Potent latency reversal by Tat RNA-containing nanoparticle enables multi-omic analysis of the HIV-1 reservoir.

The development of latency reversing agents that potently reactivate HIV without inducing global T cell activation would benefit the field of HIV reservoir research and could pave the way to a functional cure. Here, we explore the reactivation capacity of a lipid nanoparticle containing Tat mRNA (Tat-LNP) in CD4 T cells from people living with HIV undergoing antiretroviral therapy (ART). When combined with panobinostat, Tat-LNP induces latency reversal in a significantly higher proportion of latently infected cells compared to PMA/ionomycin (≈ 4-fold higher). We demonstrate that Tat-LNP does not alter the transcriptome of CD4 T cells, enabling the characterization of latently infected cells in their near-native state. Upon latency reversal, we identify transcriptomic differences between infected cells carrying an inducible provirus and non-infected cells (e.g. LINC02964, GZMA, CCL5). We confirm the transcriptomic differences at the protein level and provide evidence that the long non-coding RNA LINC02964 plays a role in active HIV infection. Furthermore, p24+ cells exhibit heightened PI3K/Akt signaling, along with downregulation of protein translation, suggesting that HIV-infected cells display distinct signatures facilitating their long-term persistence. Tat-LNP represents a valuable research tool for in vitro reservoir studies as it greatly facilitates the in-depth characterization of HIV reservoir cells' transcriptome and proteome profiles.

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latent reservoir is capable of producing replication-competent infectious viruses once prolonged suppressive cART is withdrawn.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 may 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 or any significant transcriptome perturbation.

CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design.

BACKGROUND: The study of cell metabolism is becoming central in several fields such as biotechnology, evolution/adaptation and human disease investigations. Here we present CiliateGEM, the first metabolic network reconstruction draft of the freshwater ciliate Tetrahymena thermophila. We also provide the tools and resources to simulate different growth conditions and to predict metabolic variations. CiliateGEM can be extended to other ciliates in order to set up a meta-model, i.e. a metabolic network reconstruction valid for all ciliates. Ciliates are complex unicellular eukaryotes of presumably monophyletic origin, with a phylogenetic position that is equal from plants and animals. These cells represent a new concept of unicellular system with a high degree of species, population biodiversity and cell complexity. Ciliates perform in a single cell all the functions of a pluricellular organism, including locomotion, feeding, digestion, and sexual processes. RESULTS: After generating the model, we performed an in-silico simulation with the presence and absence of glucose. The lack of this nutrient caused a 32.1% reduction rate in biomass synthesis. Despite the glucose starvation, the growth did not stop due to the use of alternative carbon sources such as amino acids. CONCLUSIONS: The future models obtained from CiliateGEM may represent a new approach to describe the metabolism of ciliates. This tool will be a useful resource for the ciliate research community in order to extend these species as model organisms in different research fields. An improved understanding of ciliate metabolism could be relevant to elucidate the basis of biological phenomena like genotype-phenotype relationships, population genetics, and cilia-related disease mechanisms.

In silico engineering of Pseudomonas metabolism reveals new biomarkers for increased biosurfactant production.

BACKGROUND: Rhamnolipids, biosurfactants with a wide range of biomedical applications, are amphiphilic molecules produced on the surfaces of or excreted extracellularly by bacteria including Pseudomonas aeruginosa. However, Pseudomonas putida is a non-pathogenic model organism with greater metabolic versatility and potential for industrial applications. METHODS: We investigate in silico the metabolic capabilities of P. putida for rhamnolipids biosynthesis using statistical, metabolic and synthetic engineering approaches after introducing key genes (RhlA and RhlB) from P. aeruginosa into a genome-scale model of P. putida. This pipeline combines machine learning methods with multi-omic modelling, and drives the engineered P. putida model toward an optimal production and export of rhamnolipids out of the membrane. RESULTS: We identify a substantial increase in synthesis of rhamnolipids by the engineered model compared to the control model. We apply statistical and machine learning techniques on the metabolic reaction rates to identify distinct features on the structure of the variables and individual components driving the variation of growth and rhamnolipids production. We finally provide a computational framework for integrating multi-omics data and identifying latent pathways and genes for the production of rhamnolipids in P. putida. CONCLUSIONS: We anticipate that our results will provide a versatile methodology for integrating multi-omics data for topological and functional analysis of P. putida toward maximization of biosurfactant production.

Mitochondrial oxodicarboxylate carrier deficiency is associated with mitochondrial DNA depletion and spinal muscular atrophy-like disease.

