Daniel ; Zhou

Yi Zhou

Pierre-Eric Lutz

El Chérif Ibrahim

Philippe Courtet

Eleni Tzavara

Raoul Belzeaux

Suicide and suicide behaviors: a comprehensive review of transcriptomics studies

Keywords: Suicide, Transcription regulation, Biomarker, Epigenetic

Aberrant gene expression can contribute to brain and nervous system dysfunction that causes many psychiatric illnesses. Here, we review how transcriptomic approaches have deepened our understanding of the neurobiological underpinnings of suicide and suicidal behaviors, as well as how such approaches have contributed to the identification of biomarkers for these disorders. We begin by providing an overview of the genetic, transcriptomic, and epigenetic factors (including non-coding RNAs) implicated in suicide and their roles in the regulation of gene expression. Then, we highlight the unique benefits and limitations of using either postmortem brain or peripheral tissues in transcriptomic analyses. We examine the current shift from candidate-gene to genome-wide approaches in psychiatric research, which are concurrently emerging with the increased consideration of the Research Domain Criteria (RDoC) framework for classifying mental illnesses. Furthermore, we discuss the use of transcription networks and how they can be integrated into multi-omic analyses. Finally, we end by highlighting recent findings of peripheral markers of suicide risk identified through the use of transcriptomic tools. Technological advancements and increased accessibility of these technologies are drastically shaping the current research landscape. We present an overview of the significant changes currently taking place to usher in a new era of psychiatric research.

Significance statement (max 100 words) Genes can cause biological processes in the brain to break down. Biological and environmental factors determine whether a gene is "turned on or off" and we can get a readout of which genes are turned on or off, whether or not they are transcribed, in certain disease conditions. Here, we review how patterns of transcribed genes can help us understanding the causes of suicide and identifying markers that predict the risk for suicide and suicidal behaviors.

Suicide is a major public health issue. According to the last world health statistics, close to 800,000 people die due to suicide every year, which is one person every 40 seconds and many more attempt suicide. Suicide occurs throughout the lifespan and is the second leading cause of death among 15-29 year olds globally (World Health Organization 2016). Although suicide is very often associated with a serious complication of major psychiatric illnesses (including mainly mood disorders and schizophrenia), it is now considered a specific behavior with specific vulnerability factors and pathophysiology. Moreover, while the burden of suicide is considerable [START_REF] Bolton | Suicide risk assessment and intervention in people with mental illness[END_REF], our understanding of its pathophysiology is limited and predictive biomarker development in this area is still in its infancy. To develop both a deeper knowledge of suicide pathophysiology and to open up an avenue for the development of predictive biomarkers of risk, transcriptomic studies of suicide behavior offer significant opportunities. In this review, we aim to detail several conceptual and methodological opportunities and caveats of transcriptomic studies according to the literature. Then, we will conduct a comprehensive review of past and present results in this field.

Concepts and methodologies used to study suicide transcriptomics: opportunities and caveats

From genes to epigenetics

The biological contributions to suicide and suicidal behaviors have been largely investigated through identifying pathophysiological processes implicated in both central and peripheral systems. Genetic association and gene expression studies aim to elucidate the molecular mechanisms by which, genes and their regulatory roles contribute to risk factors, or biological dysfunctions lead to these complex behaviors and outcomes [START_REF] Lutz | Neuropathology of suicide: recent findings and future directions[END_REF]. Suicide and suicidal behaviors have been shown to be heritable [START_REF] Egeland | Suicide and family loading for affective disorders[END_REF] with twin studies suggesting significant genetic influences contributing to suicide and suicidal behaviors F o r P e e r R e v i e w [START_REF] Pedersen | Genetic influences on suicide and nonfatal suicidal behavior: twin study findings[END_REF]. For instance, one twin study investigating suicidal behaviors in men estimated the heritability of suicide ideation and attempt to be 36% and 17%, respectively [START_REF] Fu | A twin study of genetic and environmental influences on suicidality in men[END_REF]. Accordingly, these results prompted researchers to identify specific genes associated with suicide in an effort to better understand the etiology of this disorder and identify potential targets for therapeutic intervention [START_REF] Turecki | The molecular bases of the suicidal brain[END_REF].

It should be noted that individual genetic variations associated with suicide often account only for a small proportion of the total phenotypic variation (small effect sizes) and those with larger effect sizes tend to lack reproducibility [START_REF] Perlis | Failure to replicate genetic associations with antidepressant treatment response in duloxetine-treated patients[END_REF]). As such, these trends indicate that the risk of suicide and suicidal behaviors is likely modulated by multiple genetic variations at various loci, with each contributing a small amount to the total risk of disease. Subsequently, variations at multiple genetic loci have been used to produce polygenic-risk-scores that have been more successfully able to predict the risk of suicidal behaviors [START_REF] Mullins | Genetic relationships between suicide attempts, suicidal ideation and major psychiatric disorders: a genome-wide association and polygenic scoring study[END_REF] (Sokolowski et al. 2016).

