Convergent functional genomics of psychiatric disorders

Common and distinguishing genetic factors for substance use behavior and disorder: an integrated analysis of genomic and transcriptomic studies from both human and animal studies

BACKGROUND AND AIMS

Genomic and transcriptomic findings greatly broaden the biological knowledge regarding substance use. However, systematic convergence and comparison evidence of genome-wide findings is lacking for substance use. Here, we combined all the genome-wide findings from both substance use behavior and disorder (SUBD) and identified common and distinguishing genetic factors for different SUBDs.

METHODS

Systemic literature search for genome-wide association (GWAS) and RNA-seq studies of alcohol/nicotine/drug use behavior (partially meets or not reported diagnostic criteria) and alcohol use behavior and disorder (AUBD), nicotine use behavior and disorder (NUBD) and drug use behavior and disorder (DUBD) was performed using PubMed and the GWAS catalog. Drug use was focused upon cannabis, opioid, cocaine and methamphetamine use. GWAS studies required case-control or case/cohort samples. RNA-seq studies were based on brain tissues. The genes which contained significant single nucleotide polymorphism (P ≤ 1 × 10^-6 ) in GWAS and reported as significant in RNA-seq studies were extracted. Pathway enrichment was performed by using Metascape. Gene interaction networks were identified by using the Protein Interaction Network Analysis database.

RESULTS

Total SUBD-related 2910 genes were extracted from 75 GWAS studies (2 773 889 participants) and 17 RNA-seq studies. By overlapping the genes and pathways of AUBD, NUBD and DUBD, four shared genes (CACNB2, GRIN2B, PLXDC2 and PKNOX2), four shared pathways [two Gene Ontology (GO) terms of 'modulation of chemical synaptic transmission', 'regulation of trans-synaptic signaling', two Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of 'dopaminergic synapse', 'cocaine addiction'] were identified (significantly higher than random, P < 1 × 10^-5 ). The top shared KEGG pathways (Benjamini-Hochberg-corrected P-value < 0.05) in the pairwise comparison of AUBD versus DUBD, NUBD versus DUBD, AUBD versus NUBD were 'Epstein-Barr virus infection', 'protein processing in endoplasmic reticulum' and 'neuroactive ligand-receptor interaction', respectively. We also identified substance-specific genetic factors: i.e. ADH1B and ALDH2 were unique for AUBD, while CHRNA3 and CHRNA4 were unique for NUBD.

CONCLUSIONS

This systematic review identifies the shared and unique genes and pathways for alcohol, nicotine and drug use behaviors and disorders at the genome-wide level and highlights critical biological processes for the common and distinguishing vulnerability of substance use behaviors and disorders.

Identifying the biomarkers and pathways associated with hepatocellular carcinoma based on an integrated analysis approach

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related death worldwide. The molecular mechanism underlying HCC is still unclear. In this study, we conducted a comprehensive analysis to explore the genes, pathways and their interactions involved in HCC.

METHODS

We analysed the gene expression datasets corresponding to 488 samples from 10 studies on HCC and identified the genes differentially expressed in HCC samples. Then, the genes were compared against Phenolyzer and GeneCards to screen those potentially associated with HCC. The features of the selected genes were explored by mapping them onto the human protein-protein interaction network, and a subnetwork related to HCC was constructed. Hub genes in this HCC specific subnetwork were identified, and their relevance with HCC was investigated by survival analysis.

RESULTS

We identified 444 differentially expressed genes (177 upregulated and 267 downregulated) related to HCC. Functional enrichment analysis revealed that pathways like p53 signalling and chemical carcinogenesis were eriched in HCC genes. In the subnetwork related to HCC, five disease modules were detected. Further analysis identified six hub genes from the HCC specific subnetwork. Survival analysis showed that the expression levels of these genes were negatively correlated with survival rate of HCC patients.

