Connectivity mapping uncovers small molecules that modulate neurodegeneration in Huntington's disease models

An AI Approach to Identifying Novel Therapeutics for Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that has a significant impact on quality of life and work capacity. Treatment of RA aims to control inflammation and alleviate pain; however, achieving remission with minimal toxicity is frequently not possible with the current suite of drugs. This review aims to summarise current treatment practices and highlight the urgent need for alternative pharmacogenomic approaches for novel drug discovery. These approaches can elucidate new relationships between drugs, genes, and diseases to identify additional effective and safe therapeutic options. This review discusses how computational approaches such as connectivity mapping offer the ability to repurpose FDA-approved drugs beyond their original treatment indication. This review also explores the concept of drug sensitisation to predict co-prescribed drugs with synergistic effects that produce enhanced anti-disease efficacy by involving multiple disease pathways. Challenges of this computational approach are discussed, including the availability of suitable high-quality datasets for comprehensive analysis and other data curation issues. The potential benefits include accelerated identification of novel drug combinations and the ability to trial and implement established treatments in a new index disease. This review underlines the huge opportunity to incorporate disease-related data and drug-related data to develop methods and algorithms that have strong potential to determine novel and effective treatment regimens.

A Novel Huntington's Disease Assessment Platform to Support Future Drug Discovery and Development

Huntington's disease (HD) is a lethal neurodegenerative disorder without efficient therapeutic options. The inefficient translation from preclinical and clinical research into clinical use is mainly attributed to the lack of (i) understanding of disease initiation, progression, and involved molecular mechanisms; (ii) knowledge of the possible HD target space and general data awareness; (iii) detailed characterizations of available disease models; (iv) better suitable models; and (v) reliable and sensitive biomarkers. To generate robust HD-like symptoms in a mouse model, the neomycin resistance cassette was excised from zQ175 mice, generating a new line: zQ175^Δneo. We entirely describe the dynamics of behavioral, neuropathological, and immunohistological changes from 15-57 weeks of age. Specifically, zQ175^Δneo mice showed early astrogliosis from 15 weeks; growth retardation, body weight loss, and anxiety-like behaviors from 29 weeks; motor deficits and reduced muscular strength from 36 weeks; and finally slight microgliosis at 57 weeks of age. Additionally, we collected the entire bioactivity network of small-molecule HD modulators in a multitarget dataset (HD_MDS). Hereby, we uncovered 358 unique compounds addressing over 80 different pharmacological targets and pathways. Our data will support future drug discovery approaches and may serve as useful assessment platform for drug discovery and development against HD.

Animal Models of Neurodegenerative Disease: Recent Advances in Fly Highlight Innovative Approaches to Drug Discovery

Neurodegenerative diseases represent a formidable challenge to global health. As advances in other areas of medicine grant healthy living into later decades of life, aging diseases such as Alzheimer's disease (AD) and other neurodegenerative disorders can diminish the quality of these additional years, owed largely to the lack of efficacious treatments and the absence of durable cures. Alzheimer's disease prevalence is predicted to more than double in the next 30 years, affecting nearly 15 million Americans, with AD-associated costs exceeding $1 billion by 2050. Delaying onset of AD and other neurodegenerative diseases is critical to improving the quality of life for patients and reducing the burden of disease on caregivers and healthcare systems. Significant progress has been made to model disease pathogenesis and identify points of therapeutic intervention. While some researchers have contributed to our understanding of the proteins and pathways that drive biological dysfunction in disease using in vitro and in vivo models, others have provided mathematical, biophysical, and computational technologies to identify potential therapeutic compounds using in silico modeling. The most exciting phase of the drug discovery process is now: by applying a target-directed approach that leverages the strengths of multiple techniques and validates lead hits using Drosophila as an animal model of disease, we are on the fast-track to identifying novel therapeutics to restore health to those impacted by neurodegenerative disease.

The Advantages of Connectivity Map Applied in Traditional Chinese Medicine

Amid the establishment and optimization of Connectivity Map (CMAP), the functional relationships among drugs, genes, and diseases are further explored. This biological database has been widely used to identify drugs with common mechanisms, repurpose existing drugs, discover the molecular mechanisms of unknown drugs, and find potential drugs for some diseases. Research on traditional Chinese medicine (TCM) has entered a new era in the wake of the development of bioinformatics and other subjects including network pharmacology, proteomics, metabolomics, herbgenomics, and so on. TCM gradually conforms to modern science, but there is still a torrent of limitations. In recent years, CMAP has shown its distinct advantages in the study of the components of TCM and the synergetic mechanism of TCM formulas; hence, the combination of them is inevitable.

