Functional genomic analysis delineates regulatory mechanisms of GWAS-identified bipolar disorder risk variants

Comprehensive bibliometric analysis of pharmacotherapy for bipolar disorders: Present trends and future directions

BACKGROUND

Bipolar disorder (BD) is a severe mental illness characterized by significant mood swings. Effective drug treatment modalities are crucial for managing BD.

AIM

To analyze the current status and future trends of global research on BD drug treatment over the last decade.

METHODS

The Web of Science Core Collection database spanning from 2015 to 2024 was utilized to retrieve literature related to BD drug treatment. A total of 2624 articles were extracted. Data visualization and analysis were conducted using CiteSpace, VOSviewer, Pajek, Scimago Graphica, and R-studio bibliometrix to identify research hotspots, key contributors, and future trends.

RESULTS

The United States, China, and the United Kingdom have made the most significant contributions to research on BD drug treatment and formed notable research collaboration networks. The University of Pittsburgh, Massachusetts General Hospital, and the University of Michigan have been identified as the major research institutions in this field. The Journal of Affective Disorders is the most influential journal. A keyword analysis revealed research hotspots related to clinical symptoms, drug efficacy, and genetic mechanisms. A citation analysis identified the management guidelines published by Yatham et al in 2018 as the most cited paper.

CONCLUSION

This study provides a detailed overview of the field of BD drug treatment, highlighting key contributors, research hotspots, and future directions. The study findings can be employed as a reference for future research and policymaking, which may enable further development and optimization of BD pharmacotherapy.

Association of healthy lifestyle factors and genetic liability with bipolar disorder: Findings from the UK Biobank

BACKGROUND

The interplay between genetic and lifestyle factors in the development of bipolar disorder (BD) remains unclear.

METHODS

A cohort study was carried out on 365,517 participants from the UK Biobank. Lifestyle scores, based on smoking, physical activity, diet, alcohol consumption, sedentary behavior, sleep duration, and social contact, were grouped as favorable (scores 6-7), intermediate (scores 4-5), or unfavorable (scores 0-3). The BD polygenic risk score (PRS) was also categorized into high, intermediate, and low-risk groups using PRS tertiles. Cox regression models determined hazard ratios (HRs) and 95 % confidence intervals (CIs) for BD.

RESULTS

During the 12.9-year follow-up, 529 individuals developed BD. Comparing those with favorable lifestyles to those with unfavorable participants, the HR of developing BD was 3.28 (95 % CI, 2.76-3.89). Similarly, individuals with a high PRS had a risk of 3.20 (95 % CI, 2.83-3.63) compared to those with a low PRS. Notably, individuals with both a high PRS and an unfavorable lifestyle had a significantly higher risk of BD (HR = 6.31, 95 % CI, 4.14-9.63) compared to those with a low PRS and a favorable lifestyle. Additionally, the interaction between PRS and lifestyle contributed an additional risk, with a relative excess risk of 1.74 (95 % CI, 0.40-3.07) and an attributable proportion due to the interaction of 0.37 (95 % CI, 0.16-0.58).

CONCLUSIONS

Our findings suggest that genetic liability for BD, measured as PRS, and lifestyle have an additive effect on the risk of developing BD. A favorable lifestyle was associated with a reduced risk of developing BD.

Identifying novel proteins underlying bipolar disorder via integrating pQTLs of the plasma, CSF, and brain with GWAS summary data

Bipolar disorder (BD) presents a significant challenge due to its chronic and relapsing nature, with its underlying pathogenesis remaining elusive. This study employs Mendelian randomization (MR), a widely recognized genetic approach, to unveil intricate causal associations between proteins and BD, leveraging protein quantitative trait loci (pQTL) as key exposures. We integrate pQTL data from brain, cerebrospinal fluid (CSF), and plasma with genome-wide association study (GWAS) findings of BD within a comprehensive systems analysis framework. Our analyses, including two-sample MR, Steiger filtering, and Bayesian colocalization, reveal noteworthy associations. Elevated levels of AGRP, FRZB, and IL36A in CSF exhibit significant associations with increased BD_ALL risk, while heightened levels of CTSF and LRP8 in CSF, and FLRT3 in plasma, correlate with decreased BD_ALL risk. Specifically for Bipolar I disorder (BD_I), increased CSF AGRP levels are significantly linked to heightened BD_I risk, whereas elevated CSF levels of CTSF and LRP8, and plasma FLRT3, are associated with reduced BD_I risk. Notably, genes linked to BD-related proteins demonstrate substantial enrichment in functional pathways such as "antigen processing and presentation," "metabolic regulation," and "regulation of myeloid cell differentiation." In conclusion, our findings provide beneficial evidence to support the potential causal relationship between IL36A, AGRP, FRZB, LRP8 in cerebrospinal fluid, and FLRT3 in plasma, and BD and BD_I, providing insights for future mechanistic studies and therapeutic development.

Do PACS1 variants impeding adaptor protein binding predispose to syndromic intellectual disability?

