The initiation of T cell antigen receptor signaling is a key step that can result in T cell activation and the orchestration of an adaptive immune response. Early events in T cell receptor signaling ...can distinguish between agonist and endogenous ligands with exquisite selectivity, and show extraordinary sensitivity to minute numbers of agonists in a sea of endogenous ligands. We review our current knowledge of models and crucial molecules that aim to provide a mechanistic explanation for these observations. Building on current understanding and a discussion of unresolved issues, we propose a molecular model for initiation of T cell receptor signaling that may serve as a useful guide for future studies.
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.
A phase separation model for transcription explains key features of transcription and sets enhancers, and especially super-enhancers, into the broad family of membraneless organelles.
The rise of SARS-CoV-2 variants and the history of outbreaks caused by zoonotic coronaviruses point to the need for next-generation vaccines that confer protection against variant strains. Here, we ...combined analyses of diverse sequences and structures of coronavirus spikes with data from deep mutational scanning to design SARS-CoV-2 variant antigens containing the most significant mutations that may emerge. We trained a neural network to predict RBD expression and ACE2 binding from sequence, which allowed us to determine that these antigens are stable and bind to ACE2. Thus, they represent viable variants. We then used a computational model of affinity maturation (AM) to study the antibody response to immunization with different combinations of the designed antigens. The results suggest that immunization with a cocktail of the antigens is likely to promote evolution of higher titers of antibodies that target SARS-CoV-2 variants than immunization or infection with the wildtype virus alone. Finally, our analysis of 12 coronaviruses from different genera identified the S2’ cleavage site and fusion peptide as potential pan-coronavirus vaccine targets.
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.
A prophylactic or therapeutic vaccine offers the best hope to curb the HIV-AIDS epidemic gripping sub-Saharan Africa, but it remains elusive. A major challenge is the extreme viral sequence ...variability among strains. Systematic means to guide immunogen design for highly variable pathogens like HIV are not available. Using computational models, we have developed an approach to translate available viral sequence data into quantitative landscapes of viral fitness as a function of the amino acid sequences of its constituent proteins. Predictions emerging from our computationally defined landscapes for the proteins of HIV-1 clade B Gag were positively tested against new in vitro fitness measurements and were consistent with previously defined in vitro measurements and clinical observations. These landscapes chart the peaks and valleys of viral fitness as protein sequences change and inform the design of immunogens and therapies that can target regions of the virus most vulnerable to selection pressure.
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► Quantitative fitness landscapes were extracted from viral sequence databases ► We developed a landscape for HIV Gag by using a model from statistical physics ► Predictions show good agreement with new in vitro and existing clinical data
Mass cytometry enables an unprecedented number of parameters to be measured in individual cells at a high throughput, but the large dimensionality of the resulting data severely limits approaches ...relying on manual “gating.” Clustering cells based on phenotypic similarity comes at a loss of single-cell resolution and often the number of subpopulations is unknown a priori. Here we describe ACCENSE, a tool that combines nonlinear dimensionality reduction with density-based partitioning, and displays multivariate cellular phenotypes on a 2D plot. We apply ACCENSE to 35-parameter mass cytometry data from CD8 ⁺ T cells derived from specific pathogen-free and germ-free mice, and stratify cells into phenotypic subpopulations. Our results show significant heterogeneity within the known CD8 ⁺ T-cell subpopulations, and of particular note is that we find a large novel subpopulation in both specific pathogen-free and germ-free mice that has not been described previously. This subpopulation possesses a phenotypic signature that is distinct from conventional naive and memory subpopulations when analyzed by ACCENSE, but is not distinguishable on a biaxial plot of standard markers. We are able to automatically identify cellular subpopulations based on all proteins analyzed, thus aiding the full utilization of powerful new single-cell technologies such as mass cytometry.
Macroscopic membraneless organelles containing RNA such as the nucleoli, germ granules, and the Cajal body have been known for decades. These biomolecular condensates are liquid-like bodies that can ...be formed by a phase transition. Recent evidence has revealed the presence of similar microscopic condensates associated with the transcription of genes. This brief article summarizes thoughts about the importance of condensates in the regulation of transcription and how RNA molecules, as components of such condensates, control the synthesis of RNA. Models and experimental data suggest that RNAs from enhancers facilitate the formation of a condensate that stabilizes the binding of transcription factors and accounts for a burst of transcription at the promoter. Termination of this burst is pictured as a nonequilibrium feedback loop where additional RNA destabilizes the condensate.
Long noncoding RNAs (lncRNAs) perform several important functions in cells including cis-regulation of transcription. Barring a few specific cases, the mechanisms underlying transcriptional ...regulation by lncRNAs remain poorly understood. Transcriptional proteins can form condensates via phase separation at protein-binding loci (BL) on the genome (e.g., enhancers and promoters). lncRNA-coding genes are present at loci in close genomic proximity of these BL and these RNAs can interact with transcriptional proteins via attractive heterotypic interactions mediated by their net charge. Motivated by these observations, we propose that lncRNAs can dynamically regulate transcription in cis via charge-based heterotypic interactions with transcriptional proteins in condensates. To study the consequences of this mechanism, we developed and studied a dynamical phase-field model. We find that proximal lncRNAs can promote condensate formation at the BL. Vicinally localized lncRNA can migrate to the BL to attract more protein because of favorable interaction free energies. However, increasing the distance beyond a threshold leads to a sharp decrease in protein recruitment to the BL. This finding could potentially explain why genomic distances between lncRNA-coding genes and protein-coding genes are conserved across metazoans. Finally, our model predicts that lncRNA transcription can fine-tune transcription from neighboring condensate-controlled genes, repressing transcription from highly expressed genes and enhancing transcription of genes expressed at a low level. This nonequilibrium effect can reconcile conflicting reports that lncRNAs can enhance or repress transcription from proximal genes.
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.
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.
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