Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Bacterial Phylum Cyanobacteria

Bacterial Phylum Cyanobacteria

Cyanobacteria are a diverse group of oxygenic, phototrophic bacteria that played a pivotal role in converting Earth’s atmosphere from anoxic to oxygen-rich billions of years ago. They exhibit remarkable morphological diversity, ranging from unicellular forms to filamentous types, with cell sizes varying between 0.5 μm and 100 μm. Cyanobacteria are classified into five groups: Chroococcales (unicellular, dividing by binary fission), Pleurocapsales (unicellular, dividing by...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Pannexin 1 attenuates hepatic steatosis and insulin resistance via AMPK-autophagy axis activation.

Cellular and molecular life sciences : CMLS·2026

Same author

Engineered OMVs of Salmonella displaying PlpE antigen provide effective protection against Pasteurella multocida infection in mice.

Vaccine·2026

Same author

Abnormal Glycaemic Fluctuations Associated With Early Microvascular Changes in the Retina and Choroid of Nondiabetic Adults.

Diabetes, obesity & metabolism·2026

Same author

Overweight/obesity accelerates proteinuria progression in type 2 diabetes post-COVID-19: a comparison with a pre-pandemic uninfected cohort.

Frontiers in endocrinology·2026

Same author

COVID-19 associated CKM syndrome progression in diabetic patients is linked to pancreatic beta cell dysfunction, rather than RASi use: a retrospective cohort study.

Frontiers in endocrinology·2026

Same author

Determinants of school food allergy preparedness: Insights from a National Survey in China.

Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology·2026

Same journal

Lactate Metabolism Dysregulation Drives the Pathogenesis of Acute Kidney Injury.

Metabolites·2026

Same journal

Librarian: An Open-Access Web Application for High-Resolution Mass Spectral Library Assembly.

Metabolites·2026

Same journal

Purine Metabolism Alterations in Patients with Chronic Heart Failure: A Cross-Sectional Study of Associations with Iron Status, Oxidative Stress, and Anemia.

Metabolites·2026

Same journal

The Gut Microbiome in Heart Failure: Pathways to Inflammation and Therapeutic Targets.

Metabolites·2026

Same journal

Metabolic Mechanisms of Hexavalent Chromium-Induced Splenic Immune Injury via Oxidative Stress and Ferroptosis Pathways in New Zealand Rabbits.

Metabolites·2026

Same journal

Improving Speed and Efficiency of DESI Imaging with the Xevo MRT Mass Spectrometer for Analyte Mapping.

Metabolites·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 16, 2026

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

Secondary Metabolites Predict Diazotrophic Cyanobacteria: A Model-Based Cheminformatic Approach.

James Young¹, Taufiq Nawaz¹, Liping Gu¹

¹Department of Biology and Microbiology, College of Natural Sciences, South Dakota State University, Brookings, SD 57007, USA.

|September 26, 2025

Summary

This summary is machine-generated.

This study identifies diazotrophic cyanobacteria by analyzing their secondary metabolites, paving the way for self-fertilizing crops and sustainable agriculture. The predictive model achieved 88% accuracy in identifying these nitrogen-fixing organisms.

Keywords:

biomarker chemical similarity cheminformatics diazotrophic cyanobacteria nitrogen fixation predictive modeling secondary metabolites

More Related Videos

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Published on: January 7, 2019

Related Experiment Videos

Last Updated: Jan 16, 2026

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Published on: January 7, 2019

Area of Science:

Microbiology
Biotechnology
Metabolomics

Background:

Nitrogen fixation (diazotrophy) is a key trait in cyanobacteria with agricultural and industrial potential.
Identifying diazotrophic cyanobacteria is crucial for developing sustainable agricultural practices.
This research explores the link between secondary metabolites and diazotrophy in cyanobacteria.

Purpose of the Study:

To develop a predictive model for identifying diazotrophic cyanobacteria based on their secondary metabolites.
To leverage chemical structure similarity for predicting diazotrophy.
To prioritize potential diazotrophic strains from a large dataset of metabolites.

Main Methods:

Developed a predictive algorithm using chemical fingerprint similarity of metabolites from CyanoMetDB.
Evaluated the algorithm using leave-one-out cross-validation on 133 manually labeled metabolites.
Applied the model to 1980 unlabeled metabolites to identify likely diazotrophic strains.

Main Results:

The model achieved 88% accuracy and a ROC-AUC of 0.96 in predicting diazotrophy.
Successfully prioritized potential diazotrophic strains among unlabeled metabolites.
Toxicity analysis indicated metabolites are not defensive; high nitrogen and cyclic peptides suggest signaling roles.

Conclusions:

Secondary metabolites can effectively identify diazotrophs, even without active diazotrophic physiology.
This approach advances agricultural biotechnology by enabling the discovery of more diazotrophic cyanobacteria.
Findings support the development of self-fertilizing crops and sustainable agriculture.