Predominance of positive epistasis among drug resistance-associated mutations in HIV-1 protease

Drug-resistant mutations often have deleterious impacts on replication fitness, posing a fitness cost that can only be overcome by compensatory mutations. However, the role of fitness cost in the evolution of drug resistance has often been overlooked in clinical studies or in vitro selection experiments, as these observations only capture the outcome of drug selection. In this study, we systematically profile the fitness landscape of resistance-associated sites in HIV-1 protease using deep mutational scanning. We construct a mutant library covering combinations of mutations at 11 sites in HIV-1 protease, all of which are associated with resistance to protease inhibitors in clinic. Using deep sequencing, we quantify the fitness of thousands of HIV-1 protease mutants after multiple cycles of replication in human T cells. Although the majority of resistance-associated mutations have deleterious effects on viral replication, we find that epistasis among resistance-associated mutations is predominantly positive. Furthermore, our fitness data are consistent with genetic interactions inferred directly from HIV sequence data of patients. Fitness valleys formed by strong positive epistasis reduce the likelihood of reversal of drug resistance mutations. Overall, our results support the view that strong compensatory effects are involved in the emergence of clinically observed resistance mutations and provide insights to understanding fitness barriers in the evolution and reversion of drug resistance.

Predicting the Immunogenicity of T cell epitopes: From HIV to SARS-CoV-2

We describe a physics-based learning model for predicting the immunogenicity of Cytotoxic T Lymphocyte (CTL) epitopes derived from diverse pathogens, given a Human Leukocyte Antigen (HLA) genotype. The model was trained and tested on experimental data on the relative immunodominance of CTL epitopes in Human Immunodeficiency Virus infection. The method is more accurate than publicly available models. Our model predicts that only a fraction of SARS-CoV-2 epitopes that have been predicted to bind to HLA molecules is immunogenic. The immunogenic CTL epitopes across all SARS-CoV-2 proteins are predicted to provide broad population coverage, but the immunogenic epitopes in the SARS-CoV-2 spike protein alone are unlikely to do so. Our model predicts that several immunogenic SARS-CoV-2 CTL epitopes are identical to those contained in low-pathogenicity coronaviruses circulating in the population. Thus, we suggest that some level of CTL immunity against COVID-19 may be present in some individuals prior to SARS-CoV-2 infection.

Enhancer Features that Drive Formation of Transcriptional Condensates

Enhancers are DNA elements that are bound by transcription factors (TFs), which recruit coactivators and the transcriptional machinery to genes. Phase-separated condensates of TFs and coactivators have been implicated in assembling the transcription machinery at particular enhancers, yet the role of DNA sequence in this process has not been explored. We show that DNA sequences encoding TF binding site number, density, and affinity above sharply defined thresholds drive condensation of TFs and coactivators. A combination of specific structured (TF-DNA) and weak multivalent (TF-coactivator) interactions allows for condensates to form at particular genomic loci determined by the DNA sequence and the complement of expressed TFs. DNA features found to drive condensation promote enhancer activity and transcription in cells. Our study provides a framework to understand how the genome can scaffold transcriptional condensates at specific loci and how the universal phenomenon of phase separation might regulate this process.

CD45 functions as a signaling gatekeeper in T cells

T cells require the protein tyrosine phosphatase CD45 to detect and respond to antigen because it activates the Src family kinase Lck, which phosphorylates the T cell antigen receptor (TCR) complex. CD45 activates Lck by opposing the negative regulatory kinase Csk. Paradoxically, CD45 has also been implicated in suppressing TCR signaling by dephosphorylating the same signaling motifs within the TCR complex upon which Lck acts. We sought to reconcile these observations using chemical and genetic perturbations of the Csk/CD45 regulatory axis incorporated with computational analyses. Specifically, we titrated the activities of Csk and CD45 and assessed their influence on Lck activation, TCR-associated ζ-chain phosphorylation, and more downstream signaling events. Acute inhibition of Csk revealed that CD45 suppressed ζ-chain phosphorylation and was necessary for a regulatable pool of active Lck, thereby interconnecting the activating and suppressive roles of CD45 that tune antigen discrimination. CD45 suppressed signaling events that were antigen independent or induced by low-affinity antigen but not those initiated by high-affinity antigen. Together, our findings reveal that CD45 acts as a signaling "gatekeeper," enabling graded signaling outputs while filtering weak or spurious signaling events.

