Identification of gene ontology and pathways implicated in suicide behavior: Systematic review and enrichment analysis of GWAS studies

How to save a life: From neurobiological underpinnings to psychopharmacotherapies in the prevention of suicide

The impact of suicide on our societies, mental healthcare, and public health is beyond questionable. Every year approximately 700 000 lives are lost due to suicide around the world (WHO, 2021); more people die by suicide than by homicide and war. Although suicide is a key issue and reducing suicide mortality is a global imperative, suicide is a highly complex biopsychosocial phenomenon, and in spite of several suicidal models developed in recent years and a high number of suicide risk factors identified, we still have neither a sufficient understanding of underpinnings of suicide nor adequate management strategies to reduce its prevalence. The present paper first overviews the background of suicidal behavior including its epidemiology, prevalence, age and gender correlations and its association with neuropsychiatric disorders as well as its clinical assessment. Then we give an overview of the etiological background, including its biopsychosocial contexts, genetics and neurobiology. Based on the above, we then provide a critical overview of the currently available intervention options to manage and reduce risk of suicide, including psychotherapeutic modalities, traditional medication classes also providing an up-to-date overview on the antisuicidal effects of lithium, as well as novel molecules such as esketamine and emerging medications and further molecules in development. Finally we give a critical overview on our current knowledge on using neuromodulatory and biological therapies, such as ECT, rTMS, tDCS and other options.

Big Data in Gastroenterology Research

Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of 'big data' from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.

Suicide and Changes in Expression of Neuronal miRNA Predicted by an Algorithm Search through miRNA Databases

Suicide is multifactorial and polygenic phenotype, affected by environmental and genetic factors. Among epigenetic mechanisms, miRNAs have been studied, but so far no very concise results exist. To overcome limitations of candidate miRNA and whole genome sequencing approaches, we created an in silico analysis algorithm that would help select the best suitable miRNAs that target the most interesting genes associated with suicidality. We used databases/web algorithms DIANA microT, miRDB, miRmap, miRWalk, and TargetScan and candidate genes SLC6A4, HTR1A, BDNF, NR3C1, ZNF714, and NRIP3. Based on a prediction algorithm, we have chosen miRNAs that are targeting regulation of the genes listed, and are at the same time being expressed in the brain. The highest ranking scores were obtained for hsa-miR-4516, hsa-miR-3135b, hsa-miR-124-3p, hsa-miR-129-5p, hsa-miR-27b-3p, hsa-miR-381-3p, hsa-miR-4286. Expression of these miRNAs was tested in the brain tissue of 40 suicide completers and controls, and hsa-miR-4516 and hsa-miR-381-3p showed a trend for statistical significance. We also checked the expression of the target genes of these miRNAs, and for NR3C1 expression was lower in suicide completers compared to controls, which is in accordance with the available literature results. To determine the miRNAs that are most suitable for further suicidality research, more studies, combining in silico analysis and wet lab experiments, should be performed.

Rare protein-coding variants implicate genes involved in risk of suicide death

Identification of genetic factors leading to increased risk of suicide death is critical to combat rising suicide rates, however, only a fraction of the genetic variation influencing risk has been accounted for. To address this limitation, we conducted the first comprehensive analysis of rare genetic variation in suicide death leveraging the largest suicide death biobank, the Utah Suicide Genetic Risk Study (USGRS). We conducted a single-variant association analysis of rare (minor allele frequency <1%) putatively functional single-nucleotide polymorphisms (SNPs) present on the Illumina PsychArray genotyping array in 2,672 USGRS suicide deaths of non-Finnish European (NFE) ancestry and 51,583 NFE controls from the Genome Aggregation Database. Secondary analyses used an independent control sample of 21,324 NFE controls from the Psychiatric Genomics Consortium. Five novel, high-impact, rare SNPs were identified with significant associations with suicide death (SNAPC1, rs75418419; TNKS1BP1, rs143883793; ADGRF5, rs149197213; PER1, rs145053802; and ESS2, rs62223875). 119 suicide decedents carried these high-impact SNPs. Both PER1 and SNAPC1 have other supporting gene-level evidence of suicide risk, and psychiatric associations exist for PER1 (bipolar disorder, schizophrenia), and for TNKS1BP1 and ESS2 (schizophrenia). Three of the genes (PER1, TNKS1BP1, and ADGRF5), together with additional genes implicated by genome-wide association studies on suicidal behavior, showed significant enrichment in immune system, homeostatic and signal transduction processes. No specific diagnostic phenotypes were associated with the subset of suicide deaths with the identified rare variants. These findings suggest an important role for rare variants in suicide risk and implicate genes and gene pathways for targeted replication.

