Gut Microbial Signatures Can Discriminate Unipolar from Bipolar Depression

Zheng, Peng; Yang, Jian; Li, Yifan; Wu, Jing; Liang, Weiwei; Yin, Bangmin; Tan, Xunmin; Huang, Yu; Chai, Tingjia; Zhang, Hanping; Duan, Jiajia; Zhou, Jingjing; Sun, Zuoli; Xu, Chen; Marwari, Subhi; Lai, Jianbo; Huang, Tingting; Du, Yanli; Zhang, Peifen; Perry, Seth W.; Wong, Ma‐Li; Licinio, Júlio; Hu, Shaohua; Xie, Peng; Wang, Gang

doi:10.1002/advs.201902862

Cited by 111 publications

(143 citation statements)

References 45 publications

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“…We modified the default calculation by controlling the multiple testing using Benjamini–Hochberg (BH) false discovery rate (FDR) correction procedure. LEfSe analysis was conducted under the following conditions: p < 0.05, FDR < 0.1 and LDA > 2.5 ( Erawijantari et al, 2020 ; Zheng et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

“…We modified the default calculation by controlling the multiple testing using Benjamini-Hochberg (BH) false discovery rate (FDR) correction procedure. LEfSe analysis was conducted under the following conditions: p < 0.05, 1 http://qiime.org/ 2 http://drive5.com/usearch/manual/uchime_algo.html 3 http://drive5.com/uparse/ 4 http://huttenhower.sph.harvard.edu/galaxy/ FDR < 0.1 and LDA > 2.5 (Erawijantari et al, 2020;Zheng et al, 2020).…”

Section: S Rrna Gene Sequence Analysismentioning

confidence: 99%

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Differential Gut Microbiota and Fecal Metabolites Related With the Clinical Subtypes of Myasthenia Gravis

et al. 2020

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Myasthenia gravis (MG) is a devastating acquired autoimmune disease. Previous studies have observed that disturbances of gut microbiome may attribute to the development of MG through fecal metabolomic signatures in humans. However, whether there were differential gut microbial and fecal metabolomic phenotypes in different subtypes of MG remains unclear. Here, our objective was to explore whether the microbial and metabolic signatures of ocular (OMG) and generalized myasthenia gravis (GMG) were different, and further identify the shared and distinct markers for patients with OMG and GMG. In this study, 16S ribosomal RNA (rRNA) gene sequencing and gas chromatographymass spectrometry (GC/MS) were performed to capture the microbial and metabolic signatures of OMG and GMG, respectively. Random forest (RF) classifiers was used to identify the discriminative markers for OMG and GMG. Compared with healthy control (HC) group, GMG group, but not OMG group, showed a significant decrease in α-phylogenetic diversity. Both OMG and GMG groups, however, displayed significant gut microbial and metabolic disorders. Totally, we identified 20 OTUs and 9 metabolites specific to OMG group, and 23 OTUs and 7 metabolites specific to GMG group. Moreover, combinatorial biomarkers containing 15 discriminative OTUs and 2 differential metabolites were capable of discriminating OMG and GMG from each other, as well as from HCs, with AUC values ranging from 0.934 to 0.990. In conclusion, different subtypes of MG harbored differential gut microbiota, which generated discriminative fecal metabolism.

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Section: Methodsmentioning

confidence: 99%

Section: S Rrna Gene Sequence Analysismentioning

confidence: 99%

Differential Gut Microbiota and Fecal Metabolites Related With the Clinical Subtypes of Myasthenia Gravis

et al. 2020

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“…Relative abundance of microbial compositions was different at the phylum and family levels between MDD and BD patients. Moreover, a microbial operational taxonomic unit (OTU) panel was identified that could efficiently distinguish between bipolar and unipolar depression 43 . These two studies provided preliminary evidence on biological disparities between MDD and BD, as reflected by a different microbiome in the human intestine.…”

