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Muzquiz R, Jamshidi C, Conroy DW, Jaroniec CP, Foster MP. Insights into Ligand-Mediated Activation of an Oligomeric Ring-Shaped Gene-Regulatory Protein from Solution- and Solid-State NMR. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.10.593404. [PMID: 38798368 PMCID: PMC11118279 DOI: 10.1101/2024.05.10.593404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The 91 kDa oligomeric ring-shaped ligand binding protein TRAP (trp RNA binding attenuation protein) regulates the expression of a series of genes involved in tryptophan (Trp) biosynthesis in bacilli. When cellular Trp levels rise, the free amino acid binds to sites buried in the interfaces between each of the 11 (or 12, depending on the species) protomers in the ring. Crystal structures of Trp-bound TRAP show the Trp ligands are sequestered from solvent by a pair of loops from adjacent protomers that bury the bound ligand via polar contacts to several threonine residues. Binding of the Trp ligands occurs cooperatively, such that successive binding events occur with higher apparent affinity but the structural basis for this cooperativity is poorly understood. We used solution methyl-TROSY NMR relaxation experiments focused on threonine and isoleucine sidechains, as well as magic angle spinning solid-state NMR 13C-13C and 15N-13C chemical shift correlation spectra on uniformly labeled samples recorded at 800 and 1200 MHz, to characterize the structure and dynamics of the protein. Methyl 13C relaxation dispersion experiments on ligand-free apo TRAP revealed concerted exchange dynamics on the μs-ms time scale, consistent with transient sampling of conformations that could allow ligand binding. Cross-correlated relaxation experiments revealed widespread disorder on fast timescales. Chemical shifts for methyl-bearing side chains in apo- and Trp-bound TRAP revealed subtle changes in the distribution of sampled sidechain rotameric states. These observations reveal a pathway and mechanism for induced conformational changes to generate homotropic Trp-Trp binding cooperativity.
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Affiliation(s)
- Rodrigo Muzquiz
- Ohio State Biochemistry Graduate Program, The Ohio State University, 484 West 12 Avenue, Columbus, OH 43210, USA
- Department of Chemistry and Biochemistry, The Ohio State University, 100 West 18 Avenue, Columbus, OH 43210, USA
| | - Cameron Jamshidi
- Ohio State Biochemistry Graduate Program, The Ohio State University, 484 West 12 Avenue, Columbus, OH 43210, USA
- Department of Chemistry and Biochemistry, The Ohio State University, 100 West 18 Avenue, Columbus, OH 43210, USA
| | - Daniel W. Conroy
- Department of Chemistry and Biochemistry, The Ohio State University, 100 West 18 Avenue, Columbus, OH 43210, USA
| | - Christopher P. Jaroniec
- Ohio State Biochemistry Graduate Program, The Ohio State University, 484 West 12 Avenue, Columbus, OH 43210, USA
- Department of Chemistry and Biochemistry, The Ohio State University, 100 West 18 Avenue, Columbus, OH 43210, USA
| | - Mark P. Foster
- Ohio State Biochemistry Graduate Program, The Ohio State University, 484 West 12 Avenue, Columbus, OH 43210, USA
- Department of Chemistry and Biochemistry, The Ohio State University, 100 West 18 Avenue, Columbus, OH 43210, USA
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Dupic T, Phillips AM, Desai MM. Protein sequence landscapes are not so simple: on reference-free versus reference-based inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.29.577800. [PMID: 38352387 PMCID: PMC10862727 DOI: 10.1101/2024.01.29.577800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
In a recent preprint, Park, Metzger, and Thornton reanalyze 20 empirical protein sequence-function landscapes using a "reference-free analysis" (RFA) method they recently developed. They argue that these empirical landscapes are simpler and less epistatic than earlier work suggested, and attribute the difference to limitations of the methods used in the original analyses of these landscapes, which they claim are more sensitive to measurement noise, missing data, and other artifacts. Here, we show that these claims are incorrect. Instead, we find that the RFA method introduced by Park et al. is exactly equivalent to the reference-based least-squares methods used in the original analysis of many of these empirical landscapes (and also equivalent to a Hadamard-based approach they implement). Because the reanalyzed and original landscapes are in fact identical, the different conclusions drawn by Park et al. instead reflect different interpretations of the parameters describing the inferred landscapes; we argue that these do not support the conclusion that epistasis plays only a small role in protein sequence-function landscapes.
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Affiliation(s)
- Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA
| | - Angela M Phillips
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco CA
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA
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3
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Pinto SR, Uchida N. Tonic dopamine and biases in value learning linked through a biologically inspired reinforcement learning model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.10.566580. [PMID: 38014087 PMCID: PMC10680794 DOI: 10.1101/2023.11.10.566580] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
A hallmark of various psychiatric disorders is biased future predictions. Here we examined the mechanisms for biased value learning using reinforcement learning models incorporating recent findings on synaptic plasticity and opponent circuit mechanisms in the basal ganglia. We show that variations in tonic dopamine can alter the balance between learning from positive and negative reward prediction errors, leading to biased value predictions. This bias arises from the sigmoidal shapes of the dose-occupancy curves and distinct affinities of D1- and D2-type dopamine receptors: changes in tonic dopamine differentially alters the slope of the dose-occupancy curves of these receptors, thus sensitivities, at baseline dopamine concentrations. We show that this mechanism can explain biased value learning in both mice and humans and may also contribute to symptoms observed in psychiatric disorders. Our model provides a foundation for understanding the basal ganglia circuit and underscores the significance of tonic dopamine in modulating learning processes.