Although genetic variations can be used to predict suicide risk, the mechanisms by which these variations disrupt regulatory processes or molecular pathways to contribute to pathophysiological dysfunction belong to the field of functional genomics or transcriptomics [START_REF] Cooper | Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data[END_REF][START_REF] Pastinen | Genome-wide allele-specific analysis: insights into regulatory variation[END_REF]. Gene transcription is one functional output of genetic regulation that signifies the initiation of a biological response and can be used to represent the activity of a particular molecular pathway or regulatory process. Biological responses that affect gene transcription can vary in timescales ranging from milliseconds, to seconds, to days, or even decades with various regulatory processes contributing to varying transcription kinetics [START_REF] Lenstra | Transcription Dynamics in Living Cells[END_REF]. Therefore, characterizing the transcriptional activity of genes associated with suicide can provide an important snapshot of the underlying biology contributing to the disease state. DNA to hundreds of thousands of unique oligomer probes generated from a library of known genomic sequences [START_REF] Lowe | Transcriptomics technologies[END_REF]. Later, the development of next-generation highthroughput sequencing technologies enabled RNA-sequencing of the entire transcriptome. As well, whole transcriptome sequencing allowed for the quantification of previously unannotated genes, thereby aiding novel gene discovery, which is a significant advantage over microarrays [START_REF] Trapnell | Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks[END_REF]. Importantly, the ability to generate whole-transcriptome gene expression data has led to the development of various types of global analyses, which include the identification of networks of co-expressed genes (i.e. gene which level of expression is correlated) that are more likely to underlie genes with common biological processes implicated in disease [START_REF] Langfelder | WGCNA: an R package for weighted correlation network analysis[END_REF]. Gene expression networks may even help identify common pathways between different psychiatric diseases or between different groups of patients. These gene sets and their pathways may also be easier to reproduce in different studies. This could be especially useful in the study of factors unique to suicidal behaviors, which are very often comorbid with other psychopathologies. Moreover, the identification of gene expression networks that are highly preserved in specific tissues or species can greatly facilitate the characterization of conserved biological processes, allowing for translational and biomarker studies, including in different tissues (see below).

RDoC framework for mental disorder research

Interestingly, there is strong evidence supporting the association of the serotonergic system with specific psychiatric behaviors or endophenotypes, such as lower serotonin metabolite F o r P e e r R e v i e w levels with aggression [START_REF] Brown | Aggression, suicide, and serotonin: relationships to CSF amine metabolites[END_REF] as well as serotonin transporter polymorphisms with stress susceptibility [START_REF] Karg | The serotonin transporter promoter variant (5-HTTLPR), stress, and depression meta-analysis revisited: evidence of genetic moderation[END_REF]. These findings represent risk factors and endophenotypes potentially common across several psychiatric diagnoses (such as schizophrenia, bipolar disorders, and personality disorders) that may help elucidate the common mechanisms underlying specific brain functions dysregulated in psychiatric disorders. In fact, such a framework around dimensional psychological constructs relevant to human behavior, including systems of emotion, cognition, motivation, and social behavior, has been proposed to categorize and classify research domains in mental disorders. This classification framework is called Research Domain Criteria (RDoC) and aims to aid researchers to integrate molecular, genetic, neuro-circuit information and behavioral assessments to explore human brain function and behavior [START_REF] Insel | Research domain criteria (RDoC): toward a new classification framework for research on mental disorders[END_REF]. Interestingly, potential correspondence between individual genes and specific RDoC constructs have all been recently removed from RDoC website, with a statement, as of May 2017, indicating that the "current state of the field emphasizes the need for robust evidence of association, generally resulting from adequately powered, genome wide studies, as opposed to candidate gene approaches." (https://www.nimh.nih.gov/research-priorities/rdoc/update-on-genes-inthe-rdoc-matrix.shtml). Clearly, there appears to be a shift away from candidate gene approaches that are usually limited in scope and clinical value [START_REF] Collins | Hypothesis-driven candidate genes for schizophrenia compared to genome-wide association results[END_REF][START_REF] Van Der Auwera | Genome-wide gene-environment interaction in depression: A systematic evaluation of candidate genes: The childhood trauma working-group of PGC-MDD[END_REF].

Transcriptomic Network Approaches

The movement towards large genome-wide datasets not only allows for the discovery of associations in a more unbiased approach, but the large comprehensive sets of data allows for the study of various systems in parallel and can lead to the detection of broader molecular signatures underlying suicide [START_REF] Oquendo | Toward a biosignature for suicide[END_REF]. As well, the interactions between various systems can be detected. For example, significant overlap between the HPA axis and immune system has been reported in the context of social stress [START_REF] Bekhbat | Checks and balances: The glucocorticoid receptor and NFkB in good times and bad[END_REF]) and serotonergic and dopaminergic interactions have been shown to be important in impulsive aggressive behaviors [START_REF] Sen | Serum brain-derived neurotrophic factor, depression, and antidepressant medications: meta-analyses and implications[END_REF].

One way to detect such broader signals of biological processes and interactions is through the use of transcriptomic network analyses. Transcriptomic analyses of co-expression networks can allow for the study of the molecular processes governing brain structure, circuit organization, brain function, as well as other regulatory systems including epigenetic processes and non-coding gene regulatory networks [START_REF] Gaiteri | Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders[END_REF]. Genes co-expressed in a specific spatial or temporal manner are more likely to be functionally related and have been shown to be preserved across brain regions and species [START_REF] Langfelder | Is my network module preserved and reproducible?[END_REF][START_REF] Oldham | Conservation and evolution of gene coexpression networks in human and chimpanzee brains[END_REF], as well as across psychiatric categories like depression [START_REF] Gaiteri | Differentially expressed genes in major depression reside on the periphery of resilient gene coexpression networks[END_REF].

