Highly polygenic architecture of antidepressant treatment response: Comparative analysis of SSRI and NRI treatment in an animal model of depression

Transcriptomic Studies of Antidepressant Action in Rodent Models of Depression: A First Meta-Analysis

Antidepressants (ADs) are, for now, the best everyday treatment we have for moderate to severe major depressive episodes (MDEs). ADs are among the most prescribed drugs in the Western Hemisphere; however, the trial-and-error prescription strategy and side-effects leave a lot to be desired. More than 60% of patients suffering from major depression fail to respond to the first AD they are prescribed. For those who respond, full response is only observed after several weeks of treatment. In addition, there are no biomarkers that could help with therapeutic decisions; meanwhile, this is already true in cancer and other fields of medicine. For years, many investigators have been working to decipher the underlying mechanisms of AD response. Here, we provide the first systematic review of animal models. We thoroughly searched all the studies involving rodents, profiling transcriptomic alterations consecutive to AD treatment in naïve animals or in animals subjected to stress-induced models of depression. We have been confronted by an important heterogeneity regarding the drugs and the experimental settings. Thus, we perform a meta-analysis of the AD signature of fluoxetine (FLX) in the hippocampus, the most studied target. Among genes and pathways consistently modulated across species, we identify both old players of AD action and novel transcriptional biomarker candidates that warrant further investigation. We discuss the most prominent transcripts (immediate early genes and activity-dependent synaptic plasticity pathways). We also stress the need for systematic studies of AD action in animal models that span across sex, peripheral and central tissues, and pharmacological classes.

Precision Psychiatry: Machine learning as a tool to find new pharmacological targets

There is an increasing amount of data arising from neurobehavioral sciences and medical records that cannot be adequately analyzed by traditional research methods. New drugs develop at a slow rate and seem unsatisfactory for the majority of neurobehavioral disorders. Machine learning (ML) techniques, instead, can incorporate psychopathological, computational, cognitive, and neurobiological underpinning knowledge leading to a refinement of detection, diagnosis, prognosis, treatment, research, and support. Machine and deep learning methods are currently used to accelerate the process of discovering new pharmacological targets and drugs.

OBJECTIVE

The present work reviews current evidence regarding the contribution of machine learning to the discovery of new drug targets.

METHODS

Scientific articles from PubMed, SCOPUS, EMBASE, and Web of Science Core Collection published until May 2021 were included in this review.

RESULTS

The most significant areas of research are schizophrenia, depression and anxiety, Alzheimer´s disease, and substance use disorders. ML techniques have pinpointed target gene candidates and pathways, new molecular substances, and several biomarkers regarding psychiatric disorders. Drug repositioning studies using ML have identified multiple drug candidates as promising therapeutic agents.

CONCLUSION

Next-generation ML techniques and subsequent deep learning may power new findings regarding the discovery of new pharmacological agents by bridging the gap between biological data and chemical drug information.

Uncaria rhynchophylla ameliorates unpredictable chronic mild stress-induced depression in mice via activating 5-HT1A receptor: Insights from transcriptomics

BACKGROUND

Depression is a pervasive or persistent mental disorder that causes mood, cognitive and memory deficits. Uncaria rhynchophylla has been widely used to treat central nervous system diseases for a long history, although its efficacy and potential mechanism are still uncertain.

PURPOSE

The present study aimed to investigate anti-depression effect and potential mechanism of U. rhynchophylla extract (URE).

STUDY DESIGN AND METHODS

A mouse depression model was established using unpredictable chronic mild stress (UCMS). Effects of URE on depression-like behaviours, neurotransmitters, and neuroendocrine hormones were investigated in UCMS-induced mice. The potential target of URE was analyzed by transcriptomics and bioinformatics methods and validated by RT-PCR and Western blot. The agonistic effect on 5-HT1A receptor was assayed by dual-luciferase reporter system.

RESULTS

URE ameliorated depression-like behaviours, and modulated levels of neurotransmitters and neuroendocrine hormones, including 5-hydroxytryptamine (5-HT), 5-hydroxyindole acetic acid (5-HIAA), dopamine (DA), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), corticosterone (CORT), corticotropin-releasing hormone (CRH), and adrenocorticotropic hormone (ACTH), in UCMS-induced mice. Transcriptomics and bioinformatics results indicated that URE could regulate glutamatergic, cholinergic, serotonergic, and GABAergic systems, especially neuroactive ligand-receptor and cAMP signaling pathways, revealing that Htr1a encoding 5-HT1A receptor was a potential target of URE. The expression levels of downstream proteins of 5-HT1A signaling pathway 5-HT1A, CREB, BDNF, and PKA were increased in UCMS-induced mice after URE administration, and URE also displayed an agonistic effect against 5-HT1A receptor with an EC50 value of 17.42 μg/ml.

