CD4? Most CD4 is in DSM/non-raft fraction. Why use CTxB and fractionation? Do they tell different things? What about when they don't agree? A clear setup of the why these assays have been chosen is crucial to understanding the relevance of the data.

The language should be more precise regarding evolutionary inferences, with a clear delineation of what is a statistical test and what is an inference of those tests. For example:

– The pairwise conservation/mutual information analysis detects statistical covariation which suggests coevolution. This analysis does not show coevolution.

– They find statistical signatures of selection, not identify selection.

– (ln 104-105) "how ancient and ongoing environmental challenges have influenced CD4" should read "find motifs that have been preserved".

The various monikers used for each motif are hard to keep track of. It would greatly help the reader to have a single term to apply to each motif in the text and figures (e.g. Palm and CV+C, ECD and Domain 3).

Reviewer #2 (Recommendations for the authors):

"Enhancing and inhibitory motifs have coevolved to regulate CD4 activity" takes an evolutionary approach to investigate the mechanism(s) by which CD4 regulates TCR activation and downstream functional responses. The authors identify conserved motifs in the extracellular, transmembrane, and intracellular domains of CD4 that appear to regulate multiple aspects of its function, including its localization to lipid RAFTs, its capacity to interact with Lck, and its ability to promote IL-2 production. Notably, one of these motifs, comprising a helix just N-terminal to the cysteine clasp responsible for interaction with Lck, has an inhibitory effect on TCR signaling, and it seems to have coevolved in eutherians together with a motif in extracellular domain 3 that promotes T cell activation. Collectively, these results suggest that the regulation of CD4 activity has been finely tuned during evolution such that the acquisition of activity-promoting mutations is balanced by the emergence of inhibitory regions. Although this idea is interesting, it should be accompanied by more in-depth mechanistic work to demonstrate exactly how specific parts of CD4 control TCR activation. The notion that motifs within CD4 have coevolved is not particularly unexpected. These evolutionary relationships should be connected to mechanistic and functional insights into how the molecule works.

1) Just because motifs have coevolved and their mutations have additive effects doesn't necessarily mean that they are functionally coupled. Mechanistic insight will require a more in-depth analysis of membrane proximal signaling events and the activation status of downstream pathways (e.g. Erk, Ca, NFKB).

2) The idea that the role of CD4 is to control the colocalization of the TCR and Lck to RAFTs is quite interesting, and it should be tested. Could RAFT localization be modulated independently of CD4, and would this reverse the effects of the relevant CD4 mutations?

3) It is difficult to get a sense of what the fractionation and lipid association results really mean in terms of membrane organization (e.g. Figure 2). CTxB binding in the context of a bead-based IP is particularly unphysiological. A positive control would be helpful here. What would an established disruption of RAFT localization look like in this assay (e.g. a mutation in the Lck N-terminal domain).

4) Based on the presented data, one might conclude that mutation of the ICD helix enhances T cell responses simply by inhibiting CD4 internalization, thereby maintaining the density of CD4 on the cell surface. Indeed, all of the mutations that enhance IL-2 responses appear to result in higher surface expression. Do the authors favor this hypothesis, or can it be ruled out?

5) Given the evolutionary focus of this study, it would have been more interesting if the authors had actually used sequences from non-eutherian CD4s, as opposed to Ala and Gly substitutions. Was this attempted?

6) It is hypothesized that the importance of the GGXXG motif may depend on the local cholesterol concentration of the membrane domain in which it resides. Does changing membrane cholesterol modulate the effects of mutations in the GGXXG motif?

7) The authors should confirm that the CV+C mutation actually has the intended consequence of altering CD4 palmitoylation.

Reviewer #3 (Recommendations for the authors):

In this manuscript, Lee et al., the authors interrogate the function of CD4 from an evolutionary perspective. Adaptive immunity within the jawed vertebrates is believed to have arisen as a consequence of the "Immunological Big Bang". This event marks the evolutionary origin of both T and B cell receptors, as well as many of the molecules that mediate their signaling (e.g., the TCR co-receptors CD4 and CD8, as well as the MHC I and II proteins). Although orthologs of TCRs, CD4, and MHCII are broadly conserved throughout jawed vertebrates, these proteins have not yet been identified outside of this lineage. Notably, all three molecules first appear within the cartilaginous fishes, suggesting that their functions may be tightly linked. However, most studies on TCR function focus specifically on TCRs interacting directly with either CD4 or MHCII. In this study, the authors draw on evolutionary analyses to identify broadly conserved sequence motifs that may be involved in direct interactions between CD4 and MHCII to fine-tune the strength of TCR signaling. They complement these computational analyses with biochemical studies to support their hypothesis.

The primary strength of this manuscript lies in the author’s integrative approach; their evolutionary hypotheses are considerably strengthened by the findings from the experimental techniques. However, in its current form, the manuscript is very difficult to follow for a non-specialist in TCR signaling. The authors use a considerable amount of jargon and abbreviations. Consequently, the logic underlying their experiments is not always clear. Thus, while their biological findings may be of interest to researchers interested in the specifics of TCR signaling (particularly from the standpoints of engineering biomimetics and clinically manipulating TCR signaling strength), it is difficult to extend the significance of these findings more broadly.

As mentioned above, the manuscript contains a nearly impenetrable amount of abbreviations and jargon. I suggest that the authors have the manuscript revised by someone outside the field who is familiar with the work to address this problem. This will make the manuscript more considerably more accessible to a broad range of biologists.

Two additional points that should be addressed:

1. On line 55, the authors state that "[in sharks]… an orthologous gene encoding CD4

appears to be absent, as are genes for proteins associated with CD4+ 56 T cell helper (Th) or regulatory (Treg) functions (e.g. FoxP3 and Rorc) (Venkatesh et al., 2014)" Although this was the primary finding described in the original elephant shark genome paper, subsequent analysis of this genome (JM Dijkstra, Nature 511, 2014) revealed that the CD4+ T cell lineage is likely present within cartilaginous fish.

2. The manuscript relies heavily on evolutionary analysis of CD4 molecules collected from jawed vertebrates. However, the authors do not adequately describe if/how they verified the homology of these molecules. The authors state that "The criteria used for including orthologous CD4 sequences in our analyses were that they have a domain structure consisting of four extracellular Ig domains followed by a TMD and a C-terminal ICD". Many proteins have convergently evolved this domain architecture, which makes this criteria insufficient to assign orthology. A phylogenetic approach is required to confidently determine whether or not the sequences are truly orthologous to CD4. Additionally, all accession numbers and/or aligned sequences should be made freely available in the supplemental material.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Enhancing and inhibitory motifs regulate CD4 activity." for further consideration by eLife. Your revised article has been evaluated by Carla Rothlin (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

1) The reviewers feel that the new signaling data are conceptually the right experiments but may be technically limited by overstimulating T cells and sparsely sampling antigen concentrations in some assays. As discussed by Reviewer 3, despite the sparse dose response applied in the signaling experiments, the manuscript presents the conclusions as if there can be only one interpretation of the results.

For example, whether or not the Clasp plays a positive or negative role in TCR signaling is not settled science (which is why your manuscript is interesting). The reviewers don't feel like the added experiments completely resolve these questions, so we request that your either add additional experiments or appropriately temper the presentation and discussion of your conclusions to acknowledge the remaining gaps in our understanding of this important topic.

2) We understand the difficulty of crafting a narrative that is accessible to all readers but still respects the conventions and standards of both immunology and molecular evolution. While we appreciate the revisions made to improve the readability of the manuscript, we respectfully request that you have a colleague outside the evolution and immunology read the manuscript and highlight sections or terminology or acronyms that could be modified with the goal of increasing the accessibility of the narrative. The extent and substance of these revisions are left to your discretion.

Reviewer #1 (Recommendations for the authors):

Lee and colleagues use phylogenetics and amino acid conservation/covariation to identify previously unstudied motifs within CD4 that emerged in the last common ancestor of eutherians, statistically coevolved, and have contrasting effects on downstream signaling when mutated. Historically, the activity of CD4 on TCRs has been shown to be indirect – mediated by CD4 binding Lck and Lck phosphorylating CD3. The data in this manuscript (based on mutations in positions of CD4 shown by the authors to coevolve) suggest that CD4 also directly regulates or uses an unknown cofactor (not Lck) to regulate TCR signaling.

