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Publications and source records attributed to Rife Magalis, B..
Withdrawal StatementThe authors have withdrawn this manuscript due to a duplicate posting of manuscript number BIORXIV/2022/490630. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author. The correct preprint can be found at doi: 10.1101/2022.05.04.490630.
In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful to reconstruct the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for infection forecasting in addition to backtracking, developing a phylogeny-based deep learning system, called DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, and it is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at: https://github.com/lab-smile/DeepDynaForcast. Author SummaryDuring an outbreak or sustained epidemic, accurate prediction of patterns in transmission risk can reliably inform public health strategies. Projections indicating growth or decline of transmission for specific risk groups can significantly enhance the optimization of interventions, especially when resources are limited. To address this, we present DeepDynaForecast, a cutting-edge deep learning algorithm designed for forecasting pathogen transmission dynamics. Uniquely, DeepDynaForecast was trained on in-depth simulation data and used more information from the phylogenetic tree of pathogen sequence data than any other algorithm in the field to date, allowing classification of samples according to their dynamics (growth, static, or decline) with incredible accuracy. We evaluated the models performance using both simulated outbreak data and empirical, large-scale data from the HIV epidemic in Florida between 2012 and 2020. We conclude DeepDynaForecast represents a significant advancement in genomics-mediated pathogen transmission characterization and has the potential to catalyze new research directions within virology, molecular biology, and public health.
In human immunodeficiency virus (HIV) infection, virus replication in the central nervous system (CNS) can result in HIV-associated neurocognitive deficits in approximately 25% of patients with unsuppressed viremia and is thought to be characterized by evolutionary adaptation to this unique microenvironment. While no single mutation can be agreed upon as distinguishing the neuroadapted population from virus in patients without neuropathology, earlier studies have demonstrated that a machine learning (ML) approach could be applied to identify a collection of mutational signatures within the envelope glycoprotein (Env Gp120) predictive of disease. The S[imian] IV-infected macaque is a widely used animal model of HIV neuropathology, allowing in-depth tissue sampling infeasible for human patients. Yet, translational impact of the ML approach within the context of the macaque model has not been tested, much less the capacity for early prediction in other, non-invasive tissues. We applied the previously described ML approach to prediction of SIV-mediated encephalitis (SIVE) using gp120 sequences obtained from the CNS of animals with and without SIVE with 73% accuracy. The presence of SIVE signatures at earlier time points of infection in non-CNS tissues in both SIVE and SIVnoE animals indicated these signatures cannot be used in a clinical setting. However, combined with protein structural mapping and statistical phylogenetic inference, results revealed common denominators associated with these signatures, including 2-acetamido-2-deoxy-beta-D-glucopyranose structural interactions and the infection of alveolar macrophages. Alveolar macrophages were demonstrated to harbor a relatively large proportion (35 - 100%) of SIVE-classified sequences and to be the phyloanatomic source of cranial virus in SIVE, but not SIVnoE animals. While this combined approach cannot distinguish the role of this cell population as an indicator of cellular tropism from a source of neuroadapted virus, it provides a key to understanding the function and evolution of the signatures identified as predictive of both HIV and SIV neuropathology. Author summaryHIV-associated neurocognitive disorders remain prevalent among HIV-infected individuals, even in the era of potent antiretroviral therapy, and our understanding of the mechanisms involved in disease pathogenesis, such as virus evolution and adaptation, remains elusive. In this study, we expand on a machine learning method previously used to predict neurocognitive impairment in HIV-infected individuals to the macaque model of AIDS-related neuropathology in order to characterize its translatability and predictive capacity in other sampling tissues and time points. We identified four amino acid and/or biochemical signatures associated with disease that, similar to HIV, demonstrated a proclivity for proximity to aminoglycans in the protein structure. These signatures were not, however, isolated to specific points in time or even to the central nervous system, as they could be observed at low levels during initial infection and from various tissues, most prominently in the lungs. The spatiotemporal patterns observed limit the use of these signatures as an accurate prediction for neuropathogenesis prior to the onset of symptoms, though results from this study warrant further investigation into the role of these signatures, as well as lung tissue, in viral entry to and replication in the brain.
Molecular data analysis is invaluable in understanding the overall behavior of a rapidly spreading virus population when epidemiological surveillance is problematic. It is also particularly beneficial in describing subgroups within the population, often identified as clades within a phylogenetic tree, that represent individuals connected via direct transmission or transmission via differing risk factors in viral spread. However, transmission patterns or viral dynamics within these smaller groups should not be expected to exhibit homogeneous behavior over time. As such, standard phylogenetic approaches that identify clusters based on summary statistics (e.g., median genetic distance over the clade) would not be expected to capture dynamic clusters of transmission. For this purpose, we have developed DYNAMITE (DYNAMic Identification of Transmission Epicenters), a cluster identification algorithm based on a branch-wise (rather than traditional clade-wise) search for cluster criteria, allowing partial clades to be recognized as clusters. Using simulated viral outbreaks with varying cluster types and dynamics, we show that DYNAMITE is consistently more sensitive than existing tools in detecting both static and dynamic transmission clusters. DYNAMITE has been implemented in R and released as open source at: github.com/ProsperiLab/DYNAMITE.
