Molecular Epidemiology and Bioinformatics in Pathogen Surveillance

A Special Issue of Microorganisms (ISSN 2076-2607) belonging to the section "Public Health Microbiology".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 2295

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Guest Editor
Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL 32827, USA
Interests: molecular epidemiology; genomic epidemiology; phylogenomics; microbiology; virology; phylogeny; bioinformatics; molecular microbiology; infectious diseases; public health
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Special Issue Information

Dear Colleagues,

Molecular epidemiology and bioinformatics have become central pivots of modern pathogen surveillance, driven by advances in high-throughput sequencing, computational biology, and global data sharing. These approaches enable high-resolution tracking of pathogen transmission, evolution, and antimicrobial resistance, providing critical insights for outbreak detection, public health interventions, and pandemic preparedness. By integrating genomic data with epidemiological and clinical information, molecular surveillance supports timely, evidence-based decision-making across local, national, and global health systems.

This Special Issue aims to highlight recent methodological and applied advances in molecular epidemiology and bioinformatics that enhance pathogen surveillance across human, animal, and environmental contexts. The topic aligns closely with the journal’s scope by emphasizing innovative analytical frameworks, genomic data interpretation, and translational applications that inform public health, microbiology, and infectious disease research. The goal is to assemble a focused yet diverse collection of at least ten high-quality contributions that demonstrate how molecular and computational tools are transforming surveillance strategies.

Original research articles and reviews are welcome. Suggested themes include pathogen genomics and phylogenetics, genomic epidemiology of outbreaks, bioinformatic pipelines for surveillance, antimicrobial resistance monitoring, viral and bacterial evolution, metagenomic approaches, data integration across surveillance systems