The revisions by the authors have significantly improved the manuscript by trimming potentially unsupported assertions about the interaction of distal patches on CD4 and addressing previous concerns regarding interpretation of the evolutionary analyses.. The added experiments to test the effects of various mutants on downstream signaling steps provide a limited glimpse of how the different regions of CD4 may be differentially regulating TCR signaling.

My main concern is still whether the very detailed and rigorous analysis of the interactions among CD4-Lck-CD3-TCR and the resulting findings are accessible enough for a broad readership to understand the potential impact of the finding. The evolutionary analyses are interesting, but not especially novel, so I think the impact of this paper is best judged in the context of its contribution to the CD4/TCR literature.

Reviewer #2 (Recommendations for the authors):In their manuscript, "Enhancing and inhibitory motifs regulate CD4 activity", Lee et al., employ an evolutionary approach to identify specific residues within CD4 that impact TCR signaling. CD4 is a transmembrane protein composed of four immunoglobulin domains that mediates T cell signaling in two ways: first, through direct interactions with MHC II on antigen presenting cells; and second, by facilitating intracellular signaling by interacting with the Src kinase, Lck. Traditionally, the researchers have focused on the TCR-MHC II interactions as the key determinants of T cell signaling. However, recent evidence suggests that CD4 may also regulate the outcome of T cell activation. In this study, the authors use phylogenetic analysis as a relatively unbiased means of defining residues within CD4 that are important for signaling. This is predicated on the assumption that protein evolution is constrained by function; residues that directly mediate signaling are less likely to mutate over time. CD4 is a central component of the adaptive immune system and its function is limited to this context. Accordingly, orthologs of CD4 have only been identified within vertebrates.In this study, the authors use computational analyses to characterize the evolution of CD4 within the jawed vertebrates as a foundation for functional experiments. Overall, the manuscript is well-written and the conclusions are supported by the data shown. This work could potentially be of interest to a broad group of researchers, including immunologists interested in adaptive immunity and comparative immunologists as well as cell biologists who are focused on transmembrane cell signaling proteins.

My primary concern with the manuscript is that, in its current form, it is difficult to interpret by a non-specialist in TCR signaling. The manuscript contains a considerable amount of jargon, abbreviations and specific details without the necessary context. I would suggest having the manuscript read by someone who is not familiar with the work and can highlight the more challenging sections.

In a similar vein, the figures are quite complex. The authors might consider simplifying to emphasize the main points.

Specific points to address are outlined below.

1. In the Introduction, the authors refer to the "immunological 'Big Bang' that gave rise to RAG-based antigen receptor gene rearrangement in jawed vertebrates…". This discounts several important advances in the evolution of adaptive immunity, including the characterization of the VLR-based adaptive immune system that evolved in parallel within the jawless vertebrates as well as the discovery of orthologs of the RAG1/2 and CDA enzymes within invertebrate deuterostomes. These findings, which are described and synthesized in Flajnik, 2014 (doi:10.1016/j.cub.2014.09.070), will provide important evolutionary context for this manuscript.

2. Line 69. The authors describe MHC-TCR signaling as mediated by "5 distinct modules", but it is not clear from the subsequent text what these modules are. This should be clarified.

3. Although the authors describe MHC-TCR signaling in great detail, the structure of CD4 is not described. This would be particularly useful to readers who are not specialists in T cell biology and might include a figure showing the four Ig domains, the transmembrane region, and the intracellular domain. The abbreviations ECD, TMD, and ICD should be clearly defined early in the text.

4. Line 115. The authors should briefly mention in the text which CD4 orthologs were used. For example, "X CD4 orthologs were analyzed from Y species, which included the vertebrate lineages …". This should be further elaborated in the methods section. The authors state (line 682) that "… available CD4 orthologs were downloaded from Genbank." More details are necessary. There are many transmembrane proteins that consist of four Ig domains; only a subset of these have been correctly annotated as CD4. The authors should specifically describe their inclusion/exclusion criteria (i.e., define the algorithms and parameters used and the "etc" on line 685). Accession numbers for all these sequences should be included in the manuscript.

5. Although most mammals have a single ortholog of CD4, due to whole genome duplications, many teleost genomes contain two paralogs (e.g., doi:10.4049/jimmunol.1600222). Were these included in the analysis? Conversely, the authors cite Venkatesh et al., 2014 as evidence of the absence of CD4 in sharks. This manuscript was followed by a comment (doi:10.1038/nature13446) that identified many of the "missing" components of adaptive immunity from the elephant shark genome sequence. It would be wise to double-check these findings.

6. Line 131. It is not clear how the distribution of residues subject to purifying selection suggests that "mutation putative linear motifs within the ICR is selected against". This should be clarified or justified.

Reviewer #3 (Recommendations for the authors):

This paper takes an evolutionary approach to investigate the mechanism(s) by which CD4 regulates TCR activation and downstream functional responses. The authors identify conserved motifs in the extracellular, transmembrane, and intracellular domains of CD4 that appear to regulate multiple aspects of its function, including its localization to lipid RAFTs, its capacity to interact with Lck, and its ability to elicit membrane proximal signaling and promote IL-2 production. Notably, one of these motifs, comprising a helix just N-terminal to the cysteine clasp responsible for interaction with Lck, has an inhibitory effect on TCR signaling, and it seems to have coevolved in eutherians together with a motif in extracellular domain 3 that promotes T cell activation. Collectively, these results suggest that the regulation of CD4 activity has been finely tuned during evolution such that the acquisition of activity-promoting mutations is balanced by the emergence of inhibitory regions.

The revisions and new data have improved this manuscript, although I still have some concerns.

1) The manuscript remains a difficult read. I'm not sure how best to deal with this. The acronyms get a bit overwhelming. Perhaps it would be better to remove some of the less used acronyms (e.g. MRCA) and just write the words out in full?

2) The new signaling data (Figure 6) are a welcome addition to the manuscript, but they are rather sparse. In particular, I am worried that the authors may be missing important phenotypes by overstimulating the TCR expressing cells. A dose response should be performed for each signaling readout. It's possible, for instance, that at a lower antigen dose they would observe more substantial differences in CD3z phosphorylation in Figure 6A. This would be consistent with the idea that CD4-Lck interaction matters more when antigenic peptide is limiting.

https://doi.org/10.7554/eLife.79508.sa1

Author response

[Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]

Reviewer #1 (Recommendations for the authors):

Lee and colleagues use evolutionary genomics to identify regions of CD4 that appear to have evolved under functional constraint and which were not previously appreciated to contribute to CD4 function. They proceed to test the function of these regions using in vitro assays to measure how mutations in these regions affect several aspects of CD4 biology including changes in CD4 localization on the membrane, interaction of CD4 with the downstream kinase Lck, and ultimately IL-2 production. The authors find effects on IL-2 production from mutations in all of identified motifs. Of interest, mutations in three of the regions tend to decrease IL-2 production, but mutations in one motif of the intracellular domain (termed "helix" or "IKRLL") increase IL-2 production. From this data, the authors also find evidence that interaction with the Lck kinase may not be necessary to stimulate IL-2 production, counter to the established model of CD4 signaling. Next, an analysis of statistical covariation in CD4 protein sequences showed significant covariation between the intracellular helix and most of the newly identified functional motifs in the intracellular, transmembrane, and extracellular domains. Finally, the authors combine mutations in the intracellular helix (which increase IL-2; "LL") with mutations in evolutionarily covarying regions (which decrease IL-2; "R426A, Clasp, TP") to measure the effects of the double mutants relative to each single mutant. All double mutants appear to behave as the additive effects of the individual mutations, without clear non-additive or epistatic effects.

Overall, the authors use a powerful combination of evolutionary analyses and biochemistry/molecular biology to identify novel functional regions of CD4. However, the author's conclusion that these motifs have coevolved to "finetune" the MHCII response are overstated and not supported by the functional data. Specifically, it is unclear what evidence the authors provide to support regulation of the ECD by intracellular or transmembrane regions. In figure 7, R426A, clasp, and TM mutants all decrease IL-2 on their own. The effects of these mutations in the background of the LL mutation seem to be simply additive, providing no evidence of interaction between these motifs (a la a network of coevolving residues).

