Viral Load Progression Research Articles

Story of an infection: Viral dynamics and host responses in the Caenorhabditis elegans-Orsay virus pathosystem.

Orsay virus (OrV) is the only known natural virus affecting Caenorhabditis elegans, with minimal impact on the animal's fitness due to its robust innate immune response. This study aimed to understand the interactions between C. elegans and OrV by tracking the infection's progression during larval development. Four distinct stages of infection were identified on the basis of viral load, with a peak in capsid-encoding RNA2 coinciding with the first signs of viral egression. Transcriptomic analysis revealed temporal changes in gene expression and functions induced by the infection. A specific set of up-regulated genes remained active throughout the infection, and genes correlated and anticorrelated with virus accumulation were identified. Responses to OrV mirrored reactions to other biotic stressors, distinguishing between virus-specific responses and broader immune responses. Moreover, mutants of early response genes and defense-related processes showed altered viral load progression, uncovering additional players in the antiviral defense response.

Label-free and amplification-free viral RNA quantification from primate biofluids using a trapping-assisted optofluidic nanopore platform

Viral outbreaks can cause widespread disruption, creating the need for diagnostic tools that provide high performance and sample versatility at the point of use with moderate complexity. Current gold standards such as PCR and rapid antigen tests fall short in one or more of these aspects. Here, we report a label-free and amplification-free nanopore sensor platform that overcomes these challenges via direct detection and quantification of viral RNA in clinical samples from a variety of biological fluids. The assay uses an optofluidic chip that combines optical waveguides with a fluidic channel and integrates a solid-state nanopore for sensing of individual biomolecules upon translocation through the pore. High specificity and low limit of detection are ensured by capturing RNA targets on microbeads and collecting them by optical trapping at the nanopore location where targets are released and rapidly detected. We use this device for longitudinal studies of the viral load progression for Zika and Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infections in marmoset and baboon animal models, respectively. The up to million-fold trapping-based target concentration enhancement enables amplification-free RNA quantification across the clinically relevant concentration range down to the assay limit of RT-qPCR as well as cases in which PCR failed. The assay operates across all relevant biofluids, including semen, urine, and whole blood for Zika and nasopharyngeal and throat swab, rectal swab, and bronchoalveolar lavage for SARS-CoV-2. The versatility, performance, simplicity, and potential for full microfluidic integration of the amplification-free nanopore assay points toward a unique approach to molecular diagnostics for nucleic acids, proteins, and other targets.

Innate Immunity Plays a Key Role in Controlling Viral Load in COVID-19: Mechanistic Insights from a Whole-Body Infection Dynamics Model.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a pathogen of immense public health concern. Efforts to control the disease have only proven mildly successful, and the disease will likely continue to cause excessive fatalities until effective preventative measures (such as a vaccine) are developed. To develop disease management strategies, a better understanding of SARS-CoV-2 pathogenesis and population susceptibility to infection are needed. To this end, mathematical modeling can provide a robust in silico tool to understand COVID-19 pathophysiology and the in vivo dynamics of SARS-CoV-2. Guided by ACE2-tropism (ACE2 receptor dependency for infection) of the virus and by incorporating cellular-scale viral dynamics and innate and adaptive immune responses, we have developed a multiscale mechanistic model for simulating the time-dependent evolution of viral load distribution in susceptible organs of the body (respiratory tract, gut, liver, spleen, heart, kidneys, and brain). Following parameter quantification with in vivo and clinical data, we used the model to simulate viral load progression in a virtual patient with varying degrees of compromised immune status. Further, we ranked model parameters through sensitivity analysis for their significance in governing clearance of viral load to understand the effects of physiological factors and underlying conditions on viral load dynamics. Antiviral drug therapy, interferon therapy, and their combination were simulated to study the effects on viral load kinetics of SARS-CoV-2. The model revealed the dominant role of innate immunity (specifically interferons and resident macrophages) in controlling viral load, and the importance of timing when initiating therapy after infection.

A methodology for deriving the sensitivity of pooled testing, based on viral load progression and pooling dilution

BackgroundPooled testing, in which biological specimens from multiple subjects are combined into a testing pool and tested via a single test, is a common testing method for both surveillance and screening activities. The sensitivity of pooled testing for various pool sizes is an essential input for surveillance and screening optimization, including testing pool design. However, clinical data on test sensitivity values for different pool sizes are limited, and do not provide a functional relationship between test sensitivity and pool size. We develop a novel methodology to accurately compute the sensitivity of pooled testing, while accounting for viral load progression and pooling dilution. We demonstrate our methodology on the nucleic acid amplification testing (NAT) technology for the human immunodeficiency virus (HIV).MethodsOur methodology integrates mathematical models of viral load progression and pooling dilution to derive test sensitivity values for various pool sizes. This methodology derives the conditional test sensitivity, conditioned on the number of infected specimens in a pool, and uses the law of total probability, along with higher dimensional integrals, to derive pooled test sensitivity values. We also develop a highly accurate and easy-to-compute approximation function for pooled test sensitivity of the HIV ULTRIO Plus NAT Assay. We calibrate model parameters using published efficacy data for the HIV ULTRIO Plus NAT Assay, and clinical data on viral RNA load progression in HIV-infected patients, and use this methodology to derive and validate the sensitivity of the HIV ULTRIO Plus Assay for various pool sizes.ResultsWe demonstrate the value of this methodology through optimal testing pool design for HIV prevalence estimation in Sub-Saharan Africa. This case study indicates that the optimal testing pool design is highly efficient, and outperforms a benchmark pool design.ConclusionsThe proposed methodology accounts for both viral load progression and pooling dilution, and is computationally tractable. We calibrate this model for the HIV ULTRIO Plus NAT Assay, show that it provides highly accurate sensitivity estimates for various pool sizes, and, thus, yields efficient testing pool design for HIV prevalence estimation. Our model is generic, and can be calibrated for other infections.

Effects of drug resistance on viral load in patients failing antiretroviral therapy

Previous studies on patients who develop drug resistant HIV-1 variants have shown that continued use of failing regimens might provide clinical benefit. However, the effect of long-term exposure to drug resistant variants may lead to emergence of compensatory mutations that may jeopardize this effect. In this study, we assess associations among type and number of drug resistant mutations, viral load and disease progression in patients with long-term follow up. Patients with genotypic testing performed at the time of treatment failure were enrolled. Comparison of viral load and CD4 cell count between different resistance groups was performed using analysis of variance. Multiple linear regression analysis was performed to assess the simultaneous effects of the presence of particular mutations and their accumulation on viral load. Data from 475 patients who were followed for a median of 43 months from October 1999 to July 2005 were studied. A "V shape" relationship was observed between the number of mutations and viral load. Specifically, in patients harboring up to five mutations, viral load was reduced by 0.8 log/copies when compared to wild-type variants. However, with more than six mutations viral load progressively increased. Certain reverse transcriptase mutations such as M184V/I, K70R, V108I, and protease mutations such as L33FIV, M84V, and M36I were associated with reduced viral load. Together, these findings suggest that long-term maintenance of a sub-optimal antiretroviral regimen may have deleterious consequences for the patient.