Inhibition of the DDX3 prevents HIV-1 Tat and cocaine-induced neurotoxicity by targeting microglia activation

HIV-1 Associated Neurocognitive Disorder (HAND) is commonly seen in HIV-infected patients. Viral proteins including Tat cause neuronal toxicity and is worsened by drugs of abuse. To uncover potential targets for anti-HAND therapy, we employed a literature mining system, MOLIERE. Here, we validated Dead Box RNA Helicase 3 (DDX3) as a target to treat HAND via a selective DDX3 inhibitor, RK-33. The combined neurotoxicity of Tat protein and cocaine was blocked by RK-33 in rat and mouse cortical cultures. Transcriptome analysis showed that Tat-activated transcripts include makers and regulators of microglial activation, and RK-33 blocked Tat-induced activation of these mRNAs. Elevated production of proinflammatory cytokines was also inhibited by RK-33. These findings show that DDX3 contributes to microglial activation triggered by Tat and cocaine, and DDX3 inhibition shows promise as a therapy for HAND. Moreover, DDX3 may contribute to the pathology of other neurodegenerative diseases with pathological activation of microglia. Introduction: More than 36 million people worldwide are living with HIV infection and more than 1.2 million are in the USA based on World Health Organization (1). Neurological complications associated with HIV infection have long been known (2). With the introduction of highly active antiretroviral therapy (HAART), the life span of HIV-infected individuals has increased significantly to an average of 65 years (3). The increased life expectancy of the HIV-positive population is increasing the burden of the neurological complications to these patients and society. HIV-Associated Neurocognitive Disorder (HAND), a very concerning HIV-associated dementia, is prevalent in up to half of the HIV-infected individuals and constitutes a growing health hazard in the aging population (4, 5). The risk of HAND and its associated neuropathology is higher among intravenous drug abusers (5, 6). The major drugs contributing to HIV pathogenesis are opiates and stimulants (cocaine and methamphetamine). Cocaine is the second most commonly abused drug in the US, and cocaine abuse in HIV-infected patients is associated with the worsening of HAND (7, 8). During the early stage of infection, HIV enters the brain via trafficking of infected CD4+ cells and monocytes into the CNS (9). In the brain, HIV can infect macrophages, microglia, and astrocytes, with each of these as possible sites of persistence of latent HIV (10). Though neurons are refractory to viral infection, progressive neuronal damage has been observed in HIV-infected patients (10, 11). Direct HIV-mediated neurotoxicity can be caused by viral proteins that interact with neurons resulting in neuronal damage or death by multiple mechanisms, including disruption of calcium homeostasis, perturbation in glutamate and glycolytic pathways, inhibition of calcium and potassium channels, and others (10, 12, 13). Among these viral proteins, HIV-encoded Trans Activator of Transcription (Tat) continues to be synthesized and secreted by HIV-infected cells even in patients in which HAART therapy successfully prevents viral production (14). Tat uptake by uninfected cells results in both cytoplasmic and nuclear events promoting neuronal damage and death (15). The neurotoxicity of HIV and Tat is exacerbated by drugs of abuse, including opiates and cocaine. The combination of cocaine and Tat causes neurotoxicity that greatly exceeds that caused by cocaine or Tat alone (11, 16-19). Current HIV therapy options are focused on preventing entry of the virus into the brains of infected patients, but this therapy does not directly protect neurons and is not effective after entry has occurred. Thus, there is no approved therapy for the treatment of HAND and particularly for the combined neurological effects of HIV and drugs of abuse. Consequently, there is an urgent need to discover neuroprotective therapy that can alleviate HAND symptoms. Artificial Intelligence (AI) – based discovery systems for biomedical literature analysis and hypothesis generation help researchers to navigate through the vast quantities of literature and can rapidly reveal hidden connections in the literature network (20, 21). Machine learning approaches accelerate drug repositioning to discover new applications for existing compounds (22). We recently developed a new AI-based literature mining system, MOLIERE which is different form many other literature-based analysis systems. The key difference is in the algorithmic pipeline and the amount of processed data. The MOLIERE do not filter the data extracted from the papers, whereas most other systems work with only highly filtered semantic objects (such as MESH terms or only genes). In addition to our own ranking methods, the algorithmic pipeline relies on several recently developed scalable machine learning methods that have not been adopted by other knowledge discovery systems such as low-dimensional representation manifold learning and scalable probabilistic topic modeling. (23, 24) . In the present article, we performed MOLIERE analysis for possible links of human proteins with HAND in the biomedical literature. This analysis revealed a previously unknown connection between HAND and Dead Box RNA Helicase 3 (DDX3) (25, 26). DDX3 has not previously been associated with neurodegeneration related to HIV proteins, but was shown to be essential for translation of HIV proteins and the nuclear export of HIV RNA (27). Our results show that a known inhibitor of DDX3 called RK-33, which was originally developed and tested for anti-cancer therapy (28, 29), protects primary cortical neurons from neurotoxicity and inhibits the combined Tat plus cocaine dependent activation of microglia. The results validate DDX3 inhibition as a potential target for HAND therapy.