To date, PACS1-neurodevelopmental disorder (PACS1-NDD) has been associated with recurrent variation of Arg203 and is considered diagnostic of PACS1-NDD, an autosomal dominant syndromic intellectual disability disorder. Although incompletely defined, the proposed disease mechanism for this variant is altered PACS1 affinity for its client proteins. Given this proposed mechanism, we hypothesized that PACS1 variants that interfere with binding of adaptor proteins might also give rise to syndromic intellectual disability. Herein, we report a proposita and her mother with phenotypic features overlapping PACS1-NDD and a novel PACS1 variant (NM_018026.3:c.[755C > T];[=], p.(Ser252Phe)) that impedes binding of the adaptor protein GGA3 (Golgi-associated, gamma-adaptin ear-containing, ARF-binding protein 3). We hypothesize that attenuating PACS1 binding of GGA3 also gives rise to a disorder with features overlapping those of PACS1-NDD. This observation better delineates the mechanism by which PACS1 variation predisposes to syndromic intellectual disability.

Advances in the pathophysiology of bipolar disorder

PURPOSE OF REVIEW

Due to bipolar disorder clinical heterogeneity, a plethora of studies have provided new genetic, epigenetic, molecular, and cellular findings associated with its pathophysiology.

RECENT FINDINGS

Genome-wide association studies and epigenetic evidence points to genotype-phenotype interactions associated with inflammation, oxidative stress, abnormalities in signaling pathways, hypothalamic-pituitary-adrenal axis, and circadian rhythm linked to mitochondrial dysfunction in bipolar disorder. Although the literature is constantly increasing, most of the genetic variants proposed as biomarkers remain to be validated by independent groups and use bigger samples and longitudinal approaches to enhance their power and predictive ability.

SUMMARY

Regardless of which of the mechanisms described here plays a primary or secondary role in the pathophysiology of bipolar disorder, all of these interact to worsen clinical outcomes for patients. Identifying new biomarkers for early detection, prognosis, and response to treatment might provide novel targets to prevent progression and promote general well being.

Genomic regulatory sequences in the pathogenesis of bipolar disorder

The lifetime prevalence of bipolar disorder is estimated to be about 2%. Epigenetics defines regulatory mechanisms that determine relatively stable patterns of gene expression by controlling all key steps, from DNA to messenger RNA to protein. This Mini Review highlights recent discoveries of modified epigenetic control resulting from genetic variants associated with bipolar disorder in genome-wide association studies. The revealed epigenetic abnormalities implicate gene transcription and post-transcriptional regulation. In the light of these discoveries, the Mini Review focuses on the genes PACS1, MCHR1, DCLK3, HAPLN4, LMAN2L, TMEM258, GNL3, LRRC57, CACNA1C, CACNA1D, and NOVA2 and their potential biological role in the pathogenesis of bipolar disorder. Molecular mechanisms under control of these genes do not translate into a unified picture and substantially more research is needed to fill the gaps in knowledge and to solve current limitations in prognosis and treatment of bipolar disorder. In conclusion, the genetic and functional studies confirm the complex nature of bipolar disorder and indicate future research directions to explore possible targeted treatment options, eventually working toward a personalized approach.

Optimizing CRISPR/Cas9 Editing of Repetitive Single Nucleotide Variants

CRISPR/Cas9, base editors and prime editors comprise the contemporary genome editing toolbox. Many studies have optimized the use of CRISPR/Cas9, as the original CRISPR genome editing system, in substituting single nucleotides by homology directed repair (HDR), although this remains challenging. Studies describing modifications that improve editing efficiency fall short of isolating clonal cell lines or have not been validated for challenging loci or cell models. We present data from 95 transfections using a colony forming and an immortalized cell line comparing the effect on editing efficiency of donor template modifications, concentration of components, HDR enhancing agents and cold shock. We found that in silico predictions of guide RNA efficiency correlated poorly withactivity in cells. Using NGS and ddPCR we detected editing efficiencies of 5–12% in the transfected populations which fell to 1% on clonal cell line isolation. Our data demonstrate the variability of CRISPR efficiency by cell model, target locus and other factors. Successful genome editing requires a comparison of systems and modifications to develop the optimal protocol for the cell model and locus. We describe the steps in this process in a flowchart for those embarking on genome editing using any system and incorporate validated HDR-boosting modifications for those using CRISPR/Cas9.

dbBIP: a comprehensive bipolar disorder database for genetic research

Bipolar disorder (BIP) is one of the most common hereditary psychiatric disorders worldwide. Elucidating the genetic basis of BIP will play a pivotal role in mechanistic delineation. Genome-wide association studies (GWAS) have successfully reported multiple susceptibility loci conferring BIP risk, thus providing insight into the effects of its underlying pathobiology. However, difficulties remain in the extrication of important and biologically relevant data from genetic discoveries related to psychiatric disorders such as BIP. There is an urgent need for an integrated and comprehensive online database with unified access to genetic and multi-omics data for in-depth data mining. Here, we developed the dbBIP, a database for BIP genetic research based on published data. The dbBIP consists of several modules, i.e.: (i) single nucleotide polymorphism (SNP) module, containing large-scale GWAS genetic summary statistics and functional annotation information relevant to risk variants; (ii) gene module, containing BIP-related candidate risk genes from various sources and (iii) analysis module, providing a simple and user-friendly interface to analyze one's own data. We also conducted extensive analyses, including functional SNP annotation, integration (including summary-data-based Mendelian randomization and transcriptome-wide association studies), co-expression, gene expression, tissue expression, protein-protein interaction and brain expression quantitative trait loci analyses, thus shedding light on the genetic causes of BIP. Finally, we developed a graphical browser with powerful search tools to facilitate data navigation and access. The dbBIP provides a comprehensive resource for BIP genetic research as well as an integrated analysis platform for researchers and can be accessed online at http://dbbip.xialab.info. Database URL:  http://dbbip.xialab.info.