Modelling and in vitro testing of the HIV-1 Nef fitness landscape

An effective vaccine is urgently required to curb the HIV-1 epidemic. We have previously described an approach to model the fitness landscape of several HIV-1 proteins, and have validated the results against experimental and clinical data. The fitness landscape may be used to identify mutation patterns harmful to virus viability, and consequently inform the design of immunogens that can target such regions for immunological control. Here we apply such an analysis and complementary experiments to HIV-1 Nef, a multifunctional protein which plays a key role in HIV-1 pathogenesis. We measured Nef-driven replication capacities as well as Nef-mediated CD4 and HLA-I down-modulation capacities of thirty-two different Nef mutants, and tested model predictions against these results. Furthermore, we evaluated the models using 448 patient-derived Nef sequences for which several Nef activities were previously measured. Model predictions correlated significantly with Nef-driven replication and CD4 down-modulation capacities, but not HLA-I down-modulation capacities, of the various Nef mutants. Similarly, in our analysis of patient-derived Nef sequences, CD4 down-modulation capacity correlated the most significantly with model predictions, suggesting that of the tested Nef functions, this is the most important in vivo. Overall, our results highlight how the fitness landscape inferred from patient-derived sequences captures, at least in part, the in vivo functional effects of mutations to Nef. However, the correlation between predictions of the fitness landscape and measured parameters of Nef function is not as accurate as the correlation observed in past studies for other proteins. This may be because of the additional complexity associated with inferring the cost of mutations on the diverse functions of Nef.

How nonuniform contact profiles of T cell receptors modulate thymic selection outcomes

T cell receptors (TCRs) bind foreign or self-peptides attached to major histocompatibility complex (MHC) molecules, and the strength of this interaction determines T cell activation. Optimizing the ability of T cells to recognize a diversity of foreign peptides yet be tolerant of self-peptides is crucial for the adaptive immune system to properly function. This is achieved by selection of T cells in the thymus, where immature T cells expressing unique, stochastically generated TCRs interact with a large number of self-peptide-MHC; if a TCR does not bind strongly enough to any self-peptide-MHC, or too strongly with at least one self-peptide-MHC, the T cell dies. Past theoretical work cast thymic selection as an extreme value problem and characterized the statistical enrichment or depletion of amino acids in the postselection TCR repertoire, showing how T cells are selected to be able to specifically recognize peptides derived from diverse pathogens yet have limited self-reactivity. Here, we investigate how the diversity of the postselection TCR repertoire is modified when TCRs make nonuniform contacts with peptide-MHC. Specifically, we were motivated by recent experiments showing that amino acids at certain positions of a TCR sequence have large effects on thymic selection outcomes, and crystal structure data that reveal a nonuniform contact profile between a TCR and its peptide-MHC ligand. Using a representative TCR contact profile as an illustration, we show via simulations that the statistical enrichment or depletion of amino acids now varies by position according to the contact profile, and, importantly, it depends on the implementation of nonuniform contacts during thymic selection. We explain these nontrivial results analytically. Our study has implications for understanding the selection forces that shape the functionality of the postselection TCR repertoire.

The low spike density of HIV may have evolved because of the effects of T helper cell depletion on affinity maturation

The spikes on virus surfaces bind receptors on host cells to propagate infection. High spike densities (SDs) can promote infection, but spikes are also targets of antibody-mediated immune responses. Thus, diverse evolutionary pressures can influence virus SDs. HIV's SD is about two orders of magnitude lower than that of other viruses, a surprising feature of unknown origin. By modeling antibody evolution through affinity maturation, we find that an intermediate SD maximizes the affinity of generated antibodies. We argue that this leads most viruses to evolve high SDs. T helper cells, which are depleted during early HIV infection, play a key role in antibody evolution. We find that T helper cell depletion results in high affinity antibodies when SD is high, but not if SD is low. This special feature of HIV infection may have led to the evolution of a low SD to avoid potent immune responses early in infection.