Please note that we have removed the term “epistatic effects” from the manuscript. We previously used the term in a manner that we view as consistent with ‘epistatic’ interactions as described by Starr and Thornton (Starr and Thornton, 2016). They identify “permissive epistatic interactions [that] made the residue tolerable in its native background, that restrictive epistatic mutations made it intolerable in the other, or both.” In our system, the LL mutation leads to increased IL2 production. It is likely that the increased signaling that resulted in increased IL-2 would lower the fitness of an organism that acquired this mutation, although admittedly our current experimental setup does not allow us to test this directly. Individually, mutating the other motifs lowers the IL2 response. However, when combined with the LL mutation, the effects of mutating other motifs is indistinguishable from WT, indicating that these mutations counterbalance each other. To us, this suggests that the LL mutation, likely in combination with the non-polar patch with which it co-arose, restricted the evolutionary path of CD4 in mammals leading to the acquisition of the other regulatory motifs. Therefore, in our view, the LL and non-polar patch are examples of permissive epistatic interactions that allowed (and likely necessitated) the evolution of the other motifs identified in this study. We will explore this idea at some point in a future review where this speculative idea may be more appropriate.

The covariation could reflect a 'rheostat' wherein mutations in a stimulatory domain are compensated by mutations in an enhancing domain and vice versa, but there need be no regulation in the sense of a perturbation/input at one motif affects the function of another/allostery.

The comment that motifs with covarying residues could reflect a rheostat mediated by motifs with compensatory activity rather than allosteric interactions between the motifs is in line with our thinking. Indeed, we think it is more likely that residues in the nonpolar patch that stabilize TCR-CD3-pMHCII-CD4 assemblies are not allosterically interacting with the ICD helix but rather that interactions on the outside of the cell serve to dictate the spatial position of the CD4 ICD relative to the TCR-CD3 ICDs, and that the IKRLL motif of the ICD helix determines what the CD4 ICD interacts with and recruits to TCRCD3; however, we cannot rule out an allosteric effect. We did not intend to favor one or the other in the prior submission. Based on this helpful comment, we have tried in the revised Discussion section to be clear that experimentally determining the exact mechanistic basis for the function of the motifs we are studying individually, or in concert with each other, goes beyond the scope of the present study. In short, we’ve made observations that provide evidence of activity that has not been reported before, or even considered in play for CD4. These insights would have been hard to gain from less multi-disciplinary approaches and it is our hope that this study will serve as a hypothesis generator that opens up fertile ground for new discoveries.

To be suitable for a broad readership journal like eLife, the manuscript would benefit from a figure which shows the biological context (all the relevant molecules and the regions of interest in CD4). In addition, the diagrams included in the supplementary showing the mutations and their location within CD4 could be included in this or should be moved to the main figures. Similarly, some summary figure showing the mutations and their effects in a simple manner (up/down/wt) would be immensely useful to process the proposed interaction of mutations.

Thank you for the suggestions. We have now moved the structural model of CD4 to the main Figure 1 (Figure 1C), added Table 1 to describe the mutants, and added Table 2 to summarize the biochemical and functional impacts of the CD4 mutants relative to the WT. We were unclear on what exactly is being requested with regards to a figure showing the biological context, or where to call out such a figure in the text, as we read the suggestion as requesting the types of figures typically found in review articles. We have therefore refrained from making such a figure at this time. We can add one if needed. At issue is that we are unclear as to what would satisfy the requirement of “all relevant molecules” since there could be many. We did take care to overview many of the relevant molecules in the introduction (specifically, the third paragraph). Furthermore, the citations in that paragraph refer to several reviews with figures depicting molecules that are relevant to the current study. In particular, the reviews from the Weiss and Love Labs have what we think are nice illustrations that might satisfy the request (Courtney et al., 2018; Gaud et al., 2018). It is our hope that it is sufficient to reference these reviews for those within the broader community who are looking for more information in order to put our work in a broader context. Please let us know if you would prefer that we add a similar figure, and what level of detail is suitable.

The relevance of the functions measured is not clear – what do we know about the function of raft vs non-raft CD4? Most CD4 is in DSM/non-raft fraction. Why use CTxB and fractionation? Do they tell different things? What about when they don't agree? A clear setup of the why these assays have been chosen is crucial to understanding the relevance of the data.

First, we note that rafts are and have been extensively debated. We have adopted the definition outlined by Linda Pike as reported from the Keystone symposium on lipid rafts and cell function (Pike, 2006): “Membrane rafts are small (10–200 nm) heterogeneous, highly dynamic, sterol-and sphingolipid-enriched domains that compartmentalize cellular processes. Small rafts can sometimes be stabilized to form larger platforms through protein-protein and protein-lipid interactions.” Second, we note that it is now clear that cellular membranes are complex, with a variety of membrane rafts or islands with distinct lipid and protein compositions and that these play critical roles in cellular function (Lillemeier et al., 2010; Lillemeier et al., 2006).

What is known about the function of CD4 in membrane rafts is limited. One study reported a role in immunological synapse formation and another implicated an impact on intracellular signaling after antibody crosslinking (Balamuth et al., 2004; Fragoso et al., 2003). We have not seen studies that have related CD4 membrane raft localization to a downstream function, such as IL-2 production, in response to natural agonist pMHCII ligand as we have done here.

We used CTxB as a staining agent for GM1 (a ganglioside that is reported to concentrate in membrane rafts). Given that rafts are heterogenous, and sucrose gradient fractionation only segregates membranes and associated proteins into DRMs and DSMs, we used CTxB in combination with sucrose gradient fraction to ask if the motifs we are studying influences CD4 localization to distinct membrane domains within DRMs or DSMs. In other words, we interpret CTxB staining as providing additional information about the subdomains in which CD4 resides. Although this widely used approach is limited in the information it can provide, we think it is suitable for asking basic question about protein association with membrane subdomains. The results provide clear, albeit low resolution evidence that some of the motifs we are studying do influence membrane domain fractionation and thus justify future experimentation.

The language should be more precise regarding evolutionary inferences, with a clear delineation of what is a statistical test and what is an inference of those tests. For example:

– The pairwise conservation/mutual information analysis detects statistical covariation which suggests coevolution. This analysis does not show coevolution.

– They find statistical signatures of selection, not identify selection.

– (ln 104-105) "how ancient and ongoing environmental challenges have influenced CD4" should read "find motifs that have been preserved".

To make the manuscript accessible to a broad audience we lost precision in how we described the evolutionary assays and their potential interpretation. We appreciate the reviewer pointing this out and have carefully reworded the text to clarify the distinction between test and interpretation.

The various monikers used for each motif are hard to keep track of. It would greatly help the reader to have a single term to apply to each motif in the text and figures (e.g. Palm and CV+C, ECD and Domain 3).

We appreciate that it can be hard to keep track of what mutations have been made and where they reside within CD4. To aid the reader, we have moved our model CD4 structure to the main Figure 1 and added Table 1, which lists the mutant name, location, the motif mutated, and the specific residues that are mutated for easy reference.

Reviewer #2 (Recommendations for the authors):

"Enhancing and inhibitory motifs have coevolved to regulate CD4 activity" takes an evolutionary approach to investigate the mechanism(s) by which CD4 regulates TCR activation and downstream functional responses. The authors identify conserved motifs in the extracellular, transmembrane, and intracellular domains of CD4 that appear to regulate multiple aspects of its function, including its localization to lipid RAFTs, its capacity to interact with Lck, and its ability to promote IL-2 production. Notably, one of these motifs, comprising a helix just N-terminal to the cysteine clasp responsible for interaction with Lck, has an inhibitory effect on TCR signaling, and it seems to have coevolved in eutherians together with a motif in extracellular domain 3 that promotes T cell activation. Collectively, these results suggest that the regulation of CD4 activity has been finely tuned during evolution such that the acquisition of activity-promoting mutations is balanced by the emergence of inhibitory regions. Although this idea is interesting, it should be accompanied by more in-depth mechanistic work to demonstrate exactly how specific parts of CD4 control TCR activation. The notion that motifs within CD4 have coevolved is not particularly unexpected. These evolutionary relationships should be connected to mechanistic and functional insights into how the molecule works.

Regarding: “Although this idea is interesting, it should be accompanied by more indepth mechanistic work to demonstrate exactly how specific parts of CD4 control TCR activation.”

