使用RNA定序偵測鬱期相關的生物指標

Abstract

情感性疾患是一種嚴重且具遺傳性的精神疾病，其主要包括兩種疾病：重度憂鬱症以及躁鬱症。情感性疾患高頻率的反覆發作以及慢性進程，在個人、家庭，以及社會層面都造成了相當嚴重的負擔，其中鬱症的反覆發作是重度憂鬱症以及躁鬱症重要的共同特徵。然而目前對於病人急性鬱症發作期與緩解期間，基因表現變化差異的研究相當稀少。 本研究納入了12位情感性疾患患者，從患者急性鬱症發作，追蹤至少兩個月至緩解，並同時在急性期及緩解期抽血。我們使用RNA定序，針對同一人前後測進行比較來減少個體間差異所造成的干擾，使用差異表現分析找出與鬱症相關的生物標記，同時建構與鬱症發作相關的基因網路，以找出基因共同表現模組，並進一步使用功能性分析探討差異表現基因以及模組背後的生物功能。在差異表現分析結果中，我們偵測到一些和鬱症相關的基因，其中有部分基因曾在過去研究中被發現和精神疾病相關，而形態發生、細胞發育、細胞遷移及免疫系統功能相關的基因，都在差異表現基因中佔相當高的比例。在基因網路分析中，我們找到兩個與鬱症發作顯著相關的模組，而這些模組中也有高比例的基因與免疫相關功能有關。 本研究的另一個目標，是找出鬱症發作相關狀態生物標記以及重鬱症相關疾病生物標記之間的重疊。重鬱症相關生物標記包括常見及罕見遺傳變異以及過去在重鬱症腦部發現的差異表現基因。分析結果顯示，我們的差異表現基因，顯著包括了許多跟重鬱症相關的生物標記，表示這些差異表現基因，可以提供我們重鬱症以及鬱症發作之間的連結；相反地，模組內並不顯著包含重鬱症相關的基因，表示模組可能指出了單純和鬱症發作相關的基因資訊。未來研究仍需要更大的樣本來驗證本研究找出的生物標記，並進一步確認鬱症發作以及重鬱症之間相關的生物功能。

Mood disorders, including major depressive disorder (MDD) and bipolar disorder (BPD), are severe and heritable psychiatric disorders. The high frequency of episode recurrence and disease chronicity cause great disease burden to individuals, families, and societies. For the recurrence of episode, depressive episode is suffered by both major depressive disorder (MDD) and bipolar disorder (BPD) patients. To better understand the underlying biological mechanisms of depressive episode, we aimed to investigate transcriptome changes between acute episode versus remission status for depressive episode among patients with MDD or BPD using RNA sequencing. We recruited 12 clinically diagnosed patients with BPD or MDD, who were repeatedly measured for their severity of depressive symptoms using Hamilton Depression Rating Scale (HAMD) during episode (HAMD score>=16), and follow-up for at least two-months till remission (HAMD score<=8). Blood samples were drawn at the time of depression severity assessment. We employed differential expression analysis with intra-individual comparison and gene co-expression analysis to identify differential expression genes (DEGs) and modules related to depressive episode. We further explore possible explanations of the biological functions in the DEGs and modules using functional enrichment analysis. In differential expression analysis, several DEGs were identified, such as glucosylceramidase beta (GBA, log2FC = -1.54, P = 9.06*10-5) and glutamate ionotropic receptor NMDA type subunit associated protein 1 (GRINA, log2FC = -0.54, P = 9.84*10-4), both of which have been mentioned in previous depression studies. A variety of functions were overrepresented in our DEGs, such as GOs related to morphogenesis, cellular development, cell movement, as well as immune system. In co-expression network, we detected two modules related to depressive state, and the immune system also stood out in modules associated with depressive state. Another aim of our study is to find out overlapped genes between depressive state markers and trait markers, including genetic variants and brain expression markers associated with MDD. Results revealed that trait markers were enriched in our DEGs, but not in modules related to depressive state. This indicated that the DEGs might give clues to the connection of biological functions between depressive state and trait, while the modules implied state-specific information. Further studies with a bigger sample size are needed to examine the targets detected in our study in order to identify reliable biomarkers for depressive state and confirm the biological connection between depressive state and trait.