Distinct Type 1 Immune Networks Underlie the Severity of Restrictive Lung Disease after COVID-19.

The variable etiology of persistent breathlessness after COVID-19 have confounded efforts to decipher the immunopathology of lung sequelae. Here, we analyzed hundreds of cellular and molecular features in the context of discrete pulmonary phenotypes to define the systemic immune landscape of post-COVID lung disease. Cluster analysis of lung physiology measures highlighted two phenotypes of restrictive lung disease that differed by their impaired diffusion and severity of fibrosis. Machine learning revealed marked CCR5+CD95+ CD8+ T-cell perturbations in mild-to-moderate lung disease, but attenuated T-cell responses hallmarked by elevated CXCL13 in more severe disease. Distinct sets of cells, mediators, and autoantibodies distinguished each restrictive phenotype, and differed from those of patients without significant lung involvement. These differences were reflected in divergent T-cell-based type 1 networks according to severity of lung disease. Our findings, which provide an immunological basis for active lung injury versus advanced disease after COVID-19, might offer new targets for treatment.

Quantitative mechanistic model reveals key determinants of placental IgG transfer and informs prenatal immunization strategies.

Transplacental antibody transfer is crucially important in shaping neonatal immunity. Recently, prenatal maternal immunization has been employed to boost pathogen-specific immunoglobulin G (IgG) transfer to the fetus. Multiple factors have been implicated in antibody transfer, but how these key regulators work together to elicit selective transfer is pertinent to engineering vaccines for mothers to optimally immunize their newborns. Here, we present the first quantitative mechanistic model to uncover the determinants of placental antibody transfer and inform personalized immunization approaches. We identified placental FcγRIIb expressed by endothelial cells as a limiting factor in receptor-mediated transfer, which plays a key role in promoting preferential transport of subclasses IgG1, IgG3, and IgG4, but not IgG2. Integrated computational modeling and in vitro experiments reveal that IgG subclass abundance, Fc receptor (FcR) binding affinity, and FcR abundance in syncytiotrophoblasts and endothelial cells contribute to inter-subclass competition and potentially inter- and intra-patient antibody transfer heterogeneity. We developed an in silico prenatal vaccine testbed by combining a computational model of maternal vaccination with this placental transfer model using the tetanus, diphtheria, and acellular pertussis (Tdap) vaccine as a case study. Model simulations unveiled precision prenatal immunization opportunities that account for a patient's anticipated gestational length, placental size, and FcR expression by modulating vaccine timing, dosage, and adjuvant. This computational approach provides new perspectives on the dynamics of maternal-fetal antibody transfer in humans and potential avenues to optimize prenatal vaccinations that promote neonatal immunity.

Accumulation of Neutrophil Phagocytic Antibody Features Tracks With Naturally Acquired Immunity Against Malaria in Children.

BACKGROUND: Studies have demonstrated the protective role of antibodies against malaria. Young children are known to be particularly vulnerable to malaria, pointing to the evolution of naturally acquired clinical immunity over time. However, whether changes in antibody functionality track with the acquisition of naturally acquired malaria immunity remains incompletely understood. METHODS: Using systems serology, we characterized sporozoite- and merozoite-specific antibody profiles of uninfected Malian children before the malaria season who differed in their ability to control parasitemia and fever following Plasmodium falciparum (Pf) infection. We then assessed the contributions of individual traits to overall clinical outcomes, focusing on the immunodominant sporozoite CSP and merozoite AMA1 and MSP1 antigens. RESULTS: Humoral immunity evolved with age, with an expansion of both magnitude and functional quality, particularly within blood-stage phagocytic antibody activity. Moreover, concerning clinical outcomes postinfection, protected children had higher antibody-dependent neutrophil activity along with higher levels of MSP1-specific IgG3 and IgA and CSP-specific IgG3 and IgG4 prior to the malaria season. CONCLUSIONS: These data point to the natural evolution of functional humoral immunity to Pf with age and highlight particular antibody Fc-effector profiles associated with the control of malaria in children, providing clues for the design of next-generation vaccines or therapeutics.

Quantitative mechanistic model reveals key determinants of placental IgG transfer and informs prenatal immunization strategies.