In consideration of this comment we have worked to rule in or out the most obvious mechanism, based on the TCR signaling paradigm and related models, for how the CD4 mutants with the most dramatic Lck and IL-2 phenotypes (Clasp, TP, and LL) influence TCR-CD3 signal initiation (Glaichenhaus et al., 1991; Rudd, 2021; Stepanek et al., 2014). The TCR signaling paradigm posits that CD4 contributes to TCR-CD3 signal initiation by recruiting Lck, via interactions at the CQC clasp, to phosphorylate the CD3 ITAMs when TCR-CD3 and CD4 both engage pMHCII. This model predicts that our Clasp and LL mutants, both of which reduce CD4-Lck association, should lead to reduced phosphorylation of the CD3z ITAMs. In contrast, because our TP mutant increases CD4-Lck association, it should lead to increased CD3z ITAM phosphorylation. We also analyzed phosphorylation of another substrate of Lck (Zap70), as well as an indirect downstream target (Plcg1), to gain deeper insights into how the Clasp, TP, and LL mutants impact TCR-CD3 proximal signaling events upstream of IL-2 production.

The results are presented in Figure 6. To summarize: 1) our Clasp mutant does not impact CD3z ITAM phosphorylation but does reduce Zap70 and Plcg1 phosphorylation compared to WT CD4; 2) our TP mutant reduces phosphorylation of all three molecules compared to WT CD4; 3) our LL mutant does not have a discernable impact on the phosphorylation of any of these signaling intermediates compared to WT CD4. Our interpretation of these results, and their implications, are detailed in the Discussion (for quick reference: the Clasp mutant (CQC motif) reduces CD4-Lck interactions and IL-2 production; the TP mutant (GGXXG plus CV+C motifs) increases CD4-Lck interactions, localizes CD4-Lck pairs to DSMs, and reduces IL-2; the LL mutant (IKRLL motif) decreases CD4-Lck interactions and increases IL-2 production.)

We will note here that the signaling events that originate at TCR-CD3-pMHCII-CD4 assemblies split into multiple signaling cascades, involving multiple intermediates and second messengers, to direct nuclear localization of multiple transcription factors that together drive IL-2 expression (our endpoint readout for pMHCII-specific signaling) (Courtney et al., 2018; Gaud et al., 2018; Malissen and Bongrand, 2015). We could form several hypothesize to explain how, for example, the IKRLL motif (mutated with our LL mutant) regulates TCR-CD3 signaling but we cannot test them all in one study to conclusively demonstrate how this one mutant functions. We think the most likely explanation for the inhibitory activity of the IKRLL motif is that it is mediated by an unidentified interacting partner. Finding this putative partner will require a screen and extensive characterization. Therefore, while we agree that further mechanistic analysis of the motifs described here is warranted, we ask the reviewer to please consider that demonstrating exactly how specific parts of CD4 control pMHCII-specific signaling could take years and numerous additional datasets. For this reason, we hope the reviewer will agree that doing so goes beyond the scope of the current study but that our body of data make a compelling case that further study is warranted.

Regarding: “The notion that motifs within CD4 have coevolved is not particularly unexpected.”

To our knowledge, the question has not been asked or investigated for CD4 or other components of multi-module activating immune receptors.

Regarding: “These evolutionary relationships should be connected to mechanistic and functional insights into how the molecule works.”

Our study was designed to describe evolutionary, mechanistic, and functional relationships by relating how the distinct motifs identified by our evolutionary analysis influence membrane domain localization, CD4 association with Lck, and IL-2 production in response to pMHCII. We chose membrane domain localization and Lck association because both have been proposed to regulate CD4 function, and we chose IL-2 production as an endpoint readout for differences in pMHCII-specific signaling (Fragoso et al., 2003; Glaichenhaus et al., 1991; Stepanek et al., 2014). We acknowledge that differences in IL-2 production can tell us that there is a difference in signaling between a particular mutant of CD4 and the WT molecule, but not where and in what pathway that defect lies. As detailed above, we have added analysis of TCR-CD3 proximal signaling events. We think that our results support the conclusion that CD4 function is more complex than can be explained by the TCR signaling paradigm and related models. We appreciate Reviewer #2’s view that more mechanistic work is needed and trust that, once our study is available to the field, the current study will help fuel interest in this topic.

1) Just because motifs have coevolved and their mutations have additive effects doesn't necessarily mean that they are functionally coupled. Mechanistic insight will require a more in-depth analysis of membrane proximal signaling events and the activation status of downstream pathways (e.g. Erk, Ca, NFKB).

We appreciate this comment. As with any study, we have put forth an interpretation of our data that we think is most consistent with all of the results, and other published work. We acknowledge that we cannot know with certainty why particular residues or motifs have coevolved. That being written, there is a growing body of evidence that points to a functional relationship between residues that coevolve, even if they are located at distant sites.

We have removed the term “functionally coupled” from the revision in consideration that there may be a difference in perspective regarding how it should be used or interpreted. We now use the term “counterbalance” to describe the neutral phenotype (equal to WT) we observe when we combine a motif that reduces IL-2 with one that enhances IL-2.

As discussed above, we have added additional mechanistic insights about our Clasp and TP mutants. We agree that more is needed and think that there is a long road ahead in understanding the function of the ICD helix, and IKRLL motif therein, as it is clearly a multifunctional hub with which other residues have covaried over evolutionary time. For the reasons outlined above, we think that providing the exact mechanistic details of how different motifs interact functionally requires that we first understand the function of the ICD helix, and IKRLL motif therein. We think that doing so goes beyond the scope of this study.

2) The idea that the role of CD4 is to control the colocalization of the TCR and Lck to RAFTs is quite interesting, and it should be tested. Could RAFT localization be modulated independently of CD4, and would this reverse the effects of the relevant CD4 mutations?

We agree that this is an interesting question and think that interrogating it properly would require a separate study. There are real technical challenges to doing this well.

3) It is difficult to get a sense of what the fractionation and lipid association results really mean in terms of membrane organization (e.g. Figure 2). CTxB binding in the context of a bead-based IP is particularly unphysiological. A positive control would be helpful here. What would an established disruption of RAFT localization look like in this assay (e.g. a mutation in the Lck N-terminal domain).

While membrane domains and their composition are clearly important for several biological processes, they are challenging to study. We used a classic, well-accepted biochemical technique (sucrose gradient fractionation) and a quantitative flow-based IP readout (basically a bead-based ELISA) to evaluate how the motifs we are studying impact segregation of CD4 molecules or CD4-Lck pairs into DRMs (membrane rafts) or DSMs (non-rafts)(Fragoso et al., 2003; Glassman et al., 2016; Parrish et al., 2016; Schrum et al., 2007). One reason for using this approach is that the impact of mutating some of the motifs studied here on sucrose gradient fractionation has already been published and thus those studies provide us with a benchmark for comparison [i.e. the CQC clasp motif, the CV+C palmitoylation motif, and the HXXR motif (shown in original submission but removed from the revision) (Fragoso et al., 2003; Popik and Alce, 2004)]. Those studies showed in single representative Western blots that mutating the CQC, CV+C, and HXXR motifs reduced CD4 localization to DRMs. Mutating these motifs yields similar results in our analysis. We hope that this comparison addresses the positive control comment.

We stained with CTxB because it binds the ganglioside GM1 that is reported to be enriched in membrane rafts. We agree that CTxB binding of bead-based IP is not physiological, although Western blot analysis of sucrose fractionated cell lysates is also not physiological. We looked at CTxB staining because we thought it might provide further insights into CD4 localization in membrane subdomains within DRMs and DSMs. We think the finding that our Clasp and Palm mutants have very different CTxB staining within the DRMs suggests that these two motifs control localization to distinct subdomains within DRMs for those CD4 molecules still localized in DRMs. We also think it is interesting that our TMD and Palm mutants have the same CTxB staining in DRMs as WT CD4 and that the TP mutant (TMD+Palm) has less CTxB staining that the Palm mutant. These data suggest that together, the GGXXG and CV+C motifs impact membrane domain, or subdomain, localization differently than either one individually. We do not know exactly what this means about membrane organization as the approach is admittedly low-resolution and our interpretation is limited. Nevertheless, we included the data in the manuscript because we think that some people in the field might find it interesting, useful, or otherwise hypothesis generating. We can remove the CTxB analysis from the manuscript if that is preferred.

