Targeting BMI-1 in B cells restores effective humoral immune responses and controls chronic viral infection.

Ineffective antibody-mediated responses are a key characteristic of chronic viral infection. However, our understanding of the intrinsic mechanisms that drive this dysregulation are unclear. Here, we identify that targeting the epigenetic modifier BMI-1 in mice improves humoral responses to chronic lymphocytic choriomeningitis virus. BMI-1 was upregulated by germinal center B cells in chronic viral infection, correlating with changes to the accessible chromatin landscape, compared to acute infection. B cell-intrinsic deletion of Bmi1 accelerated viral clearance, reduced splenomegaly and restored splenic architecture. Deletion of Bmi1 restored c-Myc expression in B cells, concomitant with improved quality of antibody and coupled with reduced antibody-secreting cell numbers. Specifically, BMI-1-deficiency induced antibody with increased neutralizing capacity and enhanced antibody-dependent effector function. Using a small molecule inhibitor to murine BMI-1, we could deplete antibody-secreting cells and prohibit detrimental immune complex formation in vivo. This study defines BMI-1 as a crucial immune modifier that controls antibody-mediated responses in chronic infection.

Macrophage and neutrophil death programs differentially confer resistance to tuberculosis.

Apoptosis can potently defend against intracellular pathogens by directly killing microbes and eliminating their replicative niche. However, the reported ability of Mycobacterium tuberculosis to restrict apoptotic pathways in macrophages in vitro has led to apoptosis being dismissed as a host-protective process in tuberculosis despite a lack of in vivo evidence. Here we define crucial in vivo functions of the death receptor-mediated and BCL-2-regulated apoptosis pathways in mediating protection against tuberculosis by eliminating distinct populations of infected macrophages and neutrophils and priming T cell responses. We further show that apoptotic pathways can be targeted therapeutically with clinical-stage compounds that antagonize inhibitor of apoptosis (IAP) proteins to promote clearance of M. tuberculosis in mice. These findings reveal that any inhibition of apoptosis by M. tuberculosis is incomplete in vivo, advancing our understanding of host-protective responses to tuberculosis (TB) and revealing host pathways that may be targetable for treatment of disease.

A molecular threshold for effector CD8(+) T cell differentiation controlled by transcription factors Blimp-1 and T-bet.

T cell responses are guided by cytokines that induce transcriptional regulators, which ultimately control differentiation of effector and memory T cells. However, it is unknown how the activities of these molecular regulators are coordinated and integrated during the differentiation process. Using genetic approaches and transcriptional profiling of antigen-specific CD8(+) T cells, we reveal a common program of effector differentiation that is regulated by IL-2 and IL-12 signaling and the combined activities of the transcriptional regulators Blimp-1 and T-bet. The loss of both T-bet and Blimp-1 leads to abrogated cytotoxic function and ectopic IL-17 production in CD8(+) T cells. Overall, our data reveal two major overlapping pathways of effector differentiation governed by the availability of Blimp-1 and T-bet and suggest a model for cytokine-induced transcriptional changes that combine, quantitatively and qualitatively, to promote robust effector CD8(+) T cell differentiation.

CXCR5(+) follicular cytotoxic T cells control viral infection in B cell follicles.

During unresolved infections, some viruses escape immunological control and establish a persistant reservoir in certain cell types, such as human immunodeficiency virus (HIV), which persists in follicular helper T cells (TFH cells), and Epstein-Barr virus (EBV), which persists in B cells. Here we identified a specialized group of cytotoxic T cells (TC cells) that expressed the chemokine receptor CXCR5, selectively entered B cell follicles and eradicated infected TFH cells and B cells. The differentiation of these cells, which we have called 'follicular cytotoxic T cells' (TFC cells), required the transcription factors Bcl6, E2A and TCF-1 but was inhibited by the transcriptional regulators Blimp1, Id2 and Id3. Blimp1 and E2A directly regulated Cxcr5 expression and, together with Bcl6 and TCF-1, formed a transcriptional circuit that guided TFC cell development. The identification of TFC cells has far-reaching implications for the development of strategies to control infections that target B cells and TFH cells and to treat B cell-derived malignancies.

Transcription Factor IRF4 Promotes CD8+ T Cell Exhaustion and Limits the Development of Memory-like T Cells during Chronic Infection.