Transplacental antibody transfer is crucially important in shaping neonatal immunity. Recently, prenatal maternal immunization has been employed to boost pathogen-specific immunoglobulin G (IgG) transfer to the fetus. Multiple factors have been implicated in antibody transfer, but how these key dynamic regulators work together to elicit the observed selectivity is pertinent to engineering vaccines for mothers to optimally immunize their newborns. Here, we present the first quantitative mechanistic model to uncover the determinants of placental antibody transfer and inform personalized immunization approaches. We identified placental FcγRIIb expressed by endothelial cells as a limiting factor in receptor-mediated transfer, which plays a key role in promoting preferential transport of subclasses IgG1, IgG3, and IgG4, but not IgG2. Integrated computational modeling and in vitro experiments reveal that IgG subclass abundance, Fc receptor (FcR) binding affinity, and FcR abundance in syncytiotrophoblasts and endothelial cells contribute to inter-subclass competition and potentially inter- and intra-patient antibody transfer heterogeneity. We developed an in silico prenatal vaccine testbed by combining a computational model of maternal vaccination with this placental transfer model using the tetanus, diphtheria, and acellular pertussis (Tdap) vaccine as a case study. Model simulations unveiled precision prenatal immunization opportunities that account for a patient's anticipated gestational length, placental size, and FcR expression by modulating vaccine timing, dosage, and adjuvant. This computational approach provides new perspectives on the dynamics of maternal-fetal antibody transfer in humans and potential avenues to optimize prenatal vaccinations that promote neonatal immunity.

Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation.

We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict cancer prevention, diagnosis, and treatment in individual patients. This will be realized based on advances in high performance computing, computational modeling, and an expanding repertoire of observational data across multiple scales and modalities. In 2020, the US National Cancer Institute, and the US Department of Energy, through a trans-disciplinary research community at the intersection of advanced computing and cancer research, initiated team science collaborative projects to explore the development and implementation of predictive Cancer Patient Digital Twins. Several diverse pilot projects were launched to provide key insights into important features of this emerging landscape and to determine the requirements for the development and adoption of cancer patient digital twins. Projects included exploring approaches to using a large cohort of digital twins to perform deep phenotyping and plan treatments at the individual level, prototyping self-learning digital twin platforms, using adaptive digital twin approaches to monitor treatment response and resistance, developing methods to integrate and fuse data and observations across multiple scales, and personalizing treatment based on cancer type. Collectively these efforts have yielded increased insights into the opportunities and challenges facing cancer patient digital twin approaches and helped define a path forward. Given the rapidly growing interest in patient digital twins, this manuscript provides a valuable early progress report of several CPDT pilot projects commenced in common, their overall aims, early progress, lessons learned and future directions that will increasingly involve the broader research community.

Selective transfer of maternal antibodies in preterm and fullterm children.

Preterm newborns are more likely to suffer from infectious diseases at birth compared to children delivered at term. Whether this is due to compromised cellular, humoral, or organ-specific development remains unclear. To begin to define whether maternal-fetal antibody transfer profiles differ across preterm (PT) and fullterm (FT) infants, the overall quantity and functional quality of an array of 24 vaccine-, endemic pathogen-, and common antigen-specific antibodies were assessed across a cohort of 11 PT and 12 term-delivered maternal:infant pairs from birth through week 12. While total IgG levels to influenza, pneumo, measles, rubella, EBV, and RSV were higher in FT newborns, selective Fc-receptor binding antibodies was noted in PT newborns. In fact, near equivalent antibody-effector functions were observed across PT and FT infants, despite significant quantitative differences in transferred antibody levels. Moreover, temporal transfer analysis revealed the selective early transfer of FcRn, FcγR2, and FcγR3 binding antibodies, pointing to differential placental sieving mechanisms across gestation. These data point to selectivity in placental transfer at distinct gestational ages, to ensure that children are endowed with the most robust humoral immunity even if born preterm.

Altered Maternal Antibody Profiles in Women With Human Immunodeficiency Virus Drive Changes in Transplacental Antibody Transfer.

BACKGROUND: Human immunodeficiency virus (HIV)-exposed, uninfected (HEU) children have a higher risk of severe infection, but the causes are poorly understood. Emerging data point to altered antibody transfer in women with HIV (WHIV); however, specific perturbations and the influence of antiretroviral therapy (ART) and HIV viremia remain unclear. METHODS: We evaluated antigen-specific transplacental antibody transfer across 14 antigens in paired maternal and umbilical cord plasma from 352 Ugandan women; 176 were WHIV taking ART. We measured antigen-specific immunoglobulin G (IgG) sub-class (IgG1, 2, 3, 4) levels and antibody Fcγ receptor (FcγRn, 2a, 2b, 3a, 3b) binding profiles. We used partial least squares discrimi-nant analysis to define antigen-specific transplacental antibody transfer features. RESULTS: Global antibody transfer patterns were similar by maternal HIV serostatus, pointing to effective placental function in WHIV. However, HEU umbilical cord antibody profiles were altered, driven by perturbed WHIV seroprofiles, with higher levels of herpesvirus antibodies (P < .01 for Epstein-Barr virus, herpes simplex virus) and lower levels of classic vaccine-induced antibodies (P < .01 for tetanus, polio, Haemophilus influenzae type b), suggesting that umbilical cord antibody profile differences arise from imbalanced WHIV immunity. Abnormal WHIV antibody profiles were associated with HIV viremia, lower CD4 count, and postconception ART initiation (P = .01). CONCLUSIONS: Perturbed immune-dominance profiles in WHIV shift the balance of immunity delivered to neonates. Perturbed HIV-associated maternal antibody profiles are a key determinant of com-promised neonatal immunity. Maternal vaccination interventions may promote transfer of relevant, effective antibodies to protect HEU children against early-life infections.