Network approaches, such as Weighted Gene Co-expression Network Analysis (WGCNA) [START_REF] Langfelder | WGCNA: an R package for weighted correlation network analysis[END_REF], have been used to study gene co-expression in various brain regions across psychiatric and neurodegenerative diseases including schizophrenia [START_REF] Chen | Two gene co-expression modules differentiate psychotics and controls[END_REF]) and Alzheimer's disease [START_REF] Miller | A systems level analysis of transcriptional changes in Alzheimer's disease and normal aging[END_REF][START_REF] Zhang | Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease[END_REF]. Interestingly, gene co-expression analyses have been combined with other types of data such as DNA methylation in major depression (Bustamante AC et al 2017 Psychiatry Res), miRNA, and expression quantitative trait loci (eQTL) analyses in alcohol addiction [START_REF] Mamdani | Integrating mRNA and miRNA Weighted Gene Co-Expression Networks with eQTLs in the Nucleus Accumbens of Subjects with Alcohol Dependence[END_REF][START_REF] Ponomarev | Gene coexpression networks in human brain identify epigenetic modifications in alcohol dependence[END_REF], and GWAS in autism disorder [START_REF] Voineagu | Transcriptomic analysis of autistic brain reveals convergent molecular pathology[END_REF]. As well, coexpressed genes in the dorso-lateral PFC (dlPFC) have been associated with MRI measures of cognitive decline in later life [START_REF] Yu | Association Between Brain Gene Expression, DNA Methylation, and Alteration of Ex Vivo Magnetic Resonance Imaging Transverse Relaxation in Late-Life Cognitive Decline[END_REF]. Other applications include characterizing coexpression networks across different brain regions in the same subjects to shed light onto the molecular processes underlying neural-circuit processes. For example, one study examining depressed subjects showed robust shifts in amygdala-cingulate gene synchrony involving hormone-type factors including insulin, interleukin-1, thyroid hormone, estradiol and glucocorticoids [START_REF] Gaiteri | Altered gene synchrony suggests a combined hormone-mediated dysregulated state in major depression[END_REF]. Finally, studies examining drug-induced changes in transcriptional networks hold the potential to unveil broader mechanisms of drug action and whether they are effective at targeting the molecular networks disrupted in disease states [START_REF] Iskar | Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding[END_REF]. To the best of our knowledge, no previous study focused specifically on gene networks associated with suicide behavior.

The integration of transcriptomic data with other omic pillars into "Gestaltomics" (such as the genome, epigenome, proteome, metabolome, phenome and microbiome) provides an exciting new approach to guide a broader and multi-system understanding of specific psychiatric phenotypes [START_REF] Amare | Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry[END_REF][START_REF] Najera | Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases[END_REF]. As an example, one study identified a unique co-expression network associated with cognitive performance in the blood of depressed patients, and validated the expression of various genes within this network at the proteomic level [START_REF] Schubert | Targeted proteomic analysis of cognitive dysfunction in remitted major depressive disorder: Opportunities of multi-omics approaches towards predictive, preventive, and personalized psychiatry[END_REF]). This analysis implicated molecular processes and genes related to cell-cycle signaling, protein processing, and interferon signaling. Such a multi-omic approach could facilitate biomarker discovery and can be integrated with other clinical measures to better model and predict disease.

Currently, several approaches, such as twin studies utilizing Structural Equation Modeling, exist that are capable of integrating neurocognitive, social/psychological, and biological factors (such as transcriptional networks) to model psychiatric disorders and to estimate the specific contributions of genetic and environmental factors to clinical outcomes [START_REF] Iacono | The utility of twins in developmental cognitive neuroscience research: How twins strengthen the ABCD research design[END_REF]. As the field of psychiatric research moves forward, the use of transcriptomic and other omic datasets will help provide a more comprehensive understanding of the processes that cause psychiatric diseases and hopefully help overcome current limitations in generating clinically useful tests for psychiatric diseases [START_REF] Kapur | Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it?[END_REF].

Epigenetics and non-coding RNA

Finally, an important factor contributing to gene regulation that may bridge the gap between genetic variation and transcriptomic effects in suicide, and potentially mediate the translation of social stress into biological dysfunction, is the epigenetic control of gene expression. Epigenetic mechanisms of gene regulation are heritable changes in gene function that do not involve changes in the sequence of the DNA [START_REF] Dupont | Epigenetics: definition, mechanisms and clinical perspective[END_REF]. Epigenetic mechanisms of regulation include covalent modifications of DNA, such as DNA methylation, as well as modifications of histone proteins that determine chromatin conformation, such as histone methylation and acetylation [START_REF] Allis | The molecular hallmarks of epigenetic control[END_REF]. Genome-wide DNA methylation studies in the suicidal brain have shown significant correlations between DNA methylation and gene expression [START_REF] Labonte | Genome-wide methylation changes in the brains of suicide completers[END_REF][START_REF] Schneider | Epigenetic Dysregulation in the Prefrontal Cortex of Suicide Completers[END_REF].

Additionally, non-coding RNAs (ncRNAs), such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are increasingly recognized as important regulators of gene expression and mediators of epigenetic modifications, including in suicide and related psychiatric disorders [START_REF] Lin | Noncoding RNAs in Depression[END_REF][START_REF] Serafini | The involvement of microRNAs in major depression, suicidal behavior, and related disorders: a focus on miR-185 and miR-491-3p[END_REF]. Originating from genomic regions previously thought of as "junk DNA", ncRNAs are becoming an essential component of transcriptomic analyses. While adding to the complexity of genetic regulation, ncRNAs represent a whole new world of processes and pathways that can help us better understand how environmental factors like social stress can have lasting effects in the human body that lead to suicide and other psychiatric disorders.

Small ncRNAs, such as miRNAs, around 22 nucleotides in length, have emerged as important negative post-transcriptional regulators of gene expression and have been implicated in the pathophysiology of various psychiatric illnesses [START_REF] Geaghan | MicroRNA and Posttranscriptional Dysregulation in Psychiatry[END_REF].

