The Alterations of Gut Microbiome and Lipid Metabolism in Patients with Spinal Muscular Atrophy

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Abstract

Introduction

Spinal muscular atrophy (SMA) can cause multiple system dysfunction, especially lipid metabolic disorders, for which management strategies are currently lacking. Microbes are related to metabolism and the pathogenesis of neurological diseases. This study aimed to preliminarily explore the alterations in the gut microbiota in SMA and the potential relationship between altered microbiota and lipid metabolic disorders.

Methods

Fifteen patients with SMA and 17 gender- and age-matched healthy controls were enrolled in the study. Feces and fasting plasma samples were collected. 16S ribosomal RNA sequencing and nontargeted metabolomics analysis were performed to explore the correlation between microbiota and differential lipid metabolites.

Results

No significant difference was found in microbial diversity (α- and β-diversity) between the SMA and control groups, with both groups having a relatively similar community structure. However, compared to the control group, the SMA group showed an increased relative abundance of the genera Ruminiclostridium, Gordonibacter, Enorma, Lawsonella, Frisingicoccus, and Anaerofilum and a decreased abundance of the genera Catabacter, Howardella, Marine_Methylotrophic_Group_3, and Lachnospiraceae_AC2044_group. The concurrent metabolomic analysis showed that the SMA group had 56 different kinds of lipid metabolite levels than did the control group. Additionally, the Spearman correlation suggested a correlation between the altered differential lipid metabolites and the above-mentioned altered microbiota.

Conclusions

The gut microbiome and lipid metabolites differed between the patients with SMA and the control subjects. The altered microbiota may be related with the lipid metabolic disorders in SMA. However, further study is necessary to clarify the mechanism of lipid metabolic disorders and develop management strategies to improve the related complications in SMA.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40120-023-00477-6.

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Most cited references 42

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Trimmomatic: a flexible trimmer for Illumina sequence data

Anthony M. Bolger, Marc Lohse, Bjoern Usadel (2014)

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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QIIME allows analysis of high-throughput community sequencing data.

J. Gregory Caporaso, Justin Kuczynski, Jesse Stombaugh … (2010)

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VSEARCH: a versatile open source tool for metagenomics

Torbjørn Rognes, Tomáš Flouri, Ben Nichols … (2016)

Background VSEARCH is an open source and free of charge multithreaded 64-bit tool for processing and preparing metagenomics, genomics and population genomics nucleotide sequence data. It is designed as an alternative to the widely used USEARCH tool (Edgar, 2010) for which the source code is not publicly available, algorithm details are only rudimentarily described, and only a memory-confined 32-bit version is freely available for academic use. Methods When searching nucleotide sequences, VSEARCH uses a fast heuristic based on words shared by the query and target sequences in order to quickly identify similar sequences, a similar strategy is probably used in USEARCH. VSEARCH then performs optimal global sequence alignment of the query against potential target sequences, using full dynamic programming instead of the seed-and-extend heuristic used by USEARCH. Pairwise alignments are computed in parallel using vectorisation and multiple threads. Results VSEARCH includes most commands for analysing nucleotide sequences available in USEARCH version 7 and several of those available in USEARCH version 8, including searching (exact or based on global alignment), clustering by similarity (using length pre-sorting, abundance pre-sorting or a user-defined order), chimera detection (reference-based or de novo), dereplication (full length or prefix), pairwise alignment, reverse complementation, sorting, and subsampling. VSEARCH also includes commands for FASTQ file processing, i.e., format detection, filtering, read quality statistics, and merging of paired reads. Furthermore, VSEARCH extends functionality with several new commands and improvements, including shuffling, rereplication, masking of low-complexity sequences with the well-known DUST algorithm, a choice among different similarity definitions, and FASTQ file format conversion. VSEARCH is here shown to be more accurate than USEARCH when performing searching, clustering, chimera detection and subsampling, while on a par with USEARCH for paired-ends read merging. VSEARCH is slower than USEARCH when performing clustering and chimera detection, but significantly faster when performing paired-end reads merging and dereplication. VSEARCH is available at https://github.com/torognes/vsearch under either the BSD 2-clause license or the GNU General Public License version 3.0. Discussion VSEARCH has been shown to be a fast, accurate and full-fledged alternative to USEARCH. A free and open-source versatile tool for sequence analysis is now available to the metagenomics community.

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Author and article information

Contributors

Shanshan Mao: 6307003@zju.edu.cn

Journal

Journal ID (nlm-ta): Neurol Ther

Journal ID (iso-abbrev): Neurol Ther

Title: Neurology and Therapy

Publisher: Springer Healthcare (Cheshire )

ISSN (Print): 2193-8253

ISSN (Electronic): 2193-6536

Publication date (Electronic): 27 April 2023

Publication date PMC-release: 27 April 2023

Pages: 1-16

Affiliations

[1 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Department of Neurology, , Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, ; Hangzhou, 310052 China

[2 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Department of Infection, , Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, ; Hangzhou, 310052 China

[3 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Children’s Hospital, Zhejiang University School of Medicine, , National Clinical Research Center for Child Health, ; Hangzhou, 310052 China

Article

Publisher ID: 477

DOI: 10.1007/s40120-023-00477-6

PMC ID: 10134726

PubMed ID: 37103747

SO-VID: 667fb52b-c169-4fcd-b051-79febf86ccb5

License:

Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial 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-nc/4.0/.

History

Date received : 29 January 2023

Date accepted : 4 April 2023

Funding

Funded by: National Natural Science Foundation

Award ID: 82271735

Award Recipient : Shanshan Mao

Funded by: Key R&D Program of Zhejiang Province

Award ID: 2022C03167

Award Recipient : Shanshan Mao

Funded by: FundRef http://dx.doi.org/10.13039/501100010248, Zhejiang Province Public Welfare Technology Application Research Project;

Award ID: LGC21H090001

Award Recipient : Dongming Zhou

Funded by: Key Technologies Research and Development Program of Zhejiang Province

Award ID: 2021C03099

Award Recipient : Shanshan Mao

Funded by: Scientific Research Fund of Zhejiang University

Award ID: XY2022045

Award Recipient : Yijie Feng

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The Alterations of Gut Microbiome and Lipid Metabolism in Patients with Spinal Muscular Atrophy

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Abstract

Introduction

Methods

Results

Conclusions

Supplementary Information

Related collections

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Most cited references 42

Trimmomatic: a flexible trimmer for Illumina sequence data

QIIME allows analysis of high-throughput community sequencing data.

VSEARCH: a versatile open source tool for metagenomics

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Cited by 4

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