During chronic stimulation, CD8+ T cells acquire an exhausted phenotype characterized by expression of inhibitory receptors, down-modulation of effector function, and metabolic impairments. T cell exhaustion protects from excessive immunopathology but limits clearance of virus-infected or tumor cells. We transcriptionally profiled antigen-specific T cells from mice infected with lymphocytic choriomeningitis virus strains that cause acute or chronic disease. T cell exhaustion during chronic infection was driven by high amounts of T cell receptor (TCR)-induced transcription factors IRF4, BATF, and NFATc1. These regulators promoted expression of inhibitory receptors, including PD-1, and mediated impaired cellular metabolism. Furthermore, they repressed the expression of TCF1, a transcription factor required for memory T cell differentiation. Reducing IRF4 expression restored the functional and metabolic properties of antigen-specific T cells and promoted memory-like T cell development. These findings indicate that IRF4 functions as a central node in a TCR-responsive transcriptional circuit that establishes and sustains T cell exhaustion during chronic infection.

SIDT2 Transports Extracellular dsRNA into the Cytoplasm for Innate Immune Recognition.

Double-stranded RNA (dsRNA) is a common by-product of viral infections and acts as a potent trigger of antiviral immunity. In the nematode C. elegans, sid-1 encodes a dsRNA transporter that is highly conserved throughout animal evolution, but the physiological role of SID-1 and its orthologs remains unclear. Here, we show that the mammalian SID-1 ortholog, SIDT2, is required to transport internalized extracellular dsRNA from endocytic compartments into the cytoplasm for immune activation. Sidt2-deficient mice exposed to extracellular dsRNA, encephalomyocarditis virus (EMCV), and herpes simplex virus 1 (HSV-1) show impaired production of antiviral cytokines and-in the case of EMCV and HSV-1-reduced survival. Thus, SIDT2 has retained the dsRNA transport activity of its C. elegans ortholog, and this transport is important for antiviral immunity.

IL-7 engages multiple mechanisms to overcome chronic viral infection and limit organ pathology.

Understanding the factors that impede immune responses to persistent viruses is essential in designing therapies for HIV infection. Mice infected with LCMV clone-13 have persistent high-level viremia and a dysfunctional immune response. Interleukin-7, a cytokine that is critical for immune development and homeostasis, was used here to promote immunity toward clone-13, enabling elucidation of the inhibitory pathways underlying impaired antiviral immune response. Mechanistically, IL-7 downregulated a critical repressor of cytokine signaling, Socs3, resulting in amplified cytokine production, increased T cell effector function and numbers, and viral clearance. IL-7 enhanced thymic output to expand the naive T cell pool, including T cells that were not LCMV specific. Additionally, IL-7 promoted production of cytoprotective IL-22 that abrogated liver pathology. The IL-7-mediated effects were dependent on endogenous IL-6. These attributes of IL-7 have profound implications for its use as a therapeutic in the treatment of chronic viral diseases.

The transcription factor IRF4 is essential for TCR affinity-mediated metabolic programming and clonal expansion of T cells.

During immune responses, T cells are subject to clonal competition, which leads to the predominant expansion of high-affinity clones; however, there is little understanding of how this process is controlled. We found here that the transcription factor IRF4 was induced in a manner dependent on affinity for the T cell antigen receptor (TCR) and acted as a dose-dependent regulator of the metabolic function of activated T cells. IRF4 regulated the expression of key molecules required for the aerobic glycolysis of effector T cells and was essential for the clonal expansion and maintenance of effector function of antigen-specific CD8(+) T cells. Thus, IRF4 is an indispensable molecular 'rheostat' that 'translates' TCR affinity into the appropriate transcriptional programs that link metabolic function with the clonal selection and effector differentiation of T cells.

ARIH2 is essential for embryogenesis, and its hematopoietic deficiency causes lethal activation of the immune system.

The E3 ligase ARIH2 has an unusual structure and mechanism of elongating ubiquitin chains. To understand its physiological role, we generated gene-targeted mice deficient in ARIH2. ARIH2 deficiency resulted in the embryonic death of C57BL/6 mice. On a mixed genetic background, the lethality was attenuated, with some mice surviving beyond weaning and then succumbing to an aggressive multiorgan inflammatory response. We found that in dendritic cells (DCs), ARIH2 caused degradation of the inhibitor IκBß in the nucleus, which abrogated its ability to sequester, protect and transcriptionally coactivate the transcription factor subunit p65 in the nucleus. Loss of ARIH2 caused dysregulated activation of the transcription factor NF-κB in DCs, which led to lethal activation of the immune system in ARIH2-sufficent mice reconstituted with ARIH2-deficient hematopoietic stem cells. Our data have therapeutic implications for targeting ARIH2 function.