Coactivator condensation at super-enhancers links phase separation and gene control

Super-enhancers (SEs) are clusters of enhancers that cooperatively assemble a high density of the transcriptional apparatus to drive robust expression of genes with prominent roles in cell identity. Here we demonstrate that the SE-enriched transcriptional coactivators BRD4 and MED1 form nuclear puncta at SEs that exhibit properties of liquid-like condensates and are disrupted by chemicals that perturb condensates. The intrinsically disordered regions (IDRs) of BRD4 and MED1 can form phase-separated droplets, and MED1-IDR droplets can compartmentalize and concentrate the transcription apparatus from nuclear extracts. These results support the idea that coactivators form phase-separated condensates at SEs that compartmentalize and concentrate the transcription apparatus, suggest a role for coactivator IDRs in this process, and offer insights into mechanisms involved in the control of key cell-identity genes.

Detection of classical swine fever virus E2 gene in cattle serum samples from cattle herds of Meghalaya

The present study focused on the detection and genetic characterisation of 5' untranslated region (5'UTR) and E2 gene of classical swine fever virus (CSFV, family Flaviviridae, genus Pestivirus) from bovine population of the northeastern region of India. A total of 134 cattle serum samples were collected from organised cattle farms and were screened for CSFV antigen with a commercial antigen capture enzyme linked immunosorbent assay (Ag-ELISA) and reverse transcription-polymerase chain reaction (RT-PCR). A total of 10 samples were positive for CSFV antigen by ELISA, while all of them were positive in PCR for 5'UTR region. Full length E2 region of CSFV were successfully amplified from two positive samples and used for subsequent phylogenetic analysis and determination of protein 3D structure which showed similarity with reported CSFV isolate from Assam of sub-genogroup 2.1, with minor variations in protein structure.

Role of framework mutations and antibody flexibility in the evolution of broadly neutralizing antibodies

Eliciting antibodies that are cross reactive with surface proteins of diverse strains of highly mutable pathogens (e.g., HIV, influenza) could be key for developing effective universal vaccines. Mutations in the framework regions of such broadly neutralizing antibodies (bnAbs) have been reported to play a role in determining their properties. We used molecular dynamics simulations and models of affinity maturation to study specific bnAbs against HIV. Our results suggest that there are different classes of evolutionary lineages for the bnAbs. If germline B cells that initiate affinity maturation have high affinity for the conserved residues of the targeted epitope, framework mutations increase antibody rigidity as affinity maturation progresses to evolve bnAbs. If the germline B cells exhibit weak/moderate affinity for conserved residues, an initial increase in flexibility via framework mutations may be required for the evolution of bnAbs. Subsequent mutations that increase rigidity result in highly potent bnAbs. Implications of our results for immunogen design are discussed.

A Population Dynamics Model for Clonal Diversity in a Germinal Center

Germinal centers (GCs) are micro-domains where B cells mature to develop high affinity antibodies. Inside a GC, B cells compete for antigen and T cell help, and the successful ones continue to evolve. New experimental results suggest that, under identical conditions, a wide spectrum of clonal diversity is observed in different GCs, and high affinity B cells are not always the ones selected. We use a birth, death and mutation model to study clonal competition in a GC over time. We find that, like all evolutionary processes, diversity loss is inherently stochastic. We study two selection mechanisms, birth-limited and death limited selection. While death limited selection maintains diversity and allows for slow clonal homogenization as affinity increases, birth limited selection results in more rapid takeover of successful clones. Finally, we qualitatively compare our model to experimental observations of clonal selection in mice.

