From the Cover: Functional evidence for TCR-intrinsic specificity for MHCII

How T cells become restricted to binding antigenic peptides within class I or class II major histocompatibility complex molecules (pMHCI or pMHCII, respectively) via clonotypic T-cell receptors (TCRs) remains debated. During development, if TCR–pMHC interactions exceed an affinity threshold, a signal is generated that positively selects the thymocyte to become a mature CD4⁺ or CD8⁺ T cell that can recognize foreign peptides within MHCII or MHCI, respectively. But whether TCRs possess an intrinsic, subthreshold specificity for MHC that facilitates sampling of the peptides within MHC during positive selection or T-cell activation is undefined. Here we asked if increasing the frequency of lymphocyte-specific protein tyrosine kinase (Lck)-associated CD4 molecules in T-cell hybridomas would allow for the detection of subthreshold TCR–MHC interactions. The reactivity of 10 distinct TCRs was assessed in response to selecting and nonselecting MHCII bearing cognate, null, or “shaved” peptides with alanine substitutions at known TCR contact residues: Three of the TCRs were selected on MHCII and have defined peptide specificity, two were selected on MHCI and have a known pMHC specificity, and five were generated in vitro without defined selecting or cognate pMHC. Our central finding is that IL-2 was made when each TCR interacted with selecting or nonselecting MHCII presenting shaved peptides. These responses were abrogated by anti-CD4 antibodies and mutagenesis of CD4. They were also inhibited by anti-MHC antibodies that block TCR–MHCII interactions. We interpret these data as functional evidence for TCR-intrinsic specificity for MHCII.Positive and negative selection limit the αβT-cell repertoire to cells expressing clonotypic T-cell receptors (TCRs) that distinguish the antigenicity of peptides embedded within class I and class II major histocompatibility complex molecules (pMHCI or pMHCII, respectively) based on their source of origin (i.e., self or foreign) (1–4). Approximately 7.5% of CD4⁺CD8⁺ double-positive (DP) thymocytes express TCRs that interact with self-pMHC above an affinity threshold required for positive selection, whereas 7.5% cross a higher affinity threshold that mediates negative selection and the remaining TCRs fail to direct positive selection (5). The rules that restrict TCR recognition of antigenic peptides within MHCI or MHCII are unresolved.Two models have been proposed to explain MHC restriction. One posits that restriction is imposed by CD4 or CD8 during thymocyte development to eliminate TCRs that recognize non-MHC ligands (2, 6). Here, the CD4- and CD8-associated Src kinase, p56^Lck [lymphocyte-specific protein tyrosine kinase (Lck)], is sequestered away from the immunoreceptor tyrosine-based activation motifs (ITAMs) of the TCR-associated CD3δε, CD3γε, and CD3ζζ signaling modules. Positively selecting signals are then generated in thymocytes expressing TCRs that bind MHCII or MHCI together with CD4 or CD8, respectively, as this localizes Lck to the ITAMs. Those thymocytes expressing TCRs that do not bind MHCI or MHCII would fail to localize Lck to the ITAMs and die. In the second model, germ line-encoded complementary determining regions (CDR) 1 and 2 allow each clonotypic TCR to bind distinct classes and alleles of MHC molecules via unique yet specific recognition codons that impose a canonical docking polarity and MHC restriction (1, 3, 4, 7, 8). Although it is not obvious that these models are mutually exclusive, the key distinction is that in the first model the randomly generated preselection TCR repertoire would contain TCRs that do and do not bind pMHC, whereas in the second model most if not all TCRs would have a specificity for MHC that is germ line-encoded, regardless of the class or allele of MHC.The canonical docking polarity of TCRs on MHCI or MHCII observed in crystal structures, and the CDR1 and CDR2 contacts therein, provides evidence for germ line-encoded TCR–MHC interactions for positively selected TCRs (1, 3, 4, 7, 8). But this is taken as supporting either model, as germ line-encoded contacts are likely to be required to allow the formation of a TCR–CD3–pMHC–CD4/CD8 macrocomplex that situates the CD3 ITAMs and Lck in a functionally mandated orientation (1–4, 6, 9, 10). Structural insights from positively selected TCRs thus do not allow the basis of MHC restriction to be cleanly addressed, and functional data that support either model have been reported (11–15).An open question that can shed light on the similarities and differences between the two models is whether TCRs participate in subthreshold scanning of MHC (4, 16). Scanning would allow a TCR to dock on MHC and survey its contents for peptides that increase the duration of TCR–pMHC interactions, via contacts with clonotypic CDR3s, and allow the formation of a TCR–CD3–pMHC–CD4/CD8 macrocomplex that generates signals (4, 10). In the co-receptor imposed model, a diverse preselection repertoire would contain TCRs with no intrinsic capacity to bind MHC, TCRs that interact with pMHC by atypical modalities, and TCRs that interact with a composite pMHC surface in a canonical modality in a lock-and-key manner akin to antibody–antigen recognition (2, 6). Once selected, this last group of TCRs would be predicted to scan composite pMHC with shapes (i.e., topology and chemical characteristics) related to the selecting pMHC—presumably the same MHC, or similar allelic variant, presenting related peptides. In the germ line-encoded recognition model, TCR scanning of MHC via recognition codons would be intrinsic to most if not all TCRs, regardless of the class of MHC, allelic variants, or the peptide sequence therein (4). At present, functional evidence for TCR scanning of MHC is lacking, regardless of whether it is MHC class-, allele-, and peptide sequence-dependent.Recently, the frequency of Lck-associated CD4 molecules was proposed to influence if a TCR–pMHC interaction is of sufficient duration to direct a specific cell fate decision, such as negative selection (17). We thus hypothesized that genetically increasing the frequency of CD4–Lck association should allow for the detection of subthreshold TCR–pMHC interactions that are normally of insufficient duration to elicit a functional response. Here we show that T-cell hybridomas expressing 10 distinct TCRs along with a CD4–Lck fusion make IL-2 in response to APCs expressing selecting or nonselecting MHCII, regardless of the sequence of the presented peptide. These responses were independent of positive selection on MHCII, as TCRs that were positively selected on MHCI, or generated in vitro and thus not thymically selected, yielded similar responses. These data provide functional evidence for subthreshold TCR scanning of MHCII that is independent of the class of MHC, the allele, or the peptide sequence therein.     

From the Cover: Beh?et disease-associated MHC class I residues implicate antigen binding and regulation of cell-mediated cytotoxicity