4) Based on the presented data, one might conclude that mutation of the ICD helix enhances T cell responses simply by inhibiting CD4 internalization, thereby maintaining the density of CD4 on the cell surface. Indeed, all of the mutations that enhance IL-2 responses appear to result in higher surface expression. Do the authors favor this hypothesis, or can it be ruled out?

We have added additional data to address this point. Specifically, we have analyzed additional ICD helix mutants in Figure 4 to understand how serines flanking the IKRLL motif influence the inhibitory function of the IKRLL motif. Endocytosis of all of the ICD helix mutants is impaired, yet they have distinct IL-2 phenotypes suggesting that IL-2

production is more heavily influenced by the ICDs than expression levels. In addition, we included data with a C-terminally truncated CD4 molecule, termed CD4-T1 in accordance with previous labeling, for a mutant that is truncated immediately after the CV+C motif and thus lacks the remaining ICD, including the helix (see Table1 and Figure 4 —figure supplement 5) (Glaichenhaus et al., 1991). CD4-T1 expresses at equivalent levels to the LL mutant, and fails to endocytose, but does not direct increased IL-2 production relative to the WT the way the LL mutant does. Instead, it directs slightly reduced IL-2 production. These data clearly indicate that the differences in IL-2 production between the CD4-T1 and LL mutants are due to differences in their ICDs and not their expression levels at the initiation or throughout the co-culture with APCs.

Of additional note, we have moved data concerning the H and HC mutants to Figure 3. In revising the manuscript, we realized that the cell lines used for the flow cytometry and sucrose data were not the same as those used in the IL-2 (not generated on the same day). We have now adjusted the data such that all data is from a single set of cell lines generated at the same time. Specifically, we updated the flow cytometry and sucrose data because the cell lines shown throughout the revised figure was more closely matched in CD4 expression, which is why they had originally been chosen for the IL-2 data. Now, for all figures in the manuscript, the flow data, sucrose gradient data, and IL2 data are all from the same set of lines wherein the WT and mutants being compared were generated at the same time.

5) Given the evolutionary focus of this study, it would have been more interesting if the authors had actually used sequences from non-eutherian CD4s, as opposed to Ala and Gly substitutions. Was this attempted?

The goal of this study was to better understand CD4 in general, and eutherian CD4 in particular. We focused specifically on mouse and human CD4 since the former is an extensively-used model for the latter, which has obvious implications for immunotherapeutic engineering and human health. This is in accordance with our NIH funded project. Given our goal, we decided to take a more conventional structure function approach and use mutations that would disrupt the chemical nature of the motifs under investigation.

We think that replacing eutherian sequences with those from non-eutherian CD4 would also be incredibly interesting; however, such experiments would be performed in response to a different question with a different end-goal in mind. We also think this approach may be harder to interpret because the non-eutherian CD4 homologous evolved along a different trajectory, in potentially different cellular environments (e.g. temperature, membrane composition). We do not know how to predict how non eutherian motifs would behave in the background of mouse CD4 in mouse cells. We also think there may be many caveats associated with interpreting such data. Overall, we think this is a different question that would be worthy of a separate study.

6) It is hypothesized that the importance of the GGXXG motif may depend on the local cholesterol concentration of the membrane domain in which it resides. Does changing membrane cholesterol modulate the effects of mutations in the GGXXG motif?

Changing membrane cholesterol levels or interfering in other ways is expected to have effects beyond CD4, making any results challenging to interpret (Chen et al., 2022; Swamy et al., 2016; Wang et al., 2016). Therefore, we thought that speculating about the interplay between cholesterol concentrations and the GGXXG motif was more appropriate for the Discussion.

7) The authors should confirm that the CV+C mutation actually has the intended consequence of altering CD4 palmitoylation.

Because previous publications have established that the cysteines of the CD4 CV+C motif are plamitoylated, and other studies have shown that similar motifs are also palmitoylated in other proteins, we consider this to be well established in the field (Arcaro et al., 2000; Crise and Rose, 1992; Fragoso et al., 2003; Ladygina et al., 2011).

Reviewer #3 (Recommendations for the authors):

In this manuscript, Lee et al., the authors interrogate the function of CD4 from an evolutionary perspective. Adaptive immunity within the jawed vertebrates is believed to have arisen as a consequence of the "Immunological Big Bang". This event marks the evolutionary origin of both T and B cell receptors, as well as many of the molecules that mediate their signaling (e.g., the TCR co-receptors CD4 and CD8, as well as the MHC I and II proteins). Although orthologs of TCRs, CD4, and MHCII are broadly conserved throughout jawed vertebrates, these proteins have not yet been identified outside of this lineage. Notably, all three molecules first appear within the cartilaginous fishes, suggesting that their functions may be tightly linked. However, most studies on TCR function focus specifically on TCRs interacting directly with either CD4 or MHCII. In this study, the authors draw on evolutionary analyses to identify broadly conserved sequence motifs that may be involved in direct interactions between CD4 and MHCII to fine-tune the strength of TCR signaling. They complement these computational analyses with biochemical studies to support their hypothesis.

The primary strength of this manuscript lies in the author’s integrative approach; their evolutionary hypotheses are considerably strengthened by the findings from the experimental techniques. However, in its current form, the manuscript is very difficult to follow for a non-specialist in TCR signaling. The authors use a considerable amount of jargon and abbreviations. Consequently, the logic underlying their experiments is not always clear. Thus, while their biological findings may be of interest to researchers interested in the specifics of TCR signaling (particularly from the standpoints of engineering biomimetics and clinically manipulating TCR signaling strength), it is difficult to extend the significance of these findings more broadly.

See specific comments below.

As mentioned above, the manuscript contains a nearly impenetrable amount of abbreviations and jargon. I suggest that the authors have the manuscript revised by someone outside the field who is familiar with the work to address this problem. This will make the manuscript more considerably more accessible to a broad range of biologists.

We understand and agree that field-specific nomenclature can, at times, feel like a foreign language to those outside of the field. Indeed, as an interdisciplinary collaboration, our team has had to spend time and effort learning each other’s language. This has been challenging, but also fun. We appreciate the suggestion and will note that we did have colleagues who understand the evolutionary analysis but not the immunology, and vice versa, read the manuscript and provide comments. Their suggestions were incorporated into the initial submission as we anticipated that we were likely to get one or more comments such as this and can assure the reviewer we have tried to make the manuscript accessible. We will note that our attempts backfired somewhat as Reviewer #1 took issue with the precision of our language regarding our evolutionary analysis which was intended to make the work more accessible.

We ask the reviewer to consider that we are constrained in how we relate our work to the reader. The immunological terms, including names of the cell types and proteins, must be kept consistent with the field in order for people inside and outside the field to be able to relate our work to that of others in the field. And, as called out by Reviewer #1, we must also use precision when discussing the evolutionary aspects of the work. In short, we think that we are generally obliged to stick to convention when discussing key aspects of this work. With regards to other nomenclature, or jargon, we have tried to use standard terms. For example, we used ECD, TMD, and ICD to refer to the extracellular, transmembrane, and intracellular domains of a protein as is common in biochemistry and molecular and cellular biology because these abbreviations should be broadly accessible. Similarly, referring to the detergent resistant and detergent soluble membrane fractions of sucrose gradients as DRMs and DSMs is conventional. We would be happy to use the long form instead of the abbreviations if Reviewer #3 and the editor think it would be more appropriate and we are not constrained by space.

Finally, we acknowledge that the mutant names may be a problem as we sometimes have a hard time keeping track of all the mutants. For this reason, we chose to name our mutants in a way that relates to their location or particular motif. For example, we found that calling the CQC clasp motif mutant “Clasp” and our CV+C palmitoylation motif mutant “Palm” was easier for us to keep track of than using a residue-based naming system such as C444S+C446S and C418S+C421S, respectively. We have now added Table #1 as a quick reference for the mutant names, locations, residue number being changed, and the nature of the mutation. We hope that this helps.

