Inhibition of the Dead Box RNA Helicase 3 prevents HIV-1 Tat and cocaine-induced neurotoxicity by targeting microglia activation

Abstract

HIV-1 Associated Neurocognitive Disorder (HAND) is a common and clinically detrimental complication of HIV infection. Viral proteins, including Tat, released from infected cells, cause neuronal toxicity. Substance abuse in HIV-infected patients greatly influences the severity of neuronal damage. To repurpose small molecule inhibitors for anti-HAND therapy, we employed MOLIERE, an AI-based literature mining system that we developed. All human genes were analyzed and prioritized by MOLIERE to find previously unknown targets connected to HAND. From the identified high priority genes, we narrowed the list to those with known small molecule ligands developed for other applications and lacking systemic toxicity in animal models.

To validate the AI-based process, the selective small molecule inhibitor of DDX3 helicase activity, RK-33, was chosen and tested for neuroprotective activity. The compound, previously developed for cancer treatment, was tested for the prevention of combined neurotoxicity of HIV Tat and cocaine. Rodent cortical cultures were treated with 6 or 60 ng/ml of HIV Tat and 10 or 25 μM of cocaine, which caused substantial toxicity. RK-33 at doses as low as 1 μM greatly reduced the neurotoxicity of Tat and cocaine.

Transcriptome analysis showed that most Tat-activated transcripts are microglia-specific genes and that RK-33 blocks their activation. Treatment with RK-33 inhibits the Tat and cocaine-dependent increase in the number and size of microglia and the proinflammatory cytokines IL-6, TNF-α, MCP-1/CCL2, MIP-2, IL-1α and IL-1β. These findings reveal that inhibition of DDX3 may have the potential to treat not only HAND but other neurodegenerative diseases.

Graphical Abstract

Introduction:

More than 36 million people worldwide are living with HIV infection, and more than 1.2 million are in the USA based on information from the World Health Organization (Yoshimura 2017). Neurological complications associated with HIV infection have long been known (Sabatier et al. 1991). 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 (Collaboration. 2008). 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 (Gannon et al. 2011; Heaton et al. 2010). The risk of HAND and its associated neuropathology is higher among intravenous drug abusers (Beyrer et al. 2010; Gannon et al. 2011). The major drugs contributing to HIV pathogenesis are opiates and stimulants (cocaine and methamphetamine). Cocaine is one of the most commonly abused drugs in the US (NIDA 2018), and cocaine abuse in HIV-infected patients is associated with the worsening of HAND (Atluri 2016; Avants et al. 1997).

During the early stage of infection, HIV enters the brain via trafficking of infected CD4⁺ cells and monocytes into the CNS (Ivey et al. 2009). In the brain, HIV can infect macrophages and microglia, with each of these as possible sites of the persistence of latent HIV (Kaul et al. 2001). Astrocytes were also suggested as a reservoir of latent HIV; however, their importance and route of infection are still controversial (Al-Harti et al. 2018). Though neurons are refractory to viral infection, progressive neuronal damage has been observed in HIV-infected patients (Dash et al. 2015; Kaul et al. 2001). 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 (Avdoshina et al. 2013; Kaul et al. 2001; Kovalevich and Langford 2012). 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 (Mediouni et al. 2012). Tat uptake by uninfected cells results in both cytoplasmic and nuclear events promoting neuronal damage and death (King et al. 2006). 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 (Dahal et al. 2015; Dash et al. 2015; De Simone et al. 2016; Midde et al. 2013; Midde et al. 2015). Current HIV therapy options are focused on preventing the 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 (Extance 2018; Pyysalo et al. 2018). Machine learning approaches accelerate drug repositioning to discover new applications for existing compounds (Yella et al. 2018). We recently developed a new AI-based literature mining system, MOLIERE, which is different from many other literature-based analysis systems. The key difference is in the algorithmic pipeline and the amount of processed data. The MOLIERE does not filter the data extracted from the papers, whereas most other systems work with only highly filtered semantic objects (such as Medical Subject Headings (MESH) terms or only genes). In addition to our 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. (Sybrandt et al. 2017; Sybrandt et al. 2018). 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) (Ariumi 2014; Zhao et al. 2016). 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 (Stunnenberg et al. 2018). Our results show that a known inhibitor of DDX3 called RK-33, which was originally developed and tested for anti-cancer therapy (Bol et al. 2015; Tantravedi et al. 2018), 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.

Results:

Literature mining to uncover targets and small molecules to test for HAND therapy.

To determine genes with yet-unknown implicit connections to HAND, we utilized MOLIERE, a system that automatically generates biomedical hypotheses (Sybrandt et al. 2017). The system builds a multi-modal and multi-relational network of biomedical objects extracted from Medline and ULMS datasets from the National Center for Biotechnology Information (NCBI). In the current analysis, we queried the list of all human genes downloaded from Human Genome Organization (HUGO) Gene Nomenclature Committee (Braschi et al. 2019) paired with HIV associated neurocognitive disorder. The generated hypotheses were ranked based on a number of techniques described in (Sybrandt et al. 2018). The hypotheses ranking represent the level of association each gene has with HAND. The genes were categorized as “Trivial” (top 2.5% genes with exceptionally high-ranking scores typical of well-explored prior connections clearly associated with HAND), “High Potential” (next 15% of genes), “Low Potential” (next 15% of genes) and “Random” (Fig 1A). To determine the availability of small molecule inhibitors for “High Potential” category gene products, the genes were queried through a protein-small molecule interaction database, BindingDB (Gilson et al. 2016). About 180 proteins out of 4450 genes have at least one associated small molecule (Fig 1B, Supplemental Table 1). Next, this list of the genes was narrowed down by the selection of small molecule inhibitors that have been tested in animals and did not manifest any significant systemic toxicity at therapeutic concentrations. The search of the literature revealed 52 protein-small molecule pairs (Fig 1B, Supplemental Table 1). From the shortlist we selected RK-33, an inhibitor of Dead Box RNA Helicase 3 (DDX3) for experimental evaluation for the following reasons: RK-33 was shown to be a very selective inhibitor of DDX3 ATPase activity; the compound was active in vitro in nanomolar concentrations, and in micromolar concentrations in cell culture and animal models; and it was easily available commercially. Lastly, it has been extensively tested in rodents and did not show any systemic toxicity (Bol et al. 2015; Tantravedi et al. 2018). Although DDX3 had never been associated with HAND, it was shown to be important for HIV infection (Stunnenberg et al. 2018), specifically by exporting viral RNA from nucleus to cytoplasm (Yedavalli et al. 2004). We, therefore, chose RK-33 for evaluation as a potential neuroprotective agent.