The HLA protein, HLA-B*51, encoded by HLA-B in MHC, is the strongest known genetic risk factor for Behçet disease (BD). Associations between BD and other factors within the MHC have been reported also, although strong regional linkage disequilibrium complicates their confident disentanglement from HLA-B*51. In the current study, we examined a combination of directly obtained and imputed MHC-region SNPs, directly obtained HLA-B locus types, and imputed classical HLA types with their corresponding polymorphic amino acid residues for association with BD in 1,190 cases and 1,257 controls. SNP mapping with logistic regression of the MHC identified the HLA-B/MICA region and the region between HLA-F and HLA-A as independently associated with BD (P < 1.7 × 10⁻⁸). HLA-B*51, -A*03, -B*15, -B*27, -B*49, -B*57, and -A*26 each contributed independently to BD risk. We directly examined rs116799036, a noncoding SNP upstream of HLA-B that was recently suggested to underlie the association of HLA-B*51 with BD, but we were unable to replicate that finding in our collection. Instead, we mapped the BD association to seven MHC class I (MHC-I) amino acid residues, including anchor residues that critically define the selection and binding of peptides to MHC-I molecules, residues known to influence MHC-I–killer immunoglobulin-like receptor interactions, and a residue located in the signal peptide of HLA-B. The locations of these variants collectively implicate MHC-I peptide binding in the pathophysiology of BD. Furthermore, several lines of evidence suggest a role for altered regulation of cellular cytotoxicity in BD pathogenesis.Behçet disease (BD) is a multisystem inflammatory disease of complex inheritance with a clinical course marked by recurrent episodes of oral and genital ulceration, severe ocular inflammation often leading to visual impairment or blindness, and a range of inflammatory lesions of the skin and the gastrointestinal, neurologic, and circulatory systems (1). The predominant BD susceptibility locus is the MHC on chromosome 6 (2, 3), which contains the strongest known risk factor for BD, the MHC class I (MHC-I) allele HLA-B*51 (2–5). Several recent studies have expanded the list of genes or loci implicated in the pathophysiology of BD, which now includes HLA-B, IL10, IL23R, HLA-A, CCR1, STAT4, endoplasmic reticulum amino peptidase 1 (ERAP1), the killer lectin-like receptor cluster on chromosome 12, and, most recently, TLR4 and MEFV (2, 3, 6, 7). Although these genetic studies of BD have provided new clues and insights into the pathogenesis of BD, none has provided a thorough accounting of the individual risk factors within the MHC. The lack of such a study likely reflects the absence of a BD study population of adequate size to overcome the strong linkage disequilibrium (LD) and to disentangle from HLA-B*51 the additional risk factors within the MHC.Multiple lines of evidence suggest that sources of BD risk, in addition to HLA-B*51, exist within the MHC. This evidence begins in the HLA-B locus, where associations between BD and several alleles in addition to HLA-B*51 have been reported (8–10). It also has been argued that variants in or around MHC class I polypeptide-related sequence A (MICA), the centromeric neighbor of HLA-B that encodes the MHC-I chain-related sequence A, contribute to BD susceptibility (11). However, efforts to parse the effects of MICA and HLA-B alleles definitively have been confounded by their particularly strong LD (11–14). Additionally, HLA-A has been identified as a BD susceptibility locus in numerous studies (2, 3, 14–17), and it has been suggested that HLA-C contributes to BD risk, as well (14).To understand better the sources of BD risk within the MHC, we have analyzed directly ascertained and imputed SNP genotypes, together with HLA type and amino acid data from a very large and meticulously assembled collection of Turkish subjects with BD and geographically matched, healthy Turkish individuals. Using stepwise and multivariate logistic regression, conditional analysis, and haplotype analysis, we sought to characterize the range of genetic risk factors for BD contained within the MHC.     

Functional role of T-cell receptor nanoclusters in signal initiation and antigen discrimination

Antigen recognition by the T-cell receptor (TCR) is a hallmark of the adaptive immune system. When the TCR engages a peptide bound to the restricting major histocompatibility complex molecule (pMHC), it transmits a signal via the associated CD3 complex. How the extracellular antigen recognition event leads to intracellular phosphorylation remains unclear. Here, we used single-molecule localization microscopy to quantify the organization of TCR–CD3 complexes into nanoscale clusters and to distinguish between triggered and nontriggered TCR–CD3 complexes. We found that only TCR–CD3 complexes in dense clusters were phosphorylated and associated with downstream signaling proteins, demonstrating that the molecular density within clusters dictates signal initiation. Moreover, both pMHC dose and TCR–pMHC affinity determined the density of TCR–CD3 clusters, which scaled with overall phosphorylation levels. Thus, TCR–CD3 clustering translates antigen recognition by the TCR into signal initiation by the CD3 complex, and the formation of dense signaling-competent clusters is a process of antigen discrimination.The activation of T cells orchestrates an adaptive immune response by translating antigen binding to the T-cell receptor (TCR) into appropriate cellular responses (1–4). The αβ TCR engages MHC molecules (or HLA) bound to antigenic peptides (pMHC) on the surface of antigen-presenting cells (5). The interaction of the TCR with pMHC is highly specific because T cells are able to distinguish rare foreign pMHC among abundant self pMHC molecules (6). TCR signaling is also extremely sensitive; even a single pMHC molecule is sufficient to trigger activation (7–9). TCRs are noncovalently coupled to the conserved multisubunit CD3 complex, comprising CD3εγ, CD3εδ, and CD3ζζ dimers (10), whose immunoreceptor tyrosine-based activation motifs (ITAMs) are phosphorylated upon pMHC engagement by the nonreceptor tyrosine kinase Lck (1, 2). ITAM phosphorylation is required for the recruitment and phosphorylation of the ζ-chain-associated protein kinase 70 kDa (Zap70) and the adaptor linker for activation of T cells (Lat) (11) to mediate downstream activation responses (12). Phosphorylation of the TCR–CD3 complex is one of the earliest detectable biochemical events in T-cell signaling and already at this level, important “activation decisions” are being made. For example, when the extent of ITAM phosphorylation was modulated through specific mutations, low levels of TCR–CD3 phosphorylation were sufficient for signaling through the Zap70–SLP-76–Lat pathway and cytokine production, whereas high levels of TCR–CD3 phosphorylation were required for Vav1-Numb-Notch signaling and T-cell proliferation (12–14). However, how the TCR–CD3 complex encodes both the quality and quantity of pMHC molecules and steers signaling activities toward appropriate cellular outcomes is not fully understood (1–4).Although many of the molecular players and TCR signaling pathways have been identified and characterized by biochemical and genetic approaches (12, 15), the precise mechanism by which the binding of the TCR to pMHC results in phosphorylation of the TCR–CD3 complex, referred to as TCR triggering, still remains contested (1, 16). There is increasing evidence that the spatial reorganization of the TCR into micrometer- and submicron-sized clusters is involved in regulating T-cell activation (2, 11, 17–19). With the advent of superresolution fluorescence microscopy, we have gained a much more nuanced picture of the spatial organization of TCR signaling proteins (3, 20). In particular, single-molecule localization microscopy [SMLM, including photoactivated localization microscopy (PALM) (21) and direct stochastic optical reconstruction microscopy (dSTORM) (22)] was used to report that at least a proportion of TCRs were organized into small clusters that were 30–300 nm in diameter, termed “nanoclusters” (23, 24). Similarly, Lat (23–25), Lck (26), and Zap70 (24, 27) were also found to reside in nanoclusters that are extensively remodeled during T-cell activation. The link between preexisting and pMHC-induced nanoclustering and signaling activities is not clear at present and is the focus of the present study.To identify the functional role of TCR nanoclusters, we used two-color SMLM data and integrated a cluster detection method, density-based spatial clustering of applications with noise (DBSCAN) (28) with a customized colocalization analysis (29). This process allowed us to distinguish phosphorylated from nonphosphorylated TCR–CD3 complex clusters in intact T cells and identify the spatial organization at which individual TCR–CD3 complexes had the highest signaling efficiency. We found that not all TCR–CD3 complexes had the same likelihood of being phosphorylated, even with excess doses of high-affinity pMHC molecules. The signaling efficiency of the TCR–CD3 complex was dependent upon the distance to neighboring complexes so that dense nanoclusters had the highest TCR triggering efficiency.     