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Affiliation(s)
- Sandra Romero Pinto
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Program in Speech and Hearing Bioscience and Technology, Division of Medical Sciences, Harvard Medical School, Boston, MA 02115, USA
| | - Naoshige Uchida
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
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Weaver DT, King ES, Maltas J, Scott JG. Reinforcement Learning informs optimal treatment strategies to limit antibiotic resistance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.12.523765. [PMID: 36711676 PMCID: PMC9882109 DOI: 10.1101/2023.01.12.523765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 β-lactam antibiotics with which to treat the simulated E. coli population, we demonstrate that RL agents outperform two naive treatment paradigms at minimizing the population fitness over time. We also show that RL agents approach the performance of the optimal drug cycling policy. Even when stochastic noise is introduced to the measurements of population fitness, we show that RL agents are capable of maintaining evolving populations at lower growth rates compared to controls. We further tested our approach in arbitrary fitness landscapes of up to 1024 genotypes. We show that minimization of population fitness using drug cycles is not limited by increasing genome size. Our work represents a proof-of-concept for using AI to control complex evolutionary processes.
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Affiliation(s)
- Davis T. Weaver
- Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
- Translational Hematology Oncology Research, Cleveland Clinic, Cleveland OH, 44106, USA
| | - Eshan S. King
- Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
- Translational Hematology Oncology Research, Cleveland Clinic, Cleveland OH, 44106, USA
| | - Jeff Maltas
- Translational Hematology Oncology Research, Cleveland Clinic, Cleveland OH, 44106, USA
| | - Jacob G. Scott
- Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
- Translational Hematology Oncology Research, Cleveland Clinic, Cleveland OH, 44106, USA
- Department of Physics, Case Western Reserve University, Cleveland, OH, 44106, USA
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Ahn JC, Coyle SM. Comparative profiling of cellular gait on adhesive micropatterns defines statistical patterns of activity that underlie native and cancerous cell dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.27.564389. [PMID: 37961146 PMCID: PMC10634873 DOI: 10.1101/2023.10.27.564389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Cell dynamics are powered by patterns of activity, but it is not straightforward to quantify these patterns or compare them across different environmental conditions or cell-types. Here we digitize the long-term shape fluctuations of metazoan cells grown on micropatterned fibronectin islands to define and extract statistical features of cell dynamics without the need for genetic modification or fluorescence imaging. These shape fluctuations generate single-cell morphological signals that can be decomposed into two major components: a continuous, slow-timescale meandering of morphology about an average steady-state shape; and short-lived "events" of rapid morphology change that sporadically occur throughout the timecourse. By developing statistical metrics for each of these components, we used thousands of hours of single-cell data to quantitatively define how each axis of cell dynamics was impacted by environmental conditions or cell-type. We found the size and spatial complexity of the micropattern island modulated the statistics of morphological events-lifetime, frequency, and orientation-but not its baseline shape fluctuations. Extending this approach to profile a panel of triple negative breast cancer cell-lines, we found that different cell-types could be distinguished from one another along specific and unique statistical axes of their behavior. Our results suggest that micropatterned substrates provide a generalizable method to build statistical profiles of cell dynamics to classify and compare emergent cell behaviors.
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Affiliation(s)
- John C. Ahn
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
- Integrated Program in Biochemistry Graduate Program, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Scott M. Coyle
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
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Hassani SA, Womelsdorf T. Noradrenergic alpha-2a Receptor Stimulation Enhances Prediction Error Signaling in Anterior Cingulate Cortex and Striatum. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.25.564052. [PMID: 37961384 PMCID: PMC10634832 DOI: 10.1101/2023.10.25.564052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The noradrenergic system is implicated to support behavioral flexibility by increasing exploration during periods of uncertainty and by enhancing working memory for goal-relevant stimuli. Possible sources mediating these pro-cognitive effects are α2A adrenoceptors (α2AR) in prefrontal cortex or the anterior cingulate cortex facilitating fronto-striatal learning processes. We tested this hypothesis by selectively stimulating α2ARs using Guanfacine during feature-based attentional set shifting in nonhuman primates. We found that α2A stimulation improved learning from errors and facilitates updating the target feature of an attentional set. Neural recordings in the anterior cingulate cortex (ACC), the dorsolateral prefrontal cortex (dlPFC), and the striatum showed that α2A stimulation selectively enhanced the neural representation of negative reward prediction errors in neurons of the ACC and of positive prediction errors in the striatum, but not in dlPFC. This modulation was accompanied by enhanced encoding of the feature and location of the attended target across the fronto-striatal network. Enhanced learning was paralleled by enhanced encoding of outcomes in putative fast-spiking interneurons in the ACC, dlPFC, and striatum but not in broad spiking cells, pointing to an interneuron mediated mechanism of α2AR action. These results illustrate that α2A receptors causally support the noradrenergic enhancement of updating attention sets through an enhancement of prediction error signaling in the ACC and the striatum.