Single miRNAs may regulate many target transcripts and single transcripts may be regulated by many miRNAs (ie. multiple-to-multiple relationships) thereby allowing miRNAs to broadly regulate the expression of gene networks potentially underlying complex disease processes [START_REF] Hashimoto | Multiple-to-multiple relationships between microRNAs and target genes in gastric cancer[END_REF].

There are a few other classes of ncRNAs, which have recently emerged and may have significant regulatory functions. Small nucleolar RNAs (snoRNAs) are able to guide the chemical modification of other RNA molecules, including ribosomal RNAs, transfer RNAs, and nuclear RNAs, leading to such processes as alternative splicing, and have been implicated in affective disorders [START_REF] Lin | Noncoding RNAs in Depression[END_REF].

Another class of regulatory ncRNAs is the long ncRNAs (lncRNAs), which are greater than 200 nucleotides in length and have low protein-coding potential. LncRNAs have been shown to be implicated in neurodevelopment, brain function and neurodegenerative diseases [START_REF] Wu | Roles of long noncoding RNAs in brain development, functional diversification and neurodegenerative diseases[END_REF]. Interestingly, there is seemingly no canonical mechanism by which lncRNAs mediate their regulatory processes (as is the case for miRNAs), but generally speaking, they are known to be able to regulate both nearby protein coding gene-targets in cis, through interference of transcriptional and other regulatory protein machinery, and more distal genes in trans, through the recruitment and targeting of protein complexes to target genomic loci [START_REF] Roberts | The role of long non-coding RNAs in neurodevelopment, brain function and neurological disease[END_REF]. LncRNAs are able to fold into complex 2D and 3D modular domains capable of binding to other genomic materials or protein machinery, thus contributing to their significant regulatory potential [START_REF] Ferre | Revealing protein-lncRNA interaction[END_REF][START_REF] Novikova | Sizing up long non-coding RNAs: do lncRNAs have secondary and tertiary structure?[END_REF]).

Transcriptomics across central and peripheral tissues

Of course, the extent to which suicidal thoughts and attempts, and their fatal realization share common or distinct underpinnings remains a difficult problem to resolve, primarily because of the tissue available for study. When it comes to psychiatry, the brain is thought to be the most direct organ involved in the pathophysiology of disease. Owing to its functions in cognition, emotional processing, and memory, the brain is poised to be the primary organ underlying the symptoms and diagnostic characteristics associated with psychiatric illnesses. Thus, the use of post-mortem brain for the study of psychiatric disease has been crucial to our understanding of suicide as well as the impact of social stress on brain dysfunction [START_REF] Mccullumsmith | Novel approaches to the study of postmortem brain in psychiatric illness: old limitations and new challenges[END_REF]. It has allowed scientists to carry out investigations of neurobiological processes of suicide in specific brain regions [START_REF] Furczyk | The neurobiology of suicide -A Review of post-mortem studies[END_REF]. With the latest developments in cell type specific and single cell isolation techniques, such as Fluorescence Activated Cell Sorting (FACS) and Laser-Capture Microdissection (LCM), exploration of the molecular mechanisms carried out by distinct cell populations are possible using post-mortem tissues [START_REF] Hu | Single Cell Isolation and Analysis[END_REF][START_REF] Lutz | Neuropathology of suicide: recent findings and future directions[END_REF]). Studies using these techniques have only just begun to reveal distinct molecular profiles from specific cell types and populations [START_REF] Byne | Schizophrenia and sex associated differences in the expression of neuronal and oligodendrocyte-specific genes in individual thalamic nuclei[END_REF][START_REF] Ruzicka | Selective epigenetic alteration of layer I GABAergic neurons isolated from prefrontal cortex of schizophrenia patients using laser-assisted microdissection[END_REF], which could even be used to distinguish between certain psychiatric illnesses [START_REF] Arion | Transcriptome Alterations in Prefrontal Pyramidal Cells Distinguish Schizophrenia From Bipolar and Major Depressive Disorders[END_REF]). As we uncover more of the complex nature of the brain, post-mortem brain tissues will continue to be an invaluable resource for the study of the psychiatric illnesses (de Lange 2017). Some of these molecular findings can then be investigated, at least in part, in living patients using brain imaging techniques (Oquendo et al. 2003) or studied in animal and in vitro models to provide further insight into the molecular mechanisms underlying brain dysfunction [START_REF] Mcgowan | Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse[END_REF].