Preemptive priming readily overcomes structure-based mechanisms of virus escape

A reverse-genetics approach has been used to probe the mechanism underlying immune escape for influenza A virus-specific CD8⁺ T cells responding to the immunodominant D^bNP₃₆₆ epitope. Engineered viruses with a substitution at a critical residue (position 6, P6M) all evaded recognition by WT D^bNP₃₆₆-specific CD8⁺ T cells, but only the NPM6I and NPM6T mutants altered the topography of a key residue (His155) in the MHC class I binding site. Following infection with the engineered NPM6I and NPM6T influenza viruses, both mutations were associated with a substantial “hole” in the naïve T-cell receptor repertoire, characterized by very limited T-cell receptor diversity and minimal primary responses to the NPM6I and NPM6T epitopes. Surprisingly, following respiratory challenge with a serologically distinct influenza virus carrying the same mutation, preemptive immunization against these escape variants led to the generation of secondary CD8⁺ T-cell responses that were comparable in magnitude to those found for the WT NP epitope. Consequently, it might be possible to generate broadly protective T-cell immunity against commonly occurring virus escape mutants. If this is generally true for RNA viruses (like HIV, hepatitis C virus, and influenza) that show high mutation rates, priming against predicted mutants before an initial encounter could function to prevent the emergence of escape variants in infected hosts. That process could be a step toward preserving immune control of particularly persistent RNA viruses and may be worth considering for future vaccine strategies.Virus-specific CD8⁺ T cells recognize peptide and class I MHC (pMHCI) epitopes derived predominantly from more conserved, internal proteins, offering a promising target for vaccine development. However, some RNA viruses, particularly the influenza A viruses, hepatitis C virus (HCV) and HIV, are a major challenge for preventive immunization as a low-fidelity RNA polymerase allows the rapid emergence of escape mutants. The question asked here is whether it is possible to design an immunization strategy that might minimize the likelihood that such virus variants will escape from immune control and survive.The influenza A viruses elicit robust and broad CD8⁺ T-cell immunity (1, 2) that can provide protection against serologically distinct strains, including newly emerged pandemic variants (3, 4). The experimental evidence is that influenza-specific CD8⁺ T cells, operating in either a primary response or following recall from memory, promote virus elimination and host recovery via the production of proinflammatory cytokines and the killing of virus-infected cells. At the stage of initial priming, the responding CD8⁺ cytotoxic T lymphocytes (CTLs) are selected as a consequence of the interaction between their clonotypic T-cell receptor (TCR) and pMHCI epitopes expressed on the surface of infected cells. The key to immunogenicity for CD8⁺ CTLs rests in both the nature of the TCR repertoire and the sequence, or structural complementarity, of peptides targeted in the MHCI groove (5, 6).Influenza virus-specific CD8⁺ CTLs can exert selective pressure that leads to the emergence of escape mutations in immunogenic peptides (7). This is a more familiar problem for chronic virus infections (HIV and HCV), with at least some of the variants that persist in the circulation being readily transmissible (8–10). Apart from subverting CD8⁺ T-cell–mediated control within the infected individual, this constitutes a major barrier to effective vaccine design. With influenza, although mutations have been found for >70% of immunogenic T-cell peptides (7), this finding has received little attention because of the acute nature of the disease. Even so, such escape mutants are readily generated using TCR transgenic mice (11), and “natural” variants occur within the influenza nucleoprotein (NP)₃₈₀ (HLA-B8), NP₃₈₃ (HLA-B27), and NP₄₁₈ (HLA-B35) viral peptides (12–14). Furthermore, given sufficient immune pressure and relative fitness, such mutated viruses can become fixed in the population, leading to the disappearance of WT T-cell specificities (15). For “seasonal” influenza infections, such escape from CD8⁺ T-cell–mediated immunity can also be relevant to the persistence of variants within the population (longevity and severity of influenza season). Moreover, in the face of a rapidly spreading, novel pandemic strain, established CD8⁺ T-cell memory constitutes the best protective mechanism. Clearly, any vaccine strategy that focuses on priming the CTLs needs to deal with emerging escape variants.The immunogenicity of a given epitope can be compromised in a variety of ways. Amino acid variation at an MHCI anchor residue can lead to the failure of pMHCI binding (12). Alternatively, changes at a TCR contact site (14, 16, 17) can prevent or decrease T-cell recognition, although cross-reactive T-cell immunity may still be retained between some influenza variants (18). The present study targets the mechanisms underlying virus escape at TCR contact sites and probes possible compensatory strategies using a readily manipulated C57BL/6J (B6, H2^b) influenza mouse model (17). The study focuses principally on escape variants selected in transgenic mice expressing a TCR specific for the immunodominant D^bNP₃₆₆ (ASNENMETM) epitope that reemerged from day 18 after infection and caused lethal disease within a month (11). Sequencing viral RNA recovered from the lungs of these TCR transgenic mice (11) established that NP₃₆₆ had mutated, especially at position (P) 6, and that the mice were no longer infected with the wt virus. The P6 methionine (M) is the most solvent-exposed residue for the D^bNP₃₆₆ complex (19, 20) and the change from P6M to P6A leads to loss in recognition by CD8⁺ T cells specific for the wt-D^bNP₃₆₆ epitope (17, 20, 21). The present analysis of the in vivo CTL responses to a panel of P6 mutants indicates that preemptive immunization against the escape variants can generate a good measure of protection.     

Self-assembly of alkynylplatinum(II) terpyridine amphiphiles into nanostructures via steric control and metal–metal interactions

A series of mono- and dinuclear alkynylplatinum(II) terpyridine complexes containing the hydrophilic oligo(para-phenylene ethynylene) with two 3,6,9-trioxadec-1-yloxy chains was designed and synthesized. The mononuclear alkynylplatinum(II) terpyridine complex was found to display a very strong tendency toward the formation of supramolecular structures. Interestingly, additional end-capping with another platinum(II) terpyridine moiety of various steric bulk at the terminal alkyne would lead to the formation of nanotubes or helical ribbons. These desirable nanostructures were found to be governed by the steric bulk on the platinum(II) terpyridine moieties, which modulates the directional metal−metal interactions and controls the formation of nanotubes or helical ribbons. Detailed analysis of temperature-dependent UV-visible absorption spectra of the nanostructured tubular aggregates also provided insights into the assembly mechanism and showed the role of metal−metal interactions in the cooperative supramolecular polymerization of the amphiphilic platinum(II) complexes.Square-planar d⁸ platinum(II) polypyridine complexes have long been known to exhibit intriguing spectroscopic and luminescence properties (1–54) as well as interesting solid-state polymorphism associated with metal−metal and π−π stacking interactions (1–14, 25). Earlier work by our group showed the first example, to our knowledge, of an alkynylplatinum(II) terpyridine system [Pt(tpy)(C ≡ CR)]⁺ that incorporates σ-donating and solubilizing alkynyl ligands together with the formation of Pt···Pt interactions to exhibit notable color changes and luminescence enhancements on solvent composition change (25) and polyelectrolyte addition (26). This approach has provided access to the alkynylplatinum(II) terpyridine and other related cyclometalated platinum(II) complexes, with functionalities that can self-assemble into metallogels (27–31), liquid crystals (32, 33), and other different molecular architectures, such as hairpin conformation (34), helices (35–38), nanostructures (39–45), and molecular tweezers (46, 47), as well as having a wide range of applications in molecular recognition (48–52), biomolecular labeling (48–52), and materials science (53, 54). Recently, metal-containing amphiphiles have also emerged as a building block for supramolecular architectures (42–44, 55–59). Their self-assembly has always been found to yield different molecular architectures with unprecedented complexity through the multiple noncovalent interactions on the introduction of external stimuli (42–44, 55–59).Helical architecture is one of the most exciting self-assembled morphologies because of the uniqueness for the functional and topological properties (60–69). Helical ribbons composed of amphiphiles, such as diacetylenic lipids, glutamates, and peptide-based amphiphiles, are often precursors for the growth of tubular structures on an increase in the width or the merging of the edges of ribbons (64, 65). Recently, the optimization of nanotube formation vs. helical nanostructures has aroused considerable interests and can be achieved through a fine interplay of the influence on the amphiphilic property of molecules (66), choice of counteranions (67, 68), or pH values of the media (69), which would govern the self-assembly of molecules into desirable aggregates of helical ribbons or nanotube scaffolds. However, a precise control of supramolecular morphology between helical ribbons and nanotubes remains challenging, particularly for the polycyclic aromatics in the field of molecular assembly (64–69). Oligo(para-phenylene ethynylene)s (OPEs) with solely π−π stacking interactions are well-recognized to self-assemble into supramolecular system of various nanostructures but rarely result in the formation of tubular scaffolds (70–73). In view of the rich photophysical properties of square-planar d⁸ platinum(II) systems and their propensity toward formation of directional Pt···Pt interactions in distinctive morphologies (27–31, 39–45), it is anticipated that such directional and noncovalent metal−metal interactions might be capable of directing or dictating molecular ordering and alignment to give desirable nanostructures of helical ribbons or nanotubes in a precise and controllable manner.Herein, we report the design and synthesis of mono- and dinuclear alkynylplatinum(II) terpyridine complexes containing hydrophilic OPEs with two 3,6,9-trioxadec-1-yloxy chains. The mononuclear alkynylplatinum(II) terpyridine complex with amphiphilic property is found to show a strong tendency toward the formation of supramolecular structures on diffusion of diethyl ether in dichloromethane or dimethyl sulfoxide (DMSO) solution. Interestingly, additional end-capping with another platinum(II) terpyridine moiety of various steric bulk at the terminal alkyne would result in nanotubes or helical ribbons in the self-assembly process. To the best of our knowledge, this finding represents the first example of the utilization of the steric bulk of the moieties, which modulates the formation of directional metal−metal interactions to precisely control the formation of nanotubes or helical ribbons in the self-assembly process. Application of the nucleation–elongation model into this assembly process by UV-visible (UV-vis) absorption spectroscopic studies has elucidated the nature of the molecular self-assembly, and more importantly, it has revealed the role of metal−metal interactions in the formation of these two types of nanostructures.     

