From variability to stability: Sensitivity of network properties in IBD human gut microbiome studies.

IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Computational and structural biotechnology journal Pub Date : 2025-05-10 eCollection Date: 2025-01-01 DOI:10.1016/j.csbj.2025.05.005
Theresa Geese, Astrid Dempfle
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引用次数: 0

Abstract

Background: The gut microbiome's role in inflammatory bowel disease (IBD) is well-established, but capturing its complexity is challenging. Network analysis offers a valuable approach, but selecting suitable measures is crucial. This study examines the sensitivity of network properties to abundance variations. It evaluates whether these properties reflect the microbiome in IBD or are too sensitive to variability from e.g. laboratory conditions or intra-individual changes.

Methods: Using genetically unrelated individuals from the KINDRED cohort (IBD n = 522, healthy controls n = 365) and the PRISM cohort (IBD n = 42, healthy controls n = 42), microbial networks were constructed with genera as nodes and significant pairwise correlations as edges, separately for IBD patients and controls. Important IBD-related nodes, identified through centrality measures, and non-disease-related nodes were varied in abundance ( ± 30 %), and networks were re-constructed and compared with initial networks regarding local and global properties.

Results: Network properties in IBD were sensitive to abundance variations, with small and large changes producing similar effects. Sensitivity to increasing read counts of disease-related and non-disease-related genera was similar. Local properties showed magnitude-dependent changes of up to 50 % in response to the depletion of disease-related genera, relative to no modification applied, and an almost binary sensitivity pattern when modifying non-disease-related genera. Global case network properties changed less than 10 % in most settings, potentially indicating a certain stability of dysbiosis.

Conclusion: Caution is needed with network-based approaches, as even small variations, stemming from sources of microbiome variability, can affect results and reproducibility. The relatively stable dysbiosis in IBD could pose challenges for microbiome-directed therapies.

从变异性到稳定性:IBD人类肠道微生物组研究中网络特性的敏感性。
背景:肠道微生物组在炎症性肠病(IBD)中的作用是公认的,但捕捉其复杂性是具有挑战性的。网络分析提供了一种有价值的方法,但是选择合适的度量是至关重要的。本研究考察了网络特性对丰度变化的敏感性。它评估这些特性是否反映了IBD中的微生物组,还是对实验室条件或个体内变化等变异性过于敏感。方法:利用来自KINDRED队列(IBD n = 522,健康对照 = 365)和PRISM队列(IBD n = 42,健康对照 = 42)的无亲缘关系个体,分别构建IBD患者和对照组的微生物网络,以属为节点,显著两两相关为边。通过中心性测量确定的重要ibd相关节点和非疾病相关节点的丰度不同( ± 30 %),网络被重新构建,并与局部和全局属性的初始网络进行比较。结果:IBD的网络特性对丰度变化非常敏感,大小变化产生的影响相似。对疾病相关和非疾病相关属的read计数增加的敏感性相似。相对于不进行修饰,局部特性显示出高达50% %的幅度依赖性变化,以响应疾病相关属的消耗,并且在修饰非疾病相关属时几乎呈二元敏感性模式。在大多数情况下,全球病例网络属性的变化小于10 %,这可能表明生态失调具有一定的稳定性。结论:使用基于网络的方法需要谨慎,因为即使是微小的变化,源于微生物组变异性的来源,也会影响结果和可重复性。IBD中相对稳定的生态失调可能对微生物组定向治疗提出挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computational and structural biotechnology journal
Computational and structural biotechnology journal Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
9.30
自引率
3.30%
发文量
540
审稿时长
6 weeks
期刊介绍: Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to: Structure and function of proteins, nucleic acids and other macromolecules Structure and function of multi-component complexes Protein folding, processing and degradation Enzymology Computational and structural studies of plant systems Microbial Informatics Genomics Proteomics Metabolomics Algorithms and Hypothesis in Bioinformatics Mathematical and Theoretical Biology Computational Chemistry and Drug Discovery Microscopy and Molecular Imaging Nanotechnology Systems and Synthetic Biology
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