Involvement of core clock genes in lithium response

RNA Biomarkers in Bipolar Disorder and Response to Mood Stabilizers

Bipolar disorder (BD) is a severe chronic disorder that represents one of the main causes of disability among young people. To date, no reliable biomarkers are available to inform the diagnosis of BD or clinical response to pharmacological treatment. Studies focused on coding and noncoding transcripts may provide information complementary to genome-wide association studies, allowing to correlate the dynamic evolution of different types of RNAs based on specific cell types and developmental stage with disease development or clinical course. In this narrative review, we summarize findings from human studies that evaluated the potential utility of messenger RNAs and noncoding transcripts, such as microRNAs, circular RNAs and long noncoding RNAs, as peripheral markers of BD and/or response to lithium and other mood stabilizers. The majority of available studies investigated specific targets or pathways, with large heterogeneity in the included type of cells or biofluids. However, a growing number of studies are using hypothesis-free designs, with some studies also integrating data on coding and noncoding RNAs measured in the same participants. Finally, studies conducted in neurons derived from induced-pluripotent stem cells or in brain organoids provide promising preliminary findings supporting the power and utility of these cellular models to investigate the molecular determinants of BD and clinical response.

Transcriptional biomarkers of response to pharmacological treatments in severe mental disorders: A systematic review

Variation in the expression level and activity of genes involved in drug disposition and action in tissues of pharmacological importance have been increasingly investigated in patients treated with psychotropic drugs. Findings are promising, but reliable predictive biomarkers of response have yet to be identified. Here we conducted a PRISMA-compliant systematic search of PubMed, Scopus and PsycInfo up to 12 September 2020 for studies investigating RNA expression levels in cells or biofluids from patients with major depressive disorder, schizophrenia or bipolar disorder characterized for response to psychotropic drugs (antidepressants, antipsychotics or mood stabilizers) or adverse effects. Among 5497 retrieved studies, 123 (63 on antidepressants, 33 on antipsychotics and 27 on mood stabilizers) met inclusion criteria. Studies were either focused on mRNAs (n = 96), microRNAs (n = 19) or long non-coding RNAs (n = 1), with only a minority investigating both mRNAs and microRNAs levels (n = 7). The most replicated results include genes playing a role in inflammation (antidepressants), neurotransmission (antidepressants and antipsychotics) or mitochondrial function (mood stabilizers). Compared to those investigating response to antidepressants, studies focused on antipsychotics or mood stabilizers more often showed lower sample size and lacked replication. Strengths and limitations of available studies are presented and discussed in light of the specific designs, methodology and clinical characterization of included patients for transcriptomic compared to DNA-based studies. Finally, future directions of transcriptomics of psychopharmacological interventions in psychiatric disorders are discussed.

A transcriptome analysis for 24-hour continuous sampled uterus reveals circadian regulation of the key pathways involved in eggshell formation of chicken

Circadian timing system controlled the rhythmic events, for example, ovulation and oviposition in chickens. However, how biological clock mediates eggshell formation remains obscure. Here, A 24-h mRNA transcriptome analysis was carried out in the uterus of 18 chickens with similar oviposition time points to identify the rhythmic genes and to reveal critical genes and biological pathways involved in the eggshell biomineralization. JTK_CYCLE analysis and real-time PCR revealed a total of 1,793 genes from the sequencing database with 23,513 genes (FPKM>1) were rhythmic genes regulating the rhythmic system and the expression of typical clock genes Per2, Cry1, Bmal1, Clock, Per3, and Rev-erbβ were rhythmically expressed, which suggested that endogenous clock in uterus might control the eggshell mineralization. Time of peak expression of the rhythmic genes was analyzed based on their acrophase. The main phases clustered at the periods from Zeitgeber time 0 (ZT0) to ZT4 (6:00–10:00) and from ZT10 to ZT14 (16:00-20:00). The rhythmic genes were annotated to the following Gene Ontology terms rhythmic process, lyase, ATP binding, cell membrane component. KEGG pathway enrichment analysis revealed the top 15 rhythmic genes were involved in vital biological pathways, including syndecan (1, 2, 3)-mediated signaling, post-translational regulation of adheres junction stability and disassembly, FoxO family signaling, TGF-β receptor and transport of small molecular pathways. 166 of total 1,235 genes (13.4%) were defined as rhythmic transfer factors (TFs) and they were investigated expression time distribution of cis-elements of circadian clock system D-box, E-box, B-site, and Y-Box within 24 h. Results indicated that rhythmic TFs at each phase are potential drivers of their circadian transcription activities. Compared with the control, the expression abundances of ion transport elements SCNN1G, CA2, SPP1, and ATP1B1 were significantly decreased after the interference of Bmal1 gene in synchronized uterine tubular gland cells. Clock genes changed their expression along with the eggshell formation, indicating that there is circadian clock in the uterus of chicken and it regulates the expression of eggshell formation genes.