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Affiliation(s)
- Seyed A. Hassani
- Department of Psychology, Vanderbilt University, Nashville, TN 37240
- Vanderbilt Brain Institute, Nashville, TN 37240
- National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD 20824
| | - Thilo Womelsdorf
- Department of Psychology, Vanderbilt University, Nashville, TN 37240
- Vanderbilt Brain Institute, Nashville, TN 37240
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240
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Kleinman M, Wang T, Xiao D, Feghhi E, Lee K, Carr N, Li Y, Hadidi N, Chandrasekaran C, Kao JC. A cortical information bottleneck during decision-making. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.12.548742. [PMID: 37502862 PMCID: PMC10369960 DOI: 10.1101/2023.07.12.548742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Decision-making emerges from distributed computations across multiple brain areas, but it is unclear why the brain distributes the computation. In deep learning, artificial neural networks use multiple areas (or layers) to form optimal representations of task inputs. These optimal representations are sufficient to perform the task well, but minimal so they are invariant to other irrelevant variables. We recorded single neurons and multiunits in dorsolateral prefrontal cortex (DLPFC) and dorsal premotor cortex (PMd) in monkeys during a perceptual decision-making task. We found that while DLPFC represents task-related inputs required to compute the choice, the downstream PMd contains a minimal sufficient, or optimal, representation of the choice. To identify a mechanism for how cortex may form these optimal representations, we trained a multi-area recurrent neural network (RNN) to perform the task. Remarkably, DLPFC and PMd resembling representations emerged in the early and late areas of the multi-area RNN, respectively. The DLPFC-resembling area partially orthogonalized choice information and task inputs and this choice information was preferentially propagated to downstream areas through selective alignment with inter-area connections, while remaining task information was not. Our results suggest that cortex uses multi-area computation to form minimal sufficient representations by preferential propagation of relevant information between areas.
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Affiliation(s)
- Michael Kleinman
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Tian Wang
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Derek Xiao
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Ebrahim Feghhi
- Neurosciences Program, University of California, Los Angeles, CA, USA
| | - Kenji Lee
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Nicole Carr
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Yuke Li
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Nima Hadidi
- Neurosciences Program, University of California, Los Angeles, CA, USA
| | - Chandramouli Chandrasekaran
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, USA
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Jonathan C. Kao
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
- Neurosciences Program, University of California, Los Angeles, CA, USA
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8
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Amin MR, Hasan M, Arnab SP, DeGiorgio M. Tensor decomposition based feature extraction and classification to detect natural selection from genomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.27.527731. [PMID: 37034767 PMCID: PMC10081272 DOI: 10.1101/2023.03.27.527731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under non-convex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data while preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx , which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.
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Vahidi P, Sani OG, Shanechi MM. Modeling and dissociation of intrinsic and input-driven neural population dynamics underlying behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.14.532554. [PMID: 36993213 PMCID: PMC10055042 DOI: 10.1101/2023.03.14.532554] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Neural dynamics can reflect intrinsic dynamics or dynamic inputs, such as sensory inputs or inputs from other regions. To avoid misinterpreting temporally-structured inputs as intrinsic dynamics, dynamical models of neural activity should account for measured inputs. However, incorporating measured inputs remains elusive in joint dynamical modeling of neural-behavioral data, which is important for studying neural computations of a specific behavior. We first show how training dynamical models of neural activity while considering behavior but not input, or input but not behavior may lead to misinterpretations. We then develop a novel analytical learning method that simultaneously accounts for neural activity, behavior, and measured inputs. The method provides the new capability to prioritize the learning of intrinsic behaviorally relevant neural dynamics and dissociate them from both other intrinsic dynamics and measured input dynamics. In data from a simulated brain with fixed intrinsic dynamics that performs different tasks, the method correctly finds the same intrinsic dynamics regardless of task while other methods can be influenced by the change in task. In neural datasets from three subjects performing two different motor tasks with task instruction sensory inputs, the method reveals low-dimensional intrinsic neural dynamics that are missed by other methods and are more predictive of behavior and/or neural activity. The method also uniquely finds that the intrinsic behaviorally relevant neural dynamics are largely similar across the three subjects and two tasks whereas the overall neural dynamics are not. These input-driven dynamical models of neural-behavioral data can uncover intrinsic dynamics that may otherwise be missed.