TARDiS for Philogenetics is a novel tool for optimal genetic sub-sampling. It optimizes both genetic diversity and temporal distribution through a genetic algorithm. TARDiS, along with example data sets and a user manual, is available at https://github.com/smarini/tardis-phylogenetics
Although the global response to COVID-19 has not been entirely unified, the opportunity arises to assess the impact of regional public health interventions and to classify strategies according to their outcome. Analysis of genetic sequence data gathered over the course of the pandemic allows us to link the dynamics associated with networks of connected individuals with specific interventions. In this study, clusters of transmission were inferred from a phylogenetic tree representing the relationships of patient sequences sampled from December 30, 2019 to April 17, 2020. Metadata comprising sampling time and location were used to define the global behavior of transmission over this earlier sampling period, but also the involvement of individual regions in transmission cluster dynamics. Results demonstrate a positive impact of international travel restrictions and nationwide lockdowns on global cluster dynamics. However, residual, localized clusters displayed a wide range of estimated initial secondary infection rates, for which uniform public health interventions are unlikely to have sustainable effects. Our findings highlight the presence of so-called "super-spreaders", with the propensity to infect a larger-than-average number of people, in countries, such as the USA, for which additional mitigation efforts targeting events surrounding this type of spread are urgently needed to curb further dissemination of SARS-CoV-2.
BackgroundDespite improvements in antiretroviral therapy, human immunodeficiency virus type 1 (HIV-1)-associated neurocognitive disorders (HAND) remain prevalent in subjects undergoing therapy. HAND significantly affects individuals quality of life, as well as adherence to therapy, and, despite the increasing understanding of neuropathogenesis, no definitive diagnostic or prognostic marker has been identified. ResultsWe investigated transcriptomic profiles in frontal cortex tissues of Simian immunodeficiency virus (SIV)-infected Rhesus macaques sacrificed at different stages of infection. Gene expression was compared among SIV-infected animals (n=11), with or without CD8+ lymphocyte depletion, based on detectable (n=6) or non-detectable (n=5) presence of the virus in frontal cortex tissues. Significant enrichment in activation of monocyte and macrophage cellular pathways was found in animals with detectable brain infection, independently from CD8+ lymphocyte depletion. In addition, transcripts of four poly (ADP-ribose) polymerases (PARPs) were up-regulated in the frontal cortex, which was confirmed by real-time polymerase chain reaction. ConclusionsOur results shed light on involvement of PARPs in SIV infection of the brain and their role in SIV-associated neurodegenerative processes. Inhibition of PARPs may provide an effective novel therapeutic target for HIV-related neuropathology.
A number of evolutionary hypotheses can be tested by comparing selective pressures among sets of branches in a phylogenetic tree. When the question of interest is to identify specific sites within genes that may be evolving differently, a common approach is to perform separate analyses on subsets of sequences, and compare parameter estimates in a post hoc fashion. This approach is statistically suboptimal, and not always applicable. Here, we develop a simple extension of a popular fixed effects likelihood method in the context of codon-based evolutionary phylogenetic maximum likelihood testing, Contrast-FEL. It is suitable for identifying individual alignment sites where any among the K [≥] 2 sets of branches in a phylogenetic tree have detectably different dN/dS ratios, indicative of different selective regimes. Using extensive simulations, we show that Contrast-FEL delivers good power, exceeding 90% for sufficiently large differences, while maintaining tight control over false positive rates. We conclude by applying Contrast-FEL to data from five previously published studies spanning a diverse range of organisms and focusing on different evolutionary questions.
Human immunodeficiency virus (HIV) RNA and DNA have been isolated from patient monocytes, an immune cell population that is quite different in several aspects from the canonical T-cell viral target. Because monocytes are migratory and resilient to both natural and synthetic antiviral defenses, knowledge of the contribution of monocyte infection to ongoing viral evolution and spread in vivo is of significant interest for drug development and treatment strategies. Using single viral genome sequencing from different peripheral blood compartments and phyloanatomic statistical inference, we demonstrate that productively infected monocytes follow an evolutionary trajectory that is distinct from peripheral T cells during multiple stages of disease progression. Gene flow and selection analysis reveal plasticity in the source of monocyte infection and in the region of the HIV envelope glycoprotein that experiences selection pressure across individuals. The findings, thus, point to a potential reservoir showing a range of infection and transmission dynamics, for which the current universal, T cell-targeted treatment strategies would be inadequate.\n\nAuthor summaryHuman immunodeficiency virus is a rapidly evolving virus, allowing its genetic material to act as a fingerprint for epidemiological processes among, as well as within, individual infected hosts. In this study, sampling of viral RNA from plasma and differing infected immune cell populations from the peripheral blood was undertaken for three separate individuals in order to infer such processes. The results revealed a productively infected monocyte cell population, for which distinct selection pressures were linked to differing spatiotemporal origins of infection. In light of previous evidence of the migratory nature of these cells and resilience to both natural and synthetic antiviral defenses, this study underscores the importance of further investigation into the role of monocytes, and monocyte-rich tissues, as viral reservoirs during treatment of HIV with antiretroviral therapy.