CONCLUSIONS

Based on a systems biology framework, we identified the genes, pathways, as well as the disease specific network related to HCC. We also found novel biomarkers whose expression patterns were correlated with progression of HCC, and they could be candidates for further investigation.

Using hierarchical similarity to examine the genetics of Behçet's disease

OBJECTIVE

Behçet's disease (BD) is a multisystem inflammatory disease that affects patients along the historic silk road. Thus far, the pathogenesis of the disease has proved elusive due to the complex genetic interactions of the disease. In this paper, we seek to clarify the genetic factors of the disease while also uncovering other diseases of interest that present with a similar genotype as BD.

RESULTS

To do this, we employ a convergent functional genomics approach by leveraging the hierarchical similarity tool available in Geneweaver. Through our analysis, we were able to ascertain 7 BD consensus genes and 16 autoimmune diseases with genetic overlap with BD. The results of our study will inform further research into the pathogenesis of Behçet's disease.

Comprehensive Integrative Analyses Identify TIGD5 rs75547282 as a Risk Variant for Autism Spectrum Disorder

Although recent genome-wide association studies have identified risk loci that strongly associates with autism spectrum disorder (ASD), how to pinpoint the causal genes remains a challenge. We aimed to pinpoint the potential causal genes and explore the possible susceptibility and mechanism. A convergent functional genomics (CFG) method was used to prioritize the candidate genes by combining lines of evidence, including Sherlock analysis, spatio-temporal expression patterns, expression analysis, protein-protein interactions, co-expression and association with brain structure. A higher score in the CFG approach suggested that more evidence supported this gene as an ASD risk gene. We screened genes with higher CFG scores for candidate functional single nucleotide polymorphisms (SNPs). A genotyping experiment (602 ASD children and 604 healthy sex-matched children) and the dual-luciferase reporter gene assay were followed to validate the effects of SNPs. We identified three genes (MAPT, ZNF285, and TIGD5) as candidate causal genes using the CFG approach. The genotyping experiment showed that TIGD5 rs75547282 was associated with an increased risk of ASD under the dominant model (OR = 1.37, 95% CI = 1.09-1.72, P = 0.006) though the statistical power was limited (5.2%). The T allele of rs75547282 activated the expression of TIGD5 compared with the C allele in the dual-luciferase reporter assay. Our study indicates that such comprehensive integrative analyses may be an effective way to explore promising ASD susceptibility variants and needs to be further investigated in future research. Genotyping experiments should, however, be based on a larger population sample to increase statistical power. LAY SUMMARY: We set out to pinpoint the potential causal genes of ASD and explore the possible susceptibility and mechanism by combining lines of evidence from different analyses. Our results show that TIGD5 rs75547282 is associated with the risk of ASD in the Han Chinese population. In addition, a similar framework to seek promising ASD risk variants could be further investigated in future research Autism Res 2021, 14: 631-644. © 2021 International Society for Autism Research and Wiley Periodicals LLC.

Differential Expression of Hippocampal Circular RNAs in the BTBR Mouse Model for Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition with unknown etiology. Recent experimental evidences suggest the contribution of non-coding RNAs (ncRNAs) in the pathophysiology of ASD. In this work, we aimed to investigate the expression profile of the ncRNA class of circular RNAs (circRNAs) in the hippocampus of the BTBR T + tf/J (BTBR) mouse model and age-matched C57BL/6J (B6) mice. Alongside, we analyzed BTBR hippocampal gene expression profile to evaluate possible correlations between the differential abundance of circular and linear gene products. From RNA sequencing data, we identified circRNAs highly modulated in BTBR mice. Thirteen circRNAs and their corresponding linear isoforms were validated by RT-qPCR analysis. The BTBR-regulated circCdh9 was better characterized in terms of molecular structure and expression, highlighting altered levels not only in the hippocampus, but also in the cerebellum, prefrontal cortex, and amygdala. Finally, gene expression analysis of the BTBR hippocampus pinpointed altered biological and molecular pathways relevant for the ASD phenotype. By comparison of circRNA and gene expression profiles, we identified 6 genes significantly regulated at either circRNA or mRNA gene products, suggesting low overall correlation between circRNA and host gene expression. In conclusion, our results indicate a consistent deregulation of circRNA expression in the hippocampus of BTBR mice. ASD-related circRNAs should be considered in functional studies to identify their contribution to the etiology of the disorder. In addition, as abundant and highly stable molecules, circRNAs represent interesting potential biomarkers for autism.

