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.

The Relative Contributions of Cell-Dependent Cortical Microcircuit Aging to Cognition and Anxiety.

BACKGROUND: Aging is accompanied by altered thinking (cognition) and feeling (mood), functions that depend on information processing by brain cortical cell microcircuits. We hypothesized that age-associated long-term functional and biological changes are mediated by gene transcriptomic changes within neuronal cell types forming cortical microcircuits, namely excitatory pyramidal cells (PYCs) and inhibitory gamma-aminobutyric acidergic neurons expressing vasoactive intestinal peptide (Vip), somatostatin (Sst), and parvalbumin (Pvalb). METHODS: To test this hypothesis, we assessed locomotor, anxiety-like, and cognitive behavioral changes between young (2 months of age, n = 9) and old (22 months of age, n = 12) male C57BL/6 mice, and performed frontal cortex cell type-specific molecular profiling, using laser capture microscopy and RNA sequencing. Results were analyzed by neuroinformatics and validated by fluorescent in situ hybridization. RESULTS: Old mice displayed increased anxiety and reduced working memory. The four cell types displayed distinct age-related transcriptomes and biological pathway profiles, affecting metabolic and cell signaling pathways, and selective markers of neuronal vulnerability (Ryr3), resilience (Oxr1), and mitochondrial dynamics (Opa1), suggesting high age-related vulnerability of PYCs, and variable degree of adaptation in gamma-aminobutyric acidergic neurons. Correlations between gene expression and behaviors suggest that changes in cognition and anxiety associated with age are partly mediated by normal age-related cell changes, and that additional age-independent decreases in synaptic and signaling pathways, notably in PYCs and somatostatin neurons, further contribute to behavioral changes. CONCLUSIONS: Our study demonstrates cell-dependent differential vulnerability and coordinated cell-specific cortical microcircuit molecular changes with age. Collectively, the results suggest intrinsic molecular links among aging, cognition, and mood-related behaviors, with somatostatin neurons contributing evenly to both behavioral conditions.

Ulk2 controls cortical excitatory-inhibitory balance via autophagic regulation of p62 and GABAA receptor trafficking in pyramidal neurons.

Autophagy plays an essential role in intracellular degradation and maintenance of cellular homeostasis in all cells, including neurons. Although a recent study reported a copy number variation of Ulk2, a gene essential for initiating autophagy, associated with a case of schizophrenia (SZ), it remains to be studied whether Ulk2 dysfunction could underlie the pathophysiology of the disease. Here we show that Ulk2 heterozygous (Ulk2+/-) mice have upregulated expression of sequestosome-1/p62, an autophagy-associated stress response protein, predominantly in pyramidal neurons of the prefrontal cortex (PFC), and exhibit behavioral deficits associated with the PFC functions, including attenuated sensorimotor gating and impaired cognition. Ulk2+/- neurons showed imbalanced excitatory-inhibitory neurotransmission, due in part to selective down-modulation of gamma-aminobutyric acid (GABA)A receptor surface expression in pyramidal neurons. Genetically reducing p62 gene dosage or suppressing p62 protein levels with an autophagy-inducing agent restored the GABAA receptor surface expression and rescued the behavioral deficits in Ulk2+/- mice. Moreover, expressing a short peptide that specifically interferes with the interaction of p62 and GABAA receptor-associated protein, a protein that regulates endocytic trafficking of GABAA receptors, also restored the GABAA receptor surface expression and rescued the behavioral deficits in Ulk2+/- mice. Thus, the current study reveals a novel mechanism linking deregulated autophagy to functional disturbances of the nervous system relevant to SZ, through regulation of GABAA receptor surface presentation in pyramidal neurons.

Predisposition to treatment response in major depressive episode: A peripheral blood gene coexpression network analysis.

Antidepressant efficacy is insufficient, unpredictable and poorly understood in major depressive episode (MDE). Gene expression studies allow for the identification of significantly dysregulated genes but can limit the exploration of biological pathways. In the present study, we proposed a gene coexpression analysis to investigate biological pathways associated with treatment response predisposition and their regulation by microRNAs (miRNAs) in peripheral blood samples of MDE and healthy control subjects. We used a discovery cohort that included 34 MDE patients that were given 12-week treatment with citalopram and 33 healthy controls. Two replication cohorts with similar design were also analyzed. Expression-based gene network was built to define clusters of highly correlated sets of genes, called modules. Association between each module's first principal component of the expression data and clinical improvement was tested in the three cohorts. We conducted gene ontology analysis and miRNA prediction based on the module gene list. Nine of the 59 modules from the gene coexpression network were associated with clinical improvement. The association was partially replicated in other cohorts. Gene ontology analysis demonstrated that 4 modules were associated with cytokine production, acute inflammatory response or IL-8 functions. Finally, we found 414 miRNAs that may regulate one or several modules associated with clinical improvement. By contrast, only 12 miRNAs were predicted to specifically regulate modules unrelated to clinical improvement. Our gene coexpression analysis underlines the importance of inflammation-related pathways and the involvement of a large miRNA program as biological processes predisposing associated with antidepressant response.

