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Related Concept Videos

Microbial Growth Measurement: Indirect Methods01:27

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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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Direct methods for measuring microbial populations in a culture are essential tools in microbiology, providing quantitative data for various applications. Among these, microscopic counts, plate counts, and serial dilution are widely used techniques, each with unique principles and applications.Microscopic CountsMicroscopic counting involves the use of a Petroff-Hausser chamber, a specialized microscope slide with a grid and defined depth. By observing a liquid culture under a microscope,...
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Characterizing Microbiome Dynamics &#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
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Causality and correlation analysis for deciphering the microbial interactions in activated sludge.

Weiwei Cai1, Xiangyu Han1, Thangavel Sangeetha2,3

  • 1School of Civil Engineering, Beijing Jiaotong University, Beijing, China.

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|August 22, 2022
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Summary
This summary is machine-generated.

This study uses microbial causal correlation networks to reveal ecological interactions in activated sludge. Key findings show Nitrospira

Keywords:
activated sludgecausalitycorrelationmicrobial communitynetwork

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Area of Science:

  • Microbiology
  • Environmental Science
  • Systems Biology

Background:

  • Time series data offers insights into microbial community dynamics and interactions.
  • Causality inference is crucial for understanding complex microbial ecosystems.

Purpose of the Study:

  • To construct a microbial causal correlation network (MCCN) for activated sludge.
  • To predict ecological interactions at the operational taxonomic unit (OTU)-level.

Main Methods:

  • Granger causality analysis
  • Correlation analysis
  • Microbial Causal Correlation Network (MCCN) construction
  • Phylogenetic analysis

Main Results:

  • OTU56 (Nitrospira) identified as a hub species responsible for nitrification.
  • Nitrospira exhibits amensal and commensal relationships with Proteobacteria and Bacteroidetes.
  • Zoogloea plays a foundational role in activated sludge floc formation.

Conclusions:

  • MCCN effectively reveals intricate ecological interactions in activated sludge.
  • Identified key microbial players and their roles in activated sludge ecosystems.
  • Phylogenetic analysis suggests mutualistic relationships between Nitrospira and denitrifiers.