Antibody Subclass and Glycosylation Shift Following Effective TB Treatment.

With an estimated 25% of the global population infected with Mycobacterium tuberculosis (Mtb), tuberculosis (TB) remains a leading cause of death by infectious diseases. Humoral immunity following TB treatment is largely uncharacterized, and antibody profiling could provide insights into disease resolution. Here we focused on the distinctive TB-specific serum antibody features in active TB disease (ATB) and compared them with latent TB infection (LTBI) or treated ATB (txATB). As expected, di-galactosylated glycan structures (lacking sialic acid) found on IgG-Fc differentiated LTBI from ATB, but also discriminated txATB from ATB. Moreover, TB-specific IgG4 emerged as a novel antibody feature that correlated with active disease, elevated in ATB, but significantly diminished after therapy. These findings highlight 2 novel TB-specific antibody changes that track with the resolution of TB and may provide key insights to guide TB therapy.

IgG3 collaborates with IgG1 and IgA to recruit effector function in RV144 vaccinees.

While the RV144 HIV vaccine trial led to moderately reduced risk of HIV acquisition, emerging data from the HVTN702 trial point to the critical need to reexamine RV144-based correlates of reduced risk of protection. While in RV144, the induction of V2-binding, non-IgA, IgG3 antibody responses with nonneutralizing functions were linked to reduced risk of infection, the interactions between these signatures remain unclear. Thus, here we comprehensively profile the humoral immune response in 300 RV144 vaccinees to decipher the relationships between humoral biomarkers of protection. We found that vaccine-specific IgG1, IgG3, and IgA were highly correlated. However, ratios of IgG1:IgG3:IgA provided insights into subclass/isotype polyclonal functional regulation. For instance, in the absence of high IgG1 levels, IgG3 antibodies exhibited limited functional activity, pointing to IgG3 as a critical contributor, but not sole driver, of effective antiviral humoral immunity. Higher IgA levels were linked to enhanced antibody effector function, including neutrophil phagocytosis (ADNP), complement deposition (ADCD), and antibody-dependent NK degranulation (CD107a), some of which were increased in infected vaccinees in a case/control data set, suggesting that IgA-driven functions compromised immunity. These data highlight the interplay between IgG1, IgG3, and IgA, pointing to the need to profile the relationships between subclass/isotype selection.

Fc Glycan-Mediated Regulation of Placental Antibody Transfer.

Despite the worldwide success of vaccination, newborns remain vulnerable to infections. While neonatal vaccination has been hampered by maternal antibody-mediated dampening of immune responses, enhanced regulatory and tolerogenic mechanisms, and immune system immaturity, maternal pre-natal immunization aims to boost neonatal immunity via antibody transfer to the fetus. However, emerging data suggest that antibodies are not transferred equally across the placenta. To understand this, we used systems serology to define Fc features associated with antibody transfer. The Fc-profile of neonatal and maternal antibodies differed, skewed toward natural killer (NK) cell-activating antibodies. This selective transfer was linked to digalactosylated Fc-glycans that selectively bind FcRn and FCGR3A, resulting in transfer of antibodies able to efficiently leverage innate immune cells present at birth. Given emerging data that vaccination may direct antibody glycosylation, our study provides insights for the development of next-generation maternal vaccines designed to elicit antibodies that will most effectively aid neonates.

Small-molecule control of antibody N-glycosylation in engineered mammalian cells.

