Similarly in depression, nuances of gut microbiota: Evidences from a shotgun metagenomics sequencing study on major depressive disorder versus bipolar disorder with current major depressive episode patients
Introduction
Major depressive disorder (MDD) and bipolar disorder (BD) are among the leading causes of burden and disability worldwide (Collins et al., 2011). Although the clinical features of bipolar disorder with current major depressive episode (BPD) and MDD are similar, they have different pathologies and distinct treatment protocols (Ghaemi et al., 2001). Misdiagnosis has occurred frequently due to indistinguishable symptoms and a lack of pathological detection tools (Ghaemi et al., 2001). To probe the pathological differences in the depressive symptoms connected to BPD and MDD, a large number of studies have been conducted (Batmaz et al., 2013; Carvalho et al., 2015; Cuellar et al., 2005; Deng et al., 2018; Grotegerd et al., 2013; Lan et al., 2014; Moreno et al., 2012; Parikh, 2010; Ren et al., 2017; Tas et al., 2015; Taylor, 2014; Uchida et al., 2015). In this field, the gut microbiota are considered a link between the brain, behaviour and moods (Liu and Zhu, 2018).
Gut microbiota play an important role in the pathologies of both MDD and BPD (Brietzke et al., 2011; Cattaneo et al., 2015; Winter et al., 2018). Gut microbiota studies on MDD clearly demonstrated the bi-directional interactions between the gut and the brain in three major systems, neuroimmune, neuroendocrine and sensory neural pathways (Dantzer, 2009; Evrensel and Ceylan, 2015; Mittal et al., 2017; Ng et al., 2018; Winter et al., 2018). Studies of the gut microbiota in MDD patients revealed significant alterations in the abundance of different genera within the phyla Bacteroidetes, Firmicutes, Proteobacteria and Actinobacteria (Jiang et al., 2015). Naseribafrouei et al. found an overrepresentation of the order Bacteroidales and underrepresentation of the family Lachnospiraceae at a high taxonomic level and an overrepresentation of Alistipes and Oscillibacter strains at a low taxonomic level in MDD patients (Naseribafrouei et al., 2014). In contrast, gut microbiota studies on BD, especially for BPD patients, are rare. Painold et al. found a negative correlation between gut flora alpha diversity and BD illness duration (Painold et al., 2018), and Evans et al. observed a decreased fractional representation of the phylum Firmicutes in the BD group. Furthermore, an increase in the family Faecalibacterium was associated with better physical health situations, milder depressive status, and better sleep in the BD group (Evans et al., 2017). Unfortunately, although investigating the subtle differences between MDD and BPD is important, to our knowledge, there are no studies on this topic. Depressive symptoms of MDD and BPD are similar, but due to their different pathologies (Batmaz et al., 2013; Cuellar et al., 2005; Deng et al., 2018; Grotegerd et al., 2013; Lan et al., 2014; Ren et al., 2017; Tas et al., 2015; Taylor, 2014), the clinical practice proposals for each disorder are distinct (Forty et al., 2008; Mitchell et al., 2011; Perlis et al., 2006; Uchida et al., 2015). Based on the evidence mentioned above, we hypothesized that MDD and BPD patients may have a similar gut flora structure in general but that they are significantly different when compared with that of health controls (HCs). Furthermore, we presumed that gut microbiota differences between MDD and BPD would be nuanced due to the indistinguishable clinical features of depression.
Compared with 16S rRNA sequencing (Muyzer et al., 1993), shotgun metagenomics sequencing (SMS) can offer increased resolution, enabling a more specific taxonomic and functional classification of sequences as well as the discovery of new bacterial genes and genomes (Franzosa et al., 2015). Importantly, SMS allows the simultaneous study of archaea, viruses, virophages, and eukaryotes (Norman et al., 2014). As a result, we decided to use SMS to identify the gut microbiota differences among the MDD, BPD and HC groups. We also explored the relationship between gut microbiota and clinical features.
Section snippets
Materials and methods
The study was conducted in accordance with the Declaration of Helsinki, and the protocol of this study was approved by the Human Ethic Committee of Shenzhen Kangning Hospital. Informed written consent was obtained from all of the subjects.
Subject characteristics
Due to contamination of faecal samples, failure to collect clinical information, etc., data from sequencing of 31 MDD patients, 30 BPD patients, and 30 HCs from January 2015 to 12 January 2017 were included in the final analyses. The patients and HCs had comparable demographic characteristics, and no significant demographic differences were found among the three groups. The differences in episode duration, MDQ and HCL-32 were significant between MDD and BPD patients (Table 1).
The results of alpha diversity measures
The Chao 1,
Discussion
To our knowledge, we are the first to explore the gut microbiota by using SMS among MDD, BPD, and HC participants and the first to design a novel index, the Gm coefficient, to probe the inequality of relative abundances. Our results demonstrated significant gut flora differences among the three groups. Additionally, we found some uniquely different species or subspecies between MDD and BPD patients, which may reflect the nuanced pathological differences between the two disorders with
Conclusions
By using SMS, our study provides novel evidence suggesting that gut microbiota may be involved in the intestinal pathogenesis of both MDD and BPD patients. Furthermore, the different faecal bacteria may have the potential to be biomarkers that can differentiate MDD and BPD patients.
Authors’ contributions
HR conducted the study, XHX designed the Gm coefficient algorithm. HR and XHX supervised the whole study. DX, YYG, JZ and YHL collected the data, WTL, MBW, FSH, LX and WFD analyzed the data. JZ, WTL and SXX drafted the manuscript. SXX, XHX, WFD, QFY, MBW, FSW, LX, SW, YLZ and TBL revised the manuscript. All authors read and approved the final manuscript.
Declaration of interest
None.
Role of the funding sources
The funding sources had no role in the design of this study and will not have any role during its execution, analyses, interpretation of the data, or decision to submit results.
Acknowledgements
This work was supported by the Sanming Project of Medicine in Shenzhen (Grant Number: SZSM201812052), Science and Technology Program of Huizhou (Grant Number: 2018Y128), Chinese National Natural Science Foundation (Grant Number: 81201047), and CAS Pioneer Hundred Talents Program (Grant Number: 2017-074).
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The first two authors contributed equally to this paper.