PURPOSE: To understand the role of the mitochondrial oxodicarboxylate carrier (SLC25A21) in the development of spinal muscular atrophy-like disease. METHODS: We identified a novel pathogenic variant in a patient by whole-exome sequencing. The pathogenicity of the mutation was studied by transport assays, computer modeling, followed by targeted metabolic testing and in vitro studies in human fibroblasts and neurons. RESULTS: The patient carries a homozygous pathogenic variant c.695A>G; p.(Lys232Arg) in the SLC25A21 gene, encoding the mitochondrial oxodicarboxylate carrier, and developed spinal muscular atrophy and mitochondrial myopathy. Transport assays show that the mutation renders SLC25A21 dysfunctional and 2-oxoadipate cannot be imported into the mitochondrial matrix. Computer models of central metabolism predicted that impaired transport of oxodicarboxylate disrupts the pathways of lysine and tryptophan degradation, and causes accumulation of 2-oxoadipate, pipecolic acid, and quinolinic acid, which was confirmed in the patient's urine by targeted metabolomics. Exposure to 2-oxoadipate and quinolinic acid decreased the level of mitochondrial complexes in neuronal cells (SH-SY5Y) and induced apoptosis. CONCLUSION: Mitochondrial oxodicarboxylate carrier deficiency leads to mitochondrial dysfunction and the accumulation of oxoadipate and quinolinic acid, which in turn cause toxicity in spinal motor neurons leading to spinal muscular atrophy-like disease.

MitoCore: a curated constraint-based model for simulating human central metabolism.

BACKGROUND: The complexity of metabolic networks can make the origin and impact of changes in central metabolism occurring during diseases difficult to understand. Computer simulations can help unravel this complexity, and progress has advanced in genome-scale metabolic models. However, many models produce unrealistic results when challenged to simulate abnormal metabolism as they include incorrect specification and localisation 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, making them difficult to parameterise and simulation results hard to interpret. Therefore, it remains important to develop smaller, manually curated models. RESULTS: We 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 physiological 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 interpretation of results. Its key innovations include a more accurate partitioning of metabolism between cytosol and mitochondrial matrix; better modelling of connecting transport steps; differentiation of prosthetic groups and free co-factors in reactions; and a new representation of the respiratory chain and the proton motive force. MitoCore's 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 necessary to model central metabolism more accurately. CONCLUSION: We anticipate MitoCore as a research tool for scientists, from experimentalists looking to interpret their data and test 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.

Modelling pyruvate dehydrogenase under hypoxia and its role in cancer metabolism.

Metabolism is the only biological system that can be fully modelled at genome scale. As a result, metabolic models have been increasingly used to study the molecular mechanisms of various diseases. Hypoxia, a low-oxygen tension, is a well-known characteristic of many cancer cells. Pyruvate dehydrogenase (PDH) controls the flux of metabolites between glycolysis and the tricarboxylic acid cycle and is a key enzyme in metabolic reprogramming in cancer metabolism. Here, we develop and manually curate a constraint-based metabolic model to investigate the mechanism of pyruvate dehydrogenase under hypoxia. Our results characterize the activity of pyruvate dehydrogenase and its decline during hypoxia. This results in lactate accumulation, consistent with recent hypoxia studies and a well-known feature in cancer metabolism. We apply machine-learning techniques on the flux datasets to identify reactions that drive these variations. We also identify distinct features on the structure of the variables and individual metabolic components in the switch from normoxia to hypoxia. Our results provide a framework for future studies by integrating multi-omics data to predict condition-specific metabolic phenotypes under hypoxia.

Ischaemic accumulation of succinate controls reperfusion injury through mitochondrial ROS.

Ischaemia-reperfusion injury occurs when the blood supply to an organ is disrupted and then restored, and underlies many disorders, notably heart attack and stroke. While reperfusion of ischaemic tissue is essential for survival, it also initiates oxidative damage, cell death and aberrant immune responses through the generation of mitochondrial reactive oxygen species (ROS). Although mitochondrial ROS production in ischaemia reperfusion is established, it has generally been considered a nonspecific response to reperfusion. Here we develop a comparative in vivo metabolomic analysis, and unexpectedly identify widely conserved metabolic pathways responsible for mitochondrial ROS production during ischaemia reperfusion. We show that selective accumulation of the citric acid cycle intermediate succinate is a universal metabolic signature of ischaemia in a range of tissues and is responsible for mitochondrial ROS production during reperfusion. Ischaemic succinate accumulation arises from reversal of succinate dehydrogenase, which in turn is driven by fumarate overflow from purine nucleotide breakdown and partial reversal of the malate/aspartate shuttle. After reperfusion, the accumulated succinate is rapidly re-oxidized by succinate dehydrogenase, driving extensive ROS generation by reverse electron transport at mitochondrial complex I. Decreasing ischaemic succinate accumulation by pharmacological inhibition is sufficient to ameliorate in vivo ischaemia-reperfusion injury in murine models of heart attack and stroke. Thus, we have identified a conserved metabolic response of tissues to ischaemia and reperfusion that unifies many hitherto unconnected aspects of ischaemia-reperfusion injury. Furthermore, these findings reveal a new pathway for metabolic control of ROS production in vivo, while demonstrating that inhibition of ischaemic succinate accumulation and its oxidation after subsequent reperfusion is a potential therapeutic target to decrease ischaemia-reperfusion injury in a range of pathologies.