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10
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Rife Magalis B, Autissier P, Williams KC, Chen X, Browne C, Salemi M. Predator-Prey Dynamics of Intra-Host Simian Immunodeficiency Virus Evolution Within the Untreated Host. Front Immunol 2021; 12:709962. [PMID: 34691023 PMCID: PMC8527182 DOI: 10.3389/fimmu.2021.709962] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 09/21/2021] [Indexed: 01/18/2023] Open
Abstract
The dynamic nature of the SIV population during disease progression in the SIV/macaque model of AIDS and the factors responsible for its behavior have not been documented, largely owing to the lack of sufficient spatial and temporal sampling of both viral and host data from SIV-infected animals. In this study, we detail Bayesian coalescent inference of the changing collective intra-host viral effective population size (Ne ) from various tissues over the course of infection and its relationship with what we demonstrate is a continuously changing immune cell repertoire within the blood. Although the relative contribution of these factors varied among hosts and time points, the adaptive immune response best explained the overall periodic dynamic behavior of the effective virus population. Data exposing the nature of the relationship between the virus and immune cell populations revealed the plausibility of an eco-evolutionary mathematical model, which was able to mimic the large-scale oscillations in Ne through virus escape from relatively few, early immunodominant responses, followed by slower escape from several subdominant and weakened immune populations. The results of this study suggest that SIV diversity within the untreated host is governed by a predator-prey relationship, wherein differing phases of infection are the result of adaptation in response to varying immune responses. Previous investigations into viral population dynamics using sequence data have focused on single estimates of the effective viral population size (Ne ) or point estimates over sparse sampling data to provide insight into the precise impact of immune selection on virus adaptive behavior. Herein, we describe the use of the coalescent phylogenetic frame- work to estimate the relative changes in Ne over time in order to quantify the relationship with empirical data on the dynamic immune composition of the host. This relationship has allowed us to expand on earlier simulations to build a predator-prey model that explains the deterministic behavior of the virus over the course of disease progression. We show that sequential viral adaptation can occur in response to phases of varying immune pressure, providing a broader picture of the viral response throughout the entire course of progression to AIDS.
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Affiliation(s)
- Brittany Rife Magalis
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL, United States.,Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States
| | - Patrick Autissier
- Department of Biology, Boston College, Chestnut Hill, MA, United States
| | | | - Xinguang Chen
- Department of Epidemiology, University of Florida, Gainesville, FL, United States
| | - Cameron Browne
- Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA, United States
| | - Marco Salemi
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL, United States.,Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States
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11
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Andriamandimby SF, Brook CE, Razanazatovo N, Rakotondramanga JM, Rasambainarivo F, Raharimanga V, Razanajatovo IM, Mangahasimbola R, Razafindratsimandresy R, Randrianarisoa S, Bernardson B, Rabarison JH, Randrianarisoa M, Nasolo FS, Rabetombosoa RM, Randremanana R, Héraud JM, Dussart P. Cross-sectional cycle threshold values reflect epidemic dynamics of COVID-19 in Madagascar. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.07.06.21259473. [PMID: 34268517 PMCID: PMC8282106 DOI: 10.1101/2021.07.06.21259473] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
As the national reference laboratory for febrile illness in Madagascar, we processed samples from the first epidemic wave of COVID-19, between March and September 2020. We fit generalized additive models to cycle threshold (C t ) value data from our RT-qPCR platform, demonstrating a peak in high viral load, low-C t value infections temporally coincident with peak epidemic growth rates estimated in real time from publicly-reported incidence data and retrospectively from our own laboratory testing data across three administrative regions. We additionally demonstrate a statistically significant effect of duration of time since infection onset on C t value, suggesting that C t value can be used as a biomarker of the stage at which an individual is sampled in the course of an infection trajectory. As an extension, the population-level C t distribution at a given timepoint can be used to estimate population-level epidemiological dynamics. We illustrate this concept by adopting a recently-developed, nested modeling approach, embedding a within-host viral kinetics model within a population-level Susceptible-Exposed-Infectious-Recovered (SEIR) framework, to mechanistically estimate epidemic growth rates from cross-sectional C t distributions across three regions in Madagascar. We find that C t -derived epidemic growth estimates slightly precede those derived from incidence data across the first epidemic wave, suggesting delays in surveillance and case reporting. Our findings indicate that public reporting of C t values could offer an important resource for epidemiological inference in low surveillance settings, enabling forecasts of impending incidence peaks in regions with limited case reporting.
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12
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Walsh C, Tafforeau P, Wagner WL, Jafree DJ, Bellier A, Werlein C, Kühnel MP, Boller E, Walker-Samuel S, Robertus JL, Long DA, Jacob J, Marussi S, Brown E, Holroyd N, Jonigk DD, Ackermann M, Lee PD. Multiscale three-dimensional imaging of intact human organs down to the cellular scale using hierarchical phase-contrast tomography. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.02.03.429481. [PMID: 33564772 PMCID: PMC7872374 DOI: 10.1101/2021.02.03.429481] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Human organs are complex, three-dimensional and multiscale systems. Spatially mapping the human body down through its hierarchy, from entire organs to their individual functional units and specialised cells, is a major obstacle to fully understanding health and disease. To meet this challenge, we developed hierarchical phase-contrast tomography (HiP-CT), an X-ray phase propagation technique utilising the European Synchrotron Radiation Facility's Extremely Brilliant Source: the world's first high-energy 4 th generation X-ray source. HiP-CT enabled three-dimensional and non-destructive imaging at near-micron resolution in soft tissues at one hundred thousand times the voxel size whilst maintaining the organ's structure. We applied HiP-CT to image five intact human parenchymal organs: brain, lung, heart, kidney and spleen. These were hierarchically assessed with HiP-CT, providing a structural overview of the whole organ alongside detail of the organ's individual functional units and cells. The potential applications of HiP-CT were demonstrated through quantification and morphometry of glomeruli in an intact human kidney, and identification of regional changes to the architecture of the air-tissue interface and alveolar morphology in the lung of a deceased COVID-19 patient. Overall, we show that HiP-CT is a powerful tool which can provide a comprehensive picture of structural information for whole intact human organs, encompassing precise details on functional units and their constituent cells to better understand human health and disease.