Section: Gut Microbiome and Bd In Human Studiesmentioning

confidence: 99%

Gut microbial clues to bipolar disorder: State‐of‐the‐art review of current findings and future directions

Lai

Jiang

Zhang

et al. 2020

Clinical & Translational Med

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Trillions of microorganisms inhabiting in the human gut play an essential role in maintaining physical and mental health. The connections between gut microbiome and neuropsychiatric diseases have been recently identified. The pathogenesis of bipolar disorder, a spectrum of diseases manifesting with mood and energy fluctuations, also seems to be involved in the bidirectional modulation of the microbiome‐gut‐brain (MGB) axis. In this review, we briefly introduce the concept of MGB axis, and then focus on the previous findings in human studies associated with bipolar disorder. These studies provided preliminary evidences on the gut microbial alterations in bipolar disorder. Limitations in these studies and future directions in this research field, such as fecal microbiome transplantation and microbiome‐targeted therapy, were discussed. A research framework linking gut microbiome to determinants and health‐related outcomes in BD was also proposed. Better characterizing and understanding of gut microbial biosignatures in bipolar patients contribute to clarify the etiology of this intractable disease and pave the new way for treatment innovation.

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“…This might be illustrated by the involvement of candidate hub genes underlying the pathogenesis of early death in HD, particularly CA10, which indirectly interacted with GABRB3 in the pathway of GABAergic synaptic transmission [29,34,38,41]. Five genes (CA10, WSB2, ACTR3B, PCDH19 and GABRB3) with non-zero regression coe cients were selected to conduct the ROC analysis, of which the AUC values were all greater than 70%, supporting that they were hub genes and could be served as potential predictors for early death in HD worthy of clinical application [43]. In addition, the veri cation of the Kaplan-Meier survival curve demonstrated that, compared with the high expression of CA10, WSB2, ACTR3B, PCDH19 and GABRB3, their low expression was associated with shorter overall survival of HD patients.…”

Section: Discussionmentioning

confidence: 84%

Regulatory network underlying alterations of gene expression in early death of Huntington's disease

Zhou¹,

Chen²,

Zhong³

et al. 2020

Preprint

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The aim of this study is to explore the genomic mechanism of early death in Huntington’s disease (HD). We identified 10160 and 1511 differentially expressed genes (DEGs) from comparisons of HD versus control and early versus late death, respectively. On the basis of 922 overlapped DEGs among them, six functional modules were established by weight gene correlation network analysis. The turquoise module most strongly related to overall survival of HD was significantly enriched in GABAergic synapse, retrograde endocannabinoid signaling and neuroactive ligand-receptor interaction. Low expression of five DEGs (CA10, WSB2, ACTR3B, PCDH19 and GABRB3) with highest degree of connectivity and non-zero regression coefficients were identified as hub genes, based on which the LASSO model exactly predicted the occurrence of early death in HD. Furthermore, the network analysis revealed indirect interaction of CA10 with the central hub gene GABRB3, which was involved in the pathway of GABAergic synapse. Compared with high expression of hub genes, their low expression appeared shorter overall survival of HD patients according to Kaplan-Meier survival analysis. Consequently, low expression of CA10, WSB2, ACTR3B, PCDH19 and GABRB3 were possibly associated with early death of HD. The likelihood of low expression of GABRB3 regulated by CA10 in GABAergic synapse contributed to the pathogenesis of early death in HD.

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Gut Microbial Signatures Can Discriminate Unipolar from Bipolar Depression

Cited by 111 publications

References 45 publications

Differential Gut Microbiota and Fecal Metabolites Related With the Clinical Subtypes of Myasthenia Gravis

Differential Gut Microbiota and Fecal Metabolites Related With the Clinical Subtypes of Myasthenia Gravis

Gut microbial clues to bipolar disorder: State‐of‐the‐art review of current findings and future directions

Regulatory network underlying alterations of gene expression in early death of Huntington's disease

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