A comparative study to determine the association of gut microbiome with schizophrenia in Zhejiang, China

doi:10.21203/rs.3.rs-1593406/v1

Download PDF

Research Article

A comparative study to determine the association of gut microbiome with schizophrenia in Zhejiang, China

https://doi.org/10.21203/rs.3.rs-1593406/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

With the rapid progress of high-throughput sequencing technology, characterization of schizophrenia (SZ) with underlying probing of the gut microbiome can explore pathogenic mechanisms, estimate disease risk, and allow customization of therapeutic and prophylactic modalities. In this study, we compared the differences in gut microbial diversity and composition between 50 SZ subjects and 50 healthy matched subjects in Zhejiang, China via targeted next-generation sequencing (16S rRNA amplicon).

Results

Accordingly, the alpha diversity indices (observed species index, Shannon index, and Simpson index) of the gut microbiome in the healthy control group were higher than those in the SZ group. Additionally, principal coordinate analysis and non-metric multidimensional scaling of beta diversity revealed that patients with SZ clustered more tightly than healthy controls. At the phylum level, we found that the abundance of Bacteroidetes and Proteobacteria in the SZ group was significantly increased. At the genus level, the relative abundances of Prevotella, Parabacteroides, and Sutterella were significantly higher, whereas the abundances of Faecalibacterium, Blautia, Lachnospira, Clostridium, Ruminococcus, and Coprococcus were lower than those in the healthy control group. Further analyses revealed that Succinivibrio, Megasphaera, and Nesterenkonia may serve as potential biomarkers for distinguishing patients with SZ from those in the control cohort.

Conclusions

This study profiled differences in gut microbiome diversity, taxonomic composition, and function between SZ and healthy cohorts, and the insights from this research could be used to develop targeted next-generation sequencing-based diagnoses for SZ.

gut microbiome

16S rRNA sequencing

schizophrenia

biomarker

The human gut microbiota, a complex ecosystem comprising 100 trillion microorganisms (bacterial species, fungi, and viruses) carrying over three million genes (gut microbiome), can influence human physiology, behavior, and health [1, 2]. It changes over time depending on the host’s genetic predisposition, age, behavior, dietary habits, antibiotic use, and living environment. Microorganisms located in the human gastrointestinal tract carry various functionalities, including the absorption of nutrients and minerals, the constructive synthesis of short-chain fatty acids (SCFAs) and vitamins, and the production of microbial metabolites. Converging evidence suggests that differences in the human gut microbial community contribute to various diseases, such as liver diseases, diabetes, inflammatory bowel disease, multiple sclerosis, colorectal cancer, and neuropsychiatric disorders [3–8]. In addition, a smaller number of studies have revealed predicted or actual functional alterations in microbial genes or metabolic pathways directly or indirectly associated with these diseases. Therefore, understanding the communication or signaling pathways involved in the interactions between humans and microbiota is essential for investigating pathogenic mechanisms, estimating disease risk, allowing the customization of therapeutic modalities, monitoring therapeutic progress, and developing prophylactic strategies.

Since most intestinal microorganisms are difficult to culture, next-generation sequencing (NGS) platforms with high throughput, read length, sensitivity, specificity, precision, and accuracy have been widely applied to characterize microbial composition. To increase the sensitivity of microbial identification and overcome the dilemma of amplifying low levels of microbial sequences, the process of enrichment for target and interesting sequences (16S rRNA, 18S rRNA, 23S rRNA, and internal transcribed spacer) can be performed via polymerase chain reaction (PCR) before high-throughput sequencing [9–12]. Although targeted next-generation sequencing (tNGS) only directly characterizes bacterial taxonomy, it is a cost-effective option for exhaustively covering the biodiversity (measuring the maximal dynamic range of relative abundance) of many samples via minimal sequencing. Such a powerful alternative has strongly contributed to various novel discoveries in the past decade, helping us to survey and characterize the gut microbiome from the human gut, soil and oceans [13–15]. More importantly, bioinformatics analysis may be best suited to explore predicted or actual microbial functions and metabolic pathways.