The coreceptor CD4 is expressed in distinct nanoclusters and does not colocalize with T-cell receptor and active protein tyrosine kinase p56lck

CD4 molecules on the surface of T lymphocytes greatly augment the sensitivity and activation process of these cells, but how it functions is not fully understood. Here we studied the spatial organization of CD4, and its relationship to T-cell antigen receptor (TCR) and the active form of Src kinase p56lck (Lck) using single and dual-color photoactivated localization microscopy (PALM) and direct stochastic optical reconstruction microscopy (dSTORM). In nonactivated T cells, CD4 molecules are clustered in small protein islands, as are TCR and Lck. By dual-color imaging, we find that CD4, TCR, and Lck are localized in their separate clusters with limited interactions in the interfaces between them. Upon T-cell activation, the TCR and CD4 begin clustering together, developing into microclusters, and undergo a larger scale redistribution to form supramolecluar activation clusters (SMACs). CD4 and Lck localize in the inner TCR region of the SMAC, but this redistribution of disparate cluster structures results in enhanced segregation from each other. In nonactivated cells these preclustered structures and the limited interactions between them may serve to limit spontaneous and random activation events. However, the small sizes of these island structures also ensure large interfacial surfaces for potential interactions and signal amplification when activation is initiated. In the later activation stages, the increasingly larger clusters and their segregation from each other reduce the interfacial surfaces and could have a dampening effect. These highly differentiated spatial distributions of TCR, CD4, and Lck and their changes during activation suggest that there is a more complex hierarchy than previously thought.For helper T cells, CD4 has been termed a coreceptor based on its important role in antigen recognition class II major histocompatibility complex (MHC)–peptide complexes by the αβ T-cell receptor (TCR) as well as in signal transduction. Indeed, CD4 significantly increases T-cell sensitivity to antigen upon activation (1–4). This ability of CD4 to enhance antigen recognition has often been connected to the fact that the N-terminal Ig domain of CD4 has specific affinity to invariant sites on MHC class II molecules (5, 6). It has been suggested that CD4 stabilizes the molecular complex of TCR and peptide–MHC (pMHC) by binding to the same MHC either simultaneously with the TCR (7) or shortly after TCR–pMHC engagement (2, 3). However, from more recent 2D measurements, CD4 blockades showed no effect on the stability of TCR binding to agonist peptide–MHC complexes in a synapse (8). In terms of signal transduction, the role of CD4 has been studied based on the binding ability of a cysteine motif in the cytoplasmic tail of CD4 to Src kinase p56lck (Lck) (9), which is responsible for the phosphorylation of the immunoreceptor tyrosine-based activation motif (ITAM) sequences in TCR–CD3 complex as the earliest observable biochemical event during T-cell activation (10). It has been proposed that CD4 mainly contributes to the sensitivity of T cells by facilitating the recruitment of Lck to TCR–CD3s that are actively engaged in ligand recognition (11, 12). Nevertheless, the absence of CD4 does not preclude T cells from being generated at the thymus or being activated by TCR–pMHC engagement (13, 14).It is now well appreciated that spatial reorganization and distribution of some of the membrane receptors and signaling molecules is one of the critical regulating mechanisms in T-cell activation. The molecular assembly and clusters such as supramolecular activation clusters (SMACs) (15) of immunological synapse (IS) (14), microclusters (16–20), and their roles in T-cell signaling have been widely studied. More recently, the presence and unique roles of smaller-sized protein clusters, termed “nanoclusters” or “protein islands,” of TCR–CD3 complex (21–24), linker for activation of T cell (LAT) (21, 22, 24, 25), Lck (26), and other signaling molecules (24) were revealed by electron microscopy and the newly available superresolution fluorescence microscopy.Considering that the TCR–CD3 complex, CD4, and Lck are constitutively expressed in nonactivated T cells, it is highly likely that the interaction dynamics between these components would also be controlled spatially during the T-cell activation process. Here, we studied the relative molecular distribution of these molecules using single- and dual-color photoactivated localization microscopy (PALM) (27) and direct stochastic optical reconstruction microscopy (dSTORM) (28, 29) in live and fixed T cells for both nonactivated and activating conditions. The corresponding spatial analyses were also used to quantitatively determine the sizes, degree of clustering, and degree of interactions of these clusters. We found that CD4 is also expressed in preclustered structures, separate from TCR–CD3 and LAT, and composed of three to six molecules per cluster. The interactions between these molecules occurred only in the interfaces between the clusters. Upon T-cell activation, the TCR–CD3 and CD4 molecules increased the size of their own clusters without appreciable mixing. Instead, their molecular segregation increased, whereas the T cell develops a synapse structure, often in the SMAC or “bull’s eye” pattern, with the TCR–CD3 in the central supramolecular activation cluster (cSMAC) with the CD4 and Lck clusters localizing around it. These observed clustering behaviors accompanying reorganization of spatial distributions of CD4, Lck, and TCR might be a general and effective mechanism to activate and regulate the T-cell signaling by controlling the magnitude of interfacial interactions between signaling components in each cluster.     