Bipolar disorder in the balance

Bipolar disorder (BD) is a severe mood disorder that lacks established electrophysiological, neuroimaging or biological markers to assist with both diagnosis and monitoring disease severity. This study's aim is to describe the potential of new neurophysiological features assistive in BD diagnosis and severity measurement utilizing the recording of electrical activity from the outer ear canal called Electrovestibulography (EVestG). From EVestG data sensory vestibulo-acoustic features were extracted from a single supine-vertical translation stimulus to distinguish 50 depressed and partly remitted/remitted bipolar disorder patients [18 symptomatic (BD-S, MADRS > 19), 32 reduced symptomatic (BD-R, MADRS ≤ 19)] and 31 age and gender matched healthy individuals (controls). Six features were extracted from the measured firing pattern interval histogram and the extracted shape of the average field potential response. Five of the six features had low but significant correlations (p < 0.05) with the MADRS assessment. Using leave-one-out-cross-validation, unbiased parametric and non-parametric classification routines resulted in 75-79%, 84-86%, 76-85% and 79-82% accuracy for separation of control from BD, BD-S and BD-R as well as BD-S from BD-R groups, respectively. The main limitation of this study was the inability to fully disentangle the impact of prescribed medication from the responses recorded. A mix of stationary and movement evoked EVestG features produced good discrimination between control and BD patients whether BD-S or BD-R. Moreover, BD-S and BD-R appear to have measurably different pathophysiological manifestations. The firing pattern features used were dissimilar to those observed in a prior major depressive disorder study.

Stroke health professionals' management of depression after post-stroke aphasia: a qualitative study

Background: People with post-stroke aphasia commonly experience mental health conditions, with depression having a high prevalence. An understanding of current practice may inform ways to improve psychological care for people with aphasia.Aim: To explore current practice for managing depression after post-stroke aphasia from the perspective of stroke health professionals.Method: Five semi-structured focus groups were conducted with 39 stroke health professionals across the care continuum. Focus groups were transcribed verbatim and analyzed using the method of Interpretive Description.Results: Analysis of verbatim transcripts revealed four core themes: (1) concomitant aphasia and depression after stroke is a challenging area of rehabilitation, (2) mood difficulties and depression are not always a high stroke rehabilitation priority, (3) approaches to identification and management are ad hoc, and (4) stroke health professionals are trying to bridge the gap between clients' psychological care needs and limited services. Sub-themes were also identified.Conclusion: This study demonstrates that health professionals are challenged by and limited in managing depression after post-stroke aphasia. Health professionals have the opportunity to improve services through the translation of evidence-based interventions. The integration of mental health care into stroke rehabilitation may be achieved through policy development, leadership and specialist training.Implications for rehabilitationPeople with aphasia need routine mood screening using aphasia-specific clinical tools and communication support.Stroke health professionals report a need for communication partner training to facilitate mental healthcare for people with aphasia.Family members need to be involved in aphasia rehabilitation to gain psychological care for themselves and the person with aphasia.

A comprehensive regional analysis of genome-wide expression profiles for major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a global health challenge. In recent years, a large number of genome-wide expression studies (GWES) have been carried out to identify the transcriptomic profiles for MDD. The objective of this work was to carry out a comprehensive meta-analysis of available GWES for MDD.