Schizophrenia (SZ) is a serious psychiatric disorder with a global lifetime prevalence of 0.4% and a heritability of around 0.81 (confidence interval, 0.73–0.90) [16–17]. The marked symptoms are mainly divided into five domains: hallucinations, delusions, disorganized thinking (speech), grossly disorganized, and abnormal human behaviors. Although the physiological phenomenon of SZ has not yet been explored or explained, patients diagnosed with SZ are frequently characterized by psychotic symptoms, poor social functioning, and a poor quality of life. For molecular diagnosis of SZ patients, accumulating evidence of NGS indicates that alterations in microbial diversity and taxonomic composition in the human gut are widely observed in SZ patients compared with healthy matched controls, which greatly promotes the development of biomarkers [18–20]. However, we are in the way of completely distinguishing normal microbiota from that present in SZ or recognizing how its substantial metabolic pathways act on health. In addition, there is intensive and extensive bidirectional signaling between the gut microbiota and central nervous system through the gut-brain axis [21]. This process strongly correlates with neuronal, endocrine, and immunological mechanisms, which allow the gut microbiota to influence various human psychiatric status and homeostasis. Thus, it is necessary to decipher the content, diversity, and function of the gut microbiota to evaluate therapeutic opportunities and strategies in patients with SZ.

In this study, we focused on investigating and characterizing the differences in gut microbial diversity and taxonomic composition between people with SZ and healthy people. In addition, we predicted the genetic potential of the gut microbiota and elucidated how functional differences affect the physiological processes within the human gut. We aimed to present a comprehensive insight into the predicted or actual pathogenic mechanism of the human gut microbiota of SZ, with a particular emphasis on the potential of microbe-based diagnostic biomarkers.

Sampling information

Based on the inclusion and exclusion criteria, 50 male patients with SZ and 50 healthy male individuals were recruited. The demographic and clinical characteristics of both the groups are presented in Table 1. There were no significant differences in age (p = 0.1311), weight (p = 0.1811), and body mass index (BMI, p = 0.4817). However, the NC group had a significantly greater height than the SZ group (p = 0.0460). In terms of BMI classification, there were statistical differences between the thin (BMI < 18.5, p = 0.0412) and obese (BMI ≥ 28, p = 0.0412) groups. Finally, the distribution of antipsychotic use of patients with SZ mainly consisted of risperidone (28%), clozapine (20%), olanzapine (40%), and quetiapine (24%), and 26% of them took two types of antipsychotic drugs during clinical therapy.

Microbial diversity

A total of 8,911,266 reads (1,869,170,520 bases) were obtained from the healthy control group, while 50 microbiome samples of SZ subjects consisted of 5,816,276 reads (1,320,847,922 bases). After quality filtration, adapter reduction, and paired-read assembly, we obtained 4,376,659 raw tags, 4,321,992 effective tags, and 21,507 OTUs in healthy control group, ranging from 23,459 to 414,271, 23,392 to 409,960, and 115 to 844 for each sample. Additionally, there were 2,667,140 raw tags, 2,645,060 effective tags, and 12,230 OTUs in the SZ group, ranging from 19,503 to 68,895, 19,387 to 67,766, and 101 to 516 for each sample, respectively. Both the rarefaction curve (Figure S1 A) and rank abundance curve (Figure S1 B) confirmed the validity of the high-throughput sequencing data and revealed that the abundance of the microbial community varied depending on the sample of individuals.

To characterize the richness and diversity of the microbial community, we calculated alpha indices for each sample. There were significant changes in the observed species index between the SZ and NC groups (Figure 1A, p = 6.88e-07), Shannon index (Figure 1B, p = 5.94e-05), and Simpson index (Figure 1C, p = 6.85e-04) of alpha diversity. Conversely, OTU-based beta diversity is a comparative analysis of microbial community composition between samples. We observed that the PCoA (Figure 2A) and NMDS-based map (Figure 2B, stress = 0.093) of unweighted UniFrac metrics revealed that SZ subjects were tightly clustered when NC subjects formed distinct clusters (within-group distance comparison). In addition, some NC clusters were close to those of the SZ group. Moreover, the ANOSIM analysis (Figure 2C) indicated that the microbial community structure was significantly different (unweighted UniFrac, R = 0.152, p = 0.001) between the two groups.

