Skip to content
#protein folding Open access

Genome-wide identification, characterization, evolutionary analysis, and expression profiling of the FCS-like zinc finger (FLZ) gene family in soybean (Glycine max L.) under abiotic stresses

Sep 2026 · Scientific Reports · 28 references
Soybean genetics and cultivation

Abstract

Abstract Drought and salinity limit soybean yield. Despite their role in the SnRK1 energy-sensing complex, a systematic study of FCS-Like Zinc Finger (FLZ) proteins in soybean has not been reported. We performed a genome-wide identification of the GmFLZ gene family, identifying 40 members distributed across 18 of the 20 soybean chromosomes. Phylogenetic analysis of 87 FLZ proteins from Glycine max , Arabidopsis thaliana , and Oryza sativa revealed four major evolutionary clades, suggesting that diversification predates the separation of monocots and dicots. Structural analysis identified ten conserved motifs, with Motifs 1 and 2 present in all family members. Gene duplication analysis identified 304 paralogous pairs, most arising from segmental duplication. Ka/Ks analysis indicated localized positive selection in six gene pairs and purifying selection in 97.9% of pairs. Tissue-specific expression profiling across nine tissues showed that GmFLZ5 , GmFLZ15 , GmFLZ25 , and GmFLZ34 had the highest expression levels detected across the GmFLZ family, with GmFLZ5 the most highly expressed member in leaves, nodules, and stem and showing moderate expression in pod, root, and root hairs, whereas GmFLZ18 , GmFLZ23 , and GmFLZ37 showed root-preferential expression. RT-qPCR validation under drought (20% PEG-6000) and salt (200 mM NaCl) treatments in the Giza 5 cultivar showed that 36 and 34 of the 40 GmFLZ genes, respectively, exhibited at least a two-fold change in expression, with GmFLZ21 and GmFLZ35 among the most strongly induced under salt stress. These findings provide an evolutionary and functional framework for the GmFLZ family and identify candidate genes for future functional studies in soybean stress tolerance.

Read PDF

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

Google DeepMind Blog Nov 25, 2025

AlphaFold: Five years of impact

Explore how AlphaFold has accelerated science and fueled a global wave of biological discovery.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.