The NPP-Luo Xiongjian research group used Mendelian randomization to screen potential drug targets for the treatment of mental illness

Written by Liu Jiewei

Responsible editor - Wang Sizhen

Editor-in-Charge - Xia Ye

Mental illness poses a huge economic burden to society and a global public health threat [1, 2].

Drug treatment of mental illness still faces important challenges
.
First, only a limited number of drugs can be used to treat psychiatric disorders [3-5].

Second, most approved drugs for the treatment of mental illness can only play their role through a few specific drug treatment targets
.
For example, almost all antipsychotics exert therapeutic effects by antagonizing dopaminergic receptors type 2 (DRD2) [6-8], and most antidepressants target monoaminergic systems (including dopaminergic, norepinephrine, and serotonin systems) [9, 10
].
However, in addition to the beneficial therapeutic effects, antipsychotics and antidepressants also bring considerable side effects [11-15], such as hyperlipidemia, myocarditis, weight gain, etc
.
Finally, many drugs respond slowly (about a few weeks) and many patients do not respond well to drug therapy, i.
e.
, there is a more common resistance reaction [16-19].

Therefore, the search for new drugs and drug targets that can be used to treat mental illness has become an urgent need
.

On September 16, 2022, Luo Xiongjian's team at the Kunming Institute of Zoology of the Chinese Academy of Sciences and Southeast University published a report on Neuropsychopharmacology titled "Genome-wide Mendelian randomization identifies actionable novel drug targets for psychiatric.
" disorders" article
.
The paper uses Mendelian randomization analysis to screen out drug target genes with potential application value in psychiatric diseases, which are target molecules for drugs (and drugs in clinical trials) that have been approved by the FDA, such as TIE1, AKT3, HLADRB1, P2RX7, PSMA4, MAPK3, CACNA1C, PRKCB, PSMB4, IMPDH2, GRIA1 and TAOK3
.
These results provide new ideas and research results
for the treatment of mental illness and drug development.


The rapid development of genome-wide association study (GWAS) has provided unprecedented opportunities for the development of new drugs for complex diseases [20].

Over the past two decades, GWAS has identified many risky genetic variants associated with complex human diseases, including psychiatric disorders such as schizophrenia [21, 22], depression [23, 24], bipolar disorder[25], and attention deficit and hyperactivity disorder [26].

Considering the huge time and economic cost of drug development, if new drug target molecules or drug reuse opportunities can be found from the GWAS research results of psychiatric diseases, this will undoubtedly help the drug development and clinical treatment
of mental illness.
Most of the risk genetic variants identified by GWAS are currently located in noncoding regions of the genome, suggesting that risk genetic variants may exert biological effects by influencing gene expression [27].

In recent years, Mendelian randomization (MR) analysis has been widely used to infer causal relationships between altered gene expression and complex diseases [28-32].

Through Mendelian randomization analysis, the researchers integrated GWAS results from psychiatric disorders and brain gene/protein expression quantity trait data (eQTL and pQTL) of 1263 (FDA-approved or clinically trial-phased drug target molecules) genes, and finally found 25 potential drug target molecules
that could be used in the treatment of psychiatric disorders.

Figure 1.
Manhattan plot using quantitative trait data (QTL) and MR analysis of schizophrenia GWAS data (33,640 cases of schizophrenia and 43,456 healthy controls).

(A) MR results using GTEx brain eQTL as a tool variable; (B) MR results using PsychENCODE eQTL as a tool variable; (C) MR results
using ROSMAP pQTL as an instrumental variable.
The red dotted line is the significance level
after Bonferroni correction.
(Credit: Jiewei Liu, et al.
, NPP, 2022) By using eQTL SNPs from GTEx as genetic tool variables, the researchers identified eight potential drug targets for schizophrenia (MR P < 3.
60×^10-06)
(Figure 1).

。 These potential drug target molecules include HLA-DRB1, BRD2, CHRNA2, RORB, CACNA1C, MAPK3, PTK6, and CYP2D6 (Figure 1).