Figure 1. Selection and experimental validation of RK-33, which protects neurons in primary cortical cultures from the combined neurotoxicity of Tat and cocaine.

Representation of ranking distribution of the ~27,000 HUGO genes. The ranked hypotheses representing the level of association of each gene with HAND were categorized by the following: “Trivial” (top 2.5% genes with exceptionally high ranking scores typical of well-explored prior connections clearly associated with HAND), “High Potential” (next 15% of genes), “Low Potential” (next 15% of genes) and “Random” (1A).

Representative selection of genes for experimental validation, from all the “High Potential” genes to those with known small molecule ligands that have already been tested safe for animal toxicity (1B).

Primary cortical cultures were treated with Tat (6 or 60 ng/ml) and/or cocaine (10 or 25 μM) or Tat combined with a range of RK-33 doses for 48 h prior to the addition of cocaine for another 24 h. (1C) shows cultures treated with 60 ng/ml Tat, 25 μM of cocaine, with combination of both with and without 6 μM RK-33, then fixed with 4% PFA. The dead/apoptotic cells were detected using CellEvent Caspase 3/7 Assay Kit (green, Caspase 3/7; red, Ethidium Bromide. Scale bar = 100 μm).

In (1D), Tat (60 ng/ml) and/or cocaine (25 μM) only treatments are colored in green, and the deepening blue hue represents the increasing concentration of RK-33 (from 0.25 to 12 μM).

The bar graph (1E) shows Tat (6 or 60 ng/ml) and/or cocaine (10 or 25 μM) only treatments in light blue and with 6 μM RK-33 in dark blue. The bar heights indicate mean values and error bars indicate one sample standard error from the sample mean. Each point corresponds to an image, with each image containing from 300–450 cells. The Mann-Whitney-Wilcoxon test is conducted to calculate the statistical significance, followed by Benjamini-Hochberg adjustment of p-values (*, p<0.05; **, p<0.01; ***, P<0.0001).

RK-33 protects neurons in primary cortical cultures from the combined neurotoxicity of HIV-Tat and cocaine.

To test the hypothesis that inhibition of DDX3 has neuroprotective effects in a model of HAND, we used a well-established cell culture model of rodent primary cortical neurons co-treated with Tat and cocaine in previously established concentration ranges (Aksenov et al. 2006; Bennett et al. 1993; Cunha-Oliveira et al. 2006; Nassogne et al. 1995; Nassogne et al. 1997). Meaningful in vivo measurements of Tat has been difficult (Hudson et al. 2000). However, current estimations based on Tat protein measurements in sera of HIV-positive patients have found ranges of 2–40 ng/ml (Xiao et al. 2000) and in Cerebrospinal fluid (CSF) of ~16 ng/ml (Westendorp et al. 1995). It has been suggested that the brain NIH-Tat concentration approximates that found in CSF while the local concentration near neurons can reach much higher values (Hayashi et al. 2006). The brain cocaine concentration determined postmortem in human tissues is in a range of 3 – 100 μM (Spiehler and Reed 1985), which corroborates with the concentration of cocaine in the brain of self-administered rats 10–25 μM (Zimmer et al. 2011). Therefore, we tested the effects of Tat protein at the lower and higher ends of the concentration range, 6 and 60 ng/ml, and cocaine in a range detected in brains of self-administered rats, 10 and 25 μM. The addition of Tat at a low (6 ng/ml) or high (60 ng/ml) concentration, or cocaine (10 μM or 25 μM) individually did not significantly induce the death of primary neurons during 72 h of treatment. However, pre-treatment with Tat for 48 h followed by the addition of cocaine for another 24 h drastically increased neuronal death, as measured by activated Caspase 3/7 signal and nuclear localization of ethidium bromide homodimer (Fig 1C, D, E). Treatment with 60 ng/ml of heat inactivated Tat alone or with cocaine (25 μM) did not affect neuronal survival (data not shown). Strikingly, treatment with the DDX3 inhibitor RK-33 at 6 μM resulted in a 50%−80% reduction of neuronal death caused by Tat and cocaine co-treatment (Fig 1C, E). The initial 6 μM concentration of RK-33 selected was similar to that used previously (Bol et al. 2015). RK-33 protected rat neurons from Tat/cocaine toxicity in a dose dependent manner starting as low as 1 μM (Fig 1D), which was comparable with RK-33 bioactivity observed in other cell-based assays (Bol et al. 2015; Ku et al. 2018). Nearly identical qualitative results were observed with a mouse primary neuron cortical culture regarding Tat/cocaine damage and RK-33 mediated protection from damage (Supplemental Figure S1). A notable quantitative difference was that rat primary neurons appear to be much more sensitive to cocaine relative to the mouse neurons because a 10-fold lower cocaine dose was used to promote neuronal apoptosis in combination with Tat. Hence, the results in both rat and mouse cortical cultures show that DDX3 inhibition is neuroprotective against the combined insult of Tat and cocaine.

