Nat Neurosci︱GWAS analysis reveals a new gene (new protein) that has a clear causal relationship with depression

And using rigorous academic and logical thinking to explore the mysteries of neuroscience.
Written by Wang Sizhen, edited by Wang Sizhen.
Depression is a common mental illness.
However, current treatment methods are only effective for a small number of depression patients [1-2 ].

Therefore, it is necessary to identify the underlying brain proteins in the pathogenesis of depression and determine new therapeutic targets.

 On April 12, 2021, Thomas S.
Wingo group (first author and co-corresponding author) and Aliza P.
Wingo group of Emory University School of Medicine in the United States jointly published an online publication titled Brain proteome- on Nature Neuroscience.
The latest research results of wide association study implicates novel proteins in depression pathogenesis.

The study conducted a genome-wide association analysis study (GWAS) analysis on the proteomics of the brain tissue of patients with depression, and discovered a new gene (new protein) that is causally related to the pathogenesis of depression.

 In the article, first, the researchers found 24 proteome association analysis studies (PWAS) for depression through an integrated analysis of the human brain (dorsolateral prefrontal cortex) proteome and the latest depression GWAS results [3] Genes that regulate the abundance of brain proteins in cis and are associated with depression (Figure 1, Table 1).

Among them, 19 genes such as B3GLCT, GMPPB, CTNND1, CNNM2, EPHB2, etc.
are likely to cause the onset of depression, that is, they may be causal genes for depression (Table 1).Among these 19 genes: GMPPB, B3GALTL, EPHB2, CDH13, FAHD2B, THUMPD3, TKT, NEK4, CNNM2, SLC25A12, and HIBADH are related to depression at the mRNA and protein levels; meta-analysis (meta -Analysis) results show that CTNND1, CNNM2, P2RX7, PSMB4, FAHD2B, HIBADH, CACNA2D2, SLC25A12, CDH13 these 9 genes have good reproducibility (reproducibility) of PWAS results (Table 2); human dorsolateral The single-cell RNA sequencing data of the prefrontal cortex showed that five genes were highly expressed in inhibitory neurons (CACNA2D2, CHD13, CNNM2, NEK4, and SLC25A12), and three genes were highly expressed in excitatory neurons (CHD13, CNNM2, and SLC25A12).
SLC25A12), 3 genes are highly expressed in astrocytes (CTNND1, TKT and TRPT1), 1 gene is highly expressed in oligodendrocytes (P2RX7), and 1 gene is highly expressed in microglia (LMBRD1) ( Figure 3a).

These results indicate the specificity of the expression of depression causal genes in brain cell types.

Figure 1 PWAS analysis identified 19 genes that are causally related to depression (picture quoted from: Wingo, TS, Liu, Y.
, Gerasimov, ES et al.
Nat Neurosci (2021)) Table 1 PWAS results of GWAS results Reproducibility analysis and meta-analysis (Table quoted from: Wingo, TS, Liu, Y.
, Gerasimov, ES et al.
Nat Neurosci (2021)) Table 2 Depression PWAS results (Table quoted from: Wingo, TS, Liu, Y.
, Gerasimov, ES et al.
Nat Neurosci (2021)) At the same time, reproducibility analysis and meta-analysis of PWAS results found 25 proteins that are causally related to depression (Table 3).Among the 25 genes encoding pathogenic proteins: genome network and gene enrichment analysis showed that 11 genes were respectively enriched in amyotrophic lateral sclerosis (CCS, PPP3CC), calcium signal transduction (PPP3CC) , P2RX7, ADCY3), dilated cardiomyopathy (CACNA2D2, ADCY3), oocyte meiosis (ADCY3, PPP3CC), and amino acid and its derivative metabolism (HIBADH, PSMB4) (Figure 2); gene expression cell specific The results showed: 5 highly expressed in excitatory neurons (ABCA5, CCDC92, CTNND1, RAB27B, SLC25A12), 5 highly expressed in inhibitory neurons (ABCA5, CACNA2D2, CNNM2, PPP3CC, SLC25A12), 6 in satellite Among the morphocytes (ATG7, CCS, CTNND1, LRP4, PDXDC1, PPP3CC, ABCA5), 2 in microglia (ATG7, LRP4), 1 in oligodendrocytes (P2RX7) (Figure 3b) ).

