Translating GWAS findings into therapies for depression and anxiety disorders: gene-set analyses reveal enrichment of psychiatric drug classes and implications for drug repositioning

Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications

De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.

Statins in depression: a repurposed medical treatment can provide novel insights in mental health

Depression has a large burden, but the development of new drugs for its treatment has proved difficult. Progresses in neuroscience have highlighted several physiopathological pathways, notably inflammatory and metabolic ones, likely involved in the genesis of depressive symptoms. A novel strategy proposes to repurpose established medical treatments of known safety and to investigate their potential antidepressant activity. Among numerous candidates, growing evidence suggests that statins may have a positive role in the treatment of depressive disorders, although some have raised concerns about possible depressogenic effects of these widely prescribed medications. This narrative review summarises relevant findings from translational studies implicating many interconnected neurobiological and neuropsychological, cardiovascular, endocrine-metabolic, and immunological mechanisms by which statins could influence mood. Also, the most recent clinical investigations on the effects of statins in depression are presented. Overall, the use of statins for the treatment of depressive symptoms cannot be recommended based on the available literature, though this might change as several larger, methodologically robust studies are being conducted. Nevertheless, statins can already be acknowledged as a driver of innovation in mental health, as they provide a novel perspective to the physical health of people with depression and for the development of more precise antidepressant treatments.

Predicting the Treatment Outcomes of Antidepressants Using a Deep Neural Network of Deep Learning in Drug-Naïve Major Depressive Patients

Predicting the treatment response to antidepressants by pretreatment features would be useful, as up to 70–90% of patients with major depressive disorder (MDD) do not respond to treatment as expected. Therefore, we aim to establish a deep neural network (DNN) model of deep learning to predict the treatment outcomes of antidepressants in drug-naïve and first-diagnosis MDD patients during severe depressive stage using different domains of signature profiles of clinical features, peripheral biochemistry, psychosocial factors, and genetic polymorphisms. The multilayer feedforward neural network containing two hidden layers was applied to build models with tenfold cross-validation. The areas under the curve (AUC) of the receiver operating characteristic curves were used to evaluate the performance of the models. The results demonstrated that the AUCs of the model ranged between 0.7 and 0.8 using a combination of different domains of categorical variables. Moreover, models using the extracted variables demonstrated better performance, and the best performing model was characterized by an AUC of 0.825, using the levels of cortisol and oxytocin, scales of social support and quality of life, and polymorphisms of the OXTR gene. A complex interactions model developed through DNN could be useful at the clinical level for predicting the individualized outcomes of antidepressants.

Integrative multi-omics landscape of fluoxetine action across 27 brain regions reveals global increase in energy metabolism and region-specific chromatin remodelling

Depression and anxiety are major global health burdens. Although SSRIs targeting the serotonergic system are prescribed over 200 million times annually, they have variable therapeutic efficacy and side effects, and mechanisms of action remain incompletely understood. Here, we comprehensively characterise the molecular landscape of gene regulatory changes associated with fluoxetine, a widely-used SSRI. We performed multimodal analysis of SSRI response in 27 mammalian brain regions using 310 bulk RNA-seq and H3K27ac ChIP-seq datasets, followed by in-depth characterisation of two hippocampal regions using single-cell RNA-seq (20 datasets). Remarkably, fluoxetine induced profound region-specific shifts in gene expression and chromatin state, including in the nucleus accumbens shell, locus coeruleus and septal areas, as well as in more well-studied regions such as the raphe and hippocampal dentate gyrus. Expression changes were strongly enriched at GWAS loci for depression and antidepressant drug response, stressing the relevance to human phenotypes. We observed differential expression at dozens of signalling receptors and pathways, many of which are previously unknown. Single-cell analysis revealed stark differences in fluoxetine response between the dorsal and ventral hippocampal dentate gyri, particularly in oligodendrocytes, mossy cells and inhibitory neurons. Across diverse brain regions, integrative omics analysis consistently suggested increased energy metabolism via oxidative phosphorylation and mitochondrial changes, which we corroborated in vitro; this may thus constitute a shared mechanism of action of fluoxetine. Similarly, we observed pervasive chromatin remodelling signatures across the brain. Our study reveals unexpected regional and cell type-specific heterogeneity in SSRI action, highlights under-studied brain regions that may play a major role in antidepressant response, and provides a rich resource of candidate cell types, genes, gene regulatory elements and pathways for mechanistic analysis and identifying new therapeutic targets for depression and anxiety.

