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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Alveolates are a group of organisms recognized by the presence of alveoli, which are cytoplasmic sacs located beneath the cell membrane. While their function remains uncertain, alveoli may help regulate water balance by controlling how much water enters and leaves the cell. In dinoflagellates, these structures may serve as armor plates. There are three major types of alveolates: ciliates, which move using cilia; dinoflagellates, which use flagella for movement; and apicomplexans, which are...
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Amoebozoa represent a diverse group of terrestrial and aquatic protists that utilize lobe-shaped pseudopodia for locomotion and feeding. This characteristic differentiates them from the Rhizaria, which possess threadlike pseudopodia. The primary classifications within Amoebozoa include gymnamoebas, entamoebas, and the plasmodial and cellular slime molds. Phylogenetic evidence indicates that Amoebozoa diverged from a lineage that ultimately gave rise to fungi and animals.Gymnamoebas and...
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Rhizaria are a diverse group of unicellular protists characterized by their threadlike cytoplasmic extensions known as pseudopodia. These structures aid in both locomotion and feeding, giving Rhizaria an amoeboid appearance. Their amoeboid morphology once led to taxonomic confusion, but molecular phylogenetics has clarified their evolutionary placement and emphasized their shared use of pseudopodia despite divergent lineages.This clade comprises diverse lineages such as Chlorarachniophyta,...
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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...
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Exploring biotic interactions within protist cell populations using network methods.

Shu Cheng1, Dana C Price, Slim Karkar

  • 1Department of Ecology, Evolution and Natural Resources, Rutgers University, New Brunswick, New Jersey, 08901, USA.

The Journal of Eukaryotic Microbiology
|March 18, 2014
PubMed
Summary
This summary is machine-generated.

Single cell genomics (SCG) reveals DNA differences in rhizarian cells. Protein similarity networks effectively distinguish genome features, showing SCG data follows a power law distribution.

Keywords:
AssortativityBigelowiella natansPaulinella ovalisnetwork analysisscale-free networksingle cell genomics

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cell Biology

Background:

  • Single cell genomics (SCG) is poised to revolutionize the study of diverse cell populations, including those from natural environments.
  • Understanding genomic variations within and between cells is crucial for advancing cell biology and disease research.

Purpose of the Study:

  • To explore DNA differences within and between individual rhizarian cells using SCG data.
  • To analyze protein similarity networks derived from SCG data to identify genomic distinctions.
  • To investigate the applicability of network properties in discriminating genome features and characterizing SCG data distributions.

Main Methods:

  • Utilized single cell genomics (SCG) on six rhizarian cells related to Paulinella ovalis.
  • Constructed protein similarity networks incorporating SCG data and the complete genome of Bigelowiella natans.
  • Applied network analysis metrics, including assortativity and degree, to differentiate genome features.

Main Results:

  • Identified shared and distinct DNA components within the SCG data and between P. ovalis and B. natans.
  • Demonstrated that network properties like assortativity and degree effectively discriminate genome features across SCG assemblies.
  • Observed that SCG data adheres to a power law distribution, with a few protein families dominating the networks.

Conclusions:

  • Protein similarity networks are valuable tools for dissecting genomic variations revealed by SCG.
  • Network properties provide robust metrics for distinguishing genomic features in single-cell data.
  • SCG data exhibits scale-free properties, suggesting underlying organizational principles in cellular genomes.