Effects of vitamin D-related gene polymorphisms on attempted suicide

OBJECTIVE

Emerging evidence suggests that vitamin D might protect from attempted suicide. The study aimed to investigate the associations between single-nucleotide polymorphisms (SNPs) related to vitamin D levels identified in a large genome-wide association study and attempted suicide in rural China.

METHODS

This 1:1 matched case-control study included altogether 510 suicide attempters and 510 community controls. Genotypes of four target SNPs (DHCR7-rs12785878, CYP2R1-rs10741657, GC-rs2282679, and CYP24A1-rs6013897) were determined, and a genetic risk score (GRS) was constructed to evaluate the combined effect of them. Demographic and psychological information was acquired through face-to-face interviews.

RESULTS

The A allele of CYP24A1-rs6013897 was significantly associated with attempted suicide (OR = 1.27, 95% CI, 1.03-1.58, P = 0.029), even after adjusting for demographic and psychological confounders (adjusted OR = 1.53, 95% CI, 1.01-2.30, P = 0.043). The GRS analyses revealed a significantly higher risk of attempted suicide with a greater number of low vitamin D alleles (adjusted OR = 1.33, 95% CI, 1.13-1.58, P < 0.001). Subgroup analyses stratified by sex indicated that the genetic associations were only significant among males with adjusted ORs of 3.77 (95% CI, 1.56-9.10) for the A allele of rs6013897 and 2.04 (95% CI, 1.32-3.17) for GRS.

CONCLUSIONS

Our findings identity CYP24A1-rs6013897 as a potential biomarker for attempted suicide and indicate that a genetic predisposition to lower vitamin D levels may contribute to attempted suicide. It suggests the possibility that vitamin D may have the preventive potential for attempted suicide.

Genes identified in rodent studies of alcohol intake are enriched for heritability of human substance use

BACKGROUND

Rodent paradigms and human genome-wide association studies (GWAS) on drug use have the potential to provide biological insight into the pathophysiology of addiction.

METHODS

Using GeneWeaver, we created rodent alcohol and nicotine gene-sets derived from 19 gene expression studies on alcohol and nicotine outcomes. We partitioned the SNP-heritability of these gene-sets using four large human GWAS: 1) alcoholic drinks per week, 2) problematic alcohol use, 3) cigarettes per day and 4) smoking cessation. We benchmarked our findings with curated human alcoholism and nicotine addiction gene-sets and performed specificity analyses using other rodent gene-sets (e.g., locomotor behavior) and other human GWAS (e.g., height).

RESULTS

The rodent alcohol gene-set was enriched for heritability of drinks per week, cigarettes per day, and smoking cessation, but not problematic alcohol use. However, the rodent nicotine gene-set was not significantly associated with any of these traits. Both rodent gene-sets showed enrichment for several non-substance use GWAS, and the extent of this relationship tended to increase as a function of trait heritability. In general, larger gene-sets demonstrated more significant enrichment. Finally, when evaluating human traits with similar heritabilities, both rodent gene-sets showed greater enrichment for substance use traits.

CONCLUSION

Our results suggest that rodent gene expression studies can help to identify genes that contribute to heritability of some substance use traits in humans, yet there was less specificity than expected. We outline various limitations, interpretations and considerations for future research.

Genetic contributions to suicidal thoughts and behaviors

Suicidal ideation, suicide attempt (SA) and suicide are significantly heritable phenotypes. However, the extent to which these phenotypes share genetic architecture is unclear. This question is of great relevance to determining key risk factors for suicide, and to alleviate the societal burden of suicidal thoughts and behaviors (STBs). To help address the question of heterogeneity, consortia efforts have recently shifted from a focus on suicide within the context of major psychopathology (e.g. major depressive disorder, schizophrenia) to suicide as an independent entity. Recent molecular studies of suicide risk by members of the Psychiatric Genomics Consortium and the International Suicide Genetics Consortium have identified genome-wide significant loci associated with SA and with suicide death, and have examined these phenotypes within and outside of the context of major psychopathology. This review summarizes important insights from epidemiological and biometrical research on suicide, and discusses key empirical findings from molecular genetic examinations of STBs. Polygenic risk scores for these phenotypes have been observed to be associated with case-control status and other risk phenotypes. In addition, estimated shared genetic covariance with other phenotypes suggests specific medical and psychiatric risks beyond major depressive disorder. Broadly, molecular studies suggest a complexity of suicide etiology that cannot simply be accounted for by depression. Discussion of the state of suicide genetics, a growing field, also includes important ethical and clinical implications of studying the genetic risk of suicide.