While post-mortem brain tissues offer many advantages for the study of psychiatric disease, there are also unique challenges and limitations associated with their use. Since postmortem tissues provide a snapshot of the subject's state at a specific time, they are usually used only in cross-sectional study designs that may not able to confirm cause-and-effect relationship and may have no predictive value. Several factors affecting tissue quality have varying effects on biomolecules used for study, including DNA, RNA, proteins, and lipids [START_REF] Ferrer | Brain banks: benefits, limitations and cautions concerning the use of post-mortem brain tissue for molecular studies[END_REF][START_REF] Kretzschmar | Brain banking: opportunities, challenges and meaning for the future[END_REF]. The period of time in between the moment of death to have varying effects on different classes of RNA and other biomolecules [START_REF] Nagy | Effects of postmortem interval on biomolecule integrity in the brain[END_REF] with longer PMI generally associating with higher RNA degradation [START_REF] Birdsill | Postmortem interval effect on RNA and gene expression in human brain tissue[END_REF]. It is important to note that in cases where death occurs outside of a monitored clinical setting, forensic estimates of human PMI's can be used but several factors, including environmental temperature and cause of death, can affect rates of post-mortem changes used in these estimates [START_REF] Sampaio-Silva | Profiling of RNA degradation for estimation of post mortem [corrected] interval[END_REF]. Fortunately, RNA integrity has been found to be a good indicator of tissue quality [START_REF] Stan | Human postmortem tissue: what quality markers matter?[END_REF] and can be estimated with automated highthroughput technologies [START_REF] Schroeder | The RIN: an RNA integrity number for assigning integrity values to RNA measurements[END_REF]). As such, RNA Integrity Numbers (RIN) are reported for each sample, and also frequently controlled for in post-mortem brain studies, while more recently bioinformatics approaches have been developed to measure and control for RNA degradation at the level of each transcript [START_REF] Wang | Measure transcript integrity using RNA-seq data[END_REF]. Several other factors such as mode of death (agonal conditions leading to coma and hypoxia) [START_REF] Tomita | Effect of agonal and postmortem factors on gene expression profile: quality control in microarray analyses of postmortem human brain[END_REF]), history of drug exposure [START_REF] Karege | Neurotrophin levels in postmortem brains of suicide victims and the effects of antemortem diagnosis and psychotropic drugs[END_REF], and lifetime history of disease and stress exposure [START_REF] Lupien | Effects of stress throughout the lifespan on the brain, behaviour and cognition[END_REF]) can all have significant effects on gene expression and brain function. However, these factors are difficult to control for if this information is incomplete and there is not enough statistical power to do any meaningful analyses with them. Finally, in a similar vein, almost all suicide victims are suffering from at least one axis-I disorder, including major depressive disorder and/or substance use disorder [START_REF] Henriksson | Mental disorders and comorbidity in suicide[END_REF].

As brain tissues are rare, properly controlled and well-powered studies are difficult to carry out but crucial to disentangle the specific effects of suicide on gene expression from those arising from co-morbid psychopathologies [START_REF] Zhao | Different stress-related gene expression in depression and suicide[END_REF]. The limited sample sizes may also pose challenges for case-control matching for factors such as age and sex, although the combination of samples from a network of brain banks may help overcome this issue [START_REF] Kretzschmar | Brain banking: opportunities, challenges and meaning for the future[END_REF]. It is worth noting that post-mortem brain is often used in studies investigating suicide completion while only peripheral samples, such as blood and saliva, are used in studies of other suicidal behaviors, for obvious reasons. Although most genetic association studies are not hampered with such limitations (since the DNA sequence is the same across all healthy cells and tissues), gene expression varies greatly across tissues [START_REF] Mele | Human genomics. The human transcriptome across tissues and individuals[END_REF] and is likely due to different regulatory processes and mechanisms. Whether changes in the periphery reflect changes in the central nervous system is an area of debate that justifiably requires critical investigation [START_REF] Gladkevich | Lymphocytes as a neural probe: potential for studying psychiatric disorders[END_REF][START_REF] Liew | The peripheral blood transcriptome dynamically reflects system wide biology: a potential diagnostic tool[END_REF][START_REF] Luykx | Peripheral blood gene expression profiles linked to monoamine metabolite levels in cerebrospinal fluid[END_REF][START_REF] Rollins | Analysis of whole genome biomarker expression in blood and brain[END_REF][START_REF] Sullivan | Evaluating the comparability of gene expression in blood and brain[END_REF][START_REF] Tylee | On the outside, looking in: a review and evaluation of the comparability of blood and brain "-omes[END_REF][START_REF] Van Heerden | Parallel changes in gene expression in peripheral blood mononuclear cells and the brain after maternal separation in the mouse[END_REF][START_REF] Witt | Comparison of gene expression profiles in the blood, hippocampus and prefrontal cortex of rats[END_REF].

With that being said, several candidate genes in suicide behavior are expressed in blood and several studies have demonstrated that blood provides an interesting and useful window to study the relationships between peripheral systems and brain processes in psychiatric disorders [START_REF] Belzeaux | Transcriptomic and epigenomic biomarkers of antidepressant response[END_REF]. Interestingly, some studies have shown that social stress as well as suicide behavior can affect inflammatory gene expression in peripheral immune cells. As a consequence, transcriptomics based on peripheral blood samples become fully relevant to explore such hypotheses. Additionally, the use of peripheral samples could provide invaluable information about brain processes that would be otherwise difficult to infer. As an example, peripheral gene expression could be linked to monoamine metabolite levels in the cerebral spinal fluid (CSF), which may reflect CNS metabolism [START_REF] Luykx | Peripheral blood gene expression profiles linked to monoamine metabolite levels in cerebrospinal fluid[END_REF]).

Gene expression patterns as biomarkers of suicide behavior

Biomarkers are considered by the American Food and Drug Administration as "a characteristic that is objectively measured and evaluated as an indicator of normal biological Following this simple definition, several important properties emerge: a biomarker needs to be precisely "measured" (i.e. with high fidelity and stability), and moreover, a biomarker should be an "indicator" of disease (i.e. should be related to a specific biological process). In other words, biomarker development needs to take into account biological plausibility.

Beyond the trend for hypothesis-driven research to be more impacted by error type 1 (i.e. high rate of false positive findings), high biological plausibility may lead to better reproducibility and could allow for improved therapeutic discovery and application. Lastly, a biomarker may serve different functions in the medical decision-making process. In cases of suicide prevention, clinicians could be interested in biomarkers predictive of suicide attempts or suicide death. However, the population in which such a biomarker is validated (i.e. general population vs. population with suicide behavior disorder) could largely influence its validity.