The evolution of self-control

Cognition presents evolutionary research with one of its greatest challenges. Cognitive evolution has been explained at the proximate level by shifts in absolute and relative brain volume and at the ultimate level by differences in social and dietary complexity. However, no study has integrated the experimental and phylogenetic approach at the scale required to rigorously test these explanations. Instead, previous research has largely relied on various measures of brain size as proxies for cognitive abilities. We experimentally evaluated these major evolutionary explanations by quantitatively comparing the cognitive performance of 567 individuals representing 36 species on two problem-solving tasks measuring self-control. Phylogenetic analysis revealed that absolute brain volume best predicted performance across species and accounted for considerably more variance than brain volume controlling for body mass. This result corroborates recent advances in evolutionary neurobiology and illustrates the cognitive consequences of cortical reorganization through increases in brain volume. Within primates, dietary breadth but not social group size was a strong predictor of species differences in self-control. Our results implicate robust evolutionary relationships between dietary breadth, absolute brain volume, and self-control. These findings provide a significant first step toward quantifying the primate cognitive phenome and explaining the process of cognitive evolution.Since Darwin, understanding the evolution of cognition has been widely regarded as one of the greatest challenges for evolutionary research (1). Although researchers have identified surprising cognitive flexibility in a range of species (2–40) and potentially derived features of human psychology (41–61), we know much less about the major forces shaping cognitive evolution (62–71). With the notable exception of Bitterman’s landmark studies conducted several decades ago (63, 72–74), most research comparing cognition across species has been limited to small taxonomic samples (70, 75). With limited comparable experimental data on how cognition varies across species, previous research has largely relied on proxies for cognition (e.g., brain size) or metaanalyses when testing hypotheses about cognitive evolution (76–92). The lack of cognitive data collected with similar methods across large samples of species precludes meaningful species comparisons that can reveal the major forces shaping cognitive evolution across species, including humans (48, 70, 89, 93–98).To address these challenges we measured cognitive skills for self-control in 36 species of mammals and birds (Fig. 1 and Tables S1–S4) tested using the same experimental procedures, and evaluated the leading hypotheses for the neuroanatomical underpinnings and ecological drivers of variance in animal cognition. At the proximate level, both absolute (77, 99–107) and relative brain size (108–112) have been proposed as mechanisms supporting cognitive evolution. Evolutionary increases in brain size (both absolute and relative) and cortical reorganization are hallmarks of the human lineage and are believed to index commensurate changes in cognitive abilities (52, 105, 113–115). Further, given the high metabolic costs of brain tissue (116–121) and remarkable variance in brain size across species (108, 122), it is expected that the energetic costs of large brains are offset by the advantages of improved cognition. The cortical reorganization hypothesis suggests that selection for absolutely larger brains—and concomitant cortical reorganization—was the predominant mechanism supporting cognitive evolution (77, 91, 100–106, 120). In contrast, the encephalization hypothesis argues that an increase in brain volume relative to body size was of primary importance (108, 110, 111, 123). Both of these hypotheses have received support through analyses aggregating data from published studies of primate cognition and reports of “intelligent” behavior in nature—both of which correlate with measures of brain size (76, 77, 84, 92, 110, 124).Open in a separate windowFig. 1.A phylogeny of the species included in this study. Branch lengths are proportional to time except where long branches have been truncated by parallel diagonal lines (split between mammals and birds ∼292 Mya).With respect to selective pressures, both social and dietary complexities have been proposed as ultimate causes of cognitive evolution. The social intelligence hypothesis proposes that increased social complexity (frequently indexed by social group size) was the major selective pressure in primate cognitive evolution (6, 44, 48, 50, 87, 115, 120, 125–141). This hypothesis is supported by studies showing a positive correlation between a species’ typical group size and the neocortex ratio (80, 81, 85–87, 129, 142–145), cognitive differences between closely related species with different group sizes (130, 137, 146, 147), and evidence for cognitive convergence between highly social species (26, 31, 148–150). The foraging hypothesis posits that dietary complexity, indexed by field reports of dietary breadth and reliance on fruit (a spatiotemporally distributed resource), was the primary driver of primate cognitive evolution (151–154). This hypothesis is supported by studies linking diet quality and brain size in primates (79, 81, 86, 142, 155), and experimental studies documenting species differences in cognition that relate to feeding ecology (94, 156–166).Although each of these hypotheses has received empirical support, a comparison of the relative contributions of the different proximate and ultimate explanations requires (i) a cognitive dataset covering a large number of species tested using comparable experimental procedures; (ii) cognitive tasks that allow valid measurement across a range of species with differing morphology, perception, and temperament; (iii) a representative sample within each species to obtain accurate estimates of species-typical cognition; (iv) phylogenetic comparative methods appropriate for testing evolutionary hypotheses; and (v) unprecedented collaboration to collect these data from populations of animals around the world (70).Here, we present, to our knowledge, the first large-scale collaborative dataset and comparative analysis of this kind, focusing on the evolution of self-control. We chose to measure self-control—the ability to inhibit a prepotent but ultimately counterproductive behavior—because it is a crucial and well-studied component of executive function and is involved in diverse decision-making processes (167–169). For example, animals require self-control when avoiding feeding or mating in view of a higher-ranking individual, sharing food with kin, or searching for food in a new area rather than a previously rewarding foraging site. In humans, self-control has been linked to health, economic, social, and academic achievement, and is known to be heritable (170–172). In song sparrows, a study using one of the tasks reported here found a correlation between self-control and song repertoire size, a predictor of fitness in this species (173). In primates, performance on a series of nonsocial self-control control tasks was related to variability in social systems (174), illustrating the potential link between these skills and socioecology. Thus, tasks that quantify self-control are ideal for comparison across taxa given its robust behavioral correlates, heritable basis, and potential impact on reproductive success.In this study we tested subjects on two previously implemented self-control tasks. In the A-not-B task (27 species, n = 344), subjects were first familiarized with finding food in one location (container A) for three consecutive trials. In the test trial, subjects initially saw the food hidden in the same location (container A), but then moved to a new location (container B) before they were allowed to search (Movie S1). In the cylinder task (32 species, n = 439), subjects were first familiarized with finding a piece of food hidden inside an opaque cylinder. In the following 10 test trials, a transparent cylinder was substituted for the opaque cylinder. To successfully retrieve the food, subjects needed to inhibit the impulse to reach for the food directly (bumping into the cylinder) in favor of the detour response they had used during the familiarization phase (Movie S2).Thus, the test trials in both tasks required subjects to inhibit a prepotent motor response (searching in the previously rewarded location or reaching directly for the visible food), but the nature of the correct response varied between tasks. Specifically, in the A-not-B task subjects were required to inhibit the response that was previously successful (searching in location A) whereas in the cylinder task subjects were required to perform the same response as in familiarization trials (detour response), but in the context of novel task demands (visible food directly in front of the subject).     