Imputation of Truncated p-Values For Meta-Analysis Methods and Its Genomic Application.

Microarray analysis to monitor expression activities in thousands of genes simultaneously has become routine in biomedical research during the past decade. a tremendous amount of expression profiles are generated and stored in the public domain and information integration by meta-analysis to detect differentially expressed (DE) genes has become popular to obtain increased statistical power and validated findings. Methods that aggregate transformed p-value evidence have been widely used in genomic settings, among which Fisher's and Stouffer's methods are the most popular ones. In practice, raw data and p-values of DE evidence are often not available in genomic studies that are to be combined. Instead, only the detected DE gene lists under a certain p-value threshold (e.g., DE genes with p-value < 0.001) are reported in journal publications. The truncated p-value information makes the aforementioned meta-analysis methods inapplicable and researchers are forced to apply a less efficient vote counting method or naïvely drop the studies with incomplete information. The purpose of this paper is to develop effective meta-analysis methods for such situations with partially censored p-values. We developed and compared three imputation methods-mean imputation, single random imputation and multiple imputation-for a general class of evidence aggregation methods of which Fisher's and Stouffer's methods are special examples. The null distribution of each method was analytically derived and subsequent inference and genomic analysis frameworks were established. Simulations were performed to investigate the type Ierror, power and the control of false discovery rate (FDR) for (correlated) gene expression data. The proposed methods were applied to several genomic applications in colorectal cancer, pain and liquid association analysis of major depressive disorder (MDD). The results showed that imputation methods outperformed existing naïve approaches. Mean imputation and multiple imputation methods performed the best and are recommended for future applications.

A unique gene expression signature associated with serotonin 2C receptor RNA editing in the prefrontal cortex and altered in suicide.

Editing of the pre-mRNA for the serotonin receptor 2C (5-HT2CR) by site-specific adenosine deamination (A-to-I pre-mRNA editing) substantially increases the functional plasticity of this key neurotransmitter receptor and is thought to contribute to homeostatic mechanisms in neurons. 5-HT2CR mRNA editing generates up to 24 different receptor isoforms. The extent of editing correlates with 5-HT2CR functional activity: more highly edited isoforms exhibit the least function. Altered 5-HT2CR editing has been reported in postmortem brains of suicide victims. We report a comparative analysis of the connections among 5-HT2CR editing, genome-wide gene expression and DNA methylation in suicide victims, individuals with major depressive disorder and non-psychiatric controls. The results confirm previous findings of an overrepresentation of highly edited mRNA variants (which encode hypoactive 5-HT2CR receptors) in the brains of suicide victims. A large set of genes for which the expression level is associated with editing was detected. This signature set of editing-associated genes is significantly enriched for genes that are involved in synaptic transmission, genes that are preferentially expressed in neurons, and genes whose expression is correlated with the level of DNA methylation. Notably, we report that the link between 5-HT2CR editing and gene expression is disrupted in suicide victims. The results suggest that the postulated homeostatic function of 5-HT2CR editing is dysregulated in individuals who committed suicide.

Opposing effects of acute versus chronic blockade of frontal cortex somatostatin-positive inhibitory neurons on behavioral emotionality in mice.

Reduced expression of somatostatin (SST) is reported across chronic brain conditions including major depression and normal aging. SST is a signaling neuropeptide and marker of gamma-amino butyric acid (GABA) neurons, which specifically inhibit pyramidal neuron dendrites. Studies in auditory cortex suggest that chronic reduction in dendritic inhibition induces compensatory homeostatic adaptations that oppose the effects of acute inhibition. Whether such mechanisms occur in frontal cortex (FC) and affect behavioral outcome is not known. Here, we used two complementary viral vector strategies to examine the effects of acute vs chronic inhibition of SST-positive neurons on behavioral emotionality in adult mice. SST-IRES-Cre mice were injected in FC (prelimbic/precingulate) with CRE-dependent adeno-associated viral (AAV) vector encoding the engineered Gi/o-coupled human muscarinic M4 designer receptor exclusively activated by a designer drug (DREADD-hM4Di) or a control reporter (AAV-DIO-mCherry) for acute or chronic cellular inhibition. A separate cohort was injected with CRE-dependent AAV vectors expressing diphtheria toxin (DTA) to selectively ablate FC SST neurons. Mice were assessed for anxiety- and depressive-like behaviors (defined as emotionality). Results indicate that acute inhibition of FC SST neurons increased behavioral emotionality, whereas chronic inhibition decreased behavioral emotionality. Furthermore, ablation of FC SST neurons also decreased behavioral emotionality under baseline condition and after chronic stress. Together, our results reveal opposite effects of acute and chronic inhibition of FC SST neurons on behavioral emotionality and suggest the recruitment of homeostatic plasticity mechanisms that have implications for understanding the neurobiology of chronic brain conditions affecting dendritic-targeting inhibitory neurons.

Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline.