Differences in taxonomic composition

To better understand OTU information and taxonomic annotation, tags and OTUs were calculated and summarized. As is shown in Figure 3A, the predominant bacteria in the NC group were Firmicutes (57.43%), Bacteroidetes (33.08%), Proteobacteria (4.55%), Actinobacteria (2.31%), and Fusobacteria (1.83%), whereas the SZ cohort was dominated by Firmicutes (42.93%), Bacteroidetes (42.03%), Proteobacteria (9.04%), Fusobacteria (2.55%), and Actinobacteria (1.72%). When the relative abundances of bacterial phyla were compared (Figure S2A), Bacteroidetes (p = 4.11e-03) and Proteobacteria (p = 0.0371) were found to be more abundant in the SZ than in the NC group. In terms of Firmicutes levels, the SZ group showed a significant decrease (p = 3.98e-05) compared to the NC group. At the genus level (Figure 3B), the NC group was mainly assigned to Bacteroides (24.26%), Faecalibacterium (12.59%), Roseburia (6.89%), Prevotella (4.43%), Megamonas (3.31%), Blautia (3.13%), Lachnospira (2.58%), Clostridium (2.30%), Ruminococcus (1.99%), and Coprococcus (1.81%). The most abundant genera in the SZ group were Bacteroides (25.66%), followed by Prevotella (10.24%), Faecalibacterium (7.95%), Roseburia (4.93%), Succinivibrio (3.68%), Megamonas (2.96%), Parabacteroides (2.49%), Dialister (2.00%), Sutterella (1.67%), and Clostridium (1.33%). Compared to healthy cohort (Figure S2B), the relative abundance of Prevotella (p = 0.0157), Parabacteroides (p = 0.0342), and Sutterella (p = 0.0365) was significantly higher in the SZ cohort. However, Faecalibacterium (p = 4.25e-03), Blautia (p = 3.04e-05), Lachnospira (p = 6.36e-03), Clostridium (p = 0.0287), Ruminococcus (p = 0.0380), and Coprococcus (p = 0.0258) levels were higher in the healthy cohort.

The Venn diagram illustrates the distribution of shared and specific genera to identify the candidate microbial biomarkers. As is shown in Figure 4A and 4B, all individuals, irrespective of SZ patients and healthy persons, had in common 73 genera of the total members that were consistently detected. In addition, we calculated the indicator value to identify candidate biomarkers for microbiological diagnosis. Succinivibrio (p = 0.001), Megasphaera (p = 0.001), and Nesterenkonia (p = 0.005) were more enriched in the SZ group, whereas Blautia (p = 0.001), Paracoccus (p = 0.001), Adlercreutzia (p = 0.001), Enhydrobacter (p = 0.001), Eggerthella (p = 0.002), Corynebacterium (p = 0.002), Oxalobacter (p = 0.002), and Finegoldia (p = 0.005) in healthy control subjects (Figure 4C).

Functional differences of microbiome

Tax4Fun analysis was performed to reveal and explore the differences in the function of the gut microbiome between the SZ and NC groups. As is shown in Figure 5, the metabolism of terpenoids/polyketides (p = 4.37e-07), excretory system (p = 9.50e-05), energy metabolism (p = 2.08e-04), cancers (p = 3.93e-04), circulatory system (p = 2.87e-03), nervous system (p = 4.44e-03), signal transduction (p = 5.49e-03), and xenobiotic biodegradation/metabolism (p = 0.0220) in the SZ group showed an upward trend compared to that in the NC group. However, there were significant decreases in transcription (p = 1.33e-03), nucleotide metabolism (p = 2.10e-03), immune diseases (p = 3.42e-03), replication/repair (p = 5.46e-03), membrane transport (p = 0.0131), and translation (p = 0.0139) in the SZ group. Next, we analyzed the correlation between the relative abundance of the altered genera and differentially functional pathways. Faecalibacterium, Ruminococcus, Coprococcus, Adlercreutzia, Blautia, and Paracoccus were positively associated with nucleotide metabolism, transcription, replication/repair, and translation (Figure 6). Meanwhile, Parabacteroides was positively correlated with the metabolism of terpenoids/polyketides, the nervous system, excretory system, and circulatory system when Prevotella had a positive effect on cancers. In contrast, Coprococcus, Corynebacterium, and Adlercreutzia were negatively associated with signal transduction, the nervous system, and the circulatory system. Moreover, Blautia was negatively correlated with energy metabolism and cancer.

In our study, microbial diversity and composition in the gut of patients with SZ and healthy control subjects from Zhejiang, China, were evaluated using tNGS of the V3 and V4 regions of the 16S rRNA gene with bioinformatics analysis. Our first main finding was that the microbial diversity of the human gut was altered between the SZ and NC groups. The alpha diversity indices (observed species index, Shannon index, and Simpson index) of the gut microbiome in the NC group were higher than those in the SZ group, indicating a higher richness and diversity of the microbial community in healthy subjects. It is typically observed that the scores of alpha-diversity in psychiatric populations decrease in SZ patients [22–23]. Generally, a high index of alpha diversity is considered as a marker of a healthy status. In summary, the lower alpha diversity suggested an overall abnormal microbial ecology within patients here and was linked to a range of chronic human diseases [24]. Additionally, beta diversity metrics were considered using nonphylogenetic methods. The resultant beta diversity showed that patients with SZ clustered tightly when the clusters of NC subjects were spread more widely across the PCoA and NMDS spaces. Therefore, the OTU definition and taxonomic annotation were performed for further analyses.