N-linked glycosylation in monoclonal antibodies (mAbs) is crucial for structural and functional properties of mAb therapeutics, including stability, pharmacokinetics, safety and clinical efficacy. The biopharmaceutical industry currently lacks tools to precisely control N-glycosylation levels during mAb production. In this study, we engineered Chinese hamster ovary cells with synthetic genetic circuits to tune N-glycosylation of a stably expressed IgG. We knocked out two key glycosyltransferase genes, α-1,6-fucosyltransferase (FUT8) and ß-1,4-galactosyltransferase (ß4GALT1), genomically integrated circuits expressing synthetic glycosyltransferase genes under constitutive or inducible promoters and generated antibodies with concurrently desired fucosylation (0-97%) and galactosylation (0-87%) levels. Simultaneous and independent control of FUT8 and ß4GALT1 expression was achieved using orthogonal small molecule inducers. Effector function studies confirmed that glycosylation profile changes affected antibody binding to a cell surface receptor. Precise and rational modification of N-glycosylation will allow new recombinant protein therapeutics with tailored in vitro and in vivo effects for various biotechnological and biomedical applications.

Dissecting N-Glycosylation Dynamics in Chinese Hamster Ovary Cells Fed-batch Cultures using Time Course Omics Analyses.

N-linked glycosylation affects the potency, safety, immunogenicity, and pharmacokinetic clearance of several therapeutic proteins including monoclonal antibodies. A robust control strategy is needed to dial in appropriate glycosylation profile during the course of cell culture processes accurately. However, N-glycosylation dynamics remains insufficiently understood owing to the lack of integrative analyses of factors that influence the dynamics, including sugar nucleotide donors, glycosyltransferases, and glycosidases. Here, an integrative approach involving multi-dimensional omics analyses was employed to dissect the temporal dynamics of glycoforms produced during fed-batch cultures of CHO cells. Several pathways including glycolysis, tricarboxylic citric acid cycle, and nucleotide biosynthesis exhibited temporal dynamics over the cell culture period. The steps involving galactose and sialic acid addition were determined as temporal bottlenecks. Our results show that galactose, and not manganese, is able to mitigate the temporal bottleneck, despite both being known effectors of galactosylation. Furthermore, sialylation is limited by the galactosylated precursors and autoregulation of cytidine monophosphate-sialic acid biosynthesis.

Identification of Metabolic Pathway Systems.

The estimation of parameters in even moderately large biological systems is a significant challenge. This challenge is greatly exacerbated if the mathematical formats of appropriate process descriptions are unknown. To address this challenge, the method of dynamic flux estimation (DFE) was proposed for the analysis of metabolic time series data. Under ideal conditions, the first phase of DFE yields numerical representations of all fluxes within a metabolic pathway system, either as values at each time point or as plots against their substrates and modulators. However, this numerical result does not reveal the mathematical format of each flux. Thus, the second phase of DFE selects functional formats that are consistent with the numerical trends obtained from the first phase. While greatly facilitating metabolic data analysis, DFE is only directly applicable if the pathway system contains as many dependent variables as fluxes. Because most actual systems contain more fluxes than metabolite pools, this requirement is seldom satisfied. Auxiliary methods have been proposed to alleviate this issue, but they are not general. Here we propose strategies that extend DFE toward general, slightly underdetermined pathway systems.

New insights into the complex regulation of the glycolytic pathway in Lactococcus lactis. I. Construction and diagnosis of a comprehensive dynamic model.

This article and the companion paper use computational systems modeling to decipher the complex coordination of regulatory signals controlling the glycolytic pathway in the dairy bacterium Lactococcus lactis. In this first article, the development of a comprehensive kinetic dynamic model is described. The model is based on in vivo NMR data that consist of concentration trends in key glycolytic metabolites and cofactors. The model structure and parameter values are identified with a customized optimization strategy that uses as its core the method of dynamic flux estimation. For the first time, a dynamic model with a single parameter set fits all available glycolytic time course data under anaerobic operation. The model captures observations that had not been addressed so far and suggests the existence of regulatory effects that had been observed in other species, but not in L. lactis. The companion paper uses this model to analyze details of the dynamic control of glycolysis under aerobic and anaerobic conditions.

New insights into the complex regulation of the glycolytic pathway in Lactococcus lactis. II. Inference of the precisely timed control system regulating glycolysis.

The dairy bacterium Lactococcus lactis has to master a complicated task. It must control its essentially linear glycolytic pathway in such a fashion that, when the substrate, glucose, runs out, it retains enough phosphoenolpyruvate and fructose-1,6-bisphosphate to be able to restart glycolysis as soon as new glucose becomes available. Although glycolysis is arguably the best-studied metabolic pathway, its details in L. lactis are still unclear, and it is, in particular, not understood how the bacterium manages the stop-and-start task. The primary purpose of this paper and its companion is a clarification of some of the details of the governing regulatory strategies with which L. lactis manages to retain the necessary metabolites for the restart of glycolysis after periods of starvation. The paper furthermore discusses how the bacterium changes these strategies when it is subjected to aerobic rather than anaerobic conditions.