RNA-seq transcriptomic profiling of Tat, cocaine, and RK-33 effects on cortical cultures.

To determine the effects of HIV-Tat and cocaine on the transcriptome of cortical cultures, we performed RNA sequencing (RNA-seq) of the cultures treated with Tat, cocaine, or both (Supplemental Table S4). Treatment with HIV-Tat dramatically affects the transcriptome (Supplemental Figure S2A), with over 500 genes up or down regulated by Tat (False Discovery Rate (FDR) corrected p<0.01). In stark contrast, treatment with cocaine alone did not significantly affect gene expression, with zero genes marked as significant with an FDR corrected of p<0.01 (Supplemental Figure S2B). When Tat and cocaine were combined, the results largely mirrored Tat alone, and only a few genes were differentially expressed (Supplemental Figure S2C). The results indicate that cocaine, neither alone nor in combination with Tat, significantly affects the transcriptome at the doses used here, and in the following RNA-seq experiments we focused on the Tat-treated cultures.

To explore the mechanism of RK-33 neuroprotection seen above, we performed RNA-seq of the primary cortical cultures treated with 60 ng/ml Tat in the presence or absence of 6 μM RK-33. We found differential expression of 547 genes in Tat-treated cultures compared to control (Adjusted FDR, P<0.05). Hierarchical clustering revealed that Tat-dependent upregulation of 211 genes was inhibited by RK-33 (adjusted FDR P< 0.05 for 83 genes) (Fig 2A–B, Supplemental Table 2). Strikingly, there was no statistically measurable effect (FDR of 0.05) on any gene expression changes caused by RK-33 treatment alone (Fig 2B, Supplemental Table S2). Pathway enrichment analysis of Tat-regulated genes shows that proinflammatory pathways, such as “Complement Cascade,” “Neutrophil Dysregulation” and “Cytokine Signaling” are significantly enriched, as noted by the comparisons between Tat vs control (Fig 2B, Supplemental Figure S3). In the major brain cell types (neurons, astrocytes, oligodendrocytes, epithelial and microglial cells), these pathways are known to be associated with activated microglia (Coulthard et al. 2018; Cunningham 2013; Hong et al. 2016). Notably, the expression of these same genes was suppressed by RK-33 treatment (Fig 2B, Supplemental Figure S3). Among this subset of genes upregulated by Tat alone and sensitive to RK-33 the following are worth noting: complement components C1qa, C1qc, C1qb, bona fide markers of microglial cells such as ionized calcium binding adaptor molecule 1 (Iba1 or AIf1), integrin Cd11a/b (Itgam), Ptprc (CD45), Colony-stimulating factor-1 (CSF-1), and granulocyte colony-stimulating factor (g-CSF) receptors, Csf1r and Csf3r, which are necessary for microglial survival and proliferation (Chitu et al. 2016). Further, gene set enrichment analysis (GSEA) (Subramanian et al. 2005) with custom databases of genes enriched in brain cell subtypes as defined by RNA-seq Transcriptome and splicing databases of the cerebral cortex (Zhang et al. 2014) shows significant enrichment of microglia-associated genes in Tat-regulated and RK-33 sensitive, Tat – regulated genes (Fig 2D, Supplemental Figure S4).

Figure 2. RNA-seq transcriptome profiling of cortical cultures treated with Tat and RK-33.

Cortical cultures were treated in triplicates with Tat (60 ng/ml), RK-33 (6 μM), combination of Tat (60 ng/ml) and RK-33 (6 μM) and then were compared with untreated cultures. RNA-seq transcriptomics profiling was performed as described (see Materials and Methods for details).

Hierarchical clustering of log transformed counts per million for genes differentially regulated by Tat treatment (p<0.0005) (2A).

Volcano plot comparing gene expression of control vs Tat-treated samples (upper panel), Tat plus RK-33 vs Tat-treated samples (middle panel), and control vs RK-33 treated samples (lower panel). Color indicates differentially expressing genes (FDR corrected p<0.05, absolute fold change >1.5) belonging to selected Enrichr GO categories/pathways (2B).

GSEA enrichment plot of Microglia-specific genes in Tat vs control dataset (2C).

Heatmap for expressions of selected genes representing the microglia cells (2D).

Taken together, the results of the RNA-seq analysis demonstrates that, in agreement with a previous publication (Chivero et al. 2017; Mohseni Ahooyi et al. 2018), Tat and Tat in combination with cocaine triggers the expression of genes associated with activated microglia. Moreover, this activation is inhibited by RK-33. These same genes found by RNA-seq to be regulated by HIV-Tat treatment are also found in the high-ranked genes subset of HAND-associated genes from the MOLIERE analysis (p<0.0025), including Csf1r.

The RNA-seq results were supported by analysis of the expression of Iba1 protein by Western blotting (Supplemental Figure S6). In other words, Iba1 protein is upregulated by Tat, and this upregulation is inhibited by treatment with RK-33. The level of DDX3 protein (Supplemental Figure S5) and mRNA (Supplemental Tables S2, S3) was not affected by any of the treatments, suggesting that Tat treatment does not induce DDX3 expression, and the consequences of inhibiting DDX3 with RK-33 specifically affect the enzymatic activity of DDX3.

RK-33 inhibition of the activation of microglia by Tat or combined Tat and cocaine treatment.