Bioinformatics Approaches for Anti-cancer Drug Discovery

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

Glucocorticoids improve severe or critical COVID-19 by activating ACE2 and reducing IL-6 levels

COVID-19 is a public health emergency that has rapidly spread to over 200 countries and regions, and no effective treatment has been established to date. Severe and critical cases have been associated with higher mortality due to acute respiratory distress syndrome (ARDS) and cytokine storm. Based on the novelty and recent emergence of COVID-19, no effective treatment regimen has been identified, thus prompting clinicians to engage in drug repurposing to address the immediate therapeutic need. This study focused on the molecular target angiotensin-converting enzyme 2 (ACE2) of SARS-CoV-2 and screened a group of ACE2 agonists by bioinformatics. Glucocorticoids are a type of ACE2 activator. We verified the efficacy of nine chemicals on regulating ACE2 expression in human GES-1, an upper digestive tract epithelial cell line, and THP-1, a human monocyte cell line, and found that several glucocorticoids imparted activating effects on ACE2 in both cell lines. The drugs triciribine and kinetin riboside activate ACE2 expression or inhibit IL-6 production in macrophages to some extent. In addition, we compared the efficacies of several glucocorticoids. Hydrocortisone showed the strongest effect on ACE2 activation, followed by prednisolone, dexamethasone, and methylprednisolone. We retrospectively analyzed the therapeutic efficacy of nine severe or critical patients from a cohort of 90 COVID-19 cases, who received medium to small doses of glucocorticoids from our integrated medical team in Wuhan. Seven out of nine patients revealed significant improvement in clinical parameters and chest CT images. This study provides experimental and clinical evidence that medium-to-low-dose glucocorticoids may play a protective role in the respiratory and digestive systems by activating ACE2 and suppressing cytokine storm.

A computationally inspired in-vivo approach identifies a link between amygdalar transcriptional heterogeneity, socialization and anxiety

Pharmaceutical breakthroughs for anxiety have been lackluster in the last half-century. Converging behavior and limbic molecular heterogeneity has the potential to revolutionize biomarker-driven interventions. However, current in vivo models too often deploy artificial systems including directed evolution, mutations and fear induction, which poorly mirror clinical manifestations. Here, we explore transcriptional heterogeneity of the amygdala in isogenic mice using an unbiased multi-dimensional computational approach that segregates intra-cohort reactions to moderate situational adversity and intersects it with high content molecular profiling. We show that while the computational approach stratifies known features of clinical anxiety including nitric oxide, opioid and corticotropin signaling, previously unrecognized druggable biomarkers emerge, such as calpain11 and scand1. Through ingenuity pathway analyses, we further describe a role for neurosteroid estradiol signaling, heat shock proteins, ubiquitin ligases and lipid metabolism. In addition, we report a remarkable behavioral pattern that maps to molecular features of anxiety in mice through counterphobic social attitudes, which manifest as increased, yet spatially distant socialization. These findings provide an unbiased approach for interrogating anxiolytics, and hint toward biomarkers underpinning behavioral and social patterns that merit further exploration.

In Vitro and In Vivo Pipeline for Validation of Disease-Modifying Effects of Systems Biology-Derived Network Treatments for Traumatic Brain Injury-Lessons Learned

We developed a pipeline for the discovery of transcriptomics-derived disease-modifying therapies and used it to validate treatments in vitro and in vivo that could be repurposed for TBI treatment. Desmethylclomipramine, ionomycin, sirolimus and trimipramine, identified by in silico LINCS analysis as candidate treatments modulating the TBI-induced transcriptomics networks, were tested in neuron-BV2 microglial co-cultures, using tumour necrosis factor α as a monitoring biomarker for neuroinflammation, nitrite for nitric oxide-mediated neurotoxicity and microtubule associated protein 2-based immunostaining for neuronal survival. Based on (a) therapeutic time window in silico, (b) blood-brain barrier penetration and water solubility, (c) anti-inflammatory and neuroprotective effects in vitro (p < 0.05) and (d) target engagement of Nrf2 target genes (p < 0.05), desmethylclomipramine was validated in a lateral fluid-percussion model of TBI in rats. Despite the favourable in silico and in vitro outcomes, in vivo assessment of clomipramine, which metabolizes to desmethylclomipramine, failed to demonstrate favourable effects on motor and memory tests. In fact, clomipramine treatment worsened the composite neuroscore (p < 0.05). Weight loss (p < 0.05) and prolonged upregulation of plasma cytokines (p < 0.05) may have contributed to the worsened somatomotor outcome. Our pipeline provides a rational stepwise procedure for evaluating favourable and unfavourable effects of systems-biology discovered compounds that modulate post-TBI transcriptomics.