Using common genetic variants to find drugs for common epilepsies

Better drugs are needed for common epilepsies. Drug repurposing offers the potential of significant savings in the time and cost of developing new treatments. In order to select the best candidate drug(s) to repurpose for a disease, it is desirable to predict the relative clinical efficacy that drugs will have against the disease. Common epilepsy can be divided into different types and syndromes. Different antiseizure medications are most effective for different types and syndromes of common epilepsy. For predictions of antiepileptic efficacy to be clinically translatable, it is essential that the predictions are specific to each form of common epilepsy, and reflect the patterns of drug efficacy observed in clinical studies and practice. These requirements are not fulfilled by previously published drug predictions for epilepsy. We developed a novel method for predicting the relative efficacy of drugs against any common epilepsy, by using its Genome-Wide Association Study summary statistics and drugs' activity data. The methodological advancement in our technique is that the drug predictions for a disease are based upon drugs' effects on the function and abundance of proteins, and the magnitude and direction of those effects, relative to the importance, degree and direction of the proteins' dysregulation in the disease. We used this method to predict the relative efficacy of all drugs, licensed for any condition, against each of the major types and syndromes of common epilepsy. Our predictions are concordant with findings from real-world experience and randomized clinical trials. Our method predicts the efficacy of existing antiseizure medications against common epilepsies; in this prediction, our method outperforms the best alternative existing method: area under receiver operating characteristic curve (mean ± standard deviation) 0.83 ± 0.03 and 0.63 ± 0.04, respectively. Importantly, our method predicts which antiseizure medications are amongst the more efficacious in clinical practice, and which antiseizure medications are amongst the less efficacious in clinical practice, for each of the main syndromes of common epilepsy, and it predicts the distinct order of efficacy of individual antiseizure medications in clinical trials of different common epilepsies. We identify promising candidate drugs for each of the major syndromes of common epilepsy. We screen five promising predicted drugs in an animal model: each exerts a significant dose-dependent effect upon seizures. Our predictions are a novel resource for selecting suitable candidate drugs that could potentially be repurposed for each of the major syndromes of common epilepsy. Our method is potentially generalizable to other complex diseases.

Advancing the use of genome-wide association studies for drug repurposing

Genome-wide association studies (GWAS) have revealed important biological insights into complex diseases, which are broadly expected to lead to the identification of new drug targets and opportunities for treatment. Drug development, however, remains hampered by the time taken and costs expended to achieve regulatory approval, leading many clinicians and researchers to consider alternative paths to more immediate clinical outcomes. In this Review, we explore approaches that leverage common variant genetics to identify opportunities for repurposing existing drugs, also known as drug repositioning. These approaches include the identification of compounds by linking individual loci to genes and pathways that can be pharmacologically modulated, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization, and polygenic scoring.

Genetic contributions to anxiety disorders: where we are and where we are heading

Anxiety disorders are among the most common psychiatric disorders worldwide. They often onset early in life, with symptoms and consequences that can persist for decades. This makes anxiety disorders some of the most debilitating and costly disorders of our time. Although much is known about the synaptic and circuit mechanisms of fear and anxiety, research on the underlying genetics has lagged behind that of other psychiatric disorders. However, alongside the formation of the Psychiatric Genomic Consortium Anxiety workgroup, progress is rapidly advancing, offering opportunities for future research.Here we review current knowledge about the genetics of anxiety across the lifespan from genetically informative designs (i.e. twin studies and molecular genetics). We include studies of specific anxiety disorders (e.g. panic disorder, generalised anxiety disorder) as well as those using dimensional measures of trait anxiety. We particularly address findings from large-scale genome-wide association studies and show how such discoveries may provide opportunities for translation into improved or new therapeutics for affected individuals. Finally, we describe how discoveries in anxiety genetics open the door to numerous new research possibilities, such as the investigation of specific gene-environment interactions and the disentangling of causal associations with related traits and disorders.We discuss how the field of anxiety genetics is expected to move forward. In addition to the obvious need for larger sample sizes in genome-wide studies, we highlight the need for studies among young people, focusing on specific underlying dimensional traits or components of anxiety.