A Phase Separation Model for Transcriptional Control

Phase-separated multi-molecular assemblies provide a general regulatory mechanism to compartmentalize biochemical reactions within cells. We propose that a phase separation model explains established and recently described features of transcriptional control. These features include the formation of super-enhancers, the sensitivity of super-enhancers to perturbation, the transcriptional bursting patterns of enhancers, and the ability of an enhancer to produce simultaneous activation at multiple genes. This model provides a conceptual framework to further explore principles of gene control in mammals.

Rational design of vaccine targets and strategies for HIV: a crossroad of statistical physics, biology, and medicine

Vaccination has saved more lives than any other medical procedure. Pathogens have now evolved that have not succumbed to vaccination using the empirical paradigms pioneered by Pasteur and Jenner. Vaccine design strategies that are based on a mechanistic understanding of the pertinent immunology and virology are required to confront and eliminate these scourges. In this perspective, we describe just a few examples of work aimed to achieve this goal by bringing together approaches from statistical physics with biology and clinical research.

A Perspective on the Role of Computational Models in Immunology

This is an exciting time for immunology because the future promises to be replete with exciting new discoveries that can be translated to improve health and treat disease in novel ways. Immunologists are attempting to answer increasingly complex questions concerning phenomena that range from the genetic, molecular, and cellular scales to that of organs, whole animals or humans, and populations of humans and pathogens. An important goal is to understand how the many different components involved interact with each other within and across these scales for immune responses to emerge, and how aberrant regulation of these processes causes disease. To aid this quest, large amounts of data can be collected using high-throughput instrumentation. The nonlinear, cooperative, and stochastic character of the interactions between components of the immune system as well as the overwhelming amounts of data can make it difficult to intuit patterns in the data or a mechanistic understanding of the phenomena being studied. Computational models are increasingly important in confronting and overcoming these challenges. I first describe an iterative paradigm of research that integrates laboratory experiments, clinical data, computational inference, and mechanistic computational models. I then illustrate this paradigm with a few examples from the recent literature that make vivid the power of bringing together diverse types of computational models with experimental and clinical studies to fruitfully interrogate the immune system.

Deconstruction of the Ras switching cycle through saturation mutagenesis

Ras proteins are highly conserved signaling molecules that exhibit regulated, nucleotide-dependent switching between active and inactive states. The high conservation of Ras requires mechanistic explanation, especially given the general mutational tolerance of proteins. Here, we use deep mutational scanning, biochemical analysis and molecular simulations to understand constraints on Ras sequence. Ras exhibits global sensitivity to mutation when regulated by a GTPase activating protein and a nucleotide exchange factor. Removing the regulators shifts the distribution of mutational effects to be largely neutral, and reveals hotspots of activating mutations in residues that restrain Ras dynamics and promote the inactive state. Evolutionary analysis, combined with structural and mutational data, argue that Ras has co-evolved with its regulators in the vertebrate lineage. Overall, our results show that sequence conservation in Ras depends strongly on the biochemical network in which it operates, providing a framework for understanding the origin of global selection pressures on proteins.

Hydrophobic CDR3 residues promote the development of self-reactive T cells

Studies of individual T cell antigen receptors (TCRs) have shed some light on structural features that underlie self-reactivity. However, the general rules that can be used to predict whether TCRs are self-reactive have not been fully elucidated. Here we found that the interfacial hydrophobicity of amino acids at positions 6 and 7 of the complementarity-determining region CDR3β robustly promoted the development of self-reactive TCRs. This property was found irrespective of the member of the β-chain variable region (Vβ) family present in the TCR or the length of the CDR3β. An index based on these findings distinguished Vβ2(+), Vβ6(+) and Vβ8.2(+) regulatory T cells from conventional T cells and also distinguished CD4(+) T cells selected by the major histocompatibility complex (MHC) class II molecule I-A(g7) (associated with the development of type 1 diabetes in NOD mice) from those selected by a non-autoimmunity-promoting MHC class II molecule I-A(b). Our results provide a means for distinguishing normal T cell repertoires versus autoimmunity-prone T cell repertoires.