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Affiliation(s)
- C Walsh
- Centre for Advanced Biomedical Imaging, University College London, U.K
| | - P Tafforeau
- European Synchrotron Radiation Facility, Grenoble, France
| | - Willi L Wagner
- Dept of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany Translational Lung Research Centre Heidelberg (TLRC), German Lung Research Centre (DZL), Heidelberg, Germany
| | - D J Jafree
- Developmental Biology and Cancer Programme, Great Ormond Street Institute of Child Health, University College London, UK
- UCL MB/PhD Programme, Faculty of Medical Sciences, University College London, UK
| | - A Bellier
- French Alps Laboratory of Anatomy (LADAF), Grenoble Alpes University, Grenoble, France
| | - C Werlein
- Institute of Pathology, Hannover Medical School, Hannover, Germany (Carl-Neuberg-Straße 1, 30625 Hannover)
| | - M P Kühnel
- Institute of Pathology, Hannover Medical School, Hannover, Germany (Carl-Neuberg-Straße 1, 30625 Hannover)
- Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH)
| | - E Boller
- European Synchrotron Radiation Facility, Grenoble, France
| | - S Walker-Samuel
- Centre for Advanced Biomedical Imaging, University College London, U.K
| | - J L Robertus
- Department of Histopathology, Royal Brompton and Harefield NHS Foundation Trust, London, UK
- National Heart & Lung Institute, Imperial College London, London, UK
| | - D A Long
- Developmental Biology and Cancer Programme, Great Ormond Street Institute of Child Health, University College London, UK
| | - J Jacob
- Centre for Medical Image Computing, University College London, London, UK
- Department of Radiology, University College London Hospitals NHS Foundation Trust, London, UK
| | - S Marussi
- Department of Mechanical Engineering University College London, U.K
| | - E Brown
- Centre for Advanced Biomedical Imaging, University College London, U.K
| | - N Holroyd
- Centre for Advanced Biomedical Imaging, University College London, U.K
| | - D D Jonigk
- Institute of Pathology, Hannover Medical School, Hannover, Germany (Carl-Neuberg-Straße 1, 30625 Hannover)
- Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH)
| | - M Ackermann
- Institute of Functional and Clinical Anatomy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz
| | - P D Lee
- Department of Mechanical Engineering University College London, U.K
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13
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A de novo approach to inferring within-host fitness effects during untreated HIV-1 infection. PLoS Pathog 2020; 16:e1008171. [PMID: 32492061 PMCID: PMC7295245 DOI: 10.1371/journal.ppat.1008171] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 06/15/2020] [Accepted: 05/11/2020] [Indexed: 12/15/2022] Open
Abstract
In the absence of effective antiviral therapy, HIV-1 evolves in response to the within-host environment, of which the immune system is an important aspect. During the earliest stages of infection, this process of evolution is very rapid, driven by a small number of CTL escape mutations. As the infection progresses, immune escape variants evolve under reduced magnitudes of selection, while competition between an increasing number of polymorphic alleles (i.e., clonal interference) makes it difficult to quantify the magnitude of selection acting upon specific variant alleles. To tackle this complex problem, we developed a novel multi-locus inference method to evaluate the role of selection during the chronic stage of within-host infection. We applied this method to targeted sequence data from the p24 and gp41 regions of HIV-1 collected from 34 patients with long-term untreated HIV-1 infection. We identify a broad distribution of beneficial fitness effects during infection, with a small number of variants evolving under strong selection and very many variants evolving under weaker selection. The uniquely large number of infections analysed granted a previously unparalleled statistical power to identify loci at which selection could be inferred to act with statistical confidence. Our model makes no prior assumptions about the nature of alleles under selection, such that any synonymous or non-synonymous variant may be inferred to evolve under selection. However, the majority of variants inferred with confidence to be under selection were non-synonymous in nature, and in most cases were have previously been associated with either CTL escape in p24 or neutralising antibody escape in gp41. We also identified a putative new CTL escape site (residue 286 in gag), and a region of gp41 (including residues 644, 648, 655 in env) likely to be associated with immune escape. Sites inferred to be under selection in multiple hosts have high within-host and between-host diversity although not all sites with high between-host diversity were inferred to be under selection at the within-host level. Our identification of selection at sites associated with resistance to broadly neutralising antibodies (bNAbs) highlights the need to fully understand the role of selection in untreated individuals when designing bNAb based therapies.