It is worth noting that CACNA1C is the most significant MR result (P = 3.
23 × 10⁻¹⁵, OR [95% CI] = 0.
85 [0.
81, 0.
88]).

HLA-DRB1 is supported
by MR analysis results of three different brain regions of GTEx.
BRD2 and HLA-DRB1 are located in the major histocompatibility complex (MHC) region, which contains the most significant genetically associated signals for schizophrenia [21, 22
].
When using eQTL SNPs from PsychENCODE for MR analysis as genetic tool variables, the researchers identified 3 drug target molecules (AKT3, PSMA4, and PTK6
).
Interestingly, PTK6 gets GTEx (P = 1.
53 × 10−06, OR [95%CI] = 0.
90 [0.
86, 0.
94]) and PsychENCODE(P = 2.
31 × 10⁻⁰⁶, OR [95%CI] = 0.
49 [0.
37, 0.
66] ) support for MR analysis results of eQTL datasets
.
In the results of MR analysis using proteomics, we also identified four protein molecules (HLA-DRB1, CAMKK2, P2RX7, and MAPK3) to reach a significant level of MR analysis, indicating that the expression abundance of these four proteins has a causal relationship with schizophrenia (Figure 1).

It is worth noting that two genes (MAPK3 and HLADRB1) are supported by MR analysis based on gene expression and protein expression, strongly suggesting that expression changes in these two genes and proteins may have a causal relationship with schizophrenia (Figure 1).


The researchers identified 7 potential drug target molecules
in bipolar disorder.
In MR analysis using GTEx eQTL as a genetic tool variable, the CACNA1C gene
was identified.
In the MR analysis using PsychENCODE eQTL as a genetic tool variable, 5 genes (DCLK3, SRPK2, DAGLA, PSMD3, and STK4)
were identified.
In MR analysis using ROSMAP pQTL as a genetic instrumental variable, 1 protein molecule was identified with significant MR results (PRKCB) (Table 1).

The researchers found that MR analysis of schizophrenia and bipolar disorder overlapped results (Figure 1, Table 1), and the CACNA1C gene was the most significant result in the MR analysis of bipolar disorder and schizophrenia (GTEx eQTL) (P = 3.
31 × ¹⁰⁻¹¹, OR [95% CI] = 0.
89 [0.
85 to 0.
92]) (Table 1).

。 In addition, the researchers identified seven potential drug target molecules (STK24, PSMB4, SERPINC1, IMPDH2, GRIA1, TAOK3, and P2RX7) in MR analysis of depression, 6 of which were obtained through MR analysis with ROSMAP pQTL as an instrumental variable
.
Finally, the researchers found the only significant result (TIE1) in the MR analysis of attention deficit and hyperactivity disorder (P = 2.
12 × 10⁻⁰⁷, OR [95% CI] = 1.
56 [1.
32 to 1.
85]) (Table 1).


Table 1.
Significant Mendelian randomization analysis (MR) results
for bipolar disorder (BP), depression, and attention deficit and hyperactivity disorder (ADHD).

(Table Source: Jiewei Liu, et al.
, NPP, 2022)

By using Mendelian randomization analysis, the researchers integrated genome-wide association analysis results of four psychiatric disorders and gene/protein expression quantitative trait data (eQTL and pQTL), and identified a total of 25 drug target molecules that can be used for the treatment of psychiatric disorders, 12 in schizophrenia, 7 in bipolar disorder, 7 in depression, and 1
in attention deficit and hyperactivity disorder 。 These genes are all target molecules for drugs that have been approved by the FDA or for drugs that are in the clinical development stage, and these findings provide critical drug reuse opportunities for drug treatment of psychiatric disorders and new potential drug candidate target molecules
for psychiatric drug development.
There are also certain limitations in this study, such as the sample size of proteomic data (pQTL) is much smaller than the sample size of transcriptomics (eQTL), which will lead to the number of identified protein molecules pQTL is less than eQTL, and later MR analysis
needs to be performed using a larger sample size of the pQTL dataset.
In addition, this study focused on data analysis, and the relevant results need to be verified
by further experimental and clinical data.
Overall, this research paper provides a research idea for post-GWAS analysis of mental illness GWAS data, and future integrated research on mental illness GWAS data and other omics data will promote translational research
on psychiatric disease treatment.
Original link: of Luo Xiongjian's Laboratory of Researchers