Relative rate and location of intra-host HIV evolution to evade cellular immunity are predictable

Human immunodeficiency virus (HIV) evolves within infected persons to escape being destroyed by the host immune system, thereby preventing effective immune control of infection. Here, we combine methods from evolutionary dynamics and statistical physics to simulate in vivo HIV sequence evolution, predicting the relative rate of escape and the location of escape mutations in response to T-cell-mediated immune pressure in a cohort of 17 persons with acute HIV infection. Predicted and clinically observed times to escape immune responses agree well, and we show that the mutational pathways to escape depend on the viral sequence background due to epistatic interactions. The ability to predict escape pathways and the duration over which control is maintained by specific immune responses open the door to rational design of immunotherapeutic strategies that might enable long-term control of HIV infection. Our approach enables intra-host evolution of a human pathogen to be predicted in a probabilistic framework.

Enhanced clearance of HIV-1-infected cells by broadly neutralizing antibodies against HIV-1 in vivo

Antiretroviral drugs and antibodies limit HIV-1 infection by interfering with the viral life cycle. In addition, antibodies also have the potential to guide host immune effector cells to kill HIV-1-infected cells. Examination of the kinetics of HIV-1 suppression in infected individuals by passively administered 3BNC117, a broadly neutralizing antibody, suggested that the effects of the antibody are not limited to free viral clearance and blocking new infection but also include acceleration of infected cell clearance. Consistent with these observations, we find that broadly neutralizing antibodies can target CD4(+) T cells infected with patient viruses and can decrease their in vivo half-lives by a mechanism that requires Fcγ receptor engagement in a humanized mouse model. The results indicate that passive immunotherapy can accelerate elimination of HIV-1-infected cells.

Identification of drug resistance mutations in HIV from constraints on natural evolution

Human immunodeficiency virus (HIV) evolves with extraordinary rapidity. However, its evolution is constrained by interactions between mutations in its fitness landscape. Here we show that an Ising model describing these interactions, inferred from sequence data obtained prior to the use of antiretroviral drugs, can be used to identify clinically significant sites of resistance mutations. Successful predictions of the resistance sites indicate progress in the development of successful models of real viral evolution at the single residue level and suggest that our approach may be applied to help design new therapies that are less prone to failure even where resistance data are not yet available.

Abstract B039: In silico models for B cell receptor signaling in chronic lymphocytic leukemia

Recent discoveries of ZAP-70 (ζ chain–associated protein kinase of 70 kD) and Syk (spleen tyrosine kinase) inhibitors (gefitinib1 and fostamatinib2 respectively) provide promising therapeutic options for patients with chronic lymphocytic leukemia (CLL). However, the precise action of these inhibitors in the B cell receptor (BCR) signaling pathway is not well understood. This study designs an integrated mechanistic description of the BCR signaling pathway in CLL cells, and investigates the effects of possible inhibitors on this pathway. In this work, we examined several computational models of ZAP-70 and Syk regulation in the BCR signaling pathway to explain the observed differences in the clinical behaviors of ZAP+ / ZAP- phenotypes of B-CLL patients. Specifically, we characterized the effects of different ZAP-70 and Syk expression and phosphorylation levels on the BCR activation threshold. The correlations between the observed and calculated trends are reproduced quantitatively.

Our calculations show that depending on the amount of Syk that is expressed in cells, increased ZAP-70 expression is correlated with decreased levels of phosphorylated ZAP-70 and Syk, and vice versa. ZAP-70 and Syk phosphorylation occur independently, but is dependent on the amount of ZAP-70 and Syk that is expressed in cells. These results support reported experimental observations.3 We also find that ZAP-70 is able to compensate for a missing Syk functionality with an increased BCR activation threshold in Syk-deficient B-CLL cells, similar to Syk-deficient B cell study.4 In addition, we find that selective inhibition of either Syk or ZAP-70 will result in a disease relapse. This suggests the used of two inhibitors rather than one for the treatment of ZAP-70+ patients, based on our model. Lastly, we find that enhanced or reduced BCR signaling is observed at low amounts of Syk expression in CLL cells. However, this computational prediction needs to be verified experimentally in order to identify a viable physiological range of the amount of Syk that is expressed in B-CLL cells for ZAP-70+ and ZAP-70- phenotypes.