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Garcia V, Glassberg EC, Harpak A, Feldman MW. Clonal interference can cause wavelet-like oscillations of multilocus linkage disequilibrium. J R Soc Interface 2019; 15:rsif.2017.0921. [PMID: 29563246 DOI: 10.1098/rsif.2017.0921] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 02/23/2018] [Indexed: 11/12/2022] Open
Abstract
Within-host adaptation of pathogens such as human immunodeficiency virus (HIV) often occurs at more than two loci. Multiple beneficial mutations may arise simultaneously on different genetic backgrounds and interfere, affecting each other's fixation trajectories. Here, we explore how these evolutionary dynamics are mirrored in multilocus linkage disequilibrium (MLD), a measure of multi-way associations between alleles. In the parameter regime corresponding to HIV, we show that deterministic early infection models induce MLD to oscillate over time in a wavelet-like fashion. We find that the frequency of these oscillations is proportional to the rate of adaptation. This signature is robust to drift, but can be eroded by high variation in fitness effects of beneficial mutations. Our findings suggest that MLD oscillations could be used as a signature of interference among multiple equally advantageous mutations and may aid the interpretation of MLD in data.
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Affiliation(s)
- Victor Garcia
- Department of Biology, Stanford University, 371 Serra Mall, Stanford, CA 94305, USA
| | - Emily C Glassberg
- Department of Biology, Stanford University, 371 Serra Mall, Stanford, CA 94305, USA
| | - Arbel Harpak
- Department of Biology, Stanford University, 371 Serra Mall, Stanford, CA 94305, USA
| | - Marcus W Feldman
- Department of Biology, Stanford University, 371 Serra Mall, Stanford, CA 94305, USA
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Baral S, Raja R, Sen P, Dixit NM. Towards multiscale modeling of the CD8 + T cell response to viral infections. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2019; 11:e1446. [PMID: 30811096 PMCID: PMC6614031 DOI: 10.1002/wsbm.1446] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 01/23/2019] [Accepted: 01/28/2019] [Indexed: 12/22/2022]
Abstract
The CD8+ T cell response is critical to the control of viral infections. Yet, defining the CD8+ T cell response to viral infections quantitatively has been a challenge. Following antigen recognition, which triggers an intracellular signaling cascade, CD8+ T cells can differentiate into effector cells, which proliferate rapidly and destroy infected cells. When the infection is cleared, they leave behind memory cells for quick recall following a second challenge. If the infection persists, the cells may become exhausted, retaining minimal control of the infection while preventing severe immunopathology. These activation, proliferation and differentiation processes as well as the mounting of the effector response are intrinsically multiscale and collective phenomena. Remarkable experimental advances in the recent years, especially at the single cell level, have enabled a quantitative characterization of several underlying processes. Simultaneously, sophisticated mathematical models have begun to be constructed that describe these multiscale phenomena, bringing us closer to a comprehensive description of the CD8+ T cell response to viral infections. Here, we review the advances made and summarize the challenges and opportunities ahead. This article is categorized under: Analytical and Computational Methods > Computational Methods Biological Mechanisms > Cell Fates Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models.
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Affiliation(s)
- Subhasish Baral
- Department of Chemical Engineering, Indian Institute of Science, Bangalore, India
| | - Rubesh Raja
- Department of Chemical Engineering, Indian Institute of Science, Bangalore, India
| | - Pramita Sen
- Department of Chemical Engineering, Indian Institute of Science, Bangalore, India
| | - Narendra M Dixit
- Department of Chemical Engineering, Indian Institute of Science, Bangalore, India.,Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, India
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Zinger T, Gelbart M, Miller D, Pennings PS, Stern A. Inferring population genetics parameters of evolving viruses using time-series data. Virus Evol 2019; 5:vez011. [PMID: 31191979 PMCID: PMC6555871 DOI: 10.1093/ve/vez011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
With the advent of deep sequencing techniques, it is now possible to track the evolution of viruses with ever-increasing detail. Here, we present Flexible Inference from Time-Series (FITS)-a computational tool that allows inference of one of three parameters: the fitness of a specific mutation, the mutation rate or the population size from genomic time-series sequencing data. FITS was designed first and foremost for analysis of either short-term Evolve & Resequence (E&R) experiments or rapidly recombining populations of viruses. We thoroughly explore the performance of FITS on simulated data and highlight its ability to infer the fitness/mutation rate/population size. We further show that FITS can infer meaningful information even when the input parameters are inexact. In particular, FITS is able to successfully categorize a mutation as advantageous or deleterious. We next apply FITS to empirical data from an E&R experiment on poliovirus where parameters were determined experimentally and demonstrate high accuracy in inference.
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Affiliation(s)
- Tal Zinger
- Department of Molecular Microbiology and Biotechnology, School of Molecular Cell Biology and Biotechnology, Haim Levanon Str., Tel-Aviv University, Tel-Aviv, Israel
| | - Maoz Gelbart
- Department of Molecular Microbiology and Biotechnology, School of Molecular Cell Biology and Biotechnology, Haim Levanon Str., Tel-Aviv University, Tel-Aviv, Israel
| | - Danielle Miller
- Department of Molecular Microbiology and Biotechnology, School of Molecular Cell Biology and Biotechnology, Haim Levanon Str., Tel-Aviv University, Tel-Aviv, Israel
| | - Pleuni S Pennings
- Department of Biology, San Francisco State University, 1600 Holloway Ave, San Francisco, CA, USA
| | - Adi Stern
- Department of Molecular Microbiology and Biotechnology, School of Molecular Cell Biology and Biotechnology, Haim Levanon Str., Tel-Aviv University, Tel-Aviv, Israel
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17
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Abstract
The interplay between immune response and HIV is intensely studied via mathematical modeling, with significant insights but few direct answers. In this short review, we highlight advances and knowledge gaps across different aspects of immunity. In particular, we identify the innate immune response and its role in priming the adaptive response as ripe for modeling. The latter have been the focus of most modeling studies, but we also synthesize key outstanding questions regarding effector mechanisms of cellular immunity and development of broadly neutralizing antibodies. Thus far, most modeling studies aimed to infer general immune mechanisms; we foresee that significant progress will be made next by detailed quantitative fitting of models to data, and prediction of immune responses.