A review of systems biology research of anxiety disorders

The development of "omic" technologies and deep phenotyping may facilitate a systems biology approach to understanding anxiety disorders. Systems biology approaches incorporate data from multiple modalities (e.g., genomic, neuroimaging) with functional analyses (e.g., animal and tissue culture models) and mathematical modeling (e.g., machine learning) to investigate pathological biophysical networks at various scales. Here we review: i) the neurobiology of anxiety disorders; ii) how systems biology approaches have advanced this work; and iii) the clinical implications and future directions of this research. Systems biology approaches have provided an improved functional understanding of candidate biomarkers and have suggested future potential for refining the diagnosis, prognosis, and treatment of anxiety disorders. The systems biology approach for anxiety disorders is, however, in its infancy and in some instances is characterized by insufficient power and replication. The studies reviewed here represent important steps to further untangling the pathophysiology of anxiety disorders.

Transcriptome-wide association study of treatment-resistant depression and depression subtypes for drug repurposing

Major depressive disorder (MDD) is the single largest contributor to global disability and up to 20-30% of patients do not respond to at least two antidepressants (treatment-resistant depression, TRD). This study leveraged imputed gene expression in TRD to perform a drug repurposing analysis. Among those with MDD, we defined TRD as having at least two antidepressant switches according to primary care records in UK Biobank (UKB). We performed a transcriptome-wide association study (TWAS) of TRD (n = 2165) vs healthy controls (n = 11,188) using FUSION and gene expression levels from 21 tissues. We identified compounds with opposite gene expression signatures (ConnectivityMap data) compared to our TWAS results using the Kolmogorov-Smirnov test, Spearman and Pearson correlation. As symptom patterns are routinely assessed in clinical practice and could be used to provide targeted treatments, we identified MDD subtypes associated with TRD in UKB and analysed them using the same pipeline described for TRD. Anxious MDD (n = 14,954) and MDD with weight gain (n = 4697) were associated with TRD. In the TWAS, two genes were significantly dysregulated (TMEM106B and ATP2A1 for anxious and weight gain MDD, respectively). A muscarinic receptor antagonist was identified as top candidate for repurposing in TRD; inhibition of heat shock protein 90 was the main mechanism of action identified for anxious MDD, while modulators of metabolism such as troglitazone showed promising results for MDD with weight gain. This was the first TWAS of TRD and associated MDD subtypes. Our results shed light on possible pharmacological approaches in individuals with difficult-to-treat depression.

Drug repositioning for treatment-resistant depression: Hypotheses from a pharmacogenomic study

About 20-30% of patients with major depressive disorder (MDD) develop treatment-resistant depression (TRD) and finding new effective treatments for TRD has been a challenge. This study aimed to identify new possible pharmacological options for TRD. Genes in pathways included in predictive models of TRD in a previous whole exome sequence study were compared with those coding for targets of drugs in any phase of development, nutraceuticals, proteins and peptides from Drug repurposing Hub, Drug-Gene Interaction database and DrugBank database. We tested if known gene targets were enriched in TRD-associated genes by a hypergeometric test. Compounds enriched in TRD-associated genes after false-discovery rate (FDR) correction were annotated and compared with those showing enrichment in genes associated with MDD in the last Psychiatric Genomics Consortium genome-wide association study. Among a total of 15,475 compounds, 542 were enriched in TRD-associated genes (FDR p < .05). Significant results included drugs which are currently used in TRD (e.g. lithium and ketamine), confirming the rationale of this approach. Interesting molecules included modulators of inflammation, renin-angiotensin system, proliferator-activated receptor agonists, glycogen synthase kinase 3 beta inhibitors and the rho associated kinase inhibitor fasudil. Nutraceuticals, mostly antioxidant polyphenols, were also identified. Drugs showing enrichment for TRD-associated genes had a higher probability of enrichment for MDD-associated genes compared to those having no TRD-genes enrichment (p = 6.21e-55). This study suggested new potential treatments for TRD using a in silico approach. These analyses are exploratory only but can contribute to the identification of drugs to study in future clinical trials.