The hallmarks of microglial activation are the rapid expansion of microglial cells and characteristic changes in cellular morphology (Cunningham 2013; Davis et al. 2017; Gomez-Nicola and Perry 2015). Microglial cells were analyzed by immunofluorescent staining of rat cortical cultures using antibodies against two established markers whose expression changes were seen in the RNA-seq data, namely Iba1 and CD11b/c (Galatro et al. 2017; Ito et al. 1998). Treatment with RK-33 alone did not affect the number of Iba1 positive cells. Treatment with either Tat (6 ng/ml) or cocaine (10 μM or 25 μM) increased slightly, but not significantly, the number of Iba1 positive cells, while the combined Tat and cocaine treatment caused a dramatic elevation of Iba1 positive cells (Fig 4A). The number of Iba1 positive cells in Tat and cocaine co-treated cultures also treated with RK-33 were significantly decreased from treated (Fig 3A, B, Fig 4A, B).

Figure 4. RK-33 treatment decreases the size of microglial cells and attenuates the secretion of proinflammatory cytokines induced by HIV-Tat and cocaine.

In (4A), cortical cultures were treated with Tat (6 ng/ml) alone or combined with 25 μM cocaine, or with Tat combined with RK-33 (6 μM) for 48 h prior to addition of cocaine for another 24 h. Cultures were then fixed with 4% PFA, and microglial cells were detected using anti-Iba1, anti-CD11b/c, and also anti-MAP2 antibodies as described in Materials and Methods (blue, DAPI; green, Iba1; red, MAP2. Scale bar = 50 μm).

The box-and-whisker plot graph (4B) shows Tat (6 and 60 ng/ml) and/or 25 μM cocaine only treatments in light blue and RK-33 in dark blue. The average size of Iba1-positive microglial cells was quantified using ImageJ, where each point corresponds to an image field, with each image covering a range of 300–450 cells. The boxes cover 50% of data in each condition, and the lines within the boxes indicate the median values. The Mann-Whitney- Wilcoxon test was conducted to calculate the statistical significance, followed by Benjamini-Hochberg adjustment of p-values (****, p<0.0001).

(4C) Cortical cultures were treated as above with Tat (6 ng/ml) and/or cocaine (25 μM) alone or Tat combined with RK-33 (6 μM). Conditioned media were collected from each sample. The cytokine levels were determined by 27-plex chemokine/cytokine array. (4C) shows cytokines linked to neuroinflammation with the greatest response to Tat and combined Tat+ cocaine treatment, and subsequent RK-33 treatment. Bars represent chemokine/cytokine concentrations in the medium (pg/ml). One-way ANOVA was followed with Tukey’s post hoc comparisons (**, p<0.01; ***, p<0.001)

Figure 3. RK-33 treatment suppresses the activation of microglia induced by the combination of Tat and cocaine on cortical cells.

Cortical cultures were treated with Tat alone (6 or 60 ng/ml) or Tat combined with RK-33 (6 μM) for 48 h prior to addition of cocaine (10 or 25 μM) for another 24 h. In (3A) and (3C), cultures were treated with 6 ng/ml Tat, with combination of Tat and 25 μM of cocaine, and combination of both with RK-33 treatment. Cultures were then fixed with 4% PFA, and microglial cells were detected using anti-Iba1 (3A) and anti-CD11b/c (3C) antibodies as described in Materials and Methods. Percent of Iba1-positive and CD11b/c-positive cells was estimated using several 2×2 or 2×3 tiled images and ImageJ software as described in Materials and Methods section. The number of microglia cells increased when cultured cells were subjected to Tat alone and when Tat was combined with cocaine, producing a dramatic increase of activated microglia in the cortical cell cultures, which was suppressed by RK-33 (3A: blue, DAPI; green, Iba1. Scale bar = 250 μm. 3C: blue, DAPI; purple, CD11b/c. Scale bar = 100 μm).

The box-and-whisker plot shows Tat and/or cocaine only treatments in light blue and the addition of RK-33 in dark blue (3B, 3D). In (3B), cultures were treated with 6 ng/ml Tat and 10 or 25 μM of cocaine, and percent of Iba1-positive cells was estimated. In (3D), cultures were treated with 6 or 60 ng/ml Tat and 25 μM of cocaine, and percent of CD11b/c –positive cells was estimated. The boxes cover 50% of data in each condition, and the lines within the boxes indicate the median values. The Mann-Whitney-Wilcoxon test was conducted to calculate the statistical significance, followed by Benjamini-Hochberg adjustment of p-values (*, p<0.05; **, p<0.01; ***, p<0.001).

These results were confirmed with CD11b/c, which is expressed in resting microglia and is greatly elevated upon activation (Akiyama and McGeer 1990; Galatro et al. 2017). The results were similar to the analysis of Iba1. The combined treatment of Tat and cocaine significantly elevated the number of CD11b/c positive cells (Fig. 4A), and RK-33 treatment reduced the number of CD11b/c-positive cells that were elevated by Tat/cocaine back to basal untreated control levels (Fig 3C, D, compare light blue (minus RK-33) to dark blue (+RK-33)).

Pathological stimuli are known to trigger morphological remodeling of microglia. Ramified, quiescent microglial cells transition to an intermediate form with less arborization and large soma, followed by amoeboid morphology. The size of microglial cells is greatly increased in the transition (Davis et al. 2017; Ekdahl 2012; Walker et al. 2013). Here, the combined Tat and cocaine treatment led to amoeboid morphology of microglial cells detected by immunofluorescence of both CD11b/c and Iba1 (Fig 4A, Supplemental Figure S6) and increased cell body area (Fig 4A, B). Treatment with RK-33 diminished the morphological and body changes induced by Tat/cocaine treatment (Fig 4A, B, compare light blue (minus RK-33) to dark blue (+RK-33)).

The secretion of proinflammatory cytokines induced by HIV Tat and cocaine is inhibited by RK-33.