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Affiliation(s)
- Jessica M Conway
- Department of Mathematics and Center for Infectious Disease Dynamics, Pennsylvania State University, University Park PA 16802, USA
| | - Ruy M Ribeiro
- Laboratorio de Biomatematica, Faculdade de Medicina da Universidade de Lisboa, Portugal and Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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18
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Ganusov VV. Time Intervals in Sequence Sampling, Not Data Modifications, Have a Major Impact on Estimates of HIV Escape Rates. Viruses 2018; 10:v10030099. [PMID: 29495443 PMCID: PMC5869492 DOI: 10.3390/v10030099] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Revised: 02/20/2018] [Accepted: 02/22/2018] [Indexed: 12/31/2022] Open
Abstract
The ability of human immunodeficiency virus (HIV) to avoid recognition by humoral and cellular immunity (viral escape) is well-documented, but the strength of the immune response needed to cause such a viral escape remains poorly quantified. Several previous studies observed a more rapid escape of HIV from CD8 T cell responses in the acute phase of infection compared to chronic infection. The rate of HIV escape was estimated with the help of simple mathematical models, and results were interpreted to suggest that CD8 T cell responses causing escape in acute HIV infection may be more efficient at killing virus-infected cells than responses that cause escape in chronic infection, or alternatively, that early escapes occur in epitopes mutations in which there is minimal fitness cost to the virus. However, these conclusions were challenged on several grounds, including linkage and interference of multiple escape mutations due to a low population size and because of potential issues associated with modifying the data to estimate escape rates. Here we use a sampling method which does not require data modification to show that previous results on the decline of the viral escape rate with time since infection remain unchanged. However, using this method we also show that estimates of the escape rate are highly sensitive to the time interval between measurements, with longer intervals biasing estimates of the escape rate downwards. Our results thus suggest that data modifications for early and late escapes were not the primary reason for the observed decline in the escape rate with time since infection. However, longer sampling periods for escapes in chronic infection strongly influence estimates of the escape rate. More frequent sampling of viral sequences in chronic infection may improve our understanding of factors influencing the rate of HIV escape from CD8 T cell responses.
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Affiliation(s)
- Vitaly V Ganusov
- Department of Microbiology, University of Tennessee, Knoxville, TN 37996, USA.
- Department of Mathematics, University of Tennessee, Knoxville, TN 37996, USA.
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19
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Yang Y, Ganusov VV. Kinetics of HIV-Specific CTL Responses Plays a Minimal Role in Determining HIV Escape Dynamics. Front Immunol 2018; 9:140. [PMID: 29472921 PMCID: PMC5810297 DOI: 10.3389/fimmu.2018.00140] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 01/16/2018] [Indexed: 11/13/2022] Open
Abstract
Cytotoxic T lymphocytes (CTLs) have been suggested to play an important role in controlling human immunodeficiency virus (HIV-1 or simply HIV) infection. HIV, due to its high mutation rate, can evade recognition of T cell responses by generating escape variants that cannot be recognized by HIV-specific CTLs. Although HIV escape from CTL responses has been well documented, factors contributing to the timing and the rate of viral escape from T cells have not been fully elucidated. Fitness costs associated with escape and magnitude of the epitope-specific T cell response are generally considered to be the key in determining timing of HIV escape. Several previous analyses generally ignored the kinetics of T cell responses in predicting viral escape by either considering constant or maximal T cell response; several studies also considered escape from different T cell responses to be independent. Here, we focus our analysis on data from two patients from a recent study with relatively frequent measurements of both virus sequences and HIV-specific T cell response to determine impact of CTL kinetics on viral escape. In contrast with our expectation, we found that including temporal dynamics of epitope-specific T cell response did not improve the quality of fit of different models to escape data. We also found that for well-sampled escape data, the estimates of the model parameters including T cell killing efficacy did not strongly depend on the underlying model for escapes: models assuming independent, sequential, or concurrent escapes from multiple CTL responses gave similar estimates for CTL killing efficacy. Interestingly, the model assuming sequential escapes (i.e., escapes occurring along a defined pathway) was unable to accurately describe data on escapes occurring rapidly within a short-time window, suggesting that some of model assumptions must be violated for such escapes. Our results thus suggest that the current sparse measurements of temporal CTL dynamics in blood bear little quantitative information to improve predictions of HIV escape kinetics. More frequent measurements using more sensitive techniques and sampling in secondary lymphoid tissues may allow to better understand whether and how CTL kinetics impacts viral escape.
