Evidence map›Paper›PMID 41501634›Full record

ArticleBMC genomics2026

Genomic adaptability and virulence of Bacillus anthracis: a machine learning-based pan-genome and comparative analysis.

Yamini Sri Sekar, Paulchamy Chellapandi, Kuralayanapalya Puttahonnappa Suresh, Sivashanmuga Vadivel Saranya, Mohan Papanna, Baldev Raj Gulati, Raghavendra G Amachawadi, Mehnaj Khatoon, Divakar Hemadri, Chamalapura Ashwathama Archana and 6 more

Abstract readComparative Study
In one paragraph

Article in BMC genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. DecodingPlants (Basel, Switzerland) · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Yamini Sri SekarICAR- National Institute of Veterinary Epidemiology and Disease Informatics, Bengaluru, 560119, Karnataka, India.
Paulchamy ChellapandiIndustrial Systems Biology Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, 620024, Tamil Nadu, India.
Kuralayanapalya Puttahonnappa SureshICAR- National Institute of Veterinary Epidemiology and Disease Informatics, Bengaluru, 560119, Karnataka, India. suresh.kp@icar.org.in.
Sivashanmuga Vadivel SaranyaIndustrial Systems Biology Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, 620024, Tamil Nadu, India.
Mohan PapannaHuck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, 16802, USA.
Baldev Raj GulatiICAR- National Institute of Veterinary Epidemiology and Disease Informatics, Bengaluru, 560119, Karnataka, India.
Raghavendra G AmachawadiDepartment of Clinical Sciences, College of Veterinary Medicine, Kansas State University, Manhattan, KS, 66506, USA.
Mehnaj KhatoonICAR- National Institute of Veterinary Epidemiology and Disease Informatics, Bengaluru, 560119, Karnataka, India.
Divakar HemadriICAR- National Institute of Veterinary Epidemiology and Disease Informatics, Bengaluru, 560119, Karnataka, India.
Chamalapura Ashwathama ArchanaICAR- National Institute of Veterinary Epidemiology and Disease Informatics, Bengaluru, 560119, Karnataka, India.
Swati RaniICAR- National Institute of Veterinary Epidemiology and Disease Informatics, Bengaluru, 560119, Karnataka, India.
Archana PatilICAR- National Institute of Veterinary Epidemiology and Disease Informatics, Bengaluru, 560119, Karnataka, India.
Azhahianambi PalavesamTranslational Research Platform for Veterinary Biologicals, Tamil Nadu Veterinary and Animal Sciences University, Chennai, 600051, Tamil Nadu, India.
Ningegowda SagarICAR- National Institute of Veterinary Epidemiology and Disease Informatics, Bengaluru, 560119, Karnataka, India.
Jayashree AnandICAR- National Institute of Veterinary Epidemiology and Disease Informatics, Bengaluru, 560119, Karnataka, India.
Sharanagouda S PatilICAR- National Institute of Veterinary Epidemiology and Disease Informatics, Bengaluru, 560119, Karnataka, India.

Funding

The Defense Threat Reduction Agency 47013240002
6 · The paper itself

Abstract

backgroundBacillus anthracis (B. anthracis), a Gram-positive bacterium, poses significant threats to both livestock and humans, with known potential use in bioterrorism. Despite its zoonotic importance, the pathogen’s evolutionary dynamics and metabolic genes remain poorly understood, limiting insights into its virulence mechanisms. This study aimed to: (i) evaluate the distribution of toxin genes across B. anthracis isolates, (ii) employ a machine learning algorithm to identify genetic differences among toxin- and capsule synthesis gene-carrying B. anthracis isolates, and (iii) identify shared metabolic genes of B. anthracis in humans and locate key hub genes.

resultsComprehensive genomic analyses revealed important new insights into B. anthracis diversity and pathogenesis. The study identified the thiol-activated cytolysin BAS3109 toxin as conserved across all isolates, highlighting its fundamental role alongside the canonical toxin genes (pagA, lef, cya) in virulence. Variations in the pXO1 and pXO2 plasmids significantly drive strain-specific virulence profiles and adaptation across globally diverse isolates. Using machine learning and clustering approaches, we also discovered that GuaA, a gene involved in guanine nucleotide biosynthesis, acts as a key metabolic hub gene. GuaA’s central role in metabolic pathways makes it a promising biomarker and potential target for therapeutic intervention. This study demonstrates that while the chromosomal genome remains closed and stable, plasmid variability, particularly in pXO2 constitutes the primary source of genomic diversity within B. anthracis.

conclusionThe conserved presence of BAS3109 and the identification of GuaA as a central metabolic hub emphasizes new avenues for understanding B. anthracis pathogenesis and for developing diagnostic and therapeutic strategies. Our findings establish that plasmid-driven genomic diversity combined with core metabolic functions shapes the virulence and evolutionary potential of B. anthracis. This enhanced understanding provides a robust framework for future research aimed at controlling and preventing anthrax infections.

Indexed as

Adaptation, PhysiologicalBacillus anthracisGenome, BacterialGenomicsMachine LearningBacterial ToxinsHumansPlasmidsVirulenceBacterial ToxinsBacillus anthracisGuaAMarkov clustering algorithmPathway comparisonThiol-Activated cytolysinToxin

Identifiers

PMID41501634
PMCPMC12870312

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.