The pathological effects of microglial activation are directly associated with the secretion of proinflammatory cytokines. Thus, to measure the level of cytokines we collected media of the cortical cultures treated with 6 ng/ml Tat, followed by addition of 25 μM cocaine with or without RK-33. The level of 27 cytokines was determined with the Luminex Multiplex assay. Fig. 4C shows cytokines linked to neuroinflammation with the greatest response to Tat and combined Tat+cocaine treatment, and subsequent RK-33 treatment. Tat and Tat+cocaine treatment elevated the concentration of secreted IL-6, TNF-alpha, MCP1-CCL2, MIP-2, IL-1-α and IL-1-β, while RK-33 treatment abolished the elevation. Supplemental Figure S7 shows data for all 27 cytokines measured. Interestingly, yet in agreement with the RNA-seq data, Tat and cocaine co-treatment did not increase most cytokines and only slightly increased the level of a few cytokines relative to Tat-treated culture, which suggests that the detrimental effects of cocaine in this model involve an additional mechanism that occurs post-translationally.

Discussion

We recently reported the development of MOLIERE, an AI-based literature mining system to determine target genes and associated small molecules that are potentially useful for testing novel gene-disease connections. Here, we applied MOLIERE to uncover novel targets for the treatment of HIV associated neurocognitive disorder. This usage of MOLIERE uncovered DDX3 and its specific inhibitor, RK-33, and we have experimentally verified this novel target as worthy of further investigation. To our knowledge, neither DDX3 nor RK-33 were previously linked to HAND or other neurodegenerative diseases. RK-33 was developed and tested as an antitumor drug, and importantly in this context, without any indication of systemic toxicity in mice with injections up to 20 mg/kg for seven weeks (Bol et al. 2015).

The evaluation of RK-33 in the well-established culture model of HAND augmented with a drug of abuse showed dose-dependent inhibition of neuronal apoptosis induced by combined Tat and cocaine treatment with RK-33 concentrations as low as 1 μM. Further, RNA-seq analysis and measurement of secreted pro-inflammatory cytokines demonstrated that microglial activation induced by Tat and cocaine was suppressed by RK-33, thus providing a plausible mechanism for the neuroprotective effects downstream of DDX3 inhibition. The dual role of microglial cells in HAND pathogenesis as a brain reservoir for viral replication and source of secreted viral proteins, and as a modulator of the inflammatory response is well established (Cai et al. 2016; Smail and Brew 2018). Our analysis confirmed previous results showing an activation of microglia by Tat and a synergistic effect of cocaine (Aksenov et al. 2006; Cai et al. 2016; Smith et al. 2005), as measured by elevation in the number of Iba1 and CD11b/c positive cells in cortical cultures and the induction of morphological changes and enlargement of microglial cells. The expression of both Iba-1 and CD11b were shown to be elevated with microglial activation during neurodegenerative inflammation (Roy et al. 2006; Sasaki et al. 2001). In turn, activated microglia are characterized by an elevation of CSF-1 (M-CSF) and CSF1R, the drivers of microglia proliferation in normal brain and neurodegenerative diseases (Gomez-Nicola et al. 2013; Perry and Holmes 2014). The RNA-seq results showed that Tat-dependent upregulation of CSF1 and CSF1R was inhibited by RK-33. Thus, the elevation of Iba 1/CD11 b/c positive cells can be attributed to elevation of the microglia-markers expression and Tat-dependent proliferation of microglia cells. Further studies are necessary to determine the impact of the parallel processes of activation and proliferation.

Collectively, these results are the first to connect DDX3 to the activation of microglia. Moreover, the DDX3 inhibitor RK-33 inhibits Tat/cocaine-dependent microglial activation, which both implicates DDX3 in this pathogenesis and also suggests it is a viable target for the treatment of HAND and potentially other neurocognitive diseases in which activated microglia play a role. The mechanism of DDX3-dependent regulation of microglial activation remains to be determined. However, recent results regarding the function of DDX3 in the regulation of a macrophage inflammatory response may shed light on DDX3 activity in neuroinflammation. HIV proteins activate the secretion of chemokines and cytokines by microglial cells through NF-κB, p38 and TGF-β pathways (Butovsky et al. 2014; Chen et al. 2017; Flora et al. 2006; Frakes et al. 2014). The small GTPase Rac1 was shown to be a regulator activating morphological changes in microglia cells (Neubrand et al. 2014; Persson et al. 2014). These pathways are well known to control the inflammatory response, cytokine secretion, and migration of macrophages. Importantly, it was recently shown that DDX3 directly regulates the translation of p38 MAPK, Rac1, STAT1 (TGF-β) and TAK1, which play essential roles in NF-κB regulation (Ku et al. 2018). The direct role of DDX3 in regulating pro-inflammatory responses in the pathogenesis of bowel disease and Listeria infection were also recently observed (Szappanos et al. 2018; Tantravedi et al. 2017). Inhibition of DDX3 activity stalled the translation of target proteins (Guenther et al. 2018; Ku et al. 2018), resulting in a decrease of cytokine secretion and inhibition of macrophage migration and phagocytosis (Ku et al. 2018). It, therefore, appears plausible that DDX3-dependent translational control may be the mechanism that regulates microglial activation in neuroinflammatory pathways. Experiments are ongoing to formally test this.