Modeling reveals posttranscriptional regulation of GA metabolism enzymes in response to drought and cold.

The hormone gibberellin (GA) controls plant growth and regulates growth responses to environmental stress. In monocotyledonous leaves, GA controls growth by regulating division-zone size. We used a systems approach to investigate the establishment of the GA distribution in the maize leaf growth zone to understand how drought and cold alter leaf growth. By developing and parameterizing a multiscale computational model that includes cell movement, growth-induced dilution, and metabolic activities, we revealed that the GA distribution is predominantly determined by variations in GA metabolism. Considering wild-type and UBI::GA20-OX-1 leaves, the model predicted the peak in GA concentration, which has been shown to determine division-zone size. Drought and cold modified enzyme transcript levels, although the model revealed that this did not explain the observed GA distributions. Instead, the model predicted that GA distributions are also mediated by posttranscriptional modifications increasing the activity of GA 20-oxidase in drought and of GA 2-oxidase in cold, which we confirmed by enzyme activity measurements. This work provides a mechanistic understanding of the role of GA metabolism in plant growth regulation.

Epigenetic Silencing of RIPK3 in Hepatocytes Prevents MLKL-mediated Necroptosis From Contributing to Liver Pathologies.

BACKGROUND & AIMS: Necroptosis is a highly inflammatory mode of cell death that has been implicated in causing hepatic injury including steatohepatitis/ nonalcoholic steatohepatitis (NASH); however, the evidence supporting these claims has been controversial. A comprehensive, fundamental understanding of cell death pathways involved in liver disease critically underpins rational strategies for therapeutic intervention. We sought to define the role and relevance of necroptosis in liver pathology. METHODS: Several animal models of human liver pathology, including diet-induced steatohepatitis in male mice and diverse infections in both male and female mice, were used to dissect the relevance of necroptosis in liver pathobiology. We applied necroptotic stimuli to primary mouse and human hepatocytes to measure their susceptibility to necroptosis. Paired liver biospecimens from patients with NASH, before and after intervention, were analyzed. DNA methylation sequencing was also performed to investigate the epigenetic regulation of RIPK3 expression in primary human and mouse hepatocytes. RESULTS: Identical infection kinetics and pathologic outcomes were observed in mice deficient in an essential necroptotic effector protein, MLKL, compared with control animals. Mice lacking MLKL were indistinguishable from wild-type mice when fed a high-fat diet to induce NASH. Under all conditions tested, we were unable to induce necroptosis in hepatocytes. We confirmed that a critical activator of necroptosis, RIPK3, was epigenetically silenced in mouse and human primary hepatocytes and rendered them unable to undergo necroptosis. CONCLUSIONS: We have provided compelling evidence that necroptosis is disabled in hepatocytes during homeostasis and in the pathologic conditions tested in this study.

Uncertainty and error in SARS-CoV-2 epidemiological parameters inferred from population-level epidemic models.

During the SARS-CoV-2 pandemic, epidemic models have been central to policy-making. Public health responses have been shaped by model-based projections and inferences, especially related to the impact of various non-pharmaceutical interventions. Accompanying this has been increased scrutiny over model performance, model assumptions, and the way that uncertainty is incorporated and presented. Here we consider a population-level model, focusing on how distributions representing host infectiousness and the infection-to-death times are modelled, and particularly on the impact of inferred epidemic characteristics if these distributions are mis-specified. We introduce an SIR-type model with the infected population structured by 'infected age', i.e. the number of days since first being infected, a formulation that enables distributions to be incorporated that are consistent with clinical data. We show that inference based on simpler models without infected age, which implicitly mis-specify these distributions, leads to substantial errors in inferred quantities relevant to policy-making, such as the reproduction number and the impact of interventions. We consider uncertainty quantification via a Bayesian approach, implementing this for both synthetic and real data focusing on UK data in the period 15 Feb-14 Jul 2020, and emphasising circumstances where it is misleading to neglect uncertainty. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics.