The intestinal microbiota community structure at the phylum and genus levels was mainly analyzed. Regardless of age, weight, height, BMI, and BMI classification, the gut microbial composition of normal people and patients with SZ mainly consisted of five major phyla: Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, and Fusobacteria. This result is consistent with previous researches [18, 25–26]. However, the most important alteration was that the relative abundance of Bacteroidetes (genus: Prevotella and Parabacteroides) and Proteobacteria (genus: Sutterella) was obviously higher in patients with SZ than in healthy controls. In contrast, we found that the proportion of Firmicutes (genus: Faecalibacterium, Blautia, Lachnospira, Ruminococcus, and Coprococcus) and Actinobacteria (genus: Corynebacterium and Adlercreutzia) was lower in SZ subjects. An increasing number of studies have validated that Faecalibacterium, Blautia, Lachnospira, Ruminococcus, Coprococcus, Corynebacterium, and Adlercreutzia are beneficial phylotypes. The relationship between bacterial abundance and function was analyzed and discussed. Our results revealed that Faecalibacterium, Blautia, Ruminococcus, Coprococcus, Adlercreutzia, and Paracoccus regulated diverse molecular processes, such as nucleotide metabolism, transcription, replication/repair, and translation. As is widely known, nucleotide metabolism is required for nucleic acid synthesis, DNA proliferation, DNA repair, and RNA production to maintain genome stability. Besides, RNA is translated into proteins with the correct structure and is involved in cell proliferation, maintenance, repair, and regulation at different stages of the cell cycle.

The decrease in Coprococcus, Corynebacterium, and Adlercreutzia resulted in an upward trend in signal transduction, nervous system, and circulatory system in SZ subjects. More seriously, the decreased level of Blautia caused an upregulated status of cancer. It has been reported that the decrease in Blautia, Lachnospira Coprococcus, Corynebacterium, and Adlercreutzia may be caused by antipsychotic medication and are strongly associated with a reduction in SCFAs [27]. SCFAs are capable to modulate a variety of immune and epigenetic pathways, such as barrier function in intestinal epithelial cells, obesity-associated inflammation, release of interleukin-6 and tumor necrosis factor-α from macrophages, cytokine-associated nuclear factor kappa-B signaling pathway, and inhibition of histone deacetylase [28–31]. These biological pathways have also been examined and validated to be dysregulated in SZ. Although SCFAs have anti-inflammatory properties, the central role of SCFAs in the brain and their relationship with neurobiological factors and pathways, including neurotransmitter circuits, neurotrophic factors, and other brain metabolites, remains largely unknown.

The most important limitation of this study was its small sample size, which must be acknowledged. For example, only 100 male subjects from the same area participated in this study, and one microbiome sample was tested per individual. Generally, microbial diversity and composition fluctuate across sex, area, time, and other factors [32, 34]. Likewise, we did not match the gut microbiome or control for smoking prevalence in our analyses. Patients with SZ are markedly prone to smoke tobacco and it has been suggested that biological factors may underlie the association between this disorder and tobacco use. Moreover, mechanistic studies on animal and cellular aspects are absent. All these statuses limited the extent to which we could statistically explore predicted or actual confounds on the differences in the microbiome and the power of correlational analyses between microbiota and functional differences. Therefore, these results should be considered as preliminary.

Although there are several limitations to our study, we still make a critical contribution to the monitoring of SZ. Based on the taxonomic community, the Venn diagram showed that 11 genera had the potential to serve as vital diagnostic biomarkers for distinguishing SZ. To further validate the biomarkers that contributed significantly to the prediction performance, we calculated the indicator value and performed Welch’s t-test. The predictive model for Succinivibrio, Megasphaera, and Nesterenkonia at the genus level revealed remarkable discriminating power. This microbial diagnostic strategy is highly accurate and efficacious. In the future, we might be able to assist psychiatric physicians in diagnosing SZ and predicting disease progression by measuring the tNGS-based microbiota.

In conclusion, we have confirmed that there are some differences in the composition and function of the gut microbiome between patients with SZ and healthy individuals, and the insights from this research could be used to develop a tNGS-based diagnosis for SZ. In the future, it is likely that microbial biomarkers will become fast and highly sensitive tools for the detection and diagnosis of SZ disease. Further well-designed trials are needed to validate the results and conclude causal associations using animal models in a clinical setting.

Study population and sample collection

Briefly, 100 men between the ages of 20 and 58 years participated in this study. And 50 patients with SZ were recruited from Lishui City Second People’s Hospital (Zhejiang, China), and 50 healthy matched participants (NC group) were recruited from the same province. SZ was diagnosed based on the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10, World Health Organization). In addition, the dosage of antipsychotic medication was stable within 3 months before sample collection. All eligible subjects without genetic disorders maintained a regular diet and aerobic exercise and refused to take antibiotics. Prior to the study, all eligible participants and their guardians received a full explanation of the study and provided written informed consent.

