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Wang W, Liu Y, Yao Z, Chen D, Tang Y, Cui J, Zhang J, Liu H, Hao Z. A microfluidic-based gut-on-a-chip model containing the gut microbiota of patients with depression reveals physiological characteristics similar to depression. LAB ON A CHIP 2024; 24:2537-2550. [PMID: 38623757 DOI: 10.1039/d3lc01052j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
The diverse commensal microbiome of the human intestine has been considered to play a central role in depression. However, no host-microbiota co-culture system has been developed for depression, which hinders the controlled study of the interaction between depression and gut microbiota. We designed and manufactured a microfluidic-based gut-on-a-chip model containing the gut microbiota of patients with depression (depression-on-gut-chip, DoGC), which enables the extended co-culture of viable aerobic human intestinal epithelial cells and anaerobic gut microbiota, and allows the direct study of interactions between human gut microbiota and depression. We introduced representative gut microbiota from individuals with depression into our constructed DoGC model, successfully recapitulating the gut microbiota structure of depressed patients. This further led to the manifestation of physiological characteristics resembling depression, such as reduced gut barrier function, chronic low-grade inflammatory responses and decreased neurotransmitter 5-HT levels. Metabolome analysis of substances in the DoGC revealed a significant increase in lipopolysaccharides and tyrosine, while hyodeoxycholic acid, L-proline and L-threonine were significantly reduced, indicating the occurrence of depression. The proposed DoGC can serve as an effective platform for studying the gut microbiota of patients with depression, providing important cues for their roles in the pathology of this condition and acting as a powerful tool for personalized medicine.
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Affiliation(s)
- Wenxin Wang
- Institute of Environmental Biology and Life Support Technology, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Yiyuan Liu
- Institute of Environmental Biology and Life Support Technology, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Zhikai Yao
- Institute of Environmental Biology and Life Support Technology, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Dengbo Chen
- Institute of Environmental Biology and Life Support Technology, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Yue Tang
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
| | - Jingwei Cui
- Institute of Environmental Biology and Life Support Technology, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Jiangjiang Zhang
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
| | - Hong Liu
- Institute of Environmental Biology and Life Support Technology, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Zikai Hao
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
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Gao M, Wang J, Liu P, Tu H, Zhang R, Zhang Y, Sun N, Zhang K. Gut microbiota composition in depressive disorder: a systematic review, meta-analysis, and meta-regression. Transl Psychiatry 2023; 13:379. [PMID: 38065935 PMCID: PMC10709466 DOI: 10.1038/s41398-023-02670-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/22/2023] [Accepted: 11/17/2023] [Indexed: 12/18/2023] Open
Abstract
Studies investigating gut microbiota composition in depressive disorder have yielded mixed results. The aim of our study was to compare gut microbiome between people with depressive disorder and healthy controls. We did a meta-analysis and meta-regression of studies by searching PubMed, Web of Science, Embase, Scopus, Ovid, Cochrane Library, ProQuest, and PsycINFO for articles published from database inception to March 07, 2022. Search strategies were then re-run on 12 March 2023 for an update. We undertook meta-analyses whenever values of alpha diversity and Firmicutes, Bacteroidetes (relative abundance) were available in two or more studies. A random-effects model with restricted maximum-likelihood estimator was used to synthesize the effect size (assessed by standardized mean difference [SMD]) across studies. We identified 44 studies representing 2091 patients and 2792 controls. Our study found that there were no significant differences in patients with depressive disorder on alpha diversity indices, Firmicutes and Bacteroidetes compared with healthy controls. In subgroup analyses with regional variations(east/west) as a predictor, patients who were in the West had a lower Chao1 level (SMD -0.42[-0.74 to -0.10]). Subgroup meta-analysis showed Firmicutes level was decreased in patients with depressive disorder who were medication-free (SMD -1.54[-2.36 to -0.72]), but Bacteroidetes level was increased (SMD -0.90[0.07 to 1.72]). In the meta-regression analysis, six variables cannot explain the 100% heterogeneity of the studies assessing by Chao1, Shannon index, Firmicutes, and Bacteroidetes. Depleted levels of Butyricicoccus, Coprococcus, Faecalibacterium, Fusicatenibacter, Romboutsia, and enriched levels of Eggerthella, Enterococcus, Flavonifractor, Holdemania, Streptococcus were consistently shared in depressive disorder. This systematic review and meta-analysis found that psychotropic medication and dietary habit may influence microbiota. There is reliable evidence for differences in the phylogenetic relationship in depressive disorder compared with controls, however, method of measurement and method of patient classification (symptom vs diagnosis based) may affect findings. Depressive disorder is characterized by an increase of pro-inflammatory bacteria, while anti-inflammatory butyrate-producing genera are depleted.