Considerations for Strategic Use of High-Throughput Transcriptomics Chemical Screening Data in Regulatory Decisions

Recently, numerous organizations, including governmental regulatory agencies in the U.S. and abroad, have proposed using data from New Approach Methodologies (NAMs) for augmenting and increasing the pace of chemical assessments. NAMs are broadly defined as any technology, methodology, approach or combination thereof that can be used to provide information on chemical hazard and risk assessment that avoids the use of intact animals. High-throughput transcriptomics (HTTr) is a type of NAM that uses gene expression profiling as an endpoint for rapidly evaluating the effects of large numbers of chemicals on in vitro cell culture systems. As compared to targeted high-throughput screening (HTS) approaches that measure the effect of chemical X on target Y, HTTr is a non-targeted approach that allows researchers to more broadly characterize the integrated response of an intact biological system to chemicals that may affect a specific biological target or many biological targets under a defined set of treatment conditions (time, concentration, etc.). HTTr screening performed in concentration-response mode can provide potency estimates for the concentrations of chemicals that produce perturbations in cellular response pathways. Here, we discuss study design considerations for HTTr concentration-response screening and present a framework for the use of HTTr-based biological pathway-altering concentrations (BPACs) in a screening-level, risk-based chemical prioritization approach. The framework involves concentration-response modeling of HTTr data, mapping gene level responses to biological pathways, determination of BPACs, in vitro-to-in vivo extrapolation (IVIVE) and comparison to human exposure predictions.

Neuroinflammation and ER-stress are key mechanisms of acute bilirubin toxicity and hearing loss in a mouse model

Hyperbilirubinemia (jaundice) is caused by raised levels of unconjugated bilirubin in the blood. When severe, susceptible brain regions including the cerebellum and auditory brainstem are damaged causing neurological sequelae such as ataxia, hearing loss and kernicterus. The mechanism(s) by which bilirubin exerts its toxic effect have not been completely understood to date. In this study we investigated the acute mechanisms by which bilirubin causes the neurotoxicity that contributes to hearing loss. We developed a novel mouse model that exhibits the neurological features seen in human Bilirubin-Induced Neurological Dysfunction (BIND) syndrome that we assessed with a behavioural score and auditory brainstem responses (ABR). Guided by initial experiments applying bilirubin to cultured cells in vitro, we performed whole genome gene expression measurements on mouse brain tissue (cerebellum and auditory brainstem) following bilirubin exposure to gain mechanistic insights into biochemical processes affected, and investigated further using immunoblotting. We then compared the gene changes induced by bilirubin to bacterial lipopolysaccharide (LPS), a well characterized inducer of neuroinflammation, to assess the degree of similarity between them. Finally, we examined the extent to which genetic perturbation of inflammation and both known and novel anti-inflammatory drugs could protect hearing from bilirubin-induced toxicity. The in vitro results indicated that bilirubin induces changes in gene expression consistent with endoplasmic reticulum (ER) stress and activation of the unfolded protein response (UPR). These gene changes were similar to the gene expression signature of thapsigargin–a known ER stress inducer. It also induced gene expression changes associated with inflammation and NF-κB activation. The in vivo model showed behavioural impairment and a raised auditory threshold. Whole genome gene expression analysis confirmed inflammation as a key mechanism of bilirubin neurotoxicity in the auditory pathway and shared gene expression hallmarks induced by exposure to bacterial lipopolysaccharide (LPS) a well-characterized inducer of neuroinflammation. Interestingly, bilirubin caused more severe damage to the auditory system than LPS in this model, but consistent with our hypothesis of neuroinflammation being a primary part of bilirubin toxicity, the hearing loss was protected by perturbing the inflammatory response. This was carried out genetically using lipocalin-2 (LCN2)-null mice, which is an inflammatory cytokine highly upregulated in response to bilirubin. Finally, we tested known and novel anti-inflammatory compounds (interfering with NF-κB and TNFα signalling), and also demonstrated protection of the auditory system from bilirubin toxicity. We have developed a novel, reversible, model for jaundice that shows movement impairment and auditory loss consistent with human symptoms. We used this model to establish ER-stress and inflammation as major contributors to bilirubin toxicity. Because of the rapid and reversible onset of toxicity in this novel model it represents a system to screen therapeutic compounds. We have demonstrated this by targeting inflammation genetically and with anti-inflammatory small molecules that offered protection against bilirubin toxicity. This also suggests that anti-inflammatory drugs could be of therapeutic use in hyperbilirubinemia.