As mentioned previously, DDX3 has been proposed as a target for the development of anti-viral and anti-HIV therapy (de Breyne and Ohlmann 2018; Fullam and Schroder 2013) (Brai et al. 2016; Floor et al. 2016; Kwong et al. 2005; Shadrick et al. 2013). This makes DDX3 a unique target, in which inhibition may affect both HIV-related neurotoxicity and the production of viral proteins by glial cells. However, the contribution of DDX3 to innate immunity has to be fully evaluated prior to clinical advancement of DDX3 inhibitors for HAND therapy (Gringhuis et al. 2017; Stunnenberg et al. 2018). Nonetheless, DDX3-specific inhibition as a target and RK-33 as a prototype molecule for the development of HAND therapy has been validated and should be investigated further. DDX3X mutations had been recently connected of female intellectual disability (Wang et al. 2018), the developmental syndrome linked to DDX3X effect on migration and differentiation of neuronal progenitors (Lennox et al. 2018) and neurite development (Chen et al. 2016). The later may cause potential negative effects of DDX3-specific therapy on brain development and needs to be carefully evaluated.

An interesting aspect of our results is the transcriptome regulation by combined Tat and cocaine treatment. Specifically, despite the well-known dramatic loss of neuronal survival (Buch et al. 2012; Cai et al. 2016), the addition of cocaine does not significantly alter the transcriptome composition and secretion of cytokines induced by Tat alone. Similar results of upregulation of proinflammatory pathways by Tat and Tat plus cocaine treatments in contrast to cocaine alone had been recently published (Mohseni Ahooyi et al. 2018). It is suggested that the Tat treatment primes the neurons to make them sensitive to the effects of cocaine. The exact mechanism of the priming sensitization is unclear. However, it has previously been shown that cocaine causes mitochondrial dysfunction (Chandra et al. 2017; Cunha-Oliveira et al. 2006; de Oliveira and Jardim 2016). The induction of mitochondrial dysfunction is also triggered by HIV-Tat treatment (Stevens et al. 2014) and by microglia-secreted proinflammatory cytokines such as IL-1β, TNF-α (Doll et al. 2015; Ye et al. 2013). Moreover, the synergistic effects of the interaction of Tat and cocaine (De Simone et al. 2016), or morphine (Fitting et al. 2014) on mitochondrial function has been shown. In conclusion, given the importance of microglial activation in the pathology of other neurodegenerative diseases, DDX3 targeting could be applicable for the treatment of other neurodegenerative diseases.

Materials and Methods

MOLIERE Analysis

The implementation and documentation are available at https://github.com/JSybrandt/MOLIERE. The repository also contains a list of all software dependencies to packages to compute approximate nearest neighbor graphs, low-dimensional embeddings, probabilistic topic modeling, phrase mining, and graph algorithms. The repository is organized in two major sub-projects, namely, build_network and run_query, each contains its own documentation. Preinstallation dependencies include gcc 5.4 or greater, Python 3, Java 1.8, Scons, Google Test, and Mpich 3.1.4. It is recommended to use parallel machines, as many components of the project are parallelized being too slow if executed in sequential mode. The input to the phase of building the knowledge network requires downloading full MEDLINE and UMLS. Building the network is also possible with partial MEDLINE if one wants to restrict the information domain in order to increase speed. Most algorithmic components require parameters that are provided with the code. When the knowledge network is constructed, the second phase consists of running queries using run query subproject. Running all queries, each of type gene-HAND, can be done in parallel, as all of them are independent. Each query will return the hypotheses in the form of topic model, i.e., a distribution of most representative keywords per learned topic, as well as a ranking score. In addition, the result of every single query can be visualized for further analysis using the visualization sub-project that can be found in the same repository. The visualization connects all learned topics in a network, where nodes correspond to topics, and edges represent mutual content connections. Clicking each node will bring up the most relevant to the corresponding paper topics as well as the most representative topical keywords.

Here we describe the datasets used in knowledge network construction and querying Medical Literature Analysis and Retrieval System Online (MEDLINE), the National Library of Medicine (NLM) database of biomedical abstracts, releases public yearly baselines. We used the 2017 baseline, published early that year, which was the most up-to-date at the time. This dataset consists of 26,759,399 documents; however, we found that certain short documents hinder hypothesis generation results. Therefore, we removed any document that is fewer than 20 words that also does not contain at least two “rare” words found in the bottom 85% most frequent words. The Unified Medical Language System (UMLS) consists of known medical entities, as well as their synonyms and relationships. This NLM dataset releases every six months, and we used the “2017AB” release, also the most recent available to us at the time of our experiments. This release consists of 3,639,525 entities. SemMedDB, another dataset produced by the NLM, which contains automatically extracted predicates from MEDLINE documents and keeps a six-month release schedule. We downloaded the December 31^st, 2017 release consisting of 15,836,301 unique subject-verb-object statements, as well as corresponding UMLS types and MEDLINE identifiers. Lastly, the HUGO gene dataset collects human gene symbols. Unlike the NLM sources, HUGO follows rolling updates and does not keep numbered versions. We leveraged the “complete HGNC dataset” from January 19^th, 2018, which contained 42,139 gene symbols. From this initial set, we filtered out 1,248 symbols that could not be found in either our MEDLINE subset or our UMLS release, as our system has no known information on these gene symbols. BindingDB (Gilson et al. 2016) publishes protein-ligand structures associated with gene symbols. This dataset additionally supplies rolling releases, which we accessed on January 8^th, 2018. This dataset contains 1,507,528 binding measurements, within which we identified 1,202 human gene symbols from HUGO (Braschi et al. 2019) under the field “Entry Name of Target Chain.” For each gene, we recorded both the total number of dataset occurrences as well as the distinct names found under the field “Target Name Assigned by Curator or DataSource.”

