Disease: Deciphering the triad of infection, immunity and pathology
The factors which drive and control disease progression can be inferred from mathematical models that integrate measures of immune responses, data from tissue sampling and markers of infection dynamics.
- BioQuant (Center for Quantitative Biology) at Heidelberg University, Germany
A fever, a cough, a splitting headache… Being sick often comes with tell-tale signs which worsen as the disease progresses and tissues become damaged. These symptoms result from complex interactions between the infecting pathogen, the inflammation process, and the response from the immune system. Tracking these mechanisms and how they interact, as well as identifying which factors determine when the disease recedes or progresses, is essential for establishing better treatment strategies.
In this effort, a more refined understanding of infection and immune responses has emerged from combining experimental and clinical measurements with mathematical models (Perelson, 2002). However, it is still difficult to link tissue pathology and disease severity with viral load or immune cell counts, which respectively measure the amount of virus and of certain immune actors in the body. Now, in eLife, Amber Smith and colleagues at St. Jude Children’s Research Hospital, the University of Tennessee Health Science Center and the Washington University School of Medicine – including Margaret Myers and Amanda Smith as joing first authors – report how viral infection, counteracting immune responses and lung pathology interact as mice fight off influenza A (Myers et al., 2021).
First, the team tracked how viral load and the number of CD8+ T cells, an important immune actor that helps to clear infected cells, progressed over time. In combination with mathematical models, these measurements allowed Myers et al. to estimate several parameters that reflect the pace at which the virus replicates, the strength of the immune response, and the interactions between these processes. While this had already been achieved in previous studies (e.g. Baccam et al., 2006), Myers et al. also analyzed the anatomy of the lung tissue over time, assessing the damage caused by infection and inflammation as well as how much the organ eventually regenerates.
Then, the team compared these data to values from their mathematical model that described viral load and CD8+ T cells counts, thereby linking viral load dynamics and specific immune responses to disease pathology and severity (Figure 1). In particular, the analysis shed light on how the relative number of immune cells correlates with the level of lung tissue cleared from the virus and, thus, the mice’s ability to recover from infection. These quantitative relationships could help to assess how well the virus is controlled within tissues simply by relying on easily accessible markers that are, for example, present in the blood. This would reduce the need for invasive tissue samples.
Figure 1
The triad of infection, immunity and disease pathology.
Separate data, such as viral load (left) – the quantity of virus present in a specific volume of fluid – immune cell counts (middle) and histological assessment of tissue sections (right) provide information on the dynamics of infection, immune responses and disease pathology. Mathematical modelling that integrates these measurements makes it possible to assess how the individual processes are connected, and to identify relevant prognostic markers that allow prediction of disease progression.
Individual molecular processes and specific aspects of viral replication can be studied extensively within in vitro cell culture systems. However, the full triad of infection, immunity and especially tissue pathology can only be reliably assessed within conditions that are physiologically relevant (Fackler et al., 2014). Indeed, simple cell culture systems insufficiently address the impact tissue structure can have on infection dynamics, immune activation and clearing mechanisms (Fackler et al., 2014; Imle et al., 2019).
Myers et al. used frequent samples and histological analyses to infer how infected tissues change over time. Yet, imaging technologies may continue to improve so that it becomes possible to observe the interactions between host and pathogen within tissues in real-time (Coombes and Robey, 2010). These approaches could help to investigate whether quantitative relationships as highlighted by Myers et al. also play a role in other infections and in other tissues. The expanding field of organoids – whereby simple, miniature organs are grown in the laboratory – also represents a promising step towards understanding how cells interact within structured, tissue-related environments (Gosselin et al., 2018; Bar-Ephraim et al., 2020). Combined with new technologies such as single-cell sequencing methods (Triana et al., 2021; Youk et al., 2020), these approaches will help to determine the molecular processes that govern disease progression, and how these might differ between patients.
Despite these new experimental and diagnostic technologies, data-driven mathematical modeling and analytical methods will continue to fulfil a key role for deciphering the interplay between infection, tissue pathology and disease severity. Using these models makes it possible to integrate different types of measurements from various places and times, and to disentangle the contributions of individual processes to the infection dynamics. It is only by understanding exactly how individual processes interact over time that scientists will be able to find and validate prognostic markers which predict disease progression.
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Disease: Deciphering the triad of infection, immunity and pathology
eLife 10:e72379.
https://doi.org/10.7554/eLife.72379
Further reading
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Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D structures of monoclonal antibodies and nanobodies. H3-OPT combines the strengths of AlphaFold2 with a pre-trained protein language model and provides a 2.24 Å average RMSDCα between predicted and experimentally determined CDR-H3 loops, thus outperforming other current computational methods in our non-redundant high-quality dataset. The model was validated by experimentally solving three structures of anti-VEGF nanobodies predicted by H3-OPT. We examined the potential applications of H3-OPT through analyzing antibody surface properties and antibody–antigen interactions. This structural prediction tool can be used to optimize antibody–antigen binding and engineer therapeutic antibodies with biophysical properties for specialized drug administration route.
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Background:
Preterm birth is the leading cause of neonatal morbidity and mortality worldwide. Most cases of preterm birth occur spontaneously and result from preterm labor with intact (spontaneous preterm labor [sPTL]) or ruptured (preterm prelabor rupture of membranes [PPROM]) membranes. The prediction of spontaneous preterm birth (sPTB) remains underpowered due to its syndromic nature and the dearth of independent analyses of the vaginal host immune response. Thus, we conducted the largest longitudinal investigation targeting vaginal immune mediators, referred to herein as the immunoproteome, in a population at high risk for sPTB.
Methods:
Vaginal swabs were collected across gestation from pregnant women who ultimately underwent term birth, sPTL, or PPROM. Cytokines, chemokines, growth factors, and antimicrobial peptides in the samples were quantified via specific and sensitive immunoassays. Predictive models were constructed from immune mediator concentrations.
Results:
Throughout uncomplicated gestation, the vaginal immunoproteome harbors a cytokine network with a homeostatic profile. Yet, the vaginal immunoproteome is skewed toward a pro-inflammatory state in pregnant women who ultimately experience sPTL and PPROM. Such an inflammatory profile includes increased monocyte chemoattractants, cytokines indicative of macrophage and T-cell activation, and reduced antimicrobial proteins/peptides. The vaginal immunoproteome has improved predictive value over maternal characteristics alone for identifying women at risk for early (<34 weeks) sPTB.
Conclusions:
The vaginal immunoproteome undergoes homeostatic changes throughout gestation and deviations from this shift are associated with sPTB. Furthermore, the vaginal immunoproteome can be leveraged as a potential biomarker for early sPTB, a subset of sPTB associated with extremely adverse neonatal outcomes.
Funding:
This research was conducted by the Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services (NICHD/NIH/DHHS) under contract HHSN275201300006C. ALT, KRT, and NGL were supported by the Wayne State University Perinatal Initiative in Maternal, Perinatal and Child Health.