Our results uncover molecular mechanisms of ZAP-70 and Syk regulation in the BCR signaling pathway for its future therapeutic use in B-CLL. Our in silico network analysis will help in the development of immunotherapies targeting ZAP-70 and Syk functions to regulate B-CLL responses. Furthermore, our study provides information about responses to certain cancer therapies, and tumor progression.

References:

1 Dielschneider, R. F. et al. Cell Death Dis. 2014, 5 (10), e1439.

2 Friedberg, J. W. et al. Blood 2010, 115 (13), 2578.

3 Kaplan, D. et al. Cytometry B Clin. Cytom. 2010, 78 (2), 115.

4 Kong, G. H. et al. Immunity 1995, 2 (5), 485.

Citation Format: Maria P. Frushicheva, Arup K. Chakraborty. In silico models for B cell receptor signaling in chronic lymphocytic leukemia. [abstract]. In: Proceedings of the CRI-CIMT-EATI-AACR Inaugural International Cancer Immunotherapy Conference: Translating Science into Survival; September 16-19, 2015; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(1 Suppl):Abstract nr B039.

Affinity Inequality among Serum Antibodies That Originate in Lymphoid Germinal Centers

Upon natural infection with pathogens or vaccination, antibodies are produced by a process called affinity maturation. As affinity maturation ensues, average affinity values between an antibody and ligand increase with time. Purified antibodies isolated from serum are invariably heterogeneous with respect to their affinity for the ligands they bind, whether macromolecular antigens or haptens (low molecular weight approximations of epitopes on antigens). However, less is known about how the extent of this heterogeneity evolves with time during affinity maturation. To shed light on this issue, we have taken advantage of previously published data from Eisen and Siskind (1964). Using the ratio of the strongest to the weakest binding subsets as a metric of heterogeneity (or affinity inequality), we analyzed antibodies isolated from individual serum samples. The ratios were initially as high as 50-fold, and decreased over a few weeks after a single injection of small antigen doses to around unity. This decrease in the effective heterogeneity of antibody affinities with time is consistent with Darwinian evolution in the strong selection limit. By contrast, neither the average affinity nor the heterogeneity evolves much with time for high doses of antigen, as competition between clones of the same affinity is minimal.

Theory, models and biology

Theoretical ideas have a rich history in many areas of biology, and new theories and mathematical models have much to offer in the future.

The catalytic activity of the kinase ZAP-70 mediates basal signaling and negative feedback of the T cell receptor pathway

T cell activation by antigens binding to the T cell receptor (TCR) must be properly regulated to ensure normal T cell development and effective immune responses to pathogens and transformed cells while avoiding autoimmunity. The Src family kinase Lck and the Syk family kinase ZAP-70 (ζ chain-associated protein kinase of 70 kD) are sequentially activated in response to TCR engagement and serve as critical components of the TCR signaling machinery that leads to T cell activation. We performed a mass spectrometry-based phosphoproteomic study comparing the quantitative differences in the temporal dynamics of phosphorylation in stimulated and unstimulated T cells with or without inhibition of ZAP-70 catalytic activity. The data indicated that the kinase activity of ZAP-70 stimulates negative feedback pathways that target Lck and thereby modulate the phosphorylation patterns of the immunoreceptor tyrosine-based activation motifs (ITAMs) of the CD3 and ζ chain components of the TCR and of signaling molecules downstream of Lck, including ZAP-70. We developed a computational model that provides a mechanistic explanation for the experimental findings on ITAM phosphorylation in wild-type cells, ZAP-70-deficient cells, and cells with inhibited ZAP-70 catalytic activity. This model incorporated negative feedback regulation of Lck activity by the kinase activity of ZAP-70 and predicted the order in which tyrosines in the ITAMs of TCR ζ chains must be phosphorylated to be consistent with the experimental data.