Two additional points that should be addressed:

1. On line 55, the authors state that "[in sharks]… an orthologous gene encoding CD4

appears to be absent, as are genes for proteins associated with CD4+ 56 T cell helper (Th) or regulatory (Treg) functions (e.g. FoxP3 and Rorc) (Venkatesh et al., 2014)" Although this was the primary finding described in the original elephant shark genome paper, subsequent analysis of this genome (JM Dijkstra, Nature 511, 2014) revealed that the CD4+ T cell lineage is likely present within cartilaginous fish.

We appreciate this comment and have removed this mention of the Venkatesh paper from our manuscript due to the controversial nature of their finding.

2. The manuscript relies heavily on evolutionary analysis of CD4 molecules collected from jawed vertebrates. However, the authors do not adequately describe if/how they verified the homology of these molecules. The authors state that "The criteria used for including orthologous CD4 sequences in our analyses were that they have a domain structure consisting of four extracellular Ig domains followed by a TMD and a C-terminal ICD". Many proteins have convergently evolved this domain architecture, which makes this criteria insufficient to assign orthology. A phylogenetic approach is required to confidently determine whether or not the sequences are truly orthologous to CD4. Additionally, all accession numbers and/or aligned sequences should be made freely available in the supplemental material.

As the reviewer points out, it is hard to demonstrate that proteins are orthologous. Our dataset is based on reciprocal blast-based searches of the NCBI database. Sequences were aligned and used to construct phylogenetic trees. Sequences that either did not have a canonical CD4 structure or contained large indels were removed from the analysis. Likewise, we assumed that the evolution of CD4 should reflect that of the host species. Sequences that did not cluster as expected were removed from the dataset. The final dataset, the aligned sequences, and phylogenetic trees are available from Dryad (https://doi.org/10.5061/dryad.59zw3r26z).

References cited in our response to reviewers:

Arcaro, A., Gregoire, C., Boucheron, N., Stotz, S., Palmer, E., Malissen, B., and Luescher, I.F. (2000). Essential role of CD8 palmitoylation in CD8 coreceptor function. J Immunol 165, 2068-2076.

Balamuth, F., Brogdon, J.L., and Bottomly, K. (2004). CD4 raft association and signaling regulate molecular clustering at the immunological synapse site. J Immunol 172, 58875892.

Chen, Y., Zhu, Y., Li, X., Gao, W., Zhen, Z., Dong, Huang, B., Ma, Z., Zhang, A., Song, X., et al. (2022). Cholesterol inhibits TCR signaling by directly restricting TCR-CD3 core tunnel motility. Mol Cell.

Courtney, A.H., Lo, W.L., and Weiss, A. (2018). TCR Signaling: Mechanisms of Initiation and Propagation. Trends in biochemical sciences 43, 108-123.

Crise, B., and Rose, J.K. (1992). Identification of palmitoylation sites on CD4, the human immunodeficiency virus receptor. J Biol Chem 267, 13593-13597.

Davey, N.E., Cyert, M.S., and Moses, A.M. (2015). Short linear motifs – ex nihilo evolution of protein regulation. Cell Commun Signal 13, 43.

Fragoso, R., Ren, D., Zhang, X., Su, M.W., Burakoff, S.J., and Jin, Y.J. (2003). Lipid raft distribution of CD4 depends on its palmitoylation and association with Lck, and

evidence for CD4-induced lipid raft aggregation as an additional mechanism to enhance CD3 signaling. J Immunol 170, 913-921.

Gaud, G., Lesourne, R., and Love, P.E. (2018). Regulatory mechanisms in T cell receptor signalling. Nat Rev Immunol 18, 485-497.

Glaichenhaus, N., Shastri, N., Littman, D.R., and Turner, J.M. (1991). Requirement for association of p56lck with CD4 in antigen-specific signal transduction in T cells. Cell 64, 511-520.

Glassman, C.R., Parrish, H.L., Deshpande, N.R., and Kuhns, M.S. (2016). The CD4 and CD3deltaepsilon Cytosolic Juxtamembrane Regions Are Proximal within a Compact TCR-CD3-pMHC-CD4 Macrocomplex. J Immunol 196, 4713-4722.

Kim, P.W., Sun, Z.Y., Blacklow, S.C., Wagner, G., and Eck, M.J. (2003). A zinc clasp structure tethers Lck to T cell coreceptors CD4 and CD8. Science 301, 1725-1728.

Kobayashi, S., Thelin, M.A., Parrish, H.L., Deshpande, N.R., Lee, M.S., Karimzadeh, A., Niewczas, M.A., Serwold, T., and Kuhns, M.S. (2020). A biomimetic five-module chimeric antigen receptor ((5M)CAR) designed to target and eliminate antigen-specific T cells. Proc Natl Acad Sci U S A 117, 28950-28959.

Ladygina, N., Martin, B.R., and Altman, A. (2011). Dynamic palmitoylation and the role of DHHC proteins in T cell activation and anergy. Adv Immunol 109, 1-44.

Lillemeier, B.F., Mortelmaier, M.A., Forstner, M.B., Huppa, J.B., Groves, J.T., and Davis, M.M. (2010). TCR and Lat are expressed on separate protein islands on T cell membranes and concatenate during activation. Nature immunology 11, 90-96.

Lillemeier, B.F., Pfeiffer, J.R., Surviladze, Z., Wilson, B.S., and Davis, M.M. (2006). Plasma membrane-associated proteins are clustered into islands attached to the cytoskeleton. Proc Natl Acad Sci U S A 103, 18992-18997.

Malissen, B., and Bongrand, P. (2015). Early T cell activation: integrating biochemical, structural, and biophysical cues. Annu Rev Immunol 33, 539-561.

Parrish, H.L., Deshpande, N.R., Vasic, J., and Kuhns, M.S. (2016). Functional evidence for TCR-intrinsic specificity for MHCII. Proc Natl Acad Sci U S A.

Parrish, H.L., Glassman, C.R., Keenen, M.M., Deshpande, N.R., Bronnimann, M.P., and

Kuhns, M.S. (2015). A Transmembrane Domain GGxxG Motif in CD4 Contributes to Its Lck-Independent Function but Does Not Mediate CD4 Dimerization. PLoS One 10, e0132333.

Pike, L.J. (2006). Rafts defined: a report on the Keystone Symposium on Lipid Rafts and Cell Function. J Lipid Res 47, 1597-1598.

Popik, W., and Alce, T.M. (2004). CD4 receptor localized to non-raft membrane microdomains supports HIV-1 entry. Identification of a novel raft localization marker in CD4. J Biol Chem 279, 704-712.

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Schrum, A.G., Gil, D., Dopfer, E.P., Wiest, D.L., Turka, L.A., Schamel, W.W., and Palmer, E. (2007). High-sensitivity detection and quantitative analysis of native proteinprotein interactions and multiprotein complexes by flow cytometry. Sci STKE 2007, pl2.

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Stepanek, O., Prabhakar, A.S., Osswald, C., King, C.G., Bulek, A., Naeher, D., BeaufilsHugot, M., Abanto, M.L., Galati, V., Hausmann, B., et al. (2014). Coreceptor scanning by the T cell receptor provides a mechanism for T cell tolerance. Cell 159, 333-345.

Swamy, M., Beck-Garcia, K., Beck-Garcia, E., Hartl, F.A., Morath, A., Yousefi, O.S., Dopfer, E.P., Molnar, E., Schulze, A.K., Blanco, R., et al. (2016). A Cholesterol-Based Allostery Model of T Cell Receptor Phosphorylation. Immunity 44, 1091-1101.

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[Editors’ note: what follows is the authors’ response to the second round of review.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

1) The reviewers feel that the new signaling data are conceptually the right experiments but may be technically limited by overstimulating T cells and sparsely sampling antigen concentrations in some assays. As discussed by Reviewer 3, despite the sparse dose response applied in the signaling experiments, the manuscript presents the conclusions as if there can be only one interpretation of the results.

For example, whether or not the Clasp plays a positive or negative role in TCR signaling is not settled science (which is why your manuscript is interesting). The reviewers don't feel like the added experiments completely resolve these questions, so we request that your either add additional experiments or appropriately temper the presentation and discussion of your conclusions to acknowledge the remaining gaps in our understanding of this important topic.

