Recent Research Publications
Learn more about our work from our recent publications!
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Exploring Cooperation among Social Pathogens: A Computational Perspective
Once centered on animal social behavior, investigations into cooperation have expanded across the tree of life to include microorganisms such as bacteria and viruses. Cooperative interactions are now understood to drive evolutionary dynamics within and between numerous microbial species and communities, including pathogen adaptation to and persistence in new hosts and environments. Identification and characterization of the underlying mechanisms of cooperation offer innovative opportunities for therapeutic interventions targeting difficult-to-treat pathogens through disruption of interactive networks. The current gold standards for evaluating microorganismal cooperation often rely on assessing coordinated changes of pheno-typic traits and the genetic and environmental factors that can affect them. Among these approaches, in vitro methods are labor-intensive, time-consuming, and often fail to replicate the natural microenvironment. Computational methods applied in vivo offer scalability and applicability but often require prior knowledge of metabolic pathways, restricting their use to bacterial systems. In contrast, sequence- and phylogeny-based frameworks can extend to viral datasets, though are typically con- strained by smaller sample sizes and incomplete annotations. Herein we focus on existing computational approaches used in identifying and/or characterizing cooperation and detail their advantages and limitations in shaping our understanding of cooperative pathogens.
Defective HIV DNA genomes provide ancestral relevance critical for phylogenetic inference of reservoir dynamics
During the course of infection, human immunodeficiency virus (HIV) maintains a stably integrated reservoir of replication-competent viruses within the host genome that are unaffected by antiretroviral therapy. Curative advancements rely heavily on targeting the anatomical reservoirs, though determinants of their evolutionary origins through phyloanatomic inference remain ill-supported through current sequencing and sequence analysis strategies. The vast replication-defective genomic landscape that comprises the HIV DNA population is often discarded in these evolutionary endeavors, despite key information regarding competent ancestry that can be gained from captured genomic regions outside the historically used viral envelope gene. Here, we describe the application of small-amplicon, single-cell DNA sequencing to blood and lymph node samples from a treatment-interrupted S[imian]IV-infected animal model and evaluate the contribution of genome coverage and inclusion on phylogenetic resolution and phyloanatomic inference. Findings from this study point to incomplete genomes as a significant source of phylogenetic information on movement of virus between tissue reservoirs during therapy.
Novel insights on unraveling dynamics of transmission clusters in outbreaks using phylogeny-based methods
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 would not be expected to capture dynamic clusters of transmission. We, therefore, sought to evaluate the performance of existing and adapted phylogeny-based cluster identification tools on simulated transmission clusters exhibiting dynamic transmission behavior over time. Despite the complementarity of the tools, we provide strong evidence that novel cluster identification methods are needed for reliable detection of epidemiologically linked individuals, particularly those exhibiting changing transmission dynamics during dynamic outbreak scenarios.
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 transmission 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 in reconstructing the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for transmission modeling and forecasting, developing a phylogeny-based deep learning system, referred to as DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, which 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 github.com/lab-smile/DeepDynaForcast.
Machine Learning Prediction and Phyloanatomic Modeling of Viral Neuroadaptive Signatures in the Macaque Model of HIV-Mediated Neuropathology
In human immunodeficiency virus (HIV) infection, virus replication in and adaptation to the central nervous system (CNS) can result in neurocognitive deficits in approximately 25% of patients with unsuppressed viremia. While no single viral mutation can be agreed upon as distinguishing the neuroadapted population, earlier studies have demonstrated that a machine learning (ML) approach could be applied to identify a collection of mutational signatures within the virus envelope glycoprotein (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, noninvasive 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 97% accuracy. The presence of SIVE signatures at earlier time points of infection in non-CNS tissues 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 high rate of alveolar macrophage (AM) infection. AMs were also determined to be the phyloanatomic source of cranial virus in SIVE animals, but not in animals that did not develop SIVE, implicating a role for these cells in the evolution of the signatures identified as predictive of both HIV and SIV neuropathology.