Genetics and major depressive disorder: clinical implications for disease risk, prognosis and treatment

In the post-genomic era, genetics has led to limited clinical applications in the diagnosis and treatment of major depressive disorder (MDD). Variants in genes coding for cytochrome enzymes are included in guidelines for assisting in antidepressant choice and dosing, but there are no recommendations involving genes responsible for antidepressant pharmacodynamics and no consensus applications for guiding diagnosis or prognosis. However, genetics has contributed to a better understanding of MDD pathogenesis and the mechanisms of antidepressant action, also thanks to recent methodological innovations that overcome the challenges posed by the polygenic architecture of these traits. Polygenic risk scores can be used to estimate the risk of disease at the individual level, which may have clinical relevance in cases with extremely high scores (e.g. top 1%). Genetic studies have also shed light on a wide genetic overlap between MDD and other psychiatric disorders. The relationships between genes/pathways associated with MDD and known drug targets are a promising tool for drug repurposing and identification of new pharmacological targets. Increase in power thanks to larger samples and methods integrating genetic data with gene expression, the integration of common variants and rare variants, are expected to advance our knowledge and assist in personalized psychiatry.

Turning genome-wide association study findings into opportunities for drug repositioning

Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored.

The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations.

Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.

Drug repositioning for psychiatric and neurological disorders through a network medicine approach

Psychiatric and neurological disorders (PNDs) affect millions worldwide and only a few drugs achieve complete therapeutic success in the treatment of these disorders. Due to the high cost of developing novel drugs, drug repositioning represents a promising alternative method of treatment. In this manuscript, we used a network medicine approach to investigate the molecular characteristics of PNDs and identify novel drug candidates for repositioning. Using IBM Watson for Drug Discovery, a powerful machine learning text-mining application, we built knowledge networks containing connections between PNDs and genes or drugs mentioned in the scientific literature published in the past 50 years. This approach revealed several drugs that target key PND-related genes, which have never been used to treat these disorders to date. We validate our framework by detecting drugs that have been undergoing clinical trial for treating some of the PNDs, but have no published results in their support. Our data provides comprehensive insights into the molecular pathology of PNDs and offers promising drug repositioning candidates for follow-up trials.

Pharmacogenetics and Depression: A Critical Perspective

Depression leads the higher personal and socio-economical burden within psychiatric disorders. Despite the fact that over 40 antidepressants (ADs) are available, suboptimal response still poses a major challenge and is thought to be partially a result of genetic variation. Pharmacogenetics studies the effects of genetic variants on treatment outcomes with the aim of providing tailored treatments, thereby maximizing efficacy and tolerability. After two decades of pharmacogenetic research, variants in genes coding for the cytochromes involved in ADs metabolism (CYP2D6 and CYP2C19) are now considered biomarkers with sufficient scientific support for clinical application, despite the lack of conclusive cost/effectiveness evidence. The effect of variants in genes modulating ADs mechanisms of action (pharmacodynamics) is still controversial, because of the much higher complexity of ADs pharmacodynamics compared to ADs metabolism. Considerable progress has been made since the era of candidate gene studies: the genomic revolution has made possible to assess genetic variance on an unprecedented scale, throughout the whole genome, and to analyze the cumulative effect of different variants. The results have revealed key information on the biological mechanisms mediating ADs effect and identified hypothetical new pharmacological targets. They also paved the way for future availability of polygenic pharmacogenetic panels to predict treatment outcome, which are expected to explain much higher variance in ADs response compared to CYP2D6 and CYP2C19 only. As the demand and availability of AD pharmacogenetic testing is projected to increase, it is important for clinicians to keep abreast of this evolving area to facilitate informed discussions with their patients.

Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder

The major depressive disorder (MDD) working group of the Psychiatric Genomics Consortium (PGC) has published a genome-wide association study (GWAS) for MDD in 130,664 cases, identifying 44 risk variants. We used these results to investigate potential drug targets and repurposing opportunities. We built easily interpretable bipartite drug-target networks integrating interactions between drugs and their targets, genome-wide association statistics, and genetically predicted expression levels in different tissues, using the online tool Drug Targetor ( drugtargetor.com ). We also investigated drug-target relationships that could be impacting MDD. MAGMA was used to perform pathway analyses and S-PrediXcan to investigate the directionality of tissue-specific expression levels in patients vs. controls. Outside the major histocompatibility complex (MHC) region, 153 protein-coding genes are significantly associated with MDD in MAGMA after multiple testing correction; among these, five are predicted to be down or upregulated in brain regions and 24 are known druggable genes. Several drug classes were significantly enriched, including monoamine reuptake inhibitors, sex hormones, antipsychotics, and antihistamines, indicating an effect on MDD and potential repurposing opportunities. These findings not only require validation in model systems and clinical examination, but also show that GWAS may become a rich source of new therapeutic hypotheses for MDD and other psychiatric disorders that need new-and better-treatment options.