We appreciate this comment and have taken your suggestions to modify the manuscript. In brief:

Please note that we have now also added analysis of two additional independently generated WT and Clasp mutant cell line pairs to our proximal signaling analysis in Figure 6A for a total of four independent generated lines. While we found no difference in the pCD3z MFI compared to the WT for the two independently generated cell lines we originally tested, one of the two additional cell line showed pCD3z MFI that was ~83% of WT. The other line showed a very small reduction in pCD3z MFI (~92% of WT), which is nevertheless statistically significant. The key point remains that we do not see an absence of pCD3z MFI with the Clasp mutant, nor do we see a consistent reduction in pCD3z MFI as is expected based on the prevailing paradigm.

Thank you. We followed this suggestion and asked a colleague, Dr. Benjamin Renquist who works on obesity in mice, to read the manuscript and provide suggestions on how to make the manuscript more accessible. We greatly appreciate the gift of his time and have worked to incorporate his suggestions into the manuscript.

We understand the difficulty of crafting a narrative that is accessible to all readers but still respects the conventions and standards of both immunology and molecular evolution. While we appreciate the revisions made to improve the readability of the manuscript, we respectfully request that you have a colleague outside the evolution and immunology read the manuscript and highlight sections or terminology or acronyms that could be modified with the goal of increasing the accessibility of the narrative. The extent and substance of these revisions are left to your discretion.

Reviewer #2 (Recommendations for the authors):

In their manuscript, "Enhancing and inhibitory motifs regulate CD4 activity", Lee et al., employ an evolutionary approach to identify specific residues within CD4 that impact TCR signaling. CD4 is a transmembrane protein composed of four immunoglobulin domains that mediates T cell signaling in two ways: first, through direct interactions with MHC II on antigen presenting cells; and second, by facilitating intracellular signaling by interacting with the Src kinase, Lck. Traditionally, the researchers have focused on the TCR-MHC II interactions as the key determinants of T cell signaling. However, recent evidence suggests that CD4 may also regulate the outcome of T cell activation. In this study, the authors use phylogenetic analysis as a relatively unbiased means of defining residues within CD4 that are important for signaling. This is predicated on the assumption that protein evolution is constrained by function; residues that directly mediate signaling are less likely to mutate over time. CD4 is a central component of the adaptive immune system and its function is limited to this context. Accordingly, orthologs of CD4 have only been identified within vertebrates.

In this study, the authors use computational analyses to characterize the evolution of CD4 within the jawed vertebrates as a foundation for functional experiments. Overall, the manuscript is well-written and the conclusions are supported by the data shown. This work could potentially be of interest to a broad group of researchers, including immunologists interested in adaptive immunity and comparative immunologists as well as cell biologists who are focused on transmembrane cell signaling proteins.

My primary concern with the manuscript is that, in its current form, it is difficult to interpret by a non-specialist in TCR signaling. The manuscript contains a considerable amount of jargon, abbreviations and specific details without the necessary context. I would suggest having the manuscript read by someone who is not familiar with the work and can highlight the more challenging sections.

We understand the comment and thank you for the suggestion. We did have another colleague, Dr. Benjamin Renquist who studies obesity in mouse models, read the paper to provide additional critical feedback. He also indicated that the numerous acronyms and sheer number of mutants made for a tough read. He found that he could navigate the manuscript with the aid of Figure 1C and Table 1, suggested by the reviewers (thank you), and he suggested as with Reviewer #3 that we should take the space to spell out key acronyms. We have now done this. He also suggested adding an acronym key to Table 2, which we have now done. In addition, following this line of thinking, we have added acronym keys to the figure legends.

In a similar vein, the figures are quite complex. The authors might consider simplifying to emphasize the main points.

We also appreciate this comment but are unsure how to further modify them beyond what we have already done. As noted above, we did add acronym keys to the figure legends.

Specific points to address are outlined below.

1. In the Introduction, the authors refer to the "immunological 'Big Bang' that gave rise to RAG-based antigen receptor gene rearrangement in jawed vertebrates…". This discounts several important advances in the evolution of adaptive immunity, including the characterization of the VLR-based adaptive immune system that evolved in parallel within the jawless vertebrates as well as the discovery of orthologs of the RAG1/2 and CDA enzymes within invertebrate deuterostomes. These findings, which are described and synthesized in Flajnik, 2014 (doi:10.1016/j.cub.2014.09.070), will provide important evolutionary context for this manuscript.

We appreciate the comments and suggestion. We think these are all important evolutionary developments in adaptive immunity that serve as a nice introduction to the paper from “30,000ft” before zooming in specifically on the evolution of CD4 and its role in T cell biology. We have now cited the Flajnik paper to point interested readers to a key resource for a broader sense of these important issues. We now take more care to be clear to state that this paper is focused on adaptive immunity in jawed vertebrates.

2. Line 69. The authors describe MHC-TCR signaling as mediated by "5 distinct modules", but it is not clear from the subsequent text what these modules are. This should be clarified.

We appreciate this suggestion and have now more carefully described each module.

3. Although the authors describe MHC-TCR signaling in great detail, the structure of CD4 is not described. This would be particularly useful to readers who are not specialists in T cell biology and might include a figure showing the four Ig domains, the transmembrane region, and the intracellular domain. The abbreviations ECD, TMD, and ICD should be clearly defined early in the text.

Figure 1C shows a model of CD4 with the elements highlighted as suggested. We have also added an acronym key to the figure legend to aid the reader in understanding the figure.

4. Line 115. The authors should briefly mention in the text which CD4 orthologs were used. For example, "X CD4 orthologs were analyzed from Y species, which included the vertebrate lineages …". This should be further elaborated in the methods section. The authors state (line 682) that "… available CD4 orthologs were downloaded from Genbank."

We have updated to text to read:

We performed multiple analyses of available vertebrate CD4 ortholog sequences (n=99 distinct sequences), representing ~435 million years of evolution, to understand how ancient and ongoing environmental challenges have influenced CD4. The analyzed sequences represent fish, reptiles (including birds), marsupials, and placental mammals. Details related to ortholog selection are outlined in Materials and methods. All sequences and files are available through the DataDryad repository associated with this manuscript.

More details are necessary. There are many transmembrane proteins that consist of four Ig domains; only a subset of these have been correctly annotated as CD4. The authors should specifically describe their inclusion/exclusion criteria (i.e., define the algorithms and parameters used and the "etc" on line 685).

Available CD4 orthologs were identified through reciprocal blast-based searches and downloaded from GenBank. BLAST may not only identify orthologs so additional criteria were used for including putative orthologous CD4 sequences in our analyses: presence of a domain structure consisting of four extracellular Ig domains followed by a transmembrane domain and a C-terminal intracellular domain, including the presence of the Lck binding clasp (CxC). Sequences that were shorter, contained frameshift mutations, or displayed high sequence variability were excluded from the analysis.

Accession numbers for all these sequences should be included in the manuscript.

Accession numbers are available through the DataDryad repository associated with this manuscript.

5. Although most mammals have a single ortholog of CD4, due to whole genome duplications, many teleost genomes contain two paralogs (e.g., doi:10.4049/jimmunol.1600222). Were these included in the analysis?

Based on the criteria described above we filtered out potential duplicates.

Conversely, the authors cite Venkatesh et al., 2014 as evidence of the absence of CD4 in sharks. This manuscript was followed by a comment (doi:10.1038/nature13446) that identified many of the "missing" components of adaptive immunity from the elephant shark genome sequence. It would be wise to double-check these findings.

We removed the statement in question and reference to the Venkatesh paper.

6. Line 131. It is not clear how the distribution of residues subject to purifying selection suggests that "mutation putative linear motifs within the ICR is selected against". This should be clarified or justified.

The data demonstrates that 45% of the residues in the intracellular domain of CD4 are under purifying selection. We interpret this to demonstrate that mutating these residues that are primarily located in unstructured protein regions negatively affect fitness and are selected against.

Reviewer #3 (Recommendations for the authors):

This paper takes an evolutionary approach to investigate the mechanism(s) by which CD4 regulates TCR activation and downstream functional responses. The authors identify conserved motifs in the extracellular, transmembrane, and intracellular domains of CD4 that appear to regulate multiple aspects of its function, including its localization to lipid RAFTs, its capacity to interact with Lck, and its ability to elicit membrane proximal signaling and promote IL-2 production. Notably, one of these motifs, comprising a helix just N-terminal to the cysteine clasp responsible for interaction with Lck, has an inhibitory effect on TCR signaling, and it seems to have coevolved in eutherians together with a motif in extracellular domain 3 that promotes T cell activation. Collectively, these results suggest that the regulation of CD4 activity has been finely tuned during evolution such that the acquisition of activity-promoting mutations is balanced by the emergence of inhibitory regions.

