Convergence of recent GWAS data for suicidality with previous blood biomarkers: independent reproducibility using independent methodologies in independent cohorts

Molecular-Assisted Breeding of Cadmium Pollution-Safe Cultivars

Cadmium (Cd) contamination in edible agricultural products, especially in crops intended for consumption, has raised worldwide concerns regarding food safety. Breeding of Cd pollution-safe cultivars (Cd-PSCs) is an effective solution to preventing the entry of Cd into the food chain from contaminated agricultural soil. Molecular-assisted breeding methods, based on molecular mechanisms for cultivar-dependent Cd accumulation and bioinformatic tools, have been developed to accelerate and facilitate the breeding of Cd-PSCs. This review summarizes the recent progress in the research of the low Cd accumulation traits of Cd-PSCs in different crops. Furthermore, the application of molecular-assisted breeding methods, including transgenic approaches, genome editing, marker-assisted selection, whole genome-wide association analysis, and transcriptome, has been highlighted to outline the breeding of Cd-PSCs by identifying critical genes and molecular biomarkers. This review provides a comprehensive overview of the development of Cd-PSCs and the potential future for breeding Cd-PSC using modern molecular technologies.

Detection of Embryonic Trisomy 21 in the First Trimester Using Maternal Plasma Cell-Free RNA

Prenatal trisomy 21 (T21) screening commonly involves testing a maternal blood sample for fetal DNA aneuploidy. It is reliable but poses a cost barrier to universal screening. We hypothesized maternal plasma RNA screening might provide similar reliability but at a lower cost. Discovery experiments used plasma cell-free RNA from 20 women 11−13 weeks tested by RNA and miRNA microarrays followed by qRT-PCR. Thirty-six mRNAs and 18 small RNAs of the discovery cDNA were identified by qPCR as potential markers of embryonic T21. The second objective was validation of the RNA predictors in 998 independent pregnancies at 11−13 weeks including 50 T21. Initial analyses identified 9−15 differentially expressed RNA with modest predictive power (AUC < 0.70). The 54 RNAs were then subjected to machine learning. Eleven algorithms were trained on one partition and tested on an independent partition. The three best algorithms were identified by Kappa score and the effects of training/testing partition size and dataset class imbalance on prediction were evaluated. Six to ten RNAs predicted T21 with AUCs up to 1.00. The findings suggest that maternal plasma collected at 11−13 weeks, tested by qRT-PCR, and classified by machine learning, may accurately predict T21 for a lower cost than plasma DNA, thus opening the door to universal screening.

Suicide Biomarkers to Predict Risk, Classify Diagnostic Subtypes, and Identify Novel Therapeutic Targets: 5 Years of Promising Research

BACKGROUND

Suicide is a global health crisis. However, no objective biomarkers of suicide risk currently exist, and self-reported data can be unreliable, which limits prediction, diagnostic, and treatment efforts. Reliable biomarkers that can differentiate between diagnostic subgroups, predict worsening symptoms, or suggest novel therapeutic targets would be extremely valuable for patients, researchers, and clinicians.

METHODS

MEDLINE was searched for reports published between 2016 and 2021 using search terms (suicid*) AND (biomarker*) OR (indicat*). Reports that compared biomarkers between suicidal ideation, suicide attempt, death from suicide, or any suicide subgroup against other neuropsychiatric disorders were included. Studies exclusively comparing suicidal behavior or death from suicide with healthy controls were not included to ensure that biomarkers were specific to suicide and not other psychopathology.

RESULTS

This review summarizes the last 5 years of research into suicide-associated biomarkers and provides a comprehensive guide for promising and novel biomarkers that encompass varying presentations of suicidal ideation, suicide attempt, and death by suicide. The serotonergic system, inflammation, hypothalamic-pituitary-adrenal axis, lipids, and endocannabinoids emerged as the most promising diagnostic, predictive, and therapeutic indicators.

CONCLUSIONS

The utility of diagnostic and predictive biomarkers is evident, particularly for suicide prevention. While larger-scale studies and further in-depth research are required, the last 5 years of research has uncovered essential biomarkers that could ultimately improve predictive strategies, aid diagnostics, and help develop future therapeutic targets.