RNA sequencing and analysis

RNA and library preparation, post-processing of the raw data and data analysis were performed by the USC CTT COBRE Functional genomics Core. RNAs were extracted with Qiagen RNeasy Mini kit as per manufacturer recommendations (Qiagen, Valencia, CA, USA) and RNA quality was evaluated on RNA-1000 chip using Bioanalyzer (Agilent, Santa Clara, CA, USA). RNA libraries were prepared using an established protocol with NEBNExt Ultra II Directional Library Prep Kit (NEB, Lynn, MA). Each library was made with one of the TruSeq barcode index sequences, and the Illumina sequencing was performed by GENEWIZ, Inc. (South Plainfield, NJ) with Illumina HiSeq4000 (150bp, pair-ended). Sequences were aligned to the Mus Musculus genome GRCm38.p5 (GCA_000001635.7, ensemble release-88) using STAR v2.4 (Dobin et al. 2013). Samtools (v1.2) was used to convert aligned sam files to bam files, and reads were counted using the featureCounts function of the Subreads package (Liao et al. 2014) with Gencode.vM19.basic.annotation.gtf annotation file. Only reads that were mapped uniquely to the genome were used for gene expression analysis. Differential expression analysis was performed in R using the edgeR package (Robinson et al. 2010). The average read depth for the samples was around 15 million reads, and only genes with at least one count per million average depth were considered for differential expression analysis. Raw counts were normalized using the Trimmed Mean of M-values (TMM) method. The normalized read counts were fitted to a quasi-likelihood negative binomial generalized log-linear model using the function glmQLFit, and genewise statistical tests for significant differential expression were conducted with empirical Bayes quasi-likelihood F-tests using the function Genewise Negative Binomial Generalized Linear Models With Quasi-Likelihood Tests (glmQLFTest). All gene names mentioned in the main body of manuscript and Supplemental Tables are provided according to Human Genome Organization Gene Nomenclature Committee, International Committee on Standardized Genetic Nomenclature for Mice; and The Rat Genome Nomenclature Committee (RGNC). RNA-Seq data from this study are available in the GEO database (accession GSE137988).

Pathway enrichment and GSEA analysis.

Pathway enrichment of HIV-Tat and RK-33 regulated genes were analyzed using Enrichr (Kuleshov et al. 2016) R package (https://CRAN.R-project.org/package=enrichR) with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and EnrichrPathway databases. The GSEA enrichment analysis was performed with Broad Institute software (Subramanian et al. 2005) (http://software.broadinstitute.org/gsea/index.jsp) with custom MSigDB, represented genes enriched in microglia, neurons, and astrocytes. The MSiDB were built as described in GSEA documentation (https://software.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html). The gene lists were prepared from Zhang dataset of transcriptome profiling of glia neurons and vascular cells of the cerebral cortex (Zhang et al. 2014), https://web.stanford.edu/group/barres_lab/brain_rnaseq.html. The genes which expressed 10 or more fold higher in one cell type relative to any other had been assigned as cell-type specific (Supplemental Table S3). Overrepresentation of Tat-activated genes in MOLIERE-selected subset was identified using hypergeometric distribution test.

Preparation and cultivation of primary cortical cultures.

Primary cortical cell cultures were prepared from 18-day-old Sprague-Dawley (Envigo Laboratories, Indianapolis, IN) rat fetuses of both sexes or 18-day-old C57BL/6 mouse fetuses of both sexes as previously described (Aksenova et al. 2009; Bertrand et al. 2011). Procedures were carried out in accordance with the University of South Carolina Institutional Animal Care and Use Committee. Briefly, cortical regions were dissected and incubated for 10 min in a solution of 0.05% Trypsin/EDTA in Hank’s balanced salt solution (HBSS) (Thermo Fisher Scientific). The tissue was then exposed for 5 min to soybean trypsin inhibitor (1 mg/ml in HBSS) and rinsed three times in HBSS. Cells were dissociated by trituration and distributed to poly-L-lysine coated 12-well plates with inserted round glass coverslips. Alternatively, cortical cell cultures were grown in 6-well plastic plates (VWR International, Radnor, PA).

At the time of plating, plates contained DMEM/F12 (Thermo Fisher Scientific) supplemented with 100 mL/L fetal bovine serum (HyClone, Thermo Fisher Scientific). After a 24-hr period, DMEM/F12 was replaced with an equal amount of serum-free Neurobasal medium supplemented with 2% v/v B-27, two mM GlutaMAX supplement and 0.5% w/v D-glucose (all reagents from Thermo Fisher Scientific). Half of the Neurobasal medium was replaced with freshly prepared medium of the same composition once a week. Cultures were used for experiments at the age of 10–12 days in vitro (DIV).

Experimental treatments

Primary cortical cultures were treated with recombinant Tat 1–86 (Diatheva, Italy), at concentrations ranging from 5 ng/ml up to 60 ng/ml as described in Figures and Figure legends. The concentrations of Tat had been selected to represent the estimated Tat concentration in the brain of HIV-infected patients (Xiao et al. 2000). Cocaine-HCl was obtained from Sigma Chemicals and was dissolved in sterile water immediately before the addition to cell cultures. Cocaine concentrations ranged from 10 μM up to 1000 μM as described in Figures. The concentration of cocaine is in a range of the brain cocaine concentration estimated for recreational users based on animal studies (Zimmer et al. 2011) and postmortem examination of the brain tissues in fatal cases of cocaine abuse (Spiehler and Reed 1985). RK-33, a small molecule inhibitor of DDX3, was obtained from Selleck Chemicals, (Catalog No.S8246, Selleck Chemicals, Houston, Texas). A stock solution of RK-33 was prepared in DMSO (5 mM) and was diluted to final concentrations from 0.25 μM up to 12 μM.