Interestingly, several authors suggest that follow-up biomarkers measured longitudinally in given subjects who act as their own controls, could be more accurate than biomarkers identified through absolute differences between individuals. Thus, mediator or moderator biomarkers have been defined as biomarkers whose variation within the subject is due to a specific therapeutic intervention or life event [START_REF] Belzeaux | Potential Use of MicroRNA for Monitoring Therapeutic Response to Antidepressants[END_REF]. We previously detailed the different technical steps that are critically important in biomarker development [START_REF] Belzeaux | Potential Use of MicroRNA for Monitoring Therapeutic Response to Antidepressants[END_REF]. It is worth noting that no biomarker in psychiatric disorders, to this date, has been validated at each step of this process [START_REF] Scarr | Biomarkers for Psychiatry: The Journey from Fantasy to Fact, a Report of the 2013 CINP Think Tank[END_REF]. However, due to the heterogeneity of psychiatric phenotypes, and the major issue of predictive value of clinical symptoms to predict side effects, treatment response, or clinical worsening, the development of biomarkers has been an extensive field of research in mood disorders and suicide.

Understanding suicide behavior using transcriptomics

Suicide brain

Many biological systems are implicated in suicide and several reviews extensively discuss a number of recent findings. Generally speaking, suicide is characterized by dysregulated gene expression in stress response systems (HPA axis and Locus Coeruleus/Norepinephrine system), immune/neuroinflammatory processes, the endogenous opioid system, lipid metabolic processes, and several neurotransmitter/neuromodulator systems (serotonin, dopamine, GABA, and glutamate) [START_REF] Lutz | Neuropathology of suicide: recent findings and future directions[END_REF][START_REF] Oquendo | Toward a biosignature for suicide[END_REF]).

These systems have been teased apart, predominantly with candidate gene approaches that often aim to identify a single or a handful of genes underlying a Diagnostic and Statistical Manual of Mental Disorders (DSM)-defined psychiatric diagnosis. However, the DSM categorizes psychiatric disorders by emotional, cognitive, and behavioral symptoms and signs that are extremely complex (on a biological level), and thus are not likely to be characterized by dysregulation in single biological systems, let alone a few genes. In other words, the clinical classification of psychiatric disorders by complex emotional and cognitive states and behaviors likely contributes to the extreme difficulty in finding genes that cause one of the DSM's 22 broad categories of psychiatric disorders, and the ability to reproduce these results across patient groups that are often very heterogeneous [START_REF] Demkow | Genetic tests in major psychiatric disorders-integrating molecular medicine with clinical psychiatry-why is it so difficult?[END_REF].

Candidate Gene Approaches

The last few decades of biological psychiatric research have deepened our understanding of various pathophysiological processes associated with suicide. The dysregulation of the Hypothalamic-pituitary-adrenal (HPA) axis has been consistently implicated in suicide with altered levels of Corticotropin-releasing hormone (CRH) and other stress related neuropeptides in various brain regions, generally suggesting overactive HPA axis function in depression and suicide cases [START_REF] Merali | Corticotropin-releasing hormone, arginine vasopressin, gastrin-releasing peptide, and neuromedin B alterations in stress-relevant brain regions of suicides and control subjects[END_REF]. Furthermore, increased DNA methylation and decreased mRNA expression of the glucocorticoid receptor (GR) gene, which is responsible for the negative feedback regulation of HPA axis activity, has specifically been found in the hippocampus of suicide completers with a history of childhood abuse [START_REF] Labonte | Differential glucocorticoid receptor exon 1(B), 1(C), and 1(H) expression and methylation in suicide completers with a history of childhood abuse[END_REF][START_REF] Mcgowan | Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse[END_REF]. These findings, which build on results from animal models, suggest that early life social stressors may alter the expression of genes underlying the regulation of HPA axis activity in the brain, thereby contributing to increased risk of suicide.

However, such insight into the role of the HPA axis in suicide has yet to reconcile previous studies showing the lack of consistent clinical prognostic or diagnostic value in using the dexamethasome suppression test (DST), a test measuring HPA axis function, in major depression [START_REF] Ribeiro | The DST as a predictor of outcome in depression: a meta-analysis[END_REF]) and suicide. For example, while an early study showed DST non-suppression was associated with increased risk of suicide in depressed patients [START_REF] Yerevanian | The dexamethasone suppression test as a predictor of suicidal behavior in unipolar depression[END_REF]), other later ones showed no such effect [START_REF] Jokinen | DST non-suppression predicts suicide after attempted suicide[END_REF][START_REF] Jokinen | Noradrenergic function and HPA axis dysregulation in suicidal behaviour[END_REF]. Clearly, the translation of these valuable, yet incremental, advances in understanding the pathophysiology of suicide into significant clinical applications is still a work in progress.

Other candidate gene approaches to identify biomarkers of suicide have focused on the SKA2 gene, which chaperones the GR from the cytoplasm to the nucleus, and which is differentially expressed with higher DNA methylation and lower mRNA expression in the prefrontal cortex of suicide completers [START_REF] Guintivano | Identification and replication of a combined epigenetic and genetic biomarker predicting suicide and suicidal behaviors[END_REF]. In this study, SKA2 methylation was a statistically significant factor in additive linear models of suicide and suicidal behavior across 3 independent brain gene expression cohorts and 2 blood-based gene expression datasets, respectively. However, effect sizes were quite small after accounting for genotypic variation, which seemed to be a much stronger factor associated with suicide and related behaviors. In a subsequent study, although the SKA2 methylation and genotype linear model was statistically significant for modeling suicide attempt (but not suicide ideation), it showed poor predictive accuracy in discriminating attempters from non-attempters [START_REF] Kaminsky | Epigenetic and genetic variation at SKA2 predict suicidal behavior and post-traumatic stress disorder[END_REF]. Again, while the HPA axis seems to be associated with suicide at large, findings at the single-gene level exhibit small effect sizes that, although statistically significant, likely represent only a small piece of the puzzle, with limited clinical utility.