Potential role of epicardial adipose tissue in coronary artery endothelial cell dysfunction in type 2 diabetes

Cardiovascular disease is the most prevalent cause of morbidity and mortality in diabetes. Epicardial adipose tissue (EAT) lies in direct contact with the myocardium and coronary arteries and can influence cardiac (patho) physiology through paracrine signaling pathways. This study hypothesized that the proteins released from EAT represent a critical molecular link between the diabetic state and coronary artery endothelial cell dysfunction. To simulate type 2 diabetes-associated metabolic and inflammatory status in an ex vivo tissue culture model, human EAT samples were treated with a cocktail composed of high glucose, high palmitate, and lipopolysaccharide (gplEAT) and were compared with control EAT (conEAT). Compared to conEAT, gplEAT showed a markedly increased gene expression profile of proinflammatory cytokines, corroborating EAT inflammation, a hallmark feature observed in patients with type 2 diabetes. Luminex assay of EAT-secretome identified increased release of various proinflammatory cytokines, including tumor necrosis factor-alpha (TNF-alpha), interferon-alpha 2 (IFNA2), interleukin 1 beta (IL1B), interleukin 5 (IL5), interleukin 13 (IL13), and CCL5, among others, in response to high glucose, high palmitate, and lipopolysaccharide. Conditioned culture media was used to collect the concentrated proteins (CPs). In response to gplEAT-CPs, human coronary artery endothelial cells (HCAECs) exhibited an inflammatory endothelial cell phenotype, featuring a significantly increased gene expression of proinflammatory cytokines and cell surface expression of VCAM-1. Moreover, gplEAT-CPs severely decreased Akt-eNOS signaling, nitric oxide production, and angiogenic potential of HCAECs, when compared with conEAT-CPs. These findings indicate that EAT inflammation may play a key role in coronary artery endothelial cell dysfunction in type 2 diabetes.

Machine learning as the new approach in understanding biomarkers of suicidal behavior

In psychiatry, compared to other medical fields, the identification of biological markers that would complement current clinical interview, and enable more objective and faster clinical diagnosis, implement accurate monitoring of treatment response and remission, is grave. Current technological development enables analyses of various biological marks in high throughput scale at reasonable costs, and therefore 'omic' studies are entering the psychiatry research. However, big data demands a whole new plethora of skills in data processing, before clinically useful information can be extracted. So far the classical approach to data analysis did not really contribute to identification of biomarkers in psychiatry, but the extensive amounts of data might get to a higher level, if artificial intelligence in the shape of machine learning algorithms would be applied. Not many studies on machine learning in psychiatry have been published, but we can already see from that handful of studies that the potential to build a screening portfolio of biomarkers for different psychopathologies, including suicide, exists.

Automatic Gene Function Prediction in the 2020's

The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field for decades and has made considerable progress in that time. However, it is certainly not solved. In this paper, we describe challenges that the AFP field still has to overcome in the future to increase its applicability. The challenges we consider are how to: (1) include condition-specific functional annotation, (2) predict functions for non-model species, (3) include new informative data sources, (4) deal with the biases of Gene Ontology (GO) annotations, and (5) maximally exploit the GO to obtain performance gains. We also provide recommendations for addressing those challenges, by adapting (1) the way we represent proteins and genes, (2) the way we represent gene functions, and (3) the algorithms that perform the prediction from gene to function. Together, we show that AFP is still a vibrant research area that can benefit from continuing advances in machine learning with which AFP in the 2020s can again take a large step forward reinforcing the power of computational biology.

Genetic origins of suicidality? A synopsis of genes in suicidal behaviours, with regard to evidence diversity, disorder specificity and neurodevelopmental brain transcriptomics

With regard to suicidal behavior (SB) genetics, many novel genes have been implicated over the years, in particular by a variety of hypothesis-free genomic methods (e.g. GWAS and exome sequencing). In addition, many novel SB gene findings appear enigmatic in their biological relevance and have weak statistical support, e.g. lack direct replications. Adding to this is the comorbidity between psychiatric disorders and SB. Here we provide a synopsis of SB genes, by prioritization of 106 (out of ~2500) genes based on their highest level of evidence diversity across mainly five genetic evidence types (candidate/GWAS SNP, CNV, linkage and whole exome sequencing), supplemented by three functional categories. This is a representative set of both old and new SB gene candidates, implicated by all kinds of evidence. Furthermore, we define a subset of 40 SB "specific" genes, which are not found among ~3900 genes implicated in other neuropsychiatric disorders, e.g. Autism spectrum disorders (ASD) or Schizophrenia. Biological research of suicidality contains a major developmental focus, e.g. with regard to the gene-environment interactions and epigenetic effects during childhood. Less is known about early (fetal) development and SB genes. Inspired by huge efforts to understand the role early (fetal) neurodevelopment in e.g. ASD by using brain transcriptomic data, we here also characterize the 106 SB genes. We find interesting spatiotemporal expression differences and similarities between SB specific and non-specific genes during brain neurodevelopment. These aspects are of interest to investigate further, to better understand and counteract the genetic origins suicidality.