(Photo courtesy of: Luo Xiongjian Laboratory)

About the Corresponding Author (Swipe Up and Down to Read)

Luo Xiongjian, researcher, doctoral supervisor
.
He is currently a researcher
at the Advanced Institute of Life and Health of Southeast University.
He graduated from the School of Life Sciences of Wuhan University with a bachelor's degree in 2005, received his Ph.
D.
from the Kunming Institute of Zoology of the Chinese Academy of Sciences in 2010, went to the University of Rochester Medical Center in September of the same year to engage in postdoctoral research, and returned to Kunming Institute of Zoology in September 2014
.
In 2018, he was supported
by the Outstanding Youth Science Fund of the Foundation Committee.
He has been engaged in the study of the genetic mechanism and pathogenesis of mental illness for a long time, and has used multi-omics integrated research methods to discover and identify a number of new psychiatric disease susceptibility genes such as CAMKK2, ZNF323, MKL1, GLT8D1, etc.
, and explored the role of these genes in the development of the nervous system and the possible mechanism
in the occurrence of psychiatric diseases.
In addition, the collaboration developed SZDB ( the most comprehensive genetic research database for schizophrenia to date.
At the same time, functional genomics methods are used to elucidate the gene regulation mechanism
of genetic variants of schizophrenia and depression.
At present, he has published more than 40 SCI papers
.
In recent years, he has been the author of Nature Communications (2019, 2018), Molecular Psychiatry (2020, 2014a, 2014b), Am Journal of Psychiatry, Schizophrenia Bulletin (6 articles), Neuropsychopharmacology (2019, 2018) and other journals published more than 30 papers
.
The research results have been recommended
by Faculty of 1000 three times.

J Neuroinflammation – Tang Yamei's team discovered the mechanism by which pregabalin mitigates microglial activation and neuronal damage in radiation brain injury [2] Transl Psychiatry – Accelerated aging of brain function in patients with major depression: evidence from large Chinese participants [3] Nat Commun-Xu Tianle/Li Weiguang/Zhang Siyu team work together to reveal the neuronal cluster organization of fear and regression memory competition [4] Brain-Yi Chenju/Niu Jianqin team found that activating the Wnt/β-catenin pathway can alleviate blood-brain barrier dysfunction in Alzheimer's disease [5] Prog Neurobiol Frontier Thinking Genetic factors, aging and intestinal microbial disorders on the immune response in "dry" and "wet" retinal degenerative degeneration[6] Sci Adv-Xi Zhengxiong's team discovered a new mechanism of sports reward: midbrain erythronucleus-ventral covered area glutamate neural pathway [7] Mol Psychiatry-Niu Jianqin/Xiao Lan team found that oligodendroglial precursor cells DISC-Δ3 variable shear inhibition of excitatory synaptic growth led to schizophrenia [8] PNAS | Sun Bo's research group and collaborators found that time signals are the main factors regulating multicellular information networks[9] Cell Reports—Liu Sheng/Liu Yizhi's team mapped mammalian retinal ganglion cell multimodal maps[10] Cereb Cortex - default mode network in the treatment of electroshock in patients with depression Brain imprint quality scientific research training course recommendation [1] Single-cell sequencing and spatial transcriptomics data analysis seminar (October 29-30 Tencent Online Conference) [2] Symposium on Patch Clamp and Optogenetics and Calcium Imaging Technology (October 15-16, 2022 Tencent Conference)【3】R Language Clinical Prediction Biomedical Statistics Special Training (October 15-16, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing) Conference/Forum Preview & Review

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