Structural interactions of a voltage sensor toxin with lipid membranes

Protein toxins from tarantula venom alter the activity of diverse ion channel proteins, including voltage, stretch, and ligand-activated cation channels. Although tarantula toxins have been shown to partition into membranes, and the membrane is thought to play an important role in their activity, the structural interactions between these toxins and lipid membranes are poorly understood. Here, we use solid-state NMR and neutron diffraction to investigate the interactions between a voltage sensor toxin (VSTx1) and lipid membranes, with the goal of localizing the toxin in the membrane and determining its influence on membrane structure. Our results demonstrate that VSTx1 localizes to the headgroup region of lipid membranes and produces a thinning of the bilayer. The toxin orients such that many basic residues are in the aqueous phase, all three Trp residues adopt interfacial positions, and several hydrophobic residues are within the membrane interior. One remarkable feature of this preferred orientation is that the surface of the toxin that mediates binding to voltage sensors is ideally positioned within the lipid bilayer to favor complex formation between the toxin and the voltage sensor.Protein toxins from venomous organisms have been invaluable tools for studying the ion channel proteins they target. For example, in the case of voltage-activated potassium (Kv) channels, pore-blocking scorpion toxins were used to identify the pore-forming region of the channel (1, 2), and gating modifier tarantula toxins that bind to S1–S4 voltage-sensing domains have helped to identify structural motifs that move at the protein–lipid interface (3–5). In many instances, these toxin–channel interactions are highly specific, allowing them to be used in target validation and drug development (6–8).Tarantula toxins are a particularly interesting class of protein toxins that have been found to target all three families of voltage-activated cation channels (3, 9–12), stretch-activated cation channels (13–15), as well as ligand-gated ion channels as diverse as acid-sensing ion channels (ASIC) (16–21) and transient receptor potential (TRP) channels (22, 23). The tarantula toxins targeting these ion channels belong to the inhibitor cystine knot (ICK) family of venom toxins that are stabilized by three disulfide bonds at the core of the molecule (16, 17, 24–31). Although conventional tarantula toxins vary in length from 30 to 40 aa and contain one ICK motif, the recently discovered double-knot toxin (DkTx) that specifically targets TRPV1 channels contains two separable lobes, each containing its own ICK motif (22, 23).One unifying feature of all tarantula toxins studied thus far is that they act on ion channels by modifying the gating properties of the channel. The best studied of these are the tarantula toxins targeting voltage-activated cation channels, where the toxins bind to the S3b–S4 voltage sensor paddle motif (5, 32–36), a helix-turn-helix motif within S1–S4 voltage-sensing domains that moves in response to changes in membrane voltage (37–41). Toxins binding to S3b–S4 motifs can influence voltage sensor activation, opening and closing of the pore, or the process of inactivation (4, 5, 36, 42–46). The tarantula toxin PcTx1 can promote opening of ASIC channels at neutral pH (16, 18), and DkTx opens TRPV1 in the absence of other stimuli (22, 23), suggesting that these toxin stabilize open states of their target channels.For many of these tarantula toxins, the lipid membrane plays a key role in the mechanism of inhibition. Strong membrane partitioning has been demonstrated for a range of toxins targeting S1–S4 domains in voltage-activated channels (27, 44, 47–50), and for GsMTx4 (14, 50), a tarantula toxin that inhibits opening of stretch-activated cation channels in astrocytes, as well as the cloned stretch-activated Piezo1 channel (13, 15). In experiments on stretch-activated channels, both the d- and l-enantiomers of GsMTx4 are active (14, 50), implying that the toxin may not bind directly to the channel. In addition, both forms of the toxin alter the conductance and lifetimes of gramicidin channels (14), suggesting that the toxin inhibits stretch-activated channels by perturbing the interface between the membrane and the channel. In the case of Kv channels, the S1–S4 domains are embedded in the lipid bilayer and interact intimately with lipids (48, 51, 52) and modification in the lipid composition can dramatically alter gating of the channel (48, 53–56). In one study on the gating of the Kv2.1/Kv1.2 paddle chimera (53), the tarantula toxin VSTx1 was proposed to inhibit Kv channels by modifying the forces acting between the channel and the membrane. Although these studies implicate a key role for the membrane in the activity of Kv and stretch-activated channels, and for the action of tarantula toxins, the influence of the toxin on membrane structure and dynamics have not been directly examined. The goal of the present study was to localize a tarantula toxin in membranes using structural approaches and to investigate the influence of the toxin on the structure of the lipid bilayer.     

Molecular architecture of the αβ T cell receptor–CD3 complex

αβ T-cell receptor (TCR) activation plays a crucial role for T-cell function. However, the TCR itself does not possess signaling domains. Instead, the TCR is noncovalently coupled to a conserved multisubunit signaling apparatus, the CD3 complex, that comprises the CD3εγ, CD3εδ, and CD3ζζ dimers. How antigen ligation by the TCR triggers CD3 activation and what structural role the CD3 extracellular domains (ECDs) play in the assembled TCR–CD3 complex remain unclear. Here, we use two complementary structural approaches to gain insight into the overall organization of the TCR–CD3 complex. Small-angle X-ray scattering of the soluble TCR–CD3εδ complex reveals the CD3εδ ECDs to sit underneath the TCR α-chain. The observed arrangement is consistent with EM images of the entire TCR–CD3 integral membrane complex, in which the CD3εδ and CD3εγ subunits were situated underneath the TCR α-chain and TCR β-chain, respectively. Interestingly, the TCR–CD3 transmembrane complex bound to peptide–MHC is a dimer in which two TCRs project outward from a central core composed of the CD3 ECDs and the TCR and CD3 transmembrane domains. This arrangement suggests a potential ligand-dependent dimerization mechanism for TCR signaling. Collectively, our data advance our understanding of the molecular organization of the TCR–CD3 complex, and provides a conceptual framework for the TCR activation mechanism.T cells are key mediators of the adaptive immune response. Each αβ T cell contains a unique αβ T-cell receptor (TCR), which binds antigens (Ags) displayed by major histocompatibility complexes (MHCs) and MHC-like molecules (1). The TCR serves as a remarkably sensitive driver of cellular function: although TCR ligands typically bind quite weakly (1–200 μM), even a handful of TCR ligands are sufficient to fully activate a T cell (2, 3). The TCR does not possess intracellular signaling domains, uncoupling Ag recognition from T-cell signaling. The TCR is instead noncovalently associated with a multisubunit signaling apparatus, consisting of the CD3εγ and CD3εδ heterodimers and the CD3ζζ homodimer, which collectively form the TCR–CD3 complex (4, 5). The CD3γ/δ/ε subunits each consist of a single extracellular Ig domain and a single immunoreceptor tyrosine-based activation motif (ITAM), whereas CD3ζ has a short extracellular domain (ECD) and three ITAMs (6–11). The TCR–CD3 complex exists in 1:1:1:1 stoichiometry for the αβTCR:CD3εγ:CD3εδ:CD3ζζ dimers (12). Phosphorylation of the intracellular CD3 ITAMs and recruitment of the adaptor Nck lead to T-cell activation, proliferation, and survival (13, 14). Understanding the underlying principles of TCR–CD3 architecture and T-cell signaling is of therapeutic interest. For example, TCR–CD3 is the target of therapeutic antibodies such as the immunosuppressant OKT3 (15), and there is increasing interest in manipulating T cells in an Ag-dependent manner by using naturally occurring and engineered TCRs (16).Assembly of the TCR–CD3 complex is primarily driven by each protein’s transmembrane (TM) region, enforced through the interaction of evolutionarily conserved, charged, residues in each TM region (4, 5, 12). What, if any, role interactions between TCR and CD3 ECDs play in the assembly and function of the complex remains controversial (5): there are several plausible proposed models of activation, which are not necessarily mutually exclusive (5, 17–19). Although structures of TCR–peptide–MHC (pMHC) complexes (2), TCR–MHC-I–like complexes (1), and the CD3 dimers (6–10) have been separately determined, how the αβ TCR associates with the CD3 complex is largely unknown. Here, we use two independent structural approaches to gain an understanding of the TCR–CD3 complex organization and structure.     