Both SZ and NC subjects were provided with Stool Collection Tube (Simgen, Hangzhou, China) and provided with the detailed manufacturer's protocol for collecting fresh fecal samples. All samples were stored at -80 °C.

DNA extraction and sequencing

Before DNA extraction, the samples were pre-processed according to the 16S rRNA Earth Microbiome Protocol (earthmicrobiome.org). Then, gDNA was extracted from samples using the Omega Bio-tek Stool DNA Kit (Omega Bio-Tek, Doraville, CA, USA) according to the manufacturer’s instructions. Additional positive and negative DNA controls were included so that sample exclusion based on read counts could be calculated. Thereafter, the quality of the DNA was evaluated using agarose gel electrophoresis and PicoGreen assay (Thermo Fisher Scientific, Cleveland, OH, USA). Finally, the DNA was stored at −20 °C before sequencing.

First, 341f (5’-CCTAYGGGRBGCASCAG-3’) and 806r (5’-GGACTACNNGGGTATCTAAT-3’) barcoded primers were designed using Oligo software (v7.0) [33], validated using Primer-BLAST (https://www.ncbi.nlm.nih.gov/tools/primer-blast/), and synthesized by Sangon Biotech (Shanghai, China). Second, 16s amplicons were generated via PCR based on a method described in a previous work [34]. The quality of the amplicons was evaluated using agarose gel electrophoresis and PicoGreen assay before cleaning with the Agencourt AMPure XP PCR purification kit (Beckman Coulter, Brea, CA, USA). Amplicon library amplification was performed using Nextera XT DNA Sample Prep (Illumina, San Diego, CA, USA). Then, the amplicon library was cleaned once with AMPure beads, and DNA concentration was measured on a Qubit fluorometer (Invitrogen, Carlsbad, CA, USA). Paired-end sequencing was performed on a MiSeq platform (Illumina, San Diego, CA, USA) using the MiSeq Reagent Kit v3 (Illumina, San Diego, CA, USA), generating 300 bp reads per end. Raw sequencing files were uploaded and analyzed using the MiSeq Reporter (Illumina, San Diego, CA, USA) for further analysis.

Bioinformatic data processing

The quality of raw sequencing data was assessed using FastQC v0.11.3 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc). To generate high-quality sequencing data, low-quality reads and Illumina adapters were excluded using Trimmomatic v0.36 [35]. Paired reads were merged and assembled using FLASH v1.2.11 with default settings [36]. Operational taxonomic units (OTUs) were defined as sequences with at least 85% similarity against the Greengenes database using the UClust clustering algorithm (http://drive5.com/usearch/) following the close-reference method in QIIME v1.9.1 [37, 38]. Amplicon sequence variants (ASVs) were identified using the Divisive Amplicon Denoising Algorithm 2 [39]. Taxonomic assignments of ASV representative sequences were performed with a confidence threshold of 0.8–1.0, using a pre-trained Naive Bayes classifier, which was trained on the Ribosomal Database Project classifier v11.5 [40]. Ordination was performed using principal component analysis (PCA) with the vegan function in the R package v4.0.2.

To account for both abundance and evenness, alpha diversity analyses, including the Observed Species index, Shannon diversity index [41] and Simpson diversity index [42] were performed using QIIME v1.9.1 [36]. Beta diversity was calculated using Bray–Curtis dissimilarity and unweighted UniFrac [43, 44]. To visualize the similarity between samples, output matrices were ordinated using principal coordinate analysis (PCoA) and visualized using EMPeror [45]. Non-metric multidimensional scaling (NMDS) was computed for each sample based on the total beta diversity using the R package v4.0.2 (vegan). Analysis of similarities (ANOSIM) and indicator value analysis were performed using R package v4.0.2 (vegan and labdsv), respectively. Venn diagrams illustrating the genera common to all samples were produced using the Venn program v1.6.16. For functional community profiling, Tax4Fun was evaluated using QIIME v1.9.1 with the SILVA database extension [36, 46]. The key was to compare the 16S rRNA gene sequencing data with the KEGG database to achieve functional annotation [47].

Statistical analysis

Participant demographic and clinical characteristics were summarized and analyzed using the Student’s t-test and chi-square test for continuous and discrete variables, respectively. Group significance (Welch’s t-test) and post-hoc analyses (false discovery rate and Bonferroni correction) were performed to identify ASVs that differed in abundance between the healthy peri-implant sites and those with peri-implantitis. Statistics and plots were performed using R software v4.0.2. Statistical significance was set than 0.05.