The revisions and new data have improved this manuscript, although I still have some concerns.

1) The manuscript remains a difficult read. I'm not sure how best to deal with this. The acronyms get a bit overwhelming. Perhaps it would be better to remove some of the less used acronyms (e.g. MRCA) and just write the words out in full?

We understand and appreciate both the feedback and suggestion. We have written out the acronyms in the manuscript.

2) The new signaling data (Figure 6) are a welcome addition to the manuscript, but they are rather sparse. In particular, I am worried that the authors may be missing important phenotypes by overstimulating the TCR expressing cells. A dose response should be performed for each signaling readout. It's possible, for instance, that at a lower antigen dose they would observe more substantial differences in CD3z phosphorylation in Figure 6A. This would be consistent with the idea that CD4-Lck interaction matters more when antigenic peptide is limiting.

We appreciate the comment and agree that our Clasp mutation might impair CD3z phosphorylation at low levels of agonist MCC, particularly at the levels where CD4 has been shown to be critical for calcium mobilization as a readout of proximal signaling events (<20). However, formally testing this in a convincing manner would be very challenging and would take a good deal of time. We have therefore addressed the concern as follows:

Please know that we considered the potential pitfall of overstimulation, and other technical issues, during internal deliberations about how best to study the influence of CD4 on proximal signaling events. After several pilot experiments, we settled on an approach that we consider to be robust, well-controlled, and interpretable within the parameters tested.

Key considerations in designing our experiments were that we wanted to: 1) synchronize TCR engagement on our WT and mutant cell lines; 2) use agonist pMHCII to engage TCR-CD3 and CD4 with their physiological ligands to avoid non-physiological signaling; 3) generate detectable levels of signal to be able to confidently distinguish signal from noise for data interpretation; and, 4) distinguish between differences in the frequency of responding cells and differences in the intensity of responses among responding cells, which is something that cannot be achieved with bulk cell signaling assays such as Western blots.

Antibody crosslinking (e.g. anti-CD3) is typically used in T cell signaling studies to activate cells. This approach provides reasonable control over the timing of signaling initiation, such that many cells within a sample can be activated with relative synchrony due to the high levels of antibody used to ensure robust signal. We were concerned that using anti-CD3/anti-CD4 crosslinking to synchronize activation of our sample populations would stimulate our cells in a non-physiological, and potentially overstimulating manner due to many factors including drastically slowed kinetics of engagement relative to TCR-CD3 and CD4 engagement of pMHCII on APCs (Glassman et al., 2018). We therefore reasoned that, with such an approach, any differences we might observe between the WT and mutant cells may not faithfully mirror any differences that result from engagement of agonist pMHCII, making it hard to confidently interpret the results. Using APCs expressing high densities of agonist pMHCII ensures that when the T cells and APCs are spun together and shifted to 37ºC to initiate signaling, that TCRs and CD4 should rapidly find their ligands to initiate signaling in sufficient numbers to allow for detectable signals with current approaches.

We are concerned that experiments using APCs with low doses of agonist pMHCII would be challenging for the following reasons:

https://doi.org/10.7554/eLife.79508.sa2

Article and author information

Author details

  1. Mark S Lee

    Department of Immunobiology, The University of Arizona College of Medicine, Tucson, United States
    Contribution
    Resources, Data curation, Formal analysis, Validation, Investigation, Visualization, 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, Validation, Investigation, 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, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  4. Katrina Lichauco

    Department of Immunobiology, The University of Arizona College of Medicine, Tucson, United States
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9480-2893
  5. Heather L Parrish

    Department of Immunobiology, The University of Arizona College of Medicine, Tucson, United States
    Contribution
    Conceptualization, Resources, Validation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  6. 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, 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
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft
    For correspondence
    vandoorslaer@arizona.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2985-0733
  7. 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, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    mkuhns@email.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
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0403-6313

Funding

National Institute of Allergy and Infectious Diseases (R01AI101053)

  • Michael S Kuhns

Cancer Center Support Grant (CCSG-CA 023074)

  • Michael S Kuhns

AZ TRIF Funds

  • Koenraad Van Doorslaer

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by the National Institutes of Health/National Institute of Allergy and Infections Diseases Grant R01AI101053 (MSK), the Pew Scholars Program in the Biomedical Sciences (MSK), the Cancer Center Support Grant CCSG-CA 023074 for flow cytometry (MSK), and AZ TRIF funds from the BIO5 Institute (KVD). We thank Thomas Serwold and Leslie Berg for critical feedback as well as Dominik Schenten, Caleb Glassman, Joseph Harrison, Piet Maes, and Benjamin Renquist for critically reading the manuscript.

Senior Editor

  1. Carla V Rothlin, Yale School of Medicine, United States

Reviewing Editor

  1. Richard N McLaughlin, Pacific Northwest Research Institute, United States

Publication history

  1. Preprint posted: April 30, 2021 (view preprint)
  2. Received: April 15, 2022
  3. Accepted: July 20, 2022
  4. Accepted Manuscript published: July 21, 2022 (version 1)
  5. Version of Record published: July 28, 2022 (version 2)

Copyright

© 2022, Lee et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Mark S Lee
  2. Peter J Tuohy
  3. Caleb Y Kim
  4. Katrina Lichauco
  5. Heather L Parrish
  6. Koenraad Van Doorslaer
  7. Michael S Kuhns
(2022)
Enhancing and inhibitory motifs regulate CD4 activity
eLife 11:e79508.
https://doi.org/10.7554/eLife.79508

Further reading

    1. Evolutionary Biology
    2. Microbiology and Infectious Disease
    Pramod K Jangir et al.
    Research Article

    Bacterial pathogens show high levels of chromosomal genetic diversity, but the influence of this diversity on the evolution of antibiotic resistance by plasmid acquisition remains unclear. Here, we address this problem in the context of colistin, a ‘last line of defence’ antibiotic. Using experimental evolution, we show that a plasmid carrying the MCR-1 colistin resistance gene dramatically increases the ability of Escherichia coli to evolve high-level colistin resistance by acquiring mutations in lpxC, an essential chromosomal gene involved in lipopolysaccharide biosynthesis. Crucially, lpxC mutations increase colistin resistance in the presence of the MCR-1 gene, but decrease the resistance of wild-type cells, revealing positive sign epistasis for antibiotic resistance between the chromosomal mutations and a mobile resistance gene. Analysis of public genomic datasets shows that lpxC polymorphisms are common in pathogenic E. coli, including those carrying MCR-1, highlighting the clinical relevance of this interaction. Importantly, lpxC diversity is high in pathogenic E. coli from regions with no history of MCR-1 acquisition, suggesting that pre-existing lpxC polymorphisms potentiated the evolution of high-level colistin resistance by MCR-1 acquisition. More broadly, these findings highlight the importance of standing genetic variation and plasmid/chromosomal interactions in the evolutionary dynamics of antibiotic resistance.

    1. Evolutionary Biology
    Milo S Johnson, Michael M Desai
    Research Article Updated

    As an adapting population traverses the fitness landscape, its local neighborhood (i.e., the collection of fitness effects of single-step mutations) can change shape because of interactions with mutations acquired during evolution. These changes to the distribution of fitness effects can affect both the rate of adaptation and the accumulation of deleterious mutations. However, while numerous models of fitness landscapes have been proposed in the literature, empirical data on how this distribution changes during evolution remains limited. In this study, we directly measure how the fitness landscape neighborhood changes during laboratory adaptation. Using a barcode-based mutagenesis system, we measure the fitness effects of 91 specific gene disruption mutations in genetic backgrounds spanning 8000–10,000 generations of evolution in two constant environments. We find that the mean of the distribution of fitness effects decreases in one environment, indicating a reduction in mutational robustness, but does not change in the other. We show that these distribution-level patterns result from differences in the relative frequency of certain patterns of epistasis at the level of individual mutations, including fitness-correlated and idiosyncratic epistasis.