When using mathematical models to make quantitative predictions for clinical or industrial use, it is important that predictions come with a reliable estimate of their accuracy (uncertainty quantification). Because models of complex biological systems are always large simplifications, model discrepancy arises-models fail to perfectly recapitulate the true data generating process. This presents a particular challenge for making accurate predictions, and especially for accurately quantifying uncertainty in these predictions. Experimentalists and modellers must choose which experimental procedures (protocols) are used to produce data used to train models. We propose to characterise uncertainty owing to model discrepancy with an ensemble of parameter sets, each of which results from training to data from a different protocol. The variability in predictions from this ensemble provides an empirical estimate of predictive uncertainty owing to model discrepancy, even for unseen protocols. We use the example of electrophysiology experiments that investigate the properties of hERG potassium channels. Here, 'information-rich' protocols allow mathematical models to be trained using numerous short experiments performed on the same cell. In this case, we simulate data with one model and fit it with a different (discrepant) one. For any individual experimental protocol, parameter estimates vary little under repeated samples from the assumed additive independent Gaussian noise model. Yet parameter sets arising from the same model applied to different experiments conflict-highlighting model discrepancy. Our methods will help select more suitable ion channel models for future studies, and will be widely applicable to a range of biological modelling problems.

The role of MKK4 in T-cell development and immunity to viral infections.

The stress-activated protein kinases (SAPKs)/c-Jun-N-terminal-kinases (JNK) are members of the mitogen-activated protein kinase family. These kinases are responsible for transducing cellular signals through a phosphorylation-dependent signaling cascade. JNK activation in immune cells can lead to a range of critical cellular responses that include proliferation, differentiation and apoptosis. MKK4 is a SAPK that can activate both JNK1 and JNK2; however, its role in T-cell development and function has been controversial. Additionally, loss of either JNK1 or JNK2 has opposing effects in the generation of T-cell immunity to viral infection and cancer. We used mice with a conditional loss of MKK4 in T cells to investigate the in vivo role of MKK4 in T-cell development and function during lymphocytic choriomeningitis virus (LCMV) infection. We found no physiologically relevant differences in T-cell responses or immunity to either acute or chronic LCMV in the absence of MKK4.

NLRP1 inflammasome activation induces pyroptosis of hematopoietic progenitor cells.

Cytopenias are key prognostic indicators of life-threatening infection, contributing to immunosuppression and mortality. Here we define a role for Caspase-1-dependent death, known as pyroptosis, in infection-induced cytopenias by studying inflammasome activation in hematopoietic progenitor cells. The NLRP1a inflammasome is expressed in hematopoietic progenitor cells and its activation triggers their pyroptotic death. Active NLRP1a induced a lethal systemic inflammatory disease that was driven by Caspase-1 and IL-1ß but was independent of apoptosis-associated speck-like protein containing a CARD (ASC) and ameliorated by IL-18. Surprisingly, in the absence of IL-1ß-driven inflammation, active NLRP1a triggered pyroptosis of hematopoietic progenitor cells resulting in leukopenia at steady state. During periods of hematopoietic stress induced by chemotherapy or lymphocytic choriomeningitis virus (LCMV) infection, active NLRP1a caused prolonged cytopenia, bone marrow hypoplasia, and immunosuppression. Conversely, NLRP1-deficient mice showed enhanced recovery from chemotherapy and LCMV infection, demonstrating that NLRP1 acts as a cellular sentinel to alert Caspase-1 to hematopoietic and infectious stress.

Gaussian process models of potential energy surfaces with boundary optimization.

A strategy is outlined to reduce the number of training points required to model intermolecular potentials using Gaussian processes, without reducing accuracy. An asymptotic function is used at a long range, and the crossover distance between this model and the Gaussian process is learnt from the training data. The results are presented for different implementations of this procedure, known as boundary optimization, across the following dimer systems: CO-Ne, HF-Ne, HF-Na+, CO2-Ne, and (CO2)2. The technique reduces the number of training points, at fixed accuracy, by up to ∼49%, compared to our previous work based on a sequential learning technique. The approach is readily transferable to other statistical methods of prediction or modeling problems.

Parameter inference to motivate asymptotic model reduction: An analysis of the gibberellin biosynthesis pathway.

Developing effective strategies to use models in conjunction with experimental data is essential to understand the dynamics of biological regulatory networks. In this study, we demonstrate how combining parameter estimation with asymptotic analysis can reveal the key features of a network and lead to simplified models that capture the observed network dynamics. Our approach involves fitting the model to experimental data and using the profile likelihood to identify small parameters and cases where model dynamics are insensitive to changing particular individual parameters. Such parameter diagnostics provide understanding of the dominant features of the model and motivate asymptotic model reductions to derive simpler models in terms of identifiable parameter groupings. We focus on the particular example of biosynthesis of the plant hormone gibberellin (GA), which controls plant growth and has been mutated in many current crop varieties. This pathway comprises two parallel series of enzyme-substrate reactions, which have previously been modelled using the law of mass action (Middleton et al., 2012). Considering the GA20ox-mediated steps, we analyse the identifiability of the model parameters using published experimental data; the analysis reveals the ratio between enzyme and GA levels to be small and motivates us to perform a quasi-steady state analysis to derive a reduced model. Fitting the parameters in the reduced model reveals additional features of the pathway and motivates further asymptotic analysis which produces a hierarchy of reduced models. Calculating the Akaike information criterion and parameter confidence intervals enables us to select a parsimonious model with identifiable parameters. As well as demonstrating the benefits of combining parameter estimation and asymptotic analysis, the analysis shows how GA biosynthesis is limited by the final GA20ox-mediated steps in the pathway and generates a simple mathematical description of this part of the GA biosynthesis pathway.