A Comparison of Different Approaches to Clinical Phenotyping of Lithium Response: A Proof of Principle Study Employing Genetic Variants of Three Candidate Circadian Genes

Optimal classification of the response to lithium (Li) is crucial in genetic and biomarker research. This proof of concept study aims at exploring whether different approaches to phenotyping the response to Li may influence the likelihood of detecting associations between the response and genetic markers. We operationalized Li response phenotypes using the Retrospective Assessment of Response to Lithium Scale (i.e., the Alda scale) in a sample of 164 cases with bipolar disorder (BD). Three phenotypes were defined using the established approaches, whilst two phenotypes were generated by machine learning algorithms. We examined whether these five different Li response phenotypes showed different levels of statistically significant associations with polymorphisms of three candidate circadian genes (RORA, TIMELESS and PPARGC1A), which were selected for this study because they were plausibly linked with the response to Li. The three original and two revised Alda ratings showed low levels of discordance (misclassification rates: 8–12%). However, the significance of associations with circadian genes differed when examining previously recommended categorical and continuous phenotypes versus machine-learning derived phenotypes. Findings using machine learning approaches identified more putative signals of the Li response. Established approaches to Li response phenotyping are easy to use but may lead to a significant loss of data (excluding partial responders) due to recent attempts to improve the reliability of the original rating system. While machine learning approaches require additional modeling to generate Li response phenotypes, they may offer a more nuanced approach, which, in turn, would enhance the probability of identifying significant signals in genetic studies.

Clinical, genetic, and brain imaging predictors of risk for bipolar disorder in high-risk individuals

Introduction: Early detection and intervention in bipolar disorder (BD) might reduce illness severity, slow its progression, and, in specific cases, even ward off the full-blown disorder. Therefore, identifying at-risk individuals and targeting them promptly before the illness onset is of the utmost importance. In the last decades, there has been a significant effort aimed at identifying genetic and molecular factors able to modulate risk and pharmacological outcomes.Areas covered: We performed a narrative review of articles aimed at identifying clinical, genetics, molecular, and brain imaging markers of BD specifically focusing on samples of individuals at high-risk for BD. Special emphasis was put on studies applying an integrative design, e.g. studies combining different markers such as genetic and brain imaging.Expert opinion: Findings from studies in risk individuals are still too sparse to allow drawing definite conclusions. However, the high potentiality of longitudinal studies in individuals considered at risk to develop BD supports the need for more efforts. Future investigations should focus on more homogeneous subpopulations and evaluate the cross-linking between clinical, genetic, and brain morphostructural/functional neuroimaging characteristics as predictors of risk for BD.

Whole Genome Expression Analyses of miRNAs and mRNAs Suggest the Involvement of miR-320a and miR-155-3p and their Targeted Genes in Lithium Response in Bipolar Disorder

Lithium is the mainstay in the maintenance of bipolar disorder (BD) and the most efficacious pharmacological treatment in suicide prevention. Nevertheless, its use is hampered by a high interindividual variability and important side effects. Genetic and epigenetic factors have been suggested to modulate lithium response, but findings so far have not allowed identifying molecular targets with predictive value. In this study we used next generation sequencing to measure genome-wide miRNA expression in lymphoblastoid cell lines from BD patients excellent responders (ER, n = 12) and non-responders (NR, n = 12) to lithium. These data were integrated with microarray genome-wide expression data to identify pairs of miRNA/mRNA inversely and significantly correlated. Significant pairs were prioritized based on strength of association and in-silico miRNA target prediction analyses to select candidates for validation with qRT-PCR. Thirty-one miRNAs were differentially expressed in ER vs. NR and inversely correlated with 418 genes differentially expressed between the two groups. A total of 331 of these correlations were also predicted by in-silico algorithms. miR-320a and miR-155-3p, as well as three of their targeted genes (CAPNS1 (Calpain Small Subunit 1) and RGS16 (Regulator of G Protein Signaling 16) for miR-320, SP4 (Sp4 Transcription Factor) for miR-155-3p) were validated. These miRNAs and mRNAs were previously implicated in psychiatric disorders (miR-320a and SP4), key processes of the central nervous system (CAPNS1, RGS16, SP4) or pathways involved in mental illnesses (miR-155-3p). Using an integrated approach, we identified miRNAs and their targeted genes potentially involved in lithium response in BD.