A candidate biological network formed by genes from genomic and hypothesis-free scans of suicide

Information about genes and the biology of suicidal behavior (SB) is noisy due to heterogenous outcomes (suicide attempts or deaths), as well as many different genes and overlapping biological processes implicated. One approach to test the unbiased biological coherence of disease genes, is to use genes from hypothesis-free genetic scans and to investigate if they aggregate close to each other in cellular gene and protein interaction networks ("interactomes"). Therefore, we used network methods to study the biological coherence among genes (n = 229) from genome-wide association studies (GWAS) and whole exome sequencing (WES) of suicide outcome. Results showed that the suicide GWAS+WES genes has significant aggregation in three major interactome database assemblies, a hallmark of biological similarity and increased likelihood of being involved in the same outcome (suicide). This pinpointed e.g. genes on chromosome 19, which are also associated with lipid metabolism, schizophrenia and bipolar disorder. We identified a subset of GWAS+WES "core" genes (n = 54) which are the most proximal to each other in the context of three interactome assemblies, and present a candidate network module of suicide which is specific for nervous system tissues. The n = 54 most proximal "core" genes showed overrepresentation of synaptic and nervous system development genes, as well as network paths to other SB genes having increased evidence diversity. Overall, results suggested the existence of a coherent biology in suicide outcome and provide unbiased biological support concerning links to other SB genes, as well as e.g. bipolar disorder, excitatory/inhibitory function and ketamine treatment in SB.

Can Molecular Biology Propose Reliable Biomarkers for Diagnosing Major Depression?

BACKGROUND

Modern medicine has provided considerable knowledge of the pathophysiology of mental disorders at the body, systemic, organ and neurochemical levels of the biological organization of the body. Modern clinical diagnostics of depression have some problems, that is why psychiatric society makes use of diagnostics and taxonomy of different types of depression by implemention of modern molecular biomarkers in diagnostic procedures. But up to now, there are no reliable biomarkers of major depressive disorder (MDD) and other types of depression.

OBJECTIVE

The purpose of this review is to find fundamentals in pathological mechanisms of depression, which could be a basis for development of molecular and genetic biomarkers, being the most feasible for clinical use.

METHOD

This review summarizes the published data using PubMed, Science Direct, Google Scholar and Scopus.

RESULTS

In this review, we summarized and discussed findings in molecular biology, genetics, neuroplasticity, neurotransmitters, and neuroimaging that could increase our understanding of the biological foundations of depression and show new directions for the development of reliable biomarkers. We did not find any molecular and genetic biomarker approved for the clinic. But the Genome-Wide Association Study method promises some progress in the development of biomarkers based on SNP in the future. Epigenetic factors also are a promising target for biomarkers. We have found some differences in the etiology of different types of atypical and melancholic depression. This knowledge could be the basis for development of biomarkers for clinical practice in diagnosis, prognosis and selection of treatment.

CONCLUSION

Depression is not a monoetiological disease. Many pathological mechanisms are involved in depression, thus up to now, there is no approved and reliable biomarker for diagnosis, prognosis and correction of treatment of depression. The structural and functional complexity of the brain, the lack of invasive technology, poor correlations between genetic and clinical manifestation of depression, imperfect psychiatric classification and taxonomy of subtypes of disease are the main causes of this situation. One of the possible ways to come over this situation can be to pay attention to the trigger mechanism of disease and its subtypes. Researchers and clinicians should focus their efforts on searching the trigger mechanism of depression and different types of it . HPA axis can be a candidate for such trigger in depression caused by stress, because it influences the main branches of disease: neuroinflammation, activity of biogenic amines, oxidative and nitrosative stress, epigenetic factors, metabolomics, etc. But before we shall find any trigger mechanism, we need to create complex biomarkers reflecting genetic, epigenetic, metabolomics and other pathological changes in different types of depression. Recently the most encouraging results have been obtained from genetics and neuroimaging. Continuing research in these areas should be forced by using computational, statistical and systems biology approaches, which can allow to obtain more knowledge about the neurobiology of depression. In order to obtain clinically useful tests, search for biomarkers should use appropriate research methodologies with increasing samples and identifying more homogeneous groups of depressed patients.

Identification of Gene Loci That Overlap Between Mental Disorders and Poor Prognosis of Cancers

Background: Co-morbid psychiatric disorders are common in patients with cancers, which make the treatment more difficult. Studying the connection between mental disease-related genes and the prognosis of cancers may potentially lead to novel therapeutic methods. Method: All mental disorders genes were selected from published articles. The correlations between the expression of these genes and the prognosis of different cancers were analyzed by starBase v2.0 and TIMER. The molecular functions, reactome pathways, and interactions among diverse genes were explored via the STRING tool. Results: 239 genes were identified for further survival analysis, 5 of which were overlapping genes across at least five cancer types, including RHEBL1, PDE4B, ANKRD55, EPHB2, and GIMAP7. 146 high-expression and 157 low-expression genes were found to be correlated with the unfavorable prognosis of diverse cancer types. Tight links existed among various mental disease genes. Besides, risk genes were mostly related to the dismal outcome of low-grade glioma (LGG) and kidney renal clear cell carcinoma (KIRC) patients. Gene Ontology (GO) and reactome pathway analysis revealed that most genes involved in various critical molecular functions and primarily related to metabolism, signal transduction, and hemostasis. Conclusions: To explore co-expression genes between mental illnesses and cancers may aid in finding preventive strategies and therapeutic methods for high-risk populations and patients with one or more diseases.