CONCLUSION

U. rhynchophylla ameliorated depression-like behaviours in UCMS-induced mice through activating 5-HT1A receptor.

Association study of polymorphisms within inflammatory genes and methylation status in treatment response in major depression

Background:

Although pharmacogenetics for major depressive disorder (MDD) is gaining momentum, the role of genetics in differences in response to antidepressant treatment is controversial, as they depend on multifactorial and polygenic phenotypes. Previous studies focused on the genes of the serotonergic system, leaving apart other pathological factors such as the inflammatory pathway. The main objective of the study was to assess whether treatment response might be associated with specific inflammation-related genetic variants or their methylation status.

Methods:

41 SNPs in 8 inflammatory genes: interleukin (IL) 1-β, IL2, IL6, IL6R, IL10, IL18, tumor necrosis factor (TNF)-α and interferon (IFN)-γ were genotyped in 153 patients with MDD, who were evaluated with the Mausdley Staging Method to determine treatment response profiles. Pyrosequencing reactions and methylation quantification were performed in a PyroMark Q24 in 5 selected CpG islands of IL1- β, IL6 and IL6R. Linear and logistic regression analyses were conducted, including age and gender as covariates using PLINK 1.07.

Results:

Allelic distribution of IL1- β rs1143643 was significantly associated with MSM scores (FDR corrected p = 0.04). Allelic distribution of IL6R rs57569414 showed a trend towards significance with MSM scores (p = 0.002; FDR corrected p = 0.07). Haplotype analyses showed associations between allelic combinations of IL1-β and IL10 with treatment response (FDR corrected p < 0.01). Methylation percentage of treatment responders was only higher in an IL6R CpG island (p < 0.05).

Conclusions:

These exploratory findings suggest that IL1-β and, marginally, IL6R polymorphisms may affect treatment response in major depression. If confirmed, these results may account for the heterogeneous phenotypes of major depression that underlie differences in treatment response.

A panel of miRNAs is involved in the effect of sertraline on panic disorder, as implicated by a microarray-based analysis

BACKGROUND

MiRNAs are considered to be significant contributors to the pathogenesis of psychiatric diseases, but little is known about the potential roles of miRNAs in the treatment effect of panic disorder (PD). Therefore, we aimed to identify the miRNAs association with PD over the course of sertraline treatment.

METHODS

Sixty-seven patients were collected for a 6-week period of sertraline treatment, and evaluated using HAMD-17, HAMA-14 and PDSS both at the baseline and 6 weeks later. Blood samples were collected before and after treatment, respectively. Ten pairs of samples were analyzed using miRNA array, and the differentially expressed miRNAs were further validated using RT-PCR in the whole sample.

RESULTS

miR-451a, miR-144-5p, miR-25-3p and miR-660-5p were found to be significantly up-regulated, while miR-1 and miR-148-5p significantly down-regulated after sertraline treatment. The change of miR-25-3p before and after treatment (△miR-25-3p) was positively related to both the changes of PDSS3 scores (△PDSS3) (p = 0.017, 31.5% contribution) and △ PDSS7 (p = 0.016, 32.3% contribution). The △miR-660-5p was positively related to both the △HAMA5 (p = 0.03, 26% contribution) and △PDSS7 (p = 0.032). The △miR-148-5p was positively related to the △PDSS4 (p = 0.046, 21.5% contribution), but negatively related to the △HAMA13 (p = 0.005, 41.9% contribution). The △miR-144-5p was negatively related to the △HAMA9 (p = 0.032, 25.3% contribution).

CONCLUSIONS

These findings might provide some evidences to the involvement of miRNA in the effect of anti-anxiety agents, which contributed to the better understanding the disease and developing new therapeutic genetic targets.

Animal models of depression: pros and cons

Animal models of depression are certainly needed but the question in the title has been raised owing to the controversies in the interpretation of the readout in a number of tests, to the perceived lack of progress in the development of novel treatments and to the expressed doubts in whether animal models can offer anything to make a true breakthrough in understanding the neurobiology of depression and producing novel drugs against depression. Herewith, it is argued that if anything is wrong with animal models, including those for depression, it is not about the principle of modelling complex human disorder in animals but in the way the tests are selected, conducted and interpreted. Further progress in the study of depression and in developing new treatments, will be supported by animal models of depression if these were more critically targeted to drug screening vs. studies of underlying neurobiology, clearly stratified to vulnerability and pathogenetic models, focused on well-defined endophenotypes and validated for each setting while bearing the existing limits to validation in mind. Animal models of depression need not to rely merely on behavioural readouts but increasingly incorporate neurobiological measures as the understanding of depression as human brain disorder advances. Further developments would be fostered by cross-fertilizinga translational approach that is bidirectional, research on humans making more use of neurobiological findings in animals.

Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry

Personalized medicine (personalized psychiatry in a specific setting) is a new model towards individualized care, in which knowledge from genomics and other omic pillars (microbiome, epigenomes, proteome, and metabolome) will be combined with clinical data to guide efforts to new drug development and targeted prescription of the existing treatment options. In this review, we summarize pharmacogenomic studies in mood disorders that may lay the foundation towards personalized psychiatry. In addition, we have discussed the possible strategies to integrate data from omic pillars as a future path to personalized psychiatry. So far, the progress of uncovering single nucleotide polymorphisms (SNPs) underpinning treatment efficacy in mood disorders (e.g., SNPs associated with selective serotonin re-uptake inhibitors or lithium treatment response in patients with bipolar disorder and major depressive disorder) are encouraging, but not adequate. Genetic studies have pointed to a number of SNPs located at candidate genes that possibly influence response to; (a) antidepressants COMT, HTR2A, HTR1A, CNR1, SLC6A4, NPY, MAOA, IL1B, GRIK4, BDNF, GNB3, FKBP5, CYP2D6, CYP2C19, and ABCB1 and (b) mood stabilizers (lithium) 5-HTT, TPH, DRD1, FYN, INPP1, CREB1, BDNF, GSK3β, ARNTL, TIM, DPB, NR3C1, BCR, XBP1, and CACNG2. We suggest three alternative and complementary strategies to implement knowledge gained from pharmacogenomic studies. The first strategy can be to implement diagnostic, therapeutic, or prognostic genetic testing based on candidate genes or gene products. The second alternative is an integrative analysis (systems genomics approach) to combine omics data obtained from the different pillars of omics investigation, including genomics, epigenomes, proteomics, metabolomics and microbiomes. The main goal of system genomics is an identification and understanding of biological pathways, networks, and modules underlying drug-response. The third strategy aims to the development of multivariable diagnostic or prognostic algorithms (tools) combining individual's genomic information (polygenic score) with other predictors (e.g., omics pillars, neuroimaging, and clinical characteristics) to finally predict therapeutic outcomes. An integration of molecular science with that of traditional clinical practice is the way forward to drug discoveries and novel therapeutic approaches and to characterize psychiatric disorders leading to a better predictive, preventive, and personalized medicine (PPPM) in psychiatry. With future advances in the omics technology and methodological developments for data integration, the goal of PPPM in psychiatry is promising.

Highly polygenic architecture of antidepressant treatment response: Comparative analysis of SSRI and NRI treatment in an animal model of depression

Response to antidepressant (AD) treatment may be a more polygenic trait than previously hypothesized, with many genetic variants interacting in yet unclear ways. In this study we used methods that can automatically learn to detect patterns of statistical regularity from a sparsely distributed signal across hippocampal transcriptome measurements in a large-scale animal pharmacogenomic study to uncover genomic variations associated with AD. The study used four inbred mouse strains of both sexes, two drug treatments, and a control group (escitalopram, nortriptyline, and saline). Multi-class and binary classification using Machine Learning (ML) and regularization algorithms using iterative and univariate feature selection methods, including InfoGain, mRMR, ANOVA, and Chi Square, were used to uncover genomic markers associated with AD response. Relevant genes were selected based on Jaccard distance and carried forward for gene-network analysis. Linear association methods uncovered only one gene associated with drug treatment response. The implementation of ML algorithms, together with feature reduction methods, revealed a set of 204 genes associated with SSRI and 241 genes associated with NRI response. Although only 10% of genes overlapped across the two drugs, network analysis shows that both drugs modulated the CREB pathway, through different molecular mechanisms. Through careful implementation and optimisations, the algorithms detected a weak signal used to predict whether an animal was treated with nortriptyline (77%) or escitalopram (67%) on an independent testing set. The results from this study indicate that the molecular signature of AD treatment may include a much broader range of genomic markers than previously hypothesized, suggesting that response to medication may be as complex as the pathology. The search for biomarkers of antidepressant treatment response could therefore consider a higher number of genetic markers and their interactions. Through predominately different molecular targets and mechanisms of action, the two drugs modulate the same Creb1 pathway which plays a key role in neurotrophic responses and in inflammatory processes. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.