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Biology subjects

Eyassu, F.

Publications and source records attributed to Eyassu, F..

2 recordsLinked to original sources

A truncated HIV Tat demonstrates potent and specific latency reversal activity

A major barrier to HIV-1 cure is caused by the pool of latently infected CD4 T-cells that persist under combination antiretroviral therapy (cART). This latent reservoir is capable of producing replication-competent infectious virus once prolonged suppressive cART is withdrawn. Inducing the reactivation of HIV-1 gene expression in T-cells harboring a latent provirus in people living with HIV-1 under cART will likely result in depletion of this latent reservoir due to cytopathic effects or immune clearance. Studies have investigated molecules that reactivate HIV-1 gene expression but to date no latency reversal agent has been identified to eliminate latently infected cells harboring replication-competent HIV in cART treated individuals. Stochastic fluctuations in HIV-1 tat gene expression have been described and hypothesized to allow the progression into proviral latency. We hypothesized that exposing latently infected CD4+ T-cells to Tat would result in effective latency reversal. Our results indicate the capacity of a truncated Tat protein and mRNA to reactivate HIV-1 in latently infected T-cells ex vivo to a similar degree as the protein kinase C agonist: Phorbol 12-Myristate 13-Acetate, without T-cell activation nor any significant transcriptome perturbation.

cell biology↗

MitoCore: A Curated Constraint-Based Model For Simulating Human Central Metabolism

BackgroundThe complexity of metabolic networks can make the origin and impact of profound changes in central metabolism occurring during disease difficult to understand. Computer simulations can help unravel this complexity, and progress has advanced in genome-scale metabolic models. However, many current models produce unrealistic results when challenged to simulate abnormal metabolism as they include incorrect specification and localization of reactions and transport steps, incorrect reaction parameters, and confounding of prosthetic groups and free metabolites in reactions. Other common drawbacks are due to their scale, such as being difficult to parameterise and simulation results being hard to interpret. Therefore, it remains important to develop smaller, manually curated models to represent central metabolism accurately.\n\nResultsWe present MitoCore, a manually curated constraint-based computer model of human metabolism that incorporates the complexity of central metabolism and simulates this metabolism successfully under normal and abnormal conditions, including hypoxia and mitochondrial diseases. MitoCore describes 324 metabolic reactions, 83 transport steps between mitochondrion and cytosol, and 74 metabolite inputs and outputs through the plasma membrane, to produce a model of manageable scale for easy data interpretation. Its key innovations include accurate partitioning of metabolism between cytosol and mitochondrial matrix; correct modelling of connecting transport steps; proper differentiation of prosthetic groups and free co-factors in reactions; and a new representation of the respiratory chain and the proton motive force. MitoCores default parameters simulate normal cardiomyocyte metabolism, and to improve usability and allow comparison with other models and types of analysis, its reactions and metabolites have extensive annotation, and cross-reference identifiers from Virtual Metabolic Human database and KEGG. These innovations--including over 100 reactions absent or modified from Recon 2--are essential to model central metabolism accurately.\n\nConclusionWe anticipate MitoCore as a research tool for scientists, from experimentalists looking to interpret data and generate further hypotheses, to experienced modellers predicting the consequences of disease or using computationally intensive methods that are infeasible with larger models, as well as a teaching tool for those new to modelling and needing a small manageable model on which to learn and experiment.

systems biology↗