Suicidal attempts, insomnia, and major depressive disorder among family members of suicide victims in South Korea

BACKGROUND

Suicide survivors are defined as victims who experienced suicide of siblings or parents, spouses, or lineal relatives within the fourth degree who had lived with them. We compared the effects of major depressive disorder (MDD), symptoms of posttraumatic stress disorder (PTSD), and impulsivity on lifetime suicide attempts among suicide survivors.

METHODS

Participants included 272 suicide survivors. We compared them to 5,200 members from the general population who were matched by age, sex, and years of education. We evaluated participants using the Patient Health Questionnaire-9 (PHQ-9), the EuroQuality of Life (EuroQol) scale, the Impact of Events Scale-Revised (IES-R), the Barratt Impulsiveness Scale-11 (BIS-11), and the Composite International Diagnostic Interview (K-CIDI).

RESULTS

Among the 272 suicide survivors, MDD was nine times higher (47.1% vs. 5.3%), and suicidal attempts were six times higher (20.5% vs. 3.3%) than reported among the general population. The suicide survivors showed more severe depression, greater impulsivity, and poorer quality of life than did the general population. In multivariate logistic regression analyses performed to evaluate each IES-R item, "trouble staying asleep" was the only PTSD symptom item that was significantly associated with suicide attempts among suicide survivors. Suicide survivors who had both MDD with insomnia and high impulsivity were at four times higher risk for suicide attempts than the general population with no MDD (AOR = 4.04, 95% CI: 1.25-13.09).

CONCLUSIONS

Surviving suicide by family members is an important risk factor for suicide attempts. In particular, MDD with insomnia and impulsivity are associated with suicide attempts among suicide survivors.

Identification of 40S ribosomal protein S8 as a novel biomarker for alcohol‑associated hepatocellular carcinoma using weighted gene co‑expression network analysis

Alcohol‑associated hepatocellular carcinoma (HCC) is a subtype of HCC with poor prognosis. The present study aimed to identify key biomarkers for alcohol‑associated HCC. The gene data profiles and corresponding clinical traits of patients with alcohol‑associated HCC were downloaded from The Cancer Genome Atlas (TCGA) database. Firstly, good genes and good samples were identified, which were subsequently used to conduct weighted gene co‑expression network analysis (WGCNA). Hub genes in the significant modules were selected following Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and from constructing a protein‑protein interaction (PPI) network. Real hub genes among hub genes were determined following progression, survival analysis and gene set enrichment analysis (GSEA), as well as reverse transcription‑quantitative PCR and immunohistochemical staining of non‑alcohol‑ and alcohol‑associated HCC samples. In total, 64 good samples of alcohol‑associated HCC with height score <160 were selected, from which 15,195 good genes were identified and used to conduct WGCNA; 8 gene co‑expressed modules were identified using WGCNA, while 3 modules (including pink, magenta and turquoise) were significantly associated with Child‑Pugh score, T‑stage and body weight. Following GO and KEGG analysis and construction of the PPI network, a total of 30 hub genes were identified in the aforementioned 3 gene co‑expressed modules, while 16 hub genes (including AURKB, BUB1, BUB1B, CCNB1, CCNB2, CDC20, CDCA8, CDK1, PLK1, RPS5, RPS7, RPS8, RPS14, RPS27, RPSA and TOP2A) were associated with the development of alcohol‑associated HCC, and had a significant prognosis value. Among these genes, only RPS8 was highly expressed in alcohol‑associated HCC, but not in non‑alcohol‑associated HCC, while RPS5 was not significantly associated in either alcohol‑ or non‑alcohol‑associated HCC. GSEA demonstrated that 10 pathways, including RNA polymerase and ribosome pathways were enriched in alcohol‑associated HCC samples where RPS8 was highly expressed. Taken together, the results of the present study demonstrate that RPS8 may be a novel biomarker for the diagnosis of patients with alcohol‑associated HCC.

