Call For Papers
eMicrobe
Insights into Host-microbial Pathogen Interactions and Pathogenesis through Computational Approaches
This topic is designed to provide a comprehensive platform for cutting-edge research on the molecular dynamics governing host–microbial pathogen interactions. By leveraging molecular dynamics (MD) simulations, molecular modeling, and advanced in silico analyses—such as machine learning, deep learning, and artificial intelligence—we seek contributions that offer novel insights into the complex biomolecular processes driving pathogenesis and host responses during infection. We welcome studies that simulate, visualize, and predict the functional consequences of biomolecular interactions—including protein–protein, protein–nucleic acid, and protein–lipid contacts—in infectious contexts. This encompasses mechanistic investigations into cell signaling pathways, metabolic reprogramming, cytoskeletal reorganization, membrane–protein interactions, regulation of gene expression, stress responses, and enzymatic or immunological reactions, all resolved at the molecular scale. Research that integrates computational approaches to interpret, predict, or experimentally validate molecular determinants of host–pathogen interplay, microbial adaptation, and disease progression is strongly encouraged. Special emphasis will be placed on studies exploring: Molecular dynamics simulations of interactions involving microbial and host proteins, nucleic acids, or secondary metabolites during infection The impact of microbial genetic mutations and functional adaptations on virulence and host defense mechanisms Inhibition or activation of signal transduction pathways in host–pathogen interactions Remodeling of cytoskeletal and mechano-signaling networks during infection Host and microbial stress response pathways, including heat shock, oxidative stress, and immune reactions Membrane–protein interactions, intracellular trafficking, and their roles in pathogenesis Enzyme kinetics, structural and functional crosstalk, and systems-level analyses Artificial intelligence and machine learning approaches for modeling host–pathogen dynamics and predicting infection outcomes We invite submissions of original research articles, reviews, and methodological papers spanning from fundamental molecular mechanisms to translational applications. Studies may address pathogens affecting human, animal, or plant hosts, thereby extending the scope beyond medical mycology. By integrating perspectives from biochemistry, cell biology, biophysics, computational biology, bioinformatics, and systems medicine, this issue aims to foster interdisciplinary dialogue and catalyze innovative strategies for combating infectious diseases. Please contact Dr. Monsicha Pongpom , Dr. Tanaporn Wangsanut , or Dr. Narin Lawan if you have any questions. Keywords microorganisms pathogenesis molecular dynamics simulation molecular modeling mutation effect signaling pathway heat shock response oxidative response stress response pathway enzyme reaction analysis host response immunologic response AI machine learning deep learning
eMicrobe
Deadline: 30 Jun 2026Hypervirulent Klebsiella pneumoniae Infection
Hypervirulent Klebsiella pneumoniae (hvKp) is distinguished from classical K. pneumoniae by its ability to cause community-acquired, invasive infections, such as pyogenic liver abscess and endophthalmitis, even in healthy individuals, whereas the classical form typically leads to opportunistic healthcare-associated infections. Globally, the dominant clone responsible for these hypervirulent invasive infections is K. pneumoniae clonal group 23, which includes sequence type 23. Hypervirulent K. pneumoniae clones are typically characterized by the presence of capsular serotype K1 and a set of hypervirulence-associated genes, such as ybt , iuc , iro , rmpA , and rmpA2 , often encoded on dedicated hypervirulence plasmids. These genetic determinants are central to the hypervirulent phenotype. Although antimicrobial resistance has not traditionally been a hallmark of hvKp, the recent emergence of carbapenem-resistant hypervirulent K. pneumoniae has been increasingly reported worldwide. This topic showcases cutting-edge research on the mechanisms underlying hypervirulent Klebsiella pneumoniae infections, with a particular focus on genetic and molecular factors driving both hypervirulence and antimicrobial resistance. We welcome the submission of original research articles, reviews, and perspectives that contribute to this rapidly evolving and exciting area of microbiology and medicine. Please contact Dr. Atsushi Togawa , Dr. Anthony R. Tam if you have any questions. Keywords Klebsiella pneumoniae invasive infection hypervirulence genetic and molecular determinants antimicrobial resistance
eMicrobe
Deadline: 31 Dec 2026
Suite 4002 Level 4, 447 Collins Street, Melbourne, Victoria 3000, Australia
General Inquiries: info@sciltp.com