METHODS

GWES for MDD with available raw data were searched in NCBI GEO, Array Express and Stanley databases. Raw GWES data were preprocessed and normalized and meta-analytical procedures were carried out with the Network Analyst program. 743 samples from 24 primary studies were included in our meta-analyses for blood (Blo), amygdala (Amy), cerebellum (Cer), anterior cingulate cortex (ACC) and prefrontal cortex (PFC) regions. A functional enrichment analysis was carried out.

RESULTS

We identified 35, 793, 231, 668 and 252 differentially expressed (DE) genes for Blo, Amy, Cer, ACC and PFC regions. A region-dependent significant enrichment for several functional categories, such as gene ontologies, signaling pathways and topographic parameters, was identified. There was convergence with other available genome-wide studies, such as GWAS, DNA methylation analyses and miRNA expression studies.

LIMITATIONS

Raw data were not available for several primary studies that have been published previously.

CONCLUSIONS

This is the largest meta-analysis for GWES in MDD. The examination of convergence of genome-wide evidence and of the functional enrichment analysis provides a global overview of potential neural signaling mechanisms dysregulated in MDD. Our comprehensive analysis of several brain regions identified lists of DE genes for MDD that are interesting candidates for further studies.

Physical Activity Modulates Common Neuroplasticity Substrates in Major Depressive and Bipolar Disorder

Mood disorders (MDs) are chronic, recurrent mental diseases that affect millions of individuals worldwide. Although the biogenic amine model has provided some clinical utility, a need remains to better understand the interrelated mechanisms that contribute to neuroplasticity deficits in MDs and the means by which various therapeutics mitigate them. Of those therapeutics being investigated, physical activity (PA) has shown clear and consistent promise. Accordingly, the aims of this review are to (1) explicate key modulators, processes, and interactions that impinge upon multiple susceptibility points to effectuate neuroplasticity deficits in MDs; (2) explore the putative mechanisms by which PA mitigates these features; (3) review protocols used to induce the positive effects of PA in MDs; and (4) highlight implications for clinicians and researchers.

Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders

OBJECTIVES

To provide guidance for the management of mood disorders, based on scientific evidence supplemented by expert clinical consensus and formulate recommendations to maximise clinical salience and utility.

METHODS

Articles and information sourced from search engines including PubMed and EMBASE, MEDLINE, PsycINFO and Google Scholar were supplemented by literature known to the mood disorders committee (MDC) (e.g., books, book chapters and government reports) and from published depression and bipolar disorder guidelines. Information was reviewed and discussed by members of the MDC and findings were then formulated into consensus-based recommendations and clinical guidance. The guidelines were subjected to rigorous successive consultation and external review involving: expert and clinical advisors, the public, key stakeholders, professional bodies and specialist groups with interest in mood disorders.

RESULTS

The Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders (Mood Disorders CPG) provide up-to-date guidance and advice regarding the management of mood disorders that is informed by evidence and clinical experience. The Mood Disorders CPG is intended for clinical use by psychiatrists, psychologists, physicians and others with an interest in mental health care.

CONCLUSIONS

The Mood Disorder CPG is the first Clinical Practice Guideline to address both depressive and bipolar disorders. It provides up-to-date recommendations and guidance within an evidence-based framework, supplemented by expert clinical consensus.

MOOD DISORDERS COMMITTEE

Professor Gin Malhi (Chair), Professor Darryl Bassett, Professor Philip Boyce, Professor Richard Bryant, Professor Paul Fitzgerald, Dr Kristina Fritz, Professor Malcolm Hopwood, Dr Bill Lyndon, Professor Roger Mulder, Professor Greg Murray, Professor Richard Porter and Associate Professor Ajeet Singh.

INTERNATIONAL EXPERT ADVISORS

Professor Carlo Altamura, Dr Francesco Colom, Professor Mark George, Professor Guy Goodwin, Professor Roger McIntyre, Dr Roger Ng, Professor John O'Brien, Professor Harold Sackeim, Professor Jan Scott, Dr Nobuhiro Sugiyama, Professor Eduard Vieta, Professor Lakshmi Yatham.