BMI

body mass index

NGS

next-generation sequencing

PCR

polymerase chain reaction

schizophrenia

SCFAs

short-chain fatty acids

tNGS

targeted next-generation sequencing

Availability of data and materials

The datasets used and/or analysed during the current study were submitted to the NCBI Bioproject repository.

Ethics approval and consent to participate

The study was approved by the Ethics Committee of Lishui Second People's Hospital (Approval No: 20180705-1 and date of approval: 5 July 2018). And the informed consent was obtained from all subjects involved in the study. All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Funding

This research was funded by the Academician He Lin New Medical Clinical Translation Workstation Research Fund (17332005).

Authors' contributions

G.J. designed the experiments; F.Y., L.X, X.L. and L.D. studied population and sample collection; F.Y. and L.X extracted RNA and prepared the sequencing libraries; G.J., F.Y. and L.D. analyzed the data; G.J. wrote the manuscript. All authors reviewed the manuscript.

Acknowledgements

We thank Xufang Xu and Yiyi Chen (The Second People's Hospital of Lishui, Lishui, China) for his help on the data analysis.

Sylvia KE, Demas GE. A gut feeling: Microbiome-brain-immune interactions modulate social and affective behaviors. Horm Behav. 2018; 99: 41–49.
Michail S, Lin M, Frey MR, Fanter R, Paliy O, Hilbush B, Reo NV. Altered gut microbial energy and metabolism in children with non-alcoholic fatty liver disease. FEMS Microbiol Ecol. 2015; 91(2): 1–9.
Valdes AM, Walter J, Segal E, Spector TD. Role of the gut microbiota in nutrition and health. BMJ. 2018; 361: 2179.
Wook Kim K, Allen DW, Briese T, Couper JJ, Barry SC, Colman PG, Cotterill AM, Davis EA, Giles LC, Harrison LC, Harris M, Haynes A, Horton JL, Isaacs SR, Jain K, Lipkin WI, Morahan G, Morbey C, Pang ICN, Papenfuss AT, Penno MAS, Sinnott RO, Soldatos G, Thomson RL, Vuillermin PJ, Wentworth JM, Wilkins MR, Rawlinson WD, Craig ME; ENDIA Study Group. Distinct Gut Virome Profile of Pregnant Women With Type 1 Diabetes in the ENDIA Study. Open Forum Infect Dis. 2019; 6(2): 025.
Malham M, Lilje B, Houen G, Winther K, Andersen PS, Jakobsen C. The microbiome reflects diagnosis and predicts disease severity in paediatric onset inflammatory bowel disease. Scand J Gastroenterol. 2019; 54(8): 969–975.
Coker OO, Nakatsu G, Dai RZ, Wu WKK, Wong SH, Ng SC, Chan FKL, Sung JJY, Yu J. Enteric fungal microbiota dysbiosis and ecological alterations in colorectal cancer. Gut. 2019; 68(4): 654–662.
Adams JB, Johansen LJ, Powell LD, Quig D, Rubin RA. Gastrointestinal flora and gastrointestinal status in children with autism–comparisons to typical children and correlation with autism severity. BMC Gastroenterol. 2011; 11: 22.
Aizawa E, Tsuji H, Asahara T, Takahashi T, Teraishi T, Yoshida S, Ota M, Koga N, Hattori K, Kunugi H. Possible association of Bifidobacterium and Lactobacillus in the gut microbiota of patients with major depressive disorder. J Affect Disord. 2016; 202: 254–257.
Rintala A, Pietilä S, Munukka E, Eerola E, Pursiheimo JP, Laiho A, Pekkala S, Huovinen P. Gut Microbiota Analysis Results Are Highly Dependent on the 16S rRNA Gene Target Region, Whereas the Impact of DNA Extraction Is Minor. J Biomol Tech. 2017; 28(1): 19–30.
Zahedi A, Greay TL, Paparini A, Linge KL, Joll CA, Ryan UM. Identification of eukaryotic microorganisms with 18S rRNA next-generation sequencing in wastewater treatment plants, with a more targeted NGS approach required for Cryptosporidium detection. Water Res. 2019; 158: 301–312.
Peker N, Garcia-Croes S, Dijkhuizen B, Wiersma HH, van Zanten E, Wisselink G, Friedrich AW, Kooistra-Smid M, Sinha B, Rossen JWA, Couto N. A Comparison of Three Different Bioinformatics Analyses of the 16S-23S rRNA Encoding Region for Bacterial Identification. Front Microbiol. 2019; 10: 620.