These results also indicate the specificity of the expression of depression causal genes in brain cell types.

Table 3 Meta-analysis of GWAS results and reproducibility of PWAS results (Table quoted from: Wingo, TS, Liu, Y.
, Gerasimov, ES et al.
Nat Neurosci (2021)) Figure 2 Repeatability analysis and meta-analysis of PWAS results Discovered 25 potential causes of depression and protein interactions (picture quoted from: Wingo, TS, Liu, Y.
, Gerasimov, ES et al.
Nat Neurosci (2021)) Figure 3 19 depression causal genes in brain cells Expression specificity in types (picture quoted from: Wingo, TS, Liu, Y.
, Gerasimov, ES et al.
Nat Neurosci (2021)) Neurotransmitter, body mass index, and waist-to-hip ratio are related to depression.
Genetic correlation [4-5].

So the researchers further verified the specificity of the PWAS results of these depression.

Data analysis shows that 24% of neurotransmitter genes are contained in PWAS results for depression, which reflects the high genetic correlation between PWAS results and depression.

In contrast, however, the PWAS results for depression only covered 2% of the body mass index genes and 3% of the waist-to-hip ratio genes.

Conclusion and discussion of the article is worth discussing.
Yes, among the 19 depression causal genes, B3GLCT encodes a glycosyltransferase that is involved in synapseogenesis [6].
The decrease in B3GLCT protein level may have a negative impact on synapseogenesis This may be one of the reasons for the pathogenesis of depression.

CTNND1 is involved in synaptic signaling and plays a role in the glutamatergic excitatory synaptic signaling pathway during neuron development [7].

EPHB2 and P2RX7 regulate synaptic plasticity [8-10].
EPHB2 is an important part of hippocampal synaptic plasticity [8].
Changing the expression of EPHB2 in mouse models can lead to depression-like behavior, memory impairment, and hippocampal neurogenesis.
Synaptic plasticity defects [9]; P2RX7 protein can induce synaptic plasticity in depression models [10].

CNNM2 plays an important role in magnesium homeostasis.
Magnesium is a key component of neuronal maturation and neuropathology [11].
CNNM2 is also related to schizophrenia and impaired brain development [12].

PSMB4 encodes a proteasome subunit that is responsible for protein degradation and participates in neuronal apoptosis in neuroinflammation [13].

CDH13 encodes a calcium-dependent cell adhesion protein, which is related to major depression, bipolar disorder, schizophrenia, attention deficit hyperactivity disorder and autism spectrum disorder [14].

and many more.

These analyses further show that these 19 genes are involved in many molecular pathways related to depression, especially the role of synapses in depression.

 It is worth noting that drugs targeting PSMB4, P2RX7, CACNA2D2, and EPHB2 genes have been in clinical trials [15].
PSMB4 drugs are used to treat glioblastoma multiforme, and P2RX7 drugs are used to treat rheumatoid arthritis and For osteoarthritis, CACNA2D2 drugs are used for the treatment of generalized anxiety disorder, fibromyalgia and neuropathic pain, and EPHB2 drugs can be used for the treatment of thyroid cancer.

The development of these targeted drugs will also provide insights into the mechanism research and drug development of depression.

 In total, the study found 19 potential pathogenic genes for depression, which play a pathogenic role by regulating the abundance of brain proteins; through a meta-analysis of the reproducibility analysis of PWAS results, 25 genes were identified that are related to the onset of depression.
The mechanism is a causal brain protein.

These genes and proteins may be potential targets for the research and development of the mechanism of depression in the future.

Original link: https://doi.
org/10.
1038/s41593-021-00832-6 Recommended high-quality scientific research training courses [1] Medicine plus patch clamp and optogenetic and calcium imaging technology seminar (April 24-25, 2 days and 1 night) [2] Online ︱Single Cell Sequencing Data Analysis and Research Thinking Seminar (January 16-17, 21) (courses can be booked from April to May 2021) [3] Multimodal Brain Image data processing analysis/machine learning application online training brain imaging: 17-18 Machine learning: 23-24 references (slide up and down to view) [1] Lim, GY et al.
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