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相关概念视频

Microbial Interactions: Predation01:28

Microbial Interactions: Predation

Microbial predation refers to the process by which one microorganism kills and consumes another to obtain nutrients and energy. It encompasses both bacterial and protozoan predators. This interaction plays a crucial role in shaping microbial communities and regulating nutrient cycling.Bacterial Predators: Epibiotic vs. EndobioticBacterial predators are classified based on their mode of attack as either epibiotic or endobiotic. Epibiotic predators, such as Vampirococcus, attach to the surface of...
Methods to Assess Microbial Communities01:19

Methods to Assess Microbial Communities

Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...
Microbial Interactions: Mutualism01:25

Microbial Interactions: Mutualism

Mutualism is a symbiotic interaction in which all participating organisms benefit. These relationships can be obligate or facultative and are fundamental to ecosystem functions across diverse biological systems.Plant–Fungi MutualismOne well-known example is the association between plant roots and mycorrhizal fungi, such as Rhizophagus species. The fungal hyphae penetrate the root hairs and the epidermis, forming an extensive hyphal network that establishes a symbiotic association. Through this...
Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

Assessing microbial populations is crucial for understanding microbial roles in health, ecology, and industry. Various complementary techniques—both culture-based and molecular—enable detailed analysis of microbial abundance, diversity, and function.Viable Plate CountThe viable plate count is a traditional culture-based method used to estimate the number of living microbes in a sample. After serial dilution, the sample is spread onto nutrient agar plates. Each viable cell forms a visible...
Microbial Interactions: Cooperation01:26

Microbial Interactions: Cooperation

Microbial cooperation involves beneficial interactions in which different species work together for individual or mutual advantage. These interactions can profoundly influence ecological dynamics and evolutionary processes, and they are essential to many pathogenic and symbiotic relationships.Nematode–Bacteria CooperationA striking example is the relationship between the Gram-negative bacterium Xenorhabdus nematophila and the parasitic nematode Steinernema carpocapsae. Juvenile nematodes...
Microbial Growth Measurement: Indirect Methods01:27

Microbial Growth Measurement: Indirect Methods

Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...

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相关实验视频

Updated: Jun 15, 2026

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
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使用跨物种的共同表达来预测微生物群中的代谢相互作用.

Robert A Koetsier1, Zachary L Reitz1, Clara Belzer2

  • 1Bioinformatics Group, Wageningen University, Wageningen, the Netherlands.

mSystems
|December 9, 2025
PubMed
概括

跨物种基因共同表达分析预测微生物相互作用和代谢途径. 这种数据驱动的方法确定了资源竞争和专业功能,指导微生物组研究.

关键词:
抗生素 抗生素是一种抗生素.共同表达是一种共同表达.代谢基因集群是代谢基因集群.合成社区 合成社区

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科学领域:

  • 微生物生态学 微生物生态学
  • 系统生物学 系统生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 代谢相互作用决定了微生物社区的结构和功能.
  • 通过计算预测这些相互作用至关重要,但往往缺乏机械洞察力.
  • 现有的工具往往错过了潜在的代谢途径,阻碍了实验验证.

研究的目的:

  • 开发和验证一种使用跨物种共同表达来预测微生物相互作用的新方法.
  • 确定参与竞争,交叉养和专业相互作用的特定代谢途径.
  • 通过相互作用预测评估发现新基因功能的潜力.

主要方法:

  • 应用跨物种共表达分析对微生物共培养RNA测序数据.
  • 使用的菌素和基于饮食的最小微生物组 (MDb-MM) 和树球的徒步旅行者 (THOR) 数据集.
  • 研究基因和通路的共同表达模式,以推断相互作用类型.

主要成果:

  • 在MDb-MM数据集中成功预测了涉及资源竞争的途径.
  • 在THOR数据集中确定了专业功能之间的联系,例如抗生素和多药物排放系统.
  • 在特定的微生物联盟中提供了 siderophore 同表达驱动相互作用的证据.

结论:

  • 跨物种共同表达是一种可行的数据驱动方法,用于预测微生物相互作用和潜在途径.
  • 这种方法为复杂的模型构建提供了有价值的替代方案,减少了偏差.
  • 该方法有助于发现新的基因功能,并为微生物组工程提供信息.