BACKGROUND: As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations. RESULTS: We performed 12 microarray meta-analysis methods for combining multiple simulated expression profiles, and such methods can be categorized for different hypothesis setting purposes: (1) HS(A): DE genes with non-zero effect sizes in all studies, (2) HS(B): DE genes with non-zero effect sizes in one or more studies and (3) HS(r): DE gene with non-zero effect in "majority" of studies. We then performed a comprehensive comparative analysis through six large-scale real applications using four quantitative statistical evaluation criteria: detection capability, biological association, stability and robustness. We elucidated hypothesis settings behind the methods and further apply multi-dimensional scaling (MDS) and an entropy measure to characterize the meta-analysis methods and data structure, respectively. CONCLUSIONS: The aggregated results from the simulation study categorized the 12 methods into three hypothesis settings (HS(A), HS(B), and HS(r)). Evaluation in real data and results from MDS and entropy analyses provided an insightful and practical guideline to the choice of the most suitable method in a given application. All source files for simulation and real data are available on the author's publication website.

An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection.

SUMMARY: With the rapid advances and prevalence of high-throughput genomic technologies, integrating information of multiple relevant genomic studies has brought new challenges. Microarray meta-analysis has become a frequently used tool in biomedical research. Little effort, however, has been made to develop a systematic pipeline and user-friendly software. In this article, we present MetaOmics, a suite of three R packages MetaQC, MetaDE and MetaPath, for quality control, differentially expressed gene identification and enriched pathway detection for microarray meta-analysis. MetaQC provides a quantitative and objective tool to assist study inclusion/exclusion criteria for meta-analysis. MetaDE and MetaPath were developed for candidate marker and pathway detection, which provide choices of marker detection, meta-analysis and pathway analysis methods. The system allows flexible input of experimental data, clinical outcome (case-control, multi-class, continuous or survival) and pathway databases. It allows missing values in experimental data and utilizes multi-core parallel computing for fast implementation. It generates informative summary output and visualization plots, operates on different operation systems and can be expanded to include new algorithms or combine different types of genomic data. This software suite provides a comprehensive tool to conveniently implement and compare various genomic meta-analysis pipelines. AVAILABILITY: http://www.biostat.pitt.edu/bioinfo/software.htm CONTACT: ctseng@pitt.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MetaQC: objective quality control and inclusion/exclusion criteria for genomic meta-analysis.

Genomic meta-analysis to combine relevant and homogeneous studies has been widely applied, but the quality control (QC) and objective inclusion/exclusion criteria have been largely overlooked. Currently, the inclusion/exclusion criteria mostly depend on ad-hoc expert opinion or naïve threshold by sample size or platform. There are pressing needs to develop a systematic QC methodology as the decision of study inclusion greatly impacts the final meta-analysis outcome. In this article, we propose six quantitative quality control measures, covering internal homogeneity of coexpression structure among studies, external consistency of coexpression pattern with pathway database, and accuracy and consistency of differentially expressed gene detection or enriched pathway identification. Each quality control index is defined as the minus log transformed P values from formal hypothesis testing. Principal component analysis biplots and a standardized mean rank are applied to assist visualization and decision. We applied the proposed method to 4 large-scale examples, combining 7 brain cancer, 9 prostate cancer, 8 idiopathic pulmonary fibrosis and 17 major depressive disorder studies, respectively. The identified problematic studies were further scrutinized for potential technical or biological causes of their lower quality to determine their exclusion from meta-analysis. The application and simulation results concluded a systematic quality assessment framework for genomic meta-analysis.

Altered gene synchrony suggests a combined hormone-mediated dysregulated state in major depression.

Coordinated gene transcript levels across tissues (denoted "gene synchrony") reflect converging influences of genetic, biochemical and environmental factors; hence they are informative of the biological state of an individual. So could brain gene synchrony also integrate the multiple factors engaged in neuropsychiatric disorders and reveal underlying pathologies? Using bootstrapped Pearson correlation for transcript levels for the same genes across distinct brain areas, we report robust gene transcript synchrony between the amygdala and cingulate cortex in the human postmortem brain of normal control subjects (n = 14; Control/Permutated data, p<0.000001). Coordinated expression was confirmed across distinct prefrontal cortex areas in a separate cohort (n = 19 subjects) and affected different gene sets, potentially reflecting regional network- and function-dependent transcriptional programs. Genewise regional transcript coordination was independent of age-related changes and array technical parameters. Robust shifts in amygdala-cingulate gene synchrony were observed in subjects with major depressive disorder (MDD, denoted here "depression") (n = 14; MDD/Permutated data, p<0.000001), significantly affecting between 100 and 250 individual genes (10-30% false discovery rate). Biological networks and signal transduction pathways corresponding to the identified gene set suggested putative dysregulated functions for several hormone-type factors previously implicated in depression (insulin, interleukin-1, thyroid hormone, estradiol and glucocorticoids; p<0.01 for association with depression-related networks). In summary, we showed that coordinated gene expression across brain areas may represent a novel molecular probe for brain structure/function that is sensitive to disease condition, suggesting the presence of a distinct and integrated hormone-mediated corticolimbic homeostatic, although maladaptive and pathological, state in major depression.