A similar story exists for findings implicating the serotonergic system in suicide.

Overall findings suggest lower levels of serotonergic transmission in depressed suicide completers and the up-regulation of compensatory processes [START_REF] Lutz | Neuropathology of suicide: recent findings and future directions[END_REF], such as increased 5-HT neurons in the dorsal raphe [START_REF] Arango | Serotonin 1A receptors, serotonin transporter binding and serotonin transporter mRNA expression in the brainstem of depressed suicide victims[END_REF]) and increased tyrosine hydroxylase mRNA and protein expression [START_REF] Bach-Mizrachi | Elevated expression of tryptophan hydroxylase-2 mRNA at the neuronal level in the dorsal and median raphe nuclei of depressed suicides[END_REF]. Furthermore, the literature currently consists of reports of associations between suicidal behaviors and several other genes that make up the serotonergic system, including the serotonin transporter, serotonin receptors, and monoamine oxidases, although the results are variable and sometimes inconsistent [START_REF] Antypa | Serotonergic genes and suicide: a systematic review[END_REF]. While abundant, the translation of these findings into clinical applications has been limited. For example, a recent PET study quantifying serotonin receptor 1A binding in the prefrontal cortex (PFC) showed no differences in suicide attempters and non-attempters, although serotonin receptor binding in the dorsal raphe was associated with lethality in attempters [START_REF] Sullivan | Positron emission tomography quantification of serotonin(1A) receptor binding in suicide attempters with major depressive disorder[END_REF]. miRNAs miRNAs in the brain have been implicated in affective disorders, with some specifically associated with suicidal behaviors [START_REF] Serafini | The involvement of microRNAs in major depression, suicidal behavior, and related disorders: a focus on miR-185 and miR-491-3p[END_REF]. While it has been shown that there is overall down-regulation of miRNAs in the PFC of depressed suicide completers compared to controls [START_REF] Smalheiser | MicroRNA expression is down-regulated and reorganized in prefrontal cortex of depressed suicide subjects[END_REF], other studies have shown the up-regulation of specific miRNAs in the PFC of depressed suicide completers compared to both controls and depressed patients who died by other causes [START_REF] Wang | Role of Complex Epigenetic Switching in Tumor Necrosis Factor-alpha Upregulation in the Prefrontal Cortex of Suicide Subjects[END_REF], suggesting suicide specific differences in miRNA dysregulation. Another study showed the up-regulation of several miRNAs predicted to target several polyamine-related genes, which have been previously shown to be down-regulated in the PFC of suicide completers [START_REF] Lopez | Regulatory role of miRNAs in polyamine gene expression in the prefrontal cortex of depressed suicide completers[END_REF]. Finally, miRNA expression networks associated with suicide have been examined in the brain and may lead to further characterization of more consistent miRNA-driven molecular signatures (Roy et al. 2017b).

Inconsistencies in the characterization of miRNA dysregulations, as well as other classes of molecules, may in part be attributable to heterogeneous samples grouped by broad psychiatric categories such as depression and suicide. Considering the polythetic diagnostic nature of many DSM psychiatric disorders, individual patients may share very few symptoms and still receive the same diagnosis (as in depression where only 5/9 symptom based criteria must be met) [START_REF] Olbert | Quantifying heterogeneity attributable to polythetic diagnostic criteria: theoretical framework and empirical application[END_REF]. The use of more specific patient groups, perhaps stratified by brain and cognitive function or behavioral symptomology (as proposed in the RDoC framework), could help address this issue and develop clinical measurements that cut across diagnostic categories.

Other ncRNAs

LncRNAs have been studied in the context of various psychiatric disorders, including in schizophrenia where the lncRNA Gomafu was found to bind to various splicing factors in the brain to contribute to the alternative splicing patterns of the important schizophrenia related genes DISC1 and ERBB4 [START_REF] Barry | The long non-coding RNA Gomafu is acutely regulated in response to neuronal activation and involved in schizophrenia-associated alternative splicing[END_REF]). As well, lncRNAs have been found to be dysregulated in the autistic brain [START_REF] Ziats | Aberrant expression of long noncoding RNAs in autistic brain[END_REF], and in the frontal cortex and nucleus accumbens of depressed patients [START_REF] Zeng | Genome-wide Regional Heritability Mapping Identifies a Locus Within the TOX2 Gene Associated With Major Depressive Disorder[END_REF]. Only one study reported on lncRNA dysregulation in the suicidal brain, with the association between one lncRNA and violent suicide [START_REF] Punzi | Increased expression of MARCKS in post-mortem brain of violent suicide completers is related to transcription of a long, noncoding, antisense RNA[END_REF]. Another recent study identified 6 down-regulated lncRNAs in peripheral mononuclear cells that had a negative association risk in major depressive disorder [START_REF] Cui | Long noncoding RNA expression in peripheral blood mononuclear cells and suicide risk in Chinese patients with major depressive disorder[END_REF]. With the discovery of these novel categories of ncRNAs, a whole new window has been opened to our understanding of the regulatory mechanisms governing the complex biological processes underlying psychiatric disorders. Further investigation of ncRNAs will not only increase our understanding of disease etiology, but will undoubtedly allow for the development of novel clinical tools and treatments.