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Affiliation(s)
- Mingxue Gao
- Department of Psychiatry, First Hospital of Shanxi Medical University, 030001, Taiyuan, China
- First Clinical Medical College, Shanxi Medical University, 030001, Taiyuan, China
| | - Jizhi Wang
- Department of Psychiatry, First Hospital of Shanxi Medical University, 030001, Taiyuan, China
- First Clinical Medical College, Shanxi Medical University, 030001, Taiyuan, China
| | - Penghong Liu
- Department of Psychiatry, First Hospital of Shanxi Medical University, 030001, Taiyuan, China
- First Clinical Medical College, Shanxi Medical University, 030001, Taiyuan, China
| | - Hongwei Tu
- Department of Psychiatry, First Hospital of Shanxi Medical University, 030001, Taiyuan, China
- First Clinical Medical College, Shanxi Medical University, 030001, Taiyuan, China
| | - Ruiyu Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, 030001, Taiyuan, China
- First Clinical Medical College, Shanxi Medical University, 030001, Taiyuan, China
| | - Yanyan Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, 030001, Taiyuan, China
- Basic Medical College, Shanxi Medical University, 030001, Taiyuan, China
| | - Ning Sun
- Department of Psychiatry, First Hospital of Shanxi Medical University, 030001, Taiyuan, China.
- First Clinical Medical College, Shanxi Medical University, 030001, Taiyuan, China.
| | - Kerang Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, 030001, Taiyuan, China.
- First Clinical Medical College, Shanxi Medical University, 030001, Taiyuan, China.
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Ecklu-Mensah G, Choo-Kang C, Maseng MG, Donato S, Bovet P, Viswanathan B, Bedu-Addo K, Plange-Rhule J, Oti Boateng P, Forrester TE, Williams M, Lambert EV, Rae D, Sinyanya N, Luke A, Layden BT, O'Keefe S, Gilbert JA, Dugas LR. Gut microbiota and fecal short chain fatty acids differ with adiposity and country of origin: the METS-microbiome study. Nat Commun 2023; 14:5160. [PMID: 37620311 PMCID: PMC10449869 DOI: 10.1038/s41467-023-40874-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 08/11/2023] [Indexed: 08/26/2023] Open
Abstract
The relationship between microbiota, short chain fatty acids (SCFAs), and obesity remains enigmatic. We employ amplicon sequencing and targeted metabolomics in a large (n = 1904) African origin cohort from Ghana, South Africa, Jamaica, Seychelles, and the US. Microbiota diversity and fecal SCFAs are greatest in Ghanaians, and lowest in Americans, representing each end of the urbanization spectrum. Obesity is significantly associated with a reduction in SCFA concentration, microbial diversity, and SCFA synthesizing bacteria, with country of origin being the strongest explanatory factor. Diabetes, glucose state, hypertension, obesity, and sex can be accurately predicted from the global microbiota, but when analyzed at the level of country, predictive accuracy is only universally maintained for sex. Diabetes, glucose, and hypertension are only predictive in certain low-income countries. Our findings suggest that adiposity-related microbiota differences differ between low-to-middle-income compared to high-income countries. Further investigation is needed to determine the factors driving this association.
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Affiliation(s)
- Gertrude Ecklu-Mensah
- Department of Pediatrics, Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Candice Choo-Kang
- Public Health Sciences, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, IL, USA
| | - Maria Gjerstad Maseng
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Dep. of Gastroenterology, Oslo University Hospital, Oslo, Norway
- Bio-Me, Oslo, Norway
| | - Sonya Donato
- Department of Pediatrics, Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Pascal Bovet
- University Center for Primary Care and Public Health (Unisanté), Lausanne University Hospital, Lausanne, Switzerland
- Ministry of Health, Victoria, Republic of Seychelles
| | | | - Kweku Bedu-Addo
- Department of Physiology, SMS, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Jacob Plange-Rhule
- Department of Physiology, SMS, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Prince Oti Boateng
- Department of Physiology, SMS, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Terrence E Forrester
- Solutions for Developing Countries, University of the West Indies, Mona, Kingston, Jamaica
| | - Marie Williams
- Solutions for Developing Countries, University of the West Indies, Mona, Kingston, Jamaica
| | - Estelle V Lambert
- Research Unit for Exercise Science and Sports Medicine, University of Cape Town, Cape Town, South Africa
| | - Dale Rae
- Research Unit for Exercise Science and Sports Medicine, University of Cape Town, Cape Town, South Africa
| | - Nandipha Sinyanya
- Research Unit for Exercise Science and Sports Medicine, University of Cape Town, Cape Town, South Africa
| | - Amy Luke
- Public Health Sciences, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, IL, USA
| | - Brian T Layden
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA
- Jesse Brown Veterans Affairs Medical Center, Chicago, IL, USA
| | - Stephen O'Keefe
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jack A Gilbert
- Department of Pediatrics, Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.
| | - Lara R Dugas
- Public Health Sciences, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, IL, USA.
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
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Ecklu-Mensah G, Choo-Kang C, Gjerstad Maseng M, Donato S, Bovet P, Bedu-Addo K, Plange-Rhule J, Forrester TE, Lambert EV, Rae D, Luke A, Layden BT, O’Keefe S, Gilbert JA, Dugas LR. Gut microbiota and fecal short chain fatty acids differ with adiposity and country of origin: The METS-Microbiome Study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.21.533195. [PMID: 36993742 PMCID: PMC10055249 DOI: 10.1101/2023.03.21.533195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
The relationship between the gut microbiota, short chain fatty acid (SCFA) metabolism, and obesity remains unclear due to conflicting reports from studies with limited statistical power. Additionally, this association has rarely been explored in large scale diverse populations. Here, we investigated associations between fecal microbial composition, predicted metabolic potential, SCFA concentrations, and obesity in a large ( N = 1,934) adult cohort of African-origin spanning the epidemiologic transition, from Ghana, South Africa, Jamaica, Seychelles, and the United States (US). The greatest gut microbiota diversity and total fecal SCFA concentration was found in the Ghanaian population, while the lowest levels were found in the US population, respectively representing the lowest and the highest end of the epidemiologic transition spectrum. Country-specific bacterial taxa and predicted-functional pathways were observed, including an increased prevalence of Prevotella , Butyrivibrio , Weisella and Romboutsia in Ghana and South Africa, while Bacteroides and Parabacteroides were enriched in Jamaican and the US populations. Importantly, 'VANISH' taxa, including Butyricicoccus and Succinivibrio , were significantly enriched in the Ghanaian cohort, reflecting the participants' traditional lifestyles. Obesity was significantly associated with lower SCFA concentrations, a decrease in microbial richness, and dissimilarities in community composition, and reduction in the proportion of SCFA synthesizing bacteria including Oscillospira , Christensenella , Eubacterium , Alistipes , Clostridium and Odoribacter . Further, the predicted proportions of genes in the lipopolysaccharide (LPS) synthesis pathway were enriched in obese individuals, while genes associated with butyrate synthesis via the dominant pyruvate pathway were significantly reduced in obese individuals. Using machine learning, we identified features predictive of metabolic state and country of origin. Country of origin could accurately be predicted by the fecal microbiota (AUC = 0.97), whereas obesity could not be predicted as accurately (AUC = 0.65). Participant sex (AUC = 0.75), diabetes status (AUC = 0.63), hypertensive status (AUC = 0.65), and glucose status (AUC = 0.66) could all be predicted with different success. Interestingly, within country, the predictive accuracy of the microbiota for obesity was inversely correlated to the epidemiological transition, being greatest in Ghana (AUC = 0.57). Collectively, our findings reveal profound variation in the gut microbiota, inferred functional pathways, and SCFA synthesis as a function of country of origin. While obesity could be predicted accurately from the microbiota, the variation in accuracy in parallel with the epidemiological transition suggests that differences in the microbiota between obesity and non-obesity may be larger in low-to-middle countries compared to high-income countries. Further examination of independent study populations using multi-omic approaches will be necessary to determine the factors that drive this association.
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Affiliation(s)
| | - Candice Choo-Kang
- Public Health Sciences, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, IL, USA
| | - Maria Gjerstad Maseng
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Dep. of Gastroenterology, Oslo University Hospital, Oslo, Norway
- Bio-Me, Oslo, Norway
| | - Sonya Donato
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Pascal Bovet
- University Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland& Ministry of Health, Republic of Seychelles Department of Physiology, SMS
| | - Kweku Bedu-Addo
- Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | | | - Terrence E. Forrester
- Solutions for Developing Countries, University of the West Indies, Mona, Kingston, Jamaica
| | - Estelle V. Lambert
- Research Unit for Exercise Science and Sports Medicine, University of Cape Town, Cape Town, South Africa
| | - Dale Rae
- Research Unit for Exercise Science and Sports Medicine, University of Cape Town, Cape Town, South Africa
| | - Amy Luke
- Public Health Sciences, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, IL, USA
| | - Brian T. Layden
- Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA
- Jesse Brown Veterans Affairs Medical Center, Chicago, Illinois, USA
| | | | - Jack A. Gilbert
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Lara R. Dugas
- Public Health Sciences, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, IL, USA
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
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