Apoptotic/Dead cells detection

Dead and apoptotic cells were detected using CellEvent Caspase-3/7 Kit (#C10423, Thermo Fisher Scientific) according to the manufacturer’s recommendations. Briefly, after experimental treatment, Caspase3/7 Green Detection Reagent was added directly to cells, and the plate was incubated 30 min at 37°C. The final concentration of the reagent was 500 nM. During the final 5 min of incubation, SYTOX AADvanced dead cell solution was added. The final concentration of the stain was 1 μM. Cells were rinsed with PBS, and images of live cells were taken immediately. Alternatively, cells were fixed with 4% paraformaldehyde, imaged, and used for further experiments.

Immunocytochemistry

For ICC analysis cells were plated on glass coverslips and placed inside 12-well plates. Following experimental treatment, primary neuronal cultures were fixed with 4% paraformaldehyde and permeabilized with 0.1% Triton X-100. Fixed cultures were blocked with 10% fetal bovine serum for 2 hours and then co-labeled overnight with different primary antibodies: chicken polyclonal anti-MAP2 antibodies (1:2,500) (# ab92434, Abcam, Cambridge MA), rabbit monoclonal anti-Iba1 (1:200) (# ab178847 Abcam, Cambridge MA), human recombinant anti-CD11b/c (1:50) (#130–120-288, Miltenyi Biotec, Germany). All utilized antibodies recognize rat proteins according to the manufacturers. Secondary antibodies, goat anti-chicken IgG conjugated with AlexaFluor 594, and goat anti-mouse IgG conjugated with AlexaFluor 488 (1:500; Invitrogen Life Technologies, Grand Island NY), were used for visualization. Anti-CD11b/c antibodies were originally labeled with allophycocyanine. To identify cell nuclei, DAPI was added with the final PBS wash, and coverslips were mounted on glass slides using VECTASHIELD Vibrance mounting medium (Vector Laboratories, Burlingame, CA).

Western Blot

Whole cell lysates were collected using RIPA buffer (50 mM Tris-HCl, pH 7.4; 150 mM NaCl; 5 mM EDTA; 0.5 mM EGTA; 1% Igepal CA-630; 0.5% sodium deoxycholate; and 0.1% SDS) and protein concentration was determined through Bio-Rad DC protein assay (Bio-Rad). 10 μg of proteins were resolved on precast 4–12% SDS-polyacrylamide gels (M00654, GeneScript) and transferred onto PVDF membranes (#88518, Thermo Fisher Scientific). The membranes were then stained with Ponceau Red, blocked for 1h in TBS-T buffer containing 1% Tween-20 with 5% non-fat dry milk, and incubated with primary antibodies overnight at 4°C. The primary antibodies were directed against Iba-1 (1:1000) (ab178847, Abcam, Cambridge, MA), DDX3 (1:3000) (sc-365768, Santa Cruz Biotechnology, Dallas, Texas) and GAPDH (1:10,000) (Cell Signaling Technology, Danvers, MA). Secondary antibodies were HRP-conjugated anti-mouse IgG (1:8000; 7076S), or HRP-conjugated anti-rabbit IgG (1:8000; 7074S), both from Cell Signaling. Membranes were processed with SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Scientific).

Image processing and analysis

Images were taken on a Carl Zeiss LSM 700 laser scanning confocal microscope (Carl Zeiss Meditec, Dublin, CA) equipped with 20x (Plan APO 0.8 air) or 63x (Plan APO 1.4 oil DIC) objectives. Images were captured using 1.0 scanning zoom with 312-nm (20x) or 142-nm (63x) X-Y pixel size. Fluorescence and differential interference contrast (DIC) imaging was done using single-frame or tile (2×2, 2×3, 3×3, or 3×4) modes.

ImageJ software (National Institutes of Health, USA) was used for manual or automatic analysis of microscopy images acquired using a Zeiss 700 confocal microscope. Several fields of vision were taken from at least three different wells (datapoint represents either single field image or tiled images). The total number of cells and percentage of Iba1 or CD11b/c - positive cells were estimated using segmentation of DNA channel (DAPI) followed by “Analyze Particles” ImageJ command. Size of microglia cells was estimated individually using “Freehand selections” ImageJ tool. Data were aggregated, analyzed and visualized using R ggplot2 tools.

Background correction of widefield images was performed by background (Gaussian blur) division procedure (32-bit mode) followed by image histogram adjustment for 16-bit dynamic range.

Cytokine/Chemokine Array

Cortical cultures medium was collected, frozen, and sent to Eve Technologies Corporation (Calgary, Canada) for the LUMINEX based analysis of cytokines by Featured-Rat Cytokine Array/ Chemokine Array 27-plex (RD27). The 27-plex array analyzed Eotaxin, EGF, Fractalkine, IFN-gamma,IL-1alpha, IL-1beta, IL-2, IL-4, IL-5, IL-6, IL-10, IL-12(p70), IL-13, IL-17A, IL-18, IP-10, GRO/KC, TNF-alpha, G-CSF, GM-CSF, MCP-1, Leptin, LIX, MIP-1alpha, MIP-2, RANTES, VEGF.

Data analysis and statistics:

Graphs were generated using Inkscape version 0.92 and the following R packages: cowplot version 0.9.4, data.table version 1.12.0, ggplot2 version 3.1.0, ggsignif version0.5.0. Statistical analyses were carried out using R version 3.5.3 / RStudio version 1.1.414. Due to the deviation from the normal distribution, non-parametric analyses were conducted. Statistical differences between groups were assessed using Mann-Whitney-Wilcoxon Test with Benjamini-Hochberg adjustment as indicated in the figure legends. Parametric analysis for data with a normal distribution was conducted using one-way ANOVA followed by Turkey’s post hoc test. Comparisons between relevant treatment groups are shown in each quantitative plots. All data are represented as mean ± SEM, with p<0.05 considered significant.