Three-dimensional plant architecture and sunlit-shaded patterns: a stochastic model of light dynamics in canopies.

Background and Aims: Diurnal changes in solar position and intensity combined with the structural complexity of plant architecture result in highly variable and dynamic light patterns within the plant canopy. This affects productivity through the complex ways that photosynthesis responds to changes in light intensity. Current methods to characterize light dynamics, such as ray-tracing, are able to produce data with excellent spatio-temporal resolution but are computationally intensive and the resulting data are complex and high-dimensional. This necessitates development of more economical models for summarizing the data and for simulating realistic light patterns over the course of a day. Methods: High-resolution reconstructions of field-grown plants are assembled in various configurations to form canopies, and a forward ray-tracing algorithm is applied to the canopies to compute light dynamics at high (1 min) temporal resolution. From the ray-tracer output, the sunlit or shaded state for each patch on the plants is determined, and these data are used to develop a novel stochastic model for the sunlit-shaded patterns. The model is designed to be straightforward to fit to data using maximum likelihood estimation, and fast to simulate from. Key Results: For a wide range of contrasting 3-D canopies, the stochastic model is able to summarize, and replicate in simulations, key features of the light dynamics. When light patterns simulated from the stochastic model are used as input to a model of photoinhibition, the predicted reduction in carbon gain is similar to that from calculations based on the (extremely costly) ray-tracer data. Conclusions: The model provides a way to summarize highly complex data in a small number of parameters, and a cost-effective way to simulate realistic light patterns. Simulations from the model will be particularly useful for feeding into larger-scale photosynthesis models for calculating how light dynamics affects the photosynthetic productivity of canopies.

Eliminating hepatitis B by antagonizing cellular inhibitors of apoptosis.

We have shown that cellular inhibitor of apoptosis proteins (cIAPs) impair clearance of hepatitis B virus (HBV) infection by preventing TNF-mediated killing/death of infected cells. A key question, with profound therapeutic implications, is whether this finding can be translated to the development of drugs that promote elimination of infected cells. Drug inhibitors of cIAPs were developed as cancer therapeutics to promote TNF-mediated tumor killing. These drugs are also known as Smac mimetics, because they mimic the action of the endogenous protein Smac/Diablo that antagonizes cIAP function. Here, we show using an immunocompetent mouse model of chronic HBV infection that birinapant and other Smac mimetics are able to rapidly reduce serum HBV DNA and serum HBV surface antigen, and they promote the elimination of hepatocytes containing HBV core antigen. The efficacy of Smac mimetics in treating HBV infection is dependent on their chemistry, host CD4(+) T cells, and TNF. Birinapant enhances the ability of entecavir, an antiviral nucleoside analog, to reduce viral DNA production in HBV-infected animals. These results indicate that birinapant and other Smac mimetics may have efficacy in treating HBV infection and perhaps, other intracellular infections.

Cellular inhibitor of apoptosis proteins prevent clearance of hepatitis B virus.

Hepatitis B virus (HBV) infection can result in a spectrum of outcomes from immune-mediated control to disease progression, cirrhosis, and liver cancer. The host molecular pathways that influence and contribute to these outcomes need to be defined. Using an immunocompetent mouse model of chronic HBV infection, we identified some of the host cellular and molecular factors that impact on infection outcomes. Here, we show that cellular inhibitor of apoptosis proteins (cIAPs) attenuate TNF signaling during hepatitis B infection, and they restrict the death of infected hepatocytes, thus allowing viral persistence. Animals with a liver-specific cIAP1 and total cIAP2 deficiency efficiently control HBV infection compared with WT mice. This phenotype was partly recapitulated in mice that were deficient in cIAP2 alone. These results indicate that antagonizing the function of cIAPs may promote the clearance of HBV infection.