Transcriptomic for peripheral biomarker of suicide risk

Peripheral biomarkers based on transcriptomic tools have been proposed in a few studies. One research group has proposed complex convergent functional genomics strategies in various cohorts, including some very small ones (n<10) and with patients with heterogeneous disorders, to identify biomarkers of suicide behavior (Le- [START_REF] Le-Niculescu | Discovery and validation of blood biomarkers for suicidality[END_REF][START_REF] Niculescu | Precision medicine for suicidality: from universality to subtypes and personalization[END_REF][START_REF] Niculescu | Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach[END_REF]. In one of these studies, the authors replicated the association between SAT1 gene expression and suicide that was previously described [START_REF] Fiori | Global gene expression profiling of the polyamine system in suicide completers[END_REF][START_REF] Le-Niculescu | Discovery and validation of blood biomarkers for suicidality[END_REF]), while another one replicated associations between SKA2 and suicide behavior [START_REF] Guintivano | Identification and replication of a combined epigenetic and genetic biomarker predicting suicide and suicidal behaviors[END_REF][START_REF] Niculescu | Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach[END_REF]. Although promising, associations between suicide ideation and SAT1, PTEN, MAP3K3 and MARCKS mRNAs have not been replicated by another independent group [START_REF] Mullins | Genetic relationships between suicide attempts, suicidal ideation and major psychiatric disorders: a genome-wide association and polygenic scoring study[END_REF]). In the same vein, using hypothesis-driven strategies based on the relationship between stress and suicide behavior, another study described gene expression variation of BDNF, FKBP5 and NR3C1 genes and found no differences between patients with or without suicide ideation in a crosssectional study (Roy et al. 2017a).

We also recently conducted a prospective study to identify biomarkers of suicide behavior. We focused on SLC6A4 mRNA (encoding the serotonin transporter), based on the assumption of implicated role of the serotonin system in suicide behavior (see above). Interestingly, no previous study has focused on gene expression variation of this gene to predict suicide behavior in living patients. Based on a large cohort of 148 patients with a Major Depressive Episode and 100 healthy controls, we demonstrated that a lower level of expression of SLC6A4 mRNA was predictive of suicide ideation worsening during an 8-week follow-up study [START_REF] Consoloni | Serotonin transporter gene expression predicts the worsening of suicidal ideation and suicide attempts along a long-term follow-up of a Major Depressive Episode[END_REF]. We also demonstrated that increased SLC6A4 mRNA across time was predictive of later suicide ideation as well as later suicide attempts. Taken together, our results suggested that SLC6A4 mRNA may constitute an interesting biomarker of suicide behavior. However, it is worth noting that these few studies are not sufficiently convincing to allow for a new prediction avenue based on transcriptomics. The predictive value of the most promising mRNA from these studies remains modest with an area under the curve (AUC) < 0.75 when mRNAs are used alone [START_REF] Levey | Towards understanding and predicting suicidality in women: biomarkers and clinical risk assessment[END_REF][START_REF] Niculescu | Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach[END_REF] Although some studies have used prospective and within-subject designs, future well-powered studies with precise phenotypic strategies are still required.

Conclusion

Transcriptomics offers a unique perspective through which the neurobiology of suicide and the impact of social stressors can be understood. Gene expression is a major output of biological functions, and thus a critical substrate to study in order to understand how various factors regulate the processes leading to altered brain function. Suicide and associated behaviors face unique challenges in their study and careful considerations must especially be taken when working with central and peripheral tissues as each has its own set of advantages and caveats. While post-mortem brain offers a more direct window into the mechanisms underlying dysfunctional processes in suicide, peripheral samples such as blood and saliva offer a much more practical and clinically relevant tissue from which useful biomarkers and Nevertheless, the development of clinically useful biomarkers will need to be supported by highly powered studies and confer biological plausibility. Larger network based approaches, including those integrating various omic domains, will likely play an important role in determining how specific transcriptomic bio-markers are related to broad molecular signatures characterizing disease as well as relevant systemic and clinical outcomes. Furthermore, research that stratifies psychiatric disorders by specific behavioral or clinical phenotypes will aid the discovery of novel disease-associated biological processes, such as regulation by miRNAs, lncRNAs, and other ncRNAs, with higher reproducibility and validity.

We are currently in the midst of a major change in the scientific approaches used in psychiatric research. Shifting form candidate gene to genome-wide and network approaches as well as well from a focus on broad psychiatric disorder categories to specific endophenotypes present exciting opportunities in psychiatric research. Only time will tell how these changes will impact our understanding of psychiatric disease and personalized medicine.

  With the development of transcriptomic technologies, data-driven genome-wide analysis of gene expression has enabled the 'unbiased' investigation of the suicide transcriptome. Microarrays were among the first technologies used to characterize wholetranscriptomes and were based on the hybridization of fluorescently labeled complementary

  of brain tissue is called the post-mortem-interval (PMI) and has been shown

  processes, or biological responses to a therapeutic intervention".

  on gene expression are likely to be developed in the near future.

  Placidi GP, Malone KM, Campbell C, Keilp J, Brodsky B, Kegeles LS, Cooper TB, Parsey RV, van Heertum RL, Mann JJ. 2003. Positron emission tomography of regional brain metabolic responses to a serotonergic challenge and lethality of suicide attempts in major depression. Archives of general psychiatry

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