High polygenic burden is associated with blood DNA methylation changes in individuals with suicidal behavior

Suicidal behavior is result of the interaction of several contributors, including genetic and environmental factors. The integration of approaches considering the polygenic component of suicidal behavior, such as polygenic risk scores (PRS) and DNA methylation is promising for improving our understanding of the complex interplay between genetic and environmental factors in this behavior. The aim of this study was the evaluation of DNA methylation differences between individuals with high and low genetic burden for suicidality. The present study was divided into two phases. In the first phase, genotyping with the Psycharray chip was performed in a discovery sample of 568 Mexican individuals, of which 149 had suicidal behavior (64 individuals with suicidal ideation, 50 with suicide attempt and 35 with completed suicide). Then, a PRS analysis based on summary statistics from the Psychiatric Genomic Consortium was performed in the discovery sample. In a second phase, we evaluated DNA methylation differences between individuals with high and low genetic burden for suicidality in a sub-sample of the discovery sample (target sample) of 94 subjects. We identified 153 differentially methylated sites between individuals with low and high-PRS. Among genes mapped to differentially methylated sites, we found genes involved in neurodevelopment (CHD7, RFX4, KCNA1, PLCB1, PITX1, NUMBL) and ATP binding (KIF7, NUBP2, KIF6, ATP8B1, ATP11A, CLCN7, MYLK, MAP2K5). Our results suggest that genetic variants might increase the predisposition to epigenetic variations in genes involved in neurodevelopment. This study highlights the possible implication of polygenic burden in the alteration of epigenetic changes in suicidal behavior.

Identification of Key Genes and the Pathophysiology Associated With Major Depressive Disorder Patients Based on Integrated Bioinformatics Analysis

Background: At present, laboratory blood tests to support major depressive disorder (MDD) diagnosis are not available. This study aimed to screen potential mRNAs for peripheral blood biomarkers and novel pathophysiology of MDD. Methods: The present study utilized public data from two mRNA microarray datasets to analyze the hub genes changes related to MDD. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of differentially expressed genes (DEGs) were performed. Finally, some potential mRNA quality biomarkers for hub gene expression in blood were identified. Results: A total of 25 significantly co-upregulated DEGs and 98 co-downregulated DEGs were obtained from two datasets. The pathway enrichment analyses showed that co-upregulated genes were significantly enriched in the regulation of cell-matrix adhesion and mitochondrial membrane permeability which were involved in the apoptotic process. Co-downregulated genes were mainly involved in the neutrophil activation which in turn was involved in the immune response, degranulation and cell-mediated immunity, positive regulation of immune response, the Toll-like receptor signaling pathway, and the NOD-like receptor signaling pathway. From the PPI network, 14 hub genes were obtained. Among them, the subnetworks of PLCG1, BCL2A1, TLR8, FADD, and TLR4 screened out from our study have been shown to play a role in immune and inflammation responses. Discussion: The potential molecular mechanisms that have been identified simultaneously include innate immunity, neuroinflammation, and neurotrophic factors for synapse function and development.

Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression

PURPOSE

Despite advances in characterizing the neurobiology of emotional disorders, there is still a significant lack of scientific understanding of the pathophysiological mechanisms governing major depressive disorder (MDD). This study attempted to elucidate the molecular circuitry of MDD and to identify more potential genes associated with the pathogenesis of the disease.

PATIENTS AND METHODS

Microarray data from the GSE98793 dataset were downloaded from the NCBI Gene Expression Omnibus (GEO) database, including 128 patients with MDD and 64 healthy controls. Weighted gene coexpression network analysis (WGCNA) was performed to find modules of differentially expressed genes (DEGs) with high correlations followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to obtain further biological insight into the top three key modules. The protein-protein interaction (PPI) network, the modules from the PPI network, and the gene annotation enrichment of modules were analyzed, as well.

RESULTS

We filtered 3276 genes that were considered significant DEGs for further WGCNA analysis. By performing WGCNA, we found that the turquoise, blue and brown functional modules were all strongly correlated with MDD development, including immune response, neutrophil degranulation, ribosome biogenesis, T cell activation, glycosaminoglycan biosynthetic process, and protein serine/threonine kinase activator activity. Hub genes were identified in the key functional modules that might have a role in the progression of MDD. Functional annotation showed that these modules primarily enriched such KEGG pathways as the TNF signaling pathway, T cell receptor signaling pathway, primary immunodeficiency, Th1, Th2 and Th17 cell differentiation, autophagy and RNA degradation and oxidative phosphorylation. These results suggest that these genes are closely related to autophagy and cellular immune function.

CONCLUSION

The results of this study may help to elucidate the pathophysiology of MDD development at the molecular level and explore the potential molecular mechanisms for new interventional strategies.