Precision medicine for mood disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs

Mood disorders (depression, bipolar disorders) are prevalent and disabling. They are also highly co-morbid with other psychiatric disorders. Currently there are no objective measures, such as blood tests, used in clinical practice, and available treatments do not work in everybody. The development of blood tests, as well as matching of patients with existing and new treatments, in a precise, personalized and preventive fashion, would make a significant difference at an individual and societal level. Early pilot studies by us to discover blood biomarkers for mood state were promising [1], and validated by others [2]. Recent work by us has identified blood gene expression biomarkers that track suicidality, a tragic behavioral outcome of mood disorders, using powerful longitudinal within-subject designs, validated them in suicide completers, and tested them in independent cohorts for ability to assess state (suicidal ideation), and ability to predict trait (future hospitalizations for suicidality) [3-6]. These studies showed good reproducibility with subsequent independent genetic studies [7]. More recently, we have conducted such studies also for pain [8], for stress disorders [9], and for memory/Alzheimer's Disease [10]. We endeavored to use a similar comprehensive approach to identify more definitive biomarkers for mood disorders, that are transdiagnostic, by studying mood in psychiatric disorders patients. First, we used a longitudinal within-subject design and whole-genome gene expression approach to discover biomarkers which track mood state in subjects who had diametric changes in mood state from low to high, from visit to visit, as measured by a simple visual analog scale that we had previously developed (SMS-7). Second, we prioritized these biomarkers using a convergent functional genomics (CFG) approach encompassing in a comprehensive fashion prior published evidence in the field. Third, we validated the biomarkers in an independent cohort of subjects with clinically severe depression (as measured by Hamilton Depression Scale, (HAMD)) and with clinically severe mania (as measured by the Young Mania Rating Scale (YMRS)). Adding the scores from the first three steps into an overall convergent functional evidence (CFE) score, we ended up with 26 top candidate blood gene expression biomarkers that had a CFE score as good as or better than SLC6A4, an empirical finding which we used as a de facto positive control and cutoff. Notably, there was among them an enrichment in genes involved in circadian mechanisms. We further analyzed the biological pathways and networks for the top candidate biomarkers, showing that circadian, neurotrophic, and cell differentiation functions are involved, along with serotonergic and glutamatergic signaling, supporting a view of mood as reflecting energy, activity and growth. Fourth, we tested in independent cohorts of psychiatric patients the ability of each of these 26 top candidate biomarkers to assess state (mood (SMS-7), depression (HAMD), mania (YMRS)), and to predict clinical course (future hospitalizations for depression, future hospitalizations for mania). We conducted our analyses across all patients, as well as personalized by gender and diagnosis, showing increased accuracy with the personalized approach, particularly in women. Again, using SLC6A4 as the cutoff, twelve top biomarkers had the strongest overall evidence for tracking and predicting depression after all four steps: NRG1, DOCK10, GLS, PRPS1, TMEM161B, GLO1, FANCF, HNRNPDL, CD47, OLFM1, SMAD7, and SLC6A4. Of them, six had the strongest overall evidence for tracking and predicting both depression and mania, hence bipolar mood disorders. There were also two biomarkers (RLP3 and SLC6A4) with the strongest overall evidence for mania. These panels of biomarkers have practical implications for distinguishing between depression and bipolar disorder. Next, we evaluated the evidence for our top biomarkers being targets of existing psychiatric drugs, which permits matching patients to medications in a targeted fashion, and the measuring of response to treatment. We also used the biomarker signatures to bioinformatically identify new/repurposed candidate drugs. Top drugs of interest as potential new antidepressants were pindolol, ciprofibrate, pioglitazone and adiphenine, as well as the natural compounds asiaticoside and chlorogenic acid. The last 3 had also been identified by our previous suicidality studies. Finally, we provide an example of how a report to doctors would look for a patient with depression, based on the panel of top biomarkers (12 for depression and bipolar, one for mania), with an objective depression score, risk for future depression, and risk for bipolar switching, as well as personalized lists of targeted prioritized existing psychiatric medications and new potential medications. Overall, our studies provide objective assessments, targeted therapeutics, and monitoring of response to treatment, that enable precision medicine for mood disorders.

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.