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Affiliation(s)
- Yiding Yang
- Department of Microbiology, University of Tennessee, Knoxville, TN, United States
| | - Vitaly V. Ganusov
- Department of Microbiology, University of Tennessee, Knoxville, TN, United States
- National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, TN, United States
- Department of Mathematics, University of Tennessee, Knoxville, TN, United States
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20
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Reeves DB, Peterson CW, Kiem HP, Schiffer JT. Autologous Stem Cell Transplantation Disrupts Adaptive Immune Responses during Rebound Simian/Human Immunodeficiency Virus Viremia. J Virol 2017; 91:e00095-17. [PMID: 28404854 PMCID: PMC5469274 DOI: 10.1128/jvi.00095-17] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Accepted: 04/06/2017] [Indexed: 02/07/2023] Open
Abstract
Primary HIV-1 infection induces a virus-specific adaptive/cytolytic immune response that impacts the plasma viral load set point and the rate of progression to AIDS. Combination antiretroviral therapy (cART) suppresses plasma viremia to undetectable levels that rebound upon cART treatment interruption. Following cART withdrawal, the memory component of the virus-specific adaptive immune response may improve viral control compared to primary infection. Here, using primary infection and treatment interruption data from macaques infected with simian/human immunodeficiency virus (SHIV), we observe a lower peak viral load but an unchanged viral set point during viral rebound. The addition of an autologous stem cell transplant before cART withdrawal alters viral dynamics: we found a higher rebound set point but similar peak viral loads compared to the primary infection. Mathematical modeling of the data that accounts for fundamental immune parameters achieves excellent fit to heterogeneous viral loads. Analysis of model output suggests that the rapid memory immune response following treatment interruption does not ultimately lead to better viral containment. Transplantation decreases the durability of the adaptive immune response following cART withdrawal and viral rebound. Our model's results highlight the impact of the endogenous adaptive immune response during primary SHIV infection. Moreover, because we capture adaptive immune memory and the impact of transplantation, this model will provide insight into further studies of cure strategies inspired by the Berlin patient.IMPORTANCE HIV patients who interrupt combination antiretroviral therapy (cART) eventually experience viral rebound, the return of viral loads to pretreatment levels. However, the "Berlin patient" remained free of HIV rebound over a decade after stopping cART. His cure is attributed to leukemia treatment that included an HIV-resistant stem cell transplant. Inspired by this case, we studied the impact of stem cell transplantation in a macaque simian/HIV (SHIV) system. Using a mechanistic mathematical model, we found that while primary infection generates an adaptive immune memory response, stem cell transplantation disrupts this learned immunity. The results have implications for HIV cure regimens based on stem cell transplantation.
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Affiliation(s)
- Daniel B Reeves
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Christopher W Peterson
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Hans-Peter Kiem
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
- Department of Pathology, University of Washington, Seattle, Washington, USA
| | - Joshua T Schiffer
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
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Garcia V, Feldman MW. Within-Epitope Interactions Can Bias CTL Escape Estimation in Early HIV Infection. Front Immunol 2017; 8:423. [PMID: 28507544 PMCID: PMC5410659 DOI: 10.3389/fimmu.2017.00423] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 03/27/2017] [Indexed: 01/03/2023] Open
Abstract
As human immunodeficiency virus (HIV) begins to replicate within hosts, immune responses are elicited against it. Escape mutations in viral epitopes—immunogenic peptide parts presented on the surface of infected cells—allow HIV to partially evade these responses, and thus rapidly go to fixation. The faster they go to fixation, i.e., the higher their escape rate, the larger the selective pressure exerted by the immune system is assumed to be. This relation underpins the rationale for using escapes to assess the strength of immune responses. However, escape rate estimates are often obtained by employing an aggregation procedure, where several mutations that affect the same epitope are aggregated into a single, composite epitope mutation. The aggregation procedure thus rests upon the assumption that all within-epitope mutations have indistinguishable effects on immune recognition. In this study, we investigate how violation of this assumption affects escape rate estimates. To this end, we extend a previously developed simulation model of HIV that accounts for mutation, selection, and recombination to include different distributions of fitness effects (DFEs) and inter-mutational genomic distances. We use this discrete time Wright–Fisher based model to simulate early within-host evolution of HIV for DFEs and apply standard estimation methods to infer the escape rates. We then compare true with estimated escape rate values. We also compare escape rate values obtained by applying the aggregation procedure with values estimated without use of that procedure. We find that across the DFEs analyzed, the aggregation procedure alters the detectability of escape mutations: large-effect mutations are overrepresented while small-effect mutations are concealed. The effect of the aggregation procedure is similar to extracting the largest-effect mutation appearing within an epitope. Furthermore, the more pronounced the over-exponential decay of the DFEs, the more severely true escape rates are underestimated. We conclude that the aggregation procedure has two main consequences. On the one hand, it leads to a misrepresentation of the DFE of fixed mutations. On the other hand, it conceals within-epitope interactions that may generate irregularities in mutation frequency trajectories that are thus left unexplained.
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Affiliation(s)
- Victor Garcia
- Department of Biology, Stanford University, Stanford, CA, USA
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