AUSTRALIAN AND NEW ZEALAND EXPERT ADVISORS

Professor Marie-Paule Austin, Professor Michael Berk, Dr Yulisha Byrow, Professor Helen Christensen, Dr Nick De Felice, A/Professor Seetal Dodd, A/Professor Megan Galbally, Dr Josh Geffen, Professor Philip Hazell, A/Professor David Horgan, A/Professor Felice Jacka, Professor Gordon Johnson, Professor Anthony Jorm, Dr Jon-Paul Khoo, Professor Jayashri Kulkarni, Dr Cameron Lacey, Dr Noeline Latt, Professor Florence Levy, A/Professor Andrew Lewis, Professor Colleen Loo, Dr Thomas Mayze, Dr Linton Meagher, Professor Philip Mitchell, Professor Daniel O'Connor, Dr Nick O'Connor, Dr Tim Outhred, Dr Mark Rowe, Dr Narelle Shadbolt, Dr Martien Snellen, Professor John Tiller, Dr Bill Watkins, Dr Raymond Wu.

A guide on gene prioritization in studies of psychiatric disorders

There has been an increasing interest in the identification of genetic variants causing individual differences in human behavior. Psychiatrists have contributed to the genetics field by defining the most important behavioral characteristics and by studying the association between genetic variants and behavioral differences within phenotypically well-characterized samples in which detailed assessments have been collected (e.g. neuroimaging). These samples are typically limited in size and are therefore not suitable for a genome-wide association analysis. Instead, gene association studies conducted in such samples typically focus on a few genes of interest, allowing smaller sample sizes. However, the selection of high-priority genes is not always straightforward and psychiatrists will usually have a limited background in genetics. We aim to fill this gap by (i) providing a basic introduction to genetics; (ii) showing how the selection of genes of interest can be optimized by the use of two web tools: Polysearch and Gene Prospector; (iii) illustrating how statistical power analyses can be performed and discussing the importance of sufficiently powered studies. This guide can help psychiatrists with limited experience in genetics in designing genetic studies that allow identification of specific behavioral, cognitive, or neural correlates of genetic risk variants, while avoiding common pitfalls. Copyright © 2015 John Wiley & Sons, Ltd.

Applicability of gene expression and systems biology to develop pharmacogenetic predictors; antipsychotic-induced extrapyramidal symptoms as an example

Pharmacogenetics has been driven by a candidate gene approach. The disadvantage of this approach is that is limited by our current understanding of the mechanisms by which drugs act. Gene expression could help to elucidate the molecular signatures of antipsychotic treatments searching for dysregulated molecular pathways and the relationships between gene products, especially protein-protein interactions. To embrace the complexity of drug response, machine learning methods could help to identify gene-gene interactions and develop pharmacogenetic predictors of drug response. The present review summarizes the applicability of the topics presented here (gene expression, network analysis and gene-gene interactions) in pharmacogenetics. In order to achieve this, we present an example of identifying genetic predictors of extrapyramidal symptoms induced by antipsychotic.

The landscape of copy number variations in Finnish families with autism spectrum disorders