Potentiation of cytotoxic chemotherapy by growth hormone-releasing hormone agonists

The dismal prognosis of malignant brain tumors drives the development of new treatment modalities. In view of the multiple activities of growth hormone-releasing hormone (GHRH), we hypothesized that pretreatment with a GHRH agonist, JI-34, might increase the susceptibility of U-87 MG glioblastoma multiforme (GBM) cells to subsequent treatment with the cytotoxic drug, doxorubicin (DOX). This concept was corroborated by our findings, in vivo, showing that the combination of the GHRH agonist, JI-34, and DOX inhibited the growth of GBM tumors, transplanted into nude mice, more than DOX alone. In vitro, the pretreatment of GBM cells with JI-34 potentiated inhibitory effects of DOX on cell proliferation, diminished cell size and viability, and promoted apoptotic processes, as shown by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide proliferation assay, ApoLive-Glo multiplex assay, and cell volumetric assay. Proteomic studies further revealed that the pretreatment with GHRH agonist evoked differentiation decreasing the expression of the neuroectodermal stem cell antigen, nestin, and up-regulating the glial maturation marker, GFAP. The GHRH agonist also reduced the release of humoral regulators of glial growth, such as FGF basic and TGFβ. Proteomic and gene-expression (RT-PCR) studies confirmed the strong proapoptotic activity (increase in p53, decrease in v-myc and Bcl-2) and anti-invasive potential (decrease in integrin α3) of the combination of GHRH agonist and DOX. These findings indicate that the GHRH agonists can potentiate the anticancer activity of the traditional chemotherapeutic drug, DOX, by multiple mechanisms including the induction of differentiation of cancer cells.Glioblastoma multiforme (GBM) is one of the most aggressive human cancers, and the afflicted patients inevitably succumb. The dismal outcome of this malignancy demands great efforts to find improved methods of treatment (1). Many compounds have been synthesized in our laboratory in the past few years that have proven to be effective against diverse malignant tumors (2–14). These are peptide analogs of hypothalamic hormones: luteinizing hormone-releasing hormone (LHRH), growth hormone-releasing hormone (GHRH), somatostatin, and analogs of other neuropeptides such as bombesin and gastrin-releasing peptide. The receptors for these peptides have been found to be widely distributed in the human body, including in many types of cancers (2–14). The regulatory functions of these hypothalamic hormones and other neuropeptides are not confined to the hypothalamo–hypophyseal system or, even more broadly, to the central nervous system (CNS). In particular, GHRH can induce the differentiation of ovarian granulosa cells and other cells in the reproductive system and function as a growth factor in various normal tissues, benign tumors, and malignancies (2–4, 6, 11, 14–18). Previously, we also reported that antagonistic cytototoxic derivatives of some of these neuropeptides are able to inhibit the growth of several malignant cell lines (2–14).Our earlier studies showed that treatment with antagonists of LHRH or GHRH rarely effects complete regression of glioblastoma-derived tumors (5, 7, 10, 11). Previous studies also suggested that growth factors such as EGF or agonistic analogs of LHRH serving as carriers for cytotoxic analogs and functioning as growth factors may sensitize cancer cells to cytotoxic treatments (10, 19) through the activation of maturation processes. We therefore hypothesized that pretreatment with one of our GHRH agonists, such as JI-34 (20), which has shown effects on growth and differentiation in other cell lines (17, 18, 21, 22), might decrease the pluripotency and the adaptability of GBM cells and thereby increase their susceptibility to cytotoxic treatment.In vivo, tumor cells were implanted into athymic nude mice, tumor growth was recorded weekly, and final tumor mass was measured upon autopsy. In vitro, proliferation assays were used for the determination of neoplastic proliferation and cell growth. Changes in stem (nestin) and maturation (GFAP) antigen expression was evaluated with Western blot studies in vivo and with immunocytochemistry in vitro. The production of glial growth factors (FGF basic, TGFβ) was verified by ELISA. Further, using the Human Cancer Pathway Finder real-time quantitative PCR, numerous genes that play a role in the development of cancer were evaluated. We placed particular emphasis on the measurement of apoptosis, using the ApoLive-Glo Multiplex Assay kit and by detection of the expression of the proapoptotic p53 protein. This overall approach permitted the evaluation of the effect of GHRH agonist, JI-34, on the response to chemotherapy with doxorubicin.     

Luminescence color switching of supramolecular assemblies of discrete molecular decanuclear gold(I) sulfido complexes

A series of discrete decanuclear gold(I) μ₃-sulfido complexes with alkyl chains of various lengths on the aminodiphosphine ligands, [Au₁₀{Ph₂PN(C_nH_2n+1)PPh₂}₄(μ₃-S)₄](ClO₄)₂, has been synthesized and characterized. These complexes have been shown to form supramolecular nanoaggregate assemblies upon solvent modulation. The photoluminescence (PL) colors of the nanoaggregates can be switched from green to yellow to red by varying the solvent systems from which they are formed. The PL color variation was investigated and correlated with the nanostructured morphological transformation from the spherical shape to the cube as observed by transmission electron microscopy and scanning electron microscopy. Such variations in PL colors have not been observed in their analogous complexes with short alkyl chains, suggesting that the long alkyl chains would play a key role in governing the supramolecular nanoaggregate assembly and the emission properties of the decanuclear gold(I) sulfido complexes. The long hydrophobic alkyl chains are believed to induce the formation of supramolecular nanoaggregate assemblies with different morphologies and packing densities under different solvent systems, leading to a change in the extent of Au(I)–Au(I) interactions, rigidity, and emission properties.Gold(I) complexes are one of the fascinating classes of complexes that reveal photophysical properties that are highly sensitive to the nuclearity of the metal centers and the metal–metal distances (1–59). In a certain sense, they bear an analogy or resemblance to the interesting classes of metal nanoparticles (NPs) (60–69) and quantum dots (QDs) (70–76) in that the properties of the nanostructured materials also show a strong dependence on their sizes and shapes. Interestingly, while the optical and spectroscopic properties of metal NPs and QDs show a strong dependence on the interparticle distances, those of polynuclear gold(I) complexes are known to mainly depend on the nuclearity and the internuclear separations of gold(I) centers within the individual molecular complexes or clusters, with influence of the intermolecular interactions between discrete polynuclear molecular complexes relatively less explored (34–38), and those of polynuclear gold(I) clusters not reported. Moreover, while studies on polynuclear gold(I) complexes or clusters are known (34–54), less is explored of their hierarchical assembly and nanostructures as well as the influence of intercluster aggregation on the optical properties (34–38). Among the gold(I) complexes, polynuclear gold(I) chalcogenido complexes represent an important and interesting class (44–51). While directed supramolecular assembly of discrete Au₁₂ (52), Au₁₆ (53), Au₁₈ (51), and Au₃₆ (54) metallomacrocycles as well as trinuclear gold(I) columnar stacks (34–38) have been reported, there have been no corresponding studies on the supramolecular hierarchical assembly of polynuclear gold(I) chalcogenido clusters.Based on our interests and experience in the study of gold(I) chalcogenido clusters (44–46, 51), it is believed that nanoaggegrates with interesting luminescence properties and morphology could be prepared by the judicious design of the gold(I) chalcogenido clusters. As demonstrated by our previous studies on the aggregation behavior of square-planar platinum(II) complexes (77–80) where an enhancement of the solubility of the metal complexes via introduction of solubilizing groups on the ligands and the fine control between solvophobicity and solvophilicity of the complexes would have a crucial influence on the factors governing supramolecular assembly and the formation of aggregates (80), introduction of long alkyl chains as solubilizing groups in the gold(I) sulfido clusters may serve as an effective way to enhance the solubility of the gold(I) clusters for the construction of supramolecular assemblies of novel luminescent nanoaggegrates.Herein, we report the preparation and tunable spectroscopic properties of a series of decanuclear gold(I) μ₃-sulfido complexes with alkyl chains of different lengths on the aminophosphine ligands, [Au₁₀{Ph₂PN(C_nH_2n+1)PPh₂}₄(μ₃-S)₄](ClO₄)₂ [n = 8 (1), 12 (2), 14 (3), 18 (4)] and their supramolecular assembly to form nanoaggregates. The emission colors of the nanoaggregates of 2−4 can be switched from green to yellow to red by varying the solvent systems from which they are formed. These results have been compared with their short alkyl chain-containing counterparts, 1 and a related [Au₁₀{Ph₂PN(C₃H₇)PPh₂}₄(μ₃-S)₄](ClO₄)₂ (45). The present work demonstrates that polynuclear gold(I) chalcogenides, with the introduction of appropriate functional groups, can serve as building blocks for the construction of novel hierarchical nanostructured materials with environment-responsive properties, and it represents a rare example in which nanoaggregates have been assembled with the use of discrete molecular metal clusters as building blocks.     