Huo D, Chen Y, Zheng T, Liu X, Zhang X, Yu G, Qiao Z, Li R. Characterization of Microcystis (Cyanobacteria) Genotypes Based on the Internal Transcribed Spacer Region of rRNA by Next-Generation Sequencing. Front Microbiol. 2018; 9: 971.
Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P, Bertalan M, Batto JM, Hansen T, Le Paslier D, Linneberg A, Nielsen HB, Pelletier E, Renault P, Sicheritz-Ponten T, Turner K, Zhu H, Yu C, Li S, Jian M, Zhou Y, Li Y, Zhang X, Li S, Qin N, Yang H, Wang J, Brunak S, Doré J, Guarner F, Kristiansen K, Pedersen O, Parkhill J, Weissenbach J; MetaHIT Consortium, Bork P, Ehrlich SD, Wang J. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010; 464(7285): 59–65.
Rondon MR, August PR, Bettermann AD, Brady SF, Grossman TH, Liles MR, Loiacono KA, Lynch BA, MacNeil IA, Minor C, Tiong CL, Gilman M, Osburne MS, Clardy J, Handelsman J, Goodman RM. Cloning the soil metagenome: a strategy for accessing the genetic and functional diversity of uncultured microorganisms. Appl Environ Microbiol. 2000; 66(6): 2541–2547.
Venter JC, Remington K, Heidelberg JF, Halpern AL, Rusch D, Eisen JA, Wu D, Paulsen I, Nelson KE, Nelson W, Fouts DE, Levy S, Knap AH, Lomas MW, Nealson K, White O, Peterson J, Hoffman J, Parsons R, Baden-Tillson H, Pfannkoch C, Rogers YH, Smith HO. Environmental genome shotgun sequencing of the Sargasso Sea. Science. 2004; 304(5667): 66–74.
Saha S, Chant D, Welham J, McGrath J. A systematic review of the prevalence of schizophrenia. PLoS Med. 2005; 2(5): 141.
Sullivan PF, Kendler KS, Neale MC. Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch Gen Psychiatry. 2003; 60(12): 1187–1192.
Shen Y, Xu J, Li Z, Huang Y, Yuan Y, Wang J, Zhang M, Hu S, Liang Y. Analysis of gut microbiota diversity and auxiliary diagnosis as a biomarker in patients with schizophrenia: A cross-sectional study. Schizophr Res. 2018; 197: 470–477.
Zhu F, Ju Y, Wang W, Wang Q, Guo R, Ma Q, Sun Q, Fan Y, Xie Y, Yang Z, Jie Z, Zhao B, Xiao L, Yang L, Zhang T, Feng J, Guo L, He X, Chen Y, Chen C, Gao C, Xu X, Yang H, Wang J, Dang Y, Madsen L, Brix S, Kristiansen K, Jia H, Ma X. Metagenome-wide association of gut microbiome features for schizophrenia. Nat Commun. 2020; 11(1): 1612.
Zhu C, Zheng M, Ali U, Xia Q, Wang Z, Chenlong, Yao L, Chen Y, Yan J, Wang K, Chen J, Zhang X. Association Between Abundance of Haemophilus in the Gut Microbiota and Negative Symptoms of Schizophrenia. Front Psychiatry. 2021; 12: 685910.
Grenham S, Clarke G, Cryan JF, Dinan TG. Brain-gut-microbe communication in health and disease. Front Physiol. 2011; 2: 94.
Zheng P, Zeng B, Liu M, Chen J, Pan J, Han Y, Liu Y, Cheng K, Zhou C, Wang H, Zhou X, Gui S, Perry SW, Wong ML, Licinio J, Wei H, Xie P. The gut microbiome from patients with schizophrenia modulates the glutamate-glutamine-GABA cycle and schizophrenia-relevant behaviors in mice. Sci Adv. 2019; 5(2): 8317.
Huang Y, Shi X, Li Z, Shen Y, Shi X, Wang L, Li G, Yuan Y, Wang J, Zhang Y, Zhao L, Zhang M, Kang Y, Liang Y. Possible association of Firmicutes in the gut microbiota of patients with major depressive disorder. Neuropsychiatr Dis Treat. 2018; 14: 3329–3337.
Kehrmann J, Menzel J, Saeedghalati M, Obeid R, Schulze C, Holzendorf V, Farahpour F, Reinsch N, Klein-Hitpass L, Streeck H, Hoffmann D, Buer J, Esser S; HIV-HEART Study Group. Gut Microbiota in Human Immunodeficiency Virus-Infected Individuals Linked to Coronary Heart Disease. J Infect Dis. 2019; 219(3): 497–508.
Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012; 486(7402): 207–14.
Zhang J, Guo Z, Xue Z, Sun Z, Zhang M, Wang L, Wang G, Wang F, Xu J, Cao H, Xu H, Lv Q, Zhong Z, Chen Y, Qimuge S, Menghe B, Zheng Y, Zhao L, Chen W, Zhang H. A phylo-functional core of gut microbiota in healthy young Chinese cohorts across lifestyles, geography and ethnicities. ISME J. 2015; 9(9): 1979–90.