Rare de novo and inherited copy number variations (CNVs) have been implicated in autism spectrum disorder (ASD) risk. However, the genetic underpinnings of ASD remain unknown in more than 80% of cases. Therefore, identification of novel candidate genes and corroboration of known candidate genes may broaden the horizons of determining genetic risk alleles, and subsequent development of diagnostic testing. Here, using genotyping arrays, we characterized the genetic architecture of rare CNVs (<1% frequency) in a Finnish case-control dataset. Unsurprisingly, ASD cases harbored a significant excess of rare, large (>1 Mb) CNVs and rare, exonic CNVs. The exonic rare de novo CNV rate (∼22.5%) seemed higher compared to previous reports. We identified several CNVs in well-known ASD regions including GSTM1-5, DISC1, FHIT, RBFOX1, CHRNA7, 15q11.2, 15q13.2-q13.3, 17q12, and 22q11.21. Additionally, several novel candidate genes (BDKRB1, BDKRB2, AP2M1, SPTA1, PTH1R, CYP2E1, PLCD3, F2RL1, UQCRC2, LILRB3, RPS9, and COL11A2) were identified through gene prioritization. The majority of these genes belong to neuroactive ligand-receptor interaction pathways, and calcium signaling pathways, thus suggesting that a subset of these novel candidate genes may contribute to ASD risk. Furthermore, several metabolic pathways like caffeine metabolism, drug metabolism, retinol metabolism, and calcium-signaling pathway were found to be affected by the rare exonic ASD CNVs. Additionally, biological processes such as bradykinin receptor activity, endoderm formation and development, and oxidoreductase activity were enriched among the rare exonic ASD CNVs. Overall, our findings may add data about new genes and pathways that contribute to the genetic architecture of ASD.

A brief review of exercise, bipolar disorder, and mechanistic pathways

Despite evidence that exercise has been found to be effective in the treatment of depression, it is unclear whether these data can be extrapolated to bipolar disorder. Available evidence for bipolar disorder is scant, with no existing randomized controlled trials having tested the impact of exercise on depressive, manic or hypomanic symptomatology. Although exercise is often recommended in bipolar disorder, this is based on extrapolation from the unipolar literature, theory and clinical expertise and not empirical evidence. In addition, there are currently no available empirical data on program variables, with practical implications on frequency, intensity and type of exercise derived from unipolar depression studies. The aim of the current paper is to explore the relationship between exercise and bipolar disorder and potential mechanistic pathways. Given the high rate of medical co-morbidities experienced by people with bipolar disorder, it is possible that exercise is a potentially useful and important intervention with regard to general health benefits; however, further research is required to elucidate the impact of exercise on mood symptomology.

A conserved BDNF, glutamate- and GABA-enriched gene module related to human depression identified by coexpression meta-analysis and DNA variant genome-wide association studies

Large scale gene expression (transcriptome) analysis and genome-wide association studies (GWAS) for single nucleotide polymorphisms have generated a considerable amount of gene- and disease-related information, but heterogeneity and various sources of noise have limited the discovery of disease mechanisms. As systematic dataset integration is becoming essential, we developed methods and performed meta-clustering of gene coexpression links in 11 transcriptome studies from postmortem brains of human subjects with major depressive disorder (MDD) and non-psychiatric control subjects. We next sought enrichment in the top 50 meta-analyzed coexpression modules for genes otherwise identified by GWAS for various sets of disorders. One coexpression module of 88 genes was consistently and significantly associated with GWAS for MDD, other neuropsychiatric disorders and brain functions, and for medical illnesses with elevated clinical risk of depression, but not for other diseases. In support of the superior discriminative power of this novel approach, we observed no significant enrichment for GWAS-related genes in coexpression modules extracted from single studies or in meta-modules using gene expression data from non-psychiatric control subjects. Genes in the identified module encode proteins implicated in neuronal signaling and structure, including glutamate metabotropic receptors (GRM1, GRM7), GABA receptors (GABRA2, GABRA4), and neurotrophic and development-related proteins [BDNF, reelin (RELN), Ephrin receptors (EPHA3, EPHA5)]. These results are consistent with the current understanding of molecular mechanisms of MDD and provide a set of putative interacting molecular partners, potentially reflecting components of a functional module across cells and biological pathways that are synchronously recruited in MDD, other brain disorders and MDD-related illnesses. Collectively, this study demonstrates the importance of integrating transcriptome data, gene coexpression modules and GWAS results for providing novel and complementary approaches to investigate the molecular pathology of MDD and other complex brain disorders.