Transition paths,diffusive processes,and preequilibria of protein folding

Fundamental relationships between the thermodynamics and kinetics of protein folding were investigated using chain models of natural proteins with diverse folding rates by extensive comparisons between the distribution of conformations in thermodynamic equilibrium and the distribution of conformations sampled along folding trajectories. Consistent with theory and single-molecule experiment, duration of the folding transition paths exhibits only a weak correlation with overall folding time. Conformational distributions of folding trajectories near the overall thermodynamic folding/unfolding barrier show significant deviations from preequilibrium. These deviations, the distribution of transition path times, and the variation of mean transition path time for different proteins can all be rationalized by a diffusive process that we modeled using simple Monte Carlo algorithms with an effective coordinate-independent diffusion coefficient. Conformations in the initial stages of transition paths tend to form more nonlocal contacts than typical conformations with the same number of native contacts. This statistical bias, which is indicative of preferred folding pathways, should be amenable to future single-molecule measurements. We found that the preexponential factor defined in the transition state theory of folding varies from protein to protein and that this variation can be rationalized by our Monte Carlo diffusion model. Thus, protein folding physics is different in certain fundamental respects from the physics envisioned by a simple transition-state picture. Nonetheless, transition state theory can be a useful approximate predictor of cooperative folding speed, because the height of the overall folding barrier is apparently a proxy for related rate-determining physical properties.Protein folding is an intriguing phenomenon at the interface of physics and biology. In the early days of folding kinetics studies, folding was formulated almost exclusively in terms of mass-action rate equations connecting the folded, unfolded, and possibly, one or a few intermediate states (1, 2). With the advent of site-directed mutagenesis, the concept of free energy barriers from transition state theory (TST) (3) was introduced to interpret mutational data (4), and subsequently, it was adopted for the Φ-value analysis (5). Since the 1990s, the availability of more detailed experimental data (6), in conjunction with computational development of coarse-grained chain models, has led to an energy landscape picture of folding (7–15). This perspective emphasizes the diversity of microscopic folding trajectories, and it conceptualizes folding as a diffusive process (16–25) akin to the theory of Kramers (26).For two-state-like folding, the transition path (TP), i.e., the sequence of kinetic events that leads directly from the unfolded state to the folded state (27, 28), constitutes only a tiny fraction of a folding trajectory that spends most of the time diffusing, seemingly unproductively, in the vicinity of the free energy minimum of the unfolded state. The development of ultrafast laser spectroscopy (29, 30) and single-molecule (27, 28, 31) techniques have made it possible to establish upper bounds on the transition path time (t_TP) ranging from <200 and <10 μs by earlier (27) and more recent (28), respectively, direct single-molecule FRET to <2 μs (30) by bulk relaxation measurements. Consistent with these observations, recent extensive atomic simulations have also provided estimated t_TP values of the order of ∼1 μs (32, 33). These advances offer exciting prospects of characterizing the productive events along folding TPs.It is timely, therefore, to further the theoretical investigation of TP-related questions (19). To this end, we used coarse-grained C_α models (14) to perform extensive simulations of the folding trajectories of small proteins with 56- to 86-aa residues. These tractable models are useful, because despite significant progress, current atomic models cannot provide the same degree of sampling coverage for proteins of comparable sizes (32, 33). In addition to structural insights, this study provides previously unexplored vantage points to compare the diffusion and TST pictures of folding. Deviations of folding behaviors from TST predictions are not unexpected, because TST is mostly applicable to simple gas reactions; however, the nature and extent of the deviations have not been much explored. Our explicit-chain simulation data conform well to the diffusion picture but not as well to TST. In particular, the preexponential factors of the simulated folding rates exhibit a small but appreciable variation that depends on native topology. These findings and others reported below underscore the importance of single-molecule measurements (13, 27, 28, 31, 34, 35) in assessing the merits of proposed scenarios and organizing principles of folding (7–25, 36, 37).     

From the Cover: Why the proportion of transmission during early-stage HIV infection does not predict the long-term impact of treatment on HIV incidence

Antiretroviral therapy (ART) reduces the infectiousness of HIV-infected persons, but only after testing, linkage to care, and successful viral suppression. Thus, a large proportion of HIV transmission during a period of high infectiousness in the first few months after infection (“early transmission”) is perceived as a threat to the impact of HIV “treatment-as-prevention” strategies. We created a mathematical model of a heterosexual HIV epidemic to investigate how the proportion of early transmission affects the impact of ART on reducing HIV incidence. The model includes stages of HIV infection, flexible sexual mixing, and changes in risk behavior over the epidemic. The model was calibrated to HIV prevalence data from South Africa using a Bayesian framework. Immediately after ART was introduced, more early transmission was associated with a smaller reduction in HIV incidence rate—consistent with the concern that a large amount of early transmission reduces the impact of treatment on incidence. However, the proportion of early transmission was not strongly related to the long-term reduction in incidence. This was because more early transmission resulted in a shorter generation time, in which case lower values for the basic reproductive number (R₀) are consistent with observed epidemic growth, and R₀ was negatively correlated with long-term intervention impact. The fraction of early transmission depends on biological factors, behavioral patterns, and epidemic stage and alone does not predict long-term intervention impacts. However, early transmission may be an important determinant in the outcome of short-term trials and evaluation of programs.Recent studies have confirmed that effective antiretroviral therapy (ART) reduces the transmission of HIV among stable heterosexual couples (1–3). This finding has generated interest in understanding the population-level impact of HIV treatment on reducing the rate of new HIV infections in generalized epidemic settings (4). Research, including mathematical modeling (5–10), implementation research (11), and major randomized controlled trials (12–14), are focused on how ART provision might be expanded strategically to maximize its public health benefits (15, 16).One concern is that if a large fraction of HIV transmission occurs shortly after a person becomes infected, before the person can be diagnosed and initiated on ART, this will limit the potential impact of HIV treatment on reducing HIV incidence (9, 17, 18). Data suggest that persons are more infectious during a short period of “early infection” after becoming infected with HIV (19–22), although there is debate about the extent, duration, and determinants of elevated infectiousness (18, 23). The amount of transmission that occurs also will depend on patterns of sexual behavior and sexual networks (17, 24–27). There have been estimates for the contribution of early infection to transmission from mathematical models (7, 17, 21, 24–26) and phylogenetic analyses (28–31), but these vary widely, from 5% to above 50% (23).In this study, we use a mathematical model to quantify how the proportion of transmission that comes from persons who have been infected recently affects the impact of treatment scale-up on HIV incidence. The model is calibrated to longitudinal HIV prevalence data from South Africa using a Bayesian framework. Thus, the model accounts for not only the early epidemic growth rate highlighted in previous research (5, 9, 18), but also the heterogeneity and sexual behavior change to explain the peak and decline in HIV incidence observed in sub-Saharan African HIV epidemics (32, 33).The model calibration allows uncertainty about factors that determine the amount of early transmission, including the relative infectiousness during early infection, heterogeneity in propensity for sexual risk behavior, assortativity in sexual partner selection, reduction in risk propensity over the life course, and population-wide reductions in risk behavior in response to the epidemic (32, 33). This results in multiple combinations of parameter values that are consistent with the observed epidemic and variation in the amount of early transmission. We simulated the impact of a treatment intervention and report how the proportion of early transmission correlates with the reduction in HIV incidence from the intervention over the short- and long-term.