Skip to content

Integrated transcriptomics and machine learning reveal core genes associated with Bacillus safensis-mediated resistance to tomato bacterial wilt

Oct 2026 · BMC Plant Biology
Plant-Microbe Interactions and Immunity

Abstract

Abstract Background Bacterial wilt (BW), caused by Ralstonia pseudosolanacearum , is one of the most destructive diseases of tomato, yet the molecular mechanisms underlying biological control remain poorly understood. Results This study investigated the protective effects of Bacillus safensis strain H23 using physiological, transcriptomic, network, and machine-learning analyses. H23 significantly reduced disease severity, improved plant growth under pathogen infection, enhanced antioxidant enzyme activities, and alleviated oxidative damage by reducing MDA accumulation, H 2 O 2 content, and electrolyte leakage. Across all treatment comparisons, 11,788 and 11,674 differentially expressed genes (DEGs) were identified at 5 and 10 days, respectively, indicating extensive and sustained transcriptional reprogramming during plant–microbe interactions. GO and KEGG enrichment analyses revealed that H23 primarily induced cell wall organization, polysaccharide metabolism, phenylpropanoid biosynthesis, MAPK signaling, and plant hormone signaling pathways. In contrast, Rs infection mainly activated oxidative stress, glutathione metabolism, and defense-related signaling processes. WGCNA identified five key modules associated with physiological traits and 150 hub genes associated with disease resistance, oxidative stress regulation, detoxification, hormone signaling, and secondary metabolism. Further machine-learning analysis prioritized 10 key candidate genes strongly associated with pathogen response and H23-mediated regulation. Conclusions These results demonstrate that B. safensis enhances tomato resistance to BW through coordinated regulation of antioxidant defense, structural reinforcement, immune signaling, and transcriptional reprogramming. This study provides a comprehensive molecular framework for PGPR-mediated biocontrol and identifies candidate genes to improve tomato resistance to bacterial wilt, and may serve as a basis for developing integrated disease control strategies in tomato.

View source

Similar papers

#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54
#machine learning Review Open access Jun 2014

Why Early-Stage Software Startups Fail: A Behavioral Framework

This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.

Carmine Giardino, Xiaofeng Wang, P. Abrahamsson · 175 citations · ⚡19
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#machine learning Review Open access May 2016

Key Challenges in Software Startups Across Life Cycle Stages

It is found that what perceived as biggest challenges by software startups do vary across different life cycle stages, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature.

Xiaofeng Wang, Henry Edison, Sohaib Shahid Bajwa et al. · 62 citations · ⚡6

Related blog posts

MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

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