From gene networks to drugs: systems pharmacology approaches for AUD

The alcohol research field has amassed an impressive number of gene expression datasets spanning key brain areas for addiction, species (humans as well as multiple animal models), and stages in the addiction cycle (binge/intoxication, withdrawal/negative effect, and preoccupation/anticipation). These data have improved our understanding of the molecular adaptations that eventually lead to dysregulation of brain function and the chronic, relapsing disorder of addiction. Identification of new medications to treat alcohol use disorder (AUD) will likely benefit from the integration of genetic, genomic, and behavioral information included in these important datasets. Systems pharmacology considers drug effects as the outcome of the complex network of interactions a drug has rather than a single drug-molecule interaction. Computational strategies based on this principle that integrate gene expression signatures of pharmaceuticals and disease states have shown promise for identifying treatments that ameliorate disease symptoms (called in silico gene mapping or connectivity mapping). In this review, we suggest that gene expression profiling for in silico mapping is critical to improve drug repurposing and discovery for AUD and other psychiatric illnesses. We highlight studies that successfully apply gene mapping computational approaches to identify or repurpose pharmaceutical treatments for psychiatric illnesses. Furthermore, we address important challenges that must be overcome to maximize the potential of these strategies to translate to the clinic and improve healthcare outcomes.

Genome-Wide Expression Profiles Drive Discovery of Novel Compounds that Reduce Binge Drinking in Mice

Transcriptome-based drug discovery has identified new treatments for some complex diseases, but has not been applied to alcohol use disorder (AUD) or other psychiatric diseases, where there is a critical need for improved pharmacotherapies. High Drinking in the Dark (HDID-1) mice are a genetic model of AUD risk that have been selectively bred (from the HS/Npt line) to achieve intoxicating blood alcohol levels (BALs) after binge-like drinking. We compared brain gene expression of HDID-1 and HS/Npt mice, to determine a molecular signature for genetic risk for high intensity, binge-like drinking. Using multiple computational methods, we queried LINCS-L1000 (Library of Integrated Network-Based Cellular Signatures), a database containing gene expression signatures of thousands of compounds, to predict candidate drugs with the greatest potential to decrease alcohol consumption. Our analyses predicted novel compounds for testing, many with anti-inflammatory properties, providing further support for a neuroimmune mechanism of excessive alcohol drinking. We validated the top 2 candidates in vivo as a proof-of-concept. Terreic acid (a Bruton's tyrosine kinase inhibitor) and pergolide (a dopamine and serotonin receptor agonist) robustly reduced alcohol intake and BALs in HDID-1 mice, providing the first evidence for transcriptome-based drug discovery to target an addiction trait. Effective drug treatments for many psychiatric diseases are lacking, and the emerging tools and approaches outlined here offer researchers studying complex diseases renewed opportunities to discover new or repurpose existing compounds and expedite treatment options.

Adaptive and Behavioral Changes in Kynurenine 3-Monooxygenase Knockout Mice: Relevance to Psychotic Disorders

BACKGROUND

Kynurenine 3-monooxygenase converts kynurenine to 3-hydroxykynurenine, and its inhibition shunts the kynurenine pathway-which is implicated as dysfunctional in various psychiatric disorders-toward enhanced synthesis of kynurenic acid, an antagonist of both α7 nicotinic acetylcholine and N-methyl-D-aspartate receptors. Possibly as a result of reduced kynurenine 3-monooxygenase activity, elevated central nervous system levels of kynurenic acid have been found in patients with psychotic disorders, including schizophrenia.

METHODS

In the present study, we investigated adaptive-and possibly regulatory-changes in mice with a targeted deletion of Kmo (Kmo^-/-) and characterized the kynurenine 3-monooxygenase-deficient mice using six behavioral assays relevant for the study of schizophrenia.

RESULTS

Genome-wide differential gene expression analyses in the cerebral cortex and cerebellum of these mice identified a network of schizophrenia- and psychosis-related genes, with more pronounced alterations in cerebellar tissue. Kynurenic acid levels were also increased in these brain regions in Kmo^-/- mice, with significantly higher levels in the cerebellum than in the cerebrum. Kmo^-/- mice exhibited impairments in contextual memory and spent less time than did controls interacting with an unfamiliar mouse in a social interaction paradigm. The mutant animals displayed increased anxiety-like behavior in the elevated plus maze and in a light/dark box. After a D-amphetamine challenge (5 mg/kg, intraperitoneal), Kmo^-/- mice showed potentiated horizontal activity in the open field paradigm.

CONCLUSIONS

Taken together, these results demonstrate that the elimination of Kmo in mice is associated with multiple gene and functional alterations that appear to duplicate aspects of the psychopathology of several neuropsychiatric disorders.