Li J, Zhao F, Wang Y, Chen J, Tao J, Tian G, Wu S, Liu W, Cui Q, Geng B, Zhang W, Weldon R, Auguste K, Yang L, Liu X, Chen L, Yang X, Zhu B, Cai J. Gut microbiota dysbiosis contributes to the development of hypertension. Microbiome. 2017; 5(1):14.
D'Souza WN, Douangpanya J, Mu S, Jaeckel P, Zhang M, Maxwell JR, Rottman JB, Labitzke K, Willee A, Beckmann H, Wang Y, Li Y, Schwandner R, Johnston JA, Towne JE, Hsu H. Differing roles for short chain fatty acids and GPR43 agonism in the regulation of intestinal barrier function and immune responses. PLoS One. 2017; 12(7):e0180190.
Kim CH, Park J, Kim M. Gut microbiota-derived short-chain Fatty acids, T cells, and inflammation. Immune Netw. 2014; 14(6):277–88.
Tedelind S, Westberg F, Kjerrulf M, Vidal A. Anti-inflammatory properties of the short-chain fatty acids acetate and propionate: a study with relevance to inflammatory bowel disease. World J Gastroenterol. 2007; 13(20):2826–32.
Wong JM, de Souza R, Kendall CW, Emam A, Jenkins DJ. Colonic health: fermentation and short chain fatty acids. J Clin Gastroenterol. 2006; 40(3):235–43.
Xu R, Wu B, Liang J, He F, Gu W, Li K, Luo Y, Chen J, Gao Y, Wu Z, Wang Y, Zhou W, Wang M. Altered gut microbiota and mucosal immunity in patients with schizophrenia. Brain Behav Immun. 2020; 85:120–127.
Rychlik W. OLIGO 7 primer analysis software. Methods Mol Biol. 2007; 402:35–60.
Yuan X, Wang Y, Li X, Jiang J, Kang Y, Pang L, Zhang P, Li A, Lv L, Andreassen OA, Fan X, Hu S, Song X. Gut microbial biomarkers for the treatment response in first-episode, drug-naïve schizophrenia: a 24-week follow-up study. Transl Psychiatry. 2021; 11(1):422.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014; 30(15):2114–20.
Magoč T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011; 27(21):2957–63.
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006; 72(7):5069–72.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010; 7(5):335–6.
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016; 13(7):581–3.
Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, Brown CT, Porras-Alfaro A, Kuske CR, Tiedje JM. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014; 42:633–42.
Shannon CE. The mathematical theory of communication. 1963. MD Comput. 1997; 14(4):306 – 17.
Haegeman B, Sen B, Godon JJ, Hamelin J. Only Simpson diversity can be estimated accurately from microbial community fingerprints. Microb Ecol. 2014; 68(2):169–72.
Al-Abdi S, Aljughaiman A, Alrashidi J, Aldarwish M, Zekri A, Alshamari F. A systematic comparison between infant formula compositions using the Bray-Curtis Similarity Index. Int J Pediatr Adolesc Med. 2020; 7(1):45–52.
Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005; 71(12):8228–35.
Vázquez-Baeza Y, Pirrung M, Gonzalez A, Knight R. EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience. 2013; 2(1):16.
Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, Glöckner FO. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007; 35(21):7188–96.
Kanehisa M. The KEGG database. Novartis Found Symp. 2002; 247:91–101; discussion 101-3, 119 – 28, 244 – 52.

Table 1 is available in the Supplementary Files section.

No competing interests reported.

Download PDF

Editorial decision: Major revision
23 Aug, 2022
Reviews received at journal
23 Aug, 2022
Reviewers agreed at journal
05 Aug, 2022
Reviewers invited by journal
03 Aug, 2022
Editor assigned by journal
16 May, 2022
Editor invited by journal
12 May, 2022
Submission checks completed at journal
12 May, 2022
First submitted to journal
25 Apr, 2022

You are reading this latest preprint version

A comparative study to determine the association of gut microbiome with schizophrenia in Zhejiang, China

Status:

Version 1

Abstract

Background

Results

Conclusions

Figures

Background

Results

Discussion

Conclusion

Methods

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1