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      Multi-omics reveal microbial determinants impacting the treatment outcome of antidepressants in major depressive disorder

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          Abstract

          Background

          There is a growing body of evidence suggesting that disturbance of the gut-brain axis may be one of the potential causes of major depressive disorder (MDD). However, the effects of antidepressants on the gut microbiota, and the role of gut microbiota in influencing antidepressant efficacy are still not fully understood.

          Results

          To address this knowledge gap, a multi-omics study was undertaken involving 110 MDD patients treated with escitalopram (ESC) for a period of 12 weeks. This study was conducted within a cohort and compared to a reference group of 166 healthy individuals. It was found that ESC ameliorated abnormal blood metabolism by upregulating MDD-depleted amino acids and downregulating MDD-enriched fatty acids. On the other hand, the use of ESC showed a relatively weak inhibitory effect on the gut microbiota, leading to a reduction in microbial richness and functions. Machine learning-based multi-omics integrative analysis revealed that gut microbiota contributed to the changes in plasma metabolites and was associated with several amino acids such as tryptophan and its gut microbiota-derived metabolite, indole-3-propionic acid (I3PA). Notably, a significant correlation was observed between the baseline microbial richness and clinical remission at week 12. Compared to non-remitters, individuals who achieved remission had a higher baseline microbial richness, a lower dysbiosis score, and a more complex and well-organized community structure and bacterial networks within their microbiota. These findings indicate a more resilient microbiota community in remitters. Furthermore, we also demonstrated that it was not the composition of the gut microbiota itself, but rather the presence of sporulation genes at baseline that could predict the likelihood of clinical remission following ESC treatment. The predictive model based on these genes revealed an area under the curve (AUC) performance metric of 0.71.

          Conclusion

          This study provides valuable insights into the role of the gut microbiota in the mechanism of ESC treatment efficacy for patients with MDD. The findings represent a significant advancement in understanding the intricate relationship among antidepressants, gut microbiota, and the blood metabolome. Additionally, this study offers a microbiota-centered perspective that can potentially improve antidepressant efficacy in clinical practice. By shedding light on the interplay between these factors, this research contributes to our broader understanding of the complex mechanisms underlying the treatment of MDD and opens new avenues for optimizing therapeutic approaches.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s40168-023-01635-6.

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            Regularization Paths for Generalized Linear Models via Coordinate Descent

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              CD-HIT: accelerated for clustering the next-generation sequencing data

              Summary: CD-HIT is a widely used program for clustering biological sequences to reduce sequence redundancy and improve the performance of other sequence analyses. In response to the rapid increase in the amount of sequencing data produced by the next-generation sequencing technologies, we have developed a new CD-HIT program accelerated with a novel parallelization strategy and some other techniques to allow efficient clustering of such datasets. Our tests demonstrated very good speedup derived from the parallelization for up to ∼24 cores and a quasi-linear speedup for up to ∼8 cores. The enhanced CD-HIT is capable of handling very large datasets in much shorter time than previous versions. Availability: http://cd-hit.org. Contact: liwz@sdsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                gangwangdoc@ccmu.edu.cn
                yangjian@ccmu.edu.cn
                Journal
                Microbiome
                Microbiome
                Microbiome
                BioMed Central (London )
                2049-2618
                28 August 2023
                28 August 2023
                2023
                : 11
                : 195
                Affiliations
                [1 ]GRID grid.24696.3f, ISNI 0000 0004 0369 153X, Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, ; Beijing, 100088 China
                [2 ]GRID grid.24696.3f, ISNI 0000 0004 0369 153X, Advanced Innovation Center for Human Brain Protection, , Capital Medical University, ; Beijing, 100069 China
                [3 ]Beijing WeGenome Paradigm Co., Ltd, Beijing, China
                [4 ]GRID grid.452206.7, ISNI 0000 0004 1758 417X, Department of Neurology, , The First Affiliated Hospital of Chongqing Medical University, ; Chongqing, 400016 China
                [5 ]GRID grid.452206.7, ISNI 0000 0004 1758 417X, NHC Key Laboratory of Diagnosis and Treatment On Brain Functional Diseases, , The First Affiliated Hospital of Chongqing Medical University, ; Chongqing, 400016 China
                Article
                1635
                10.1186/s40168-023-01635-6
                10464022
                37641148
                1008b65b-d406-4d13-baa2-977d252ddeb3
                © BioMed Central Ltd., part of Springer Nature 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 10 June 2023
                : 30 July 2023
                Funding
                Funded by: STI2030-Major Projects
                Award ID: 2021ZD0200600
                Award Recipient :
                Funded by: Beijing Municipal Science & Technology Commission
                Award ID: Z221100007422049
                Award Recipient :
                Funded by: National Natural Science Foundation Project of China
                Award ID: 82171526
                Award Recipient :
                Funded by: Beijing Talents Project
                Award ID: 2020A38
                Award Recipient :
                Funded by: Beijing Biobank of Clinical Resources-Mental Disorders (BBCR-MD)
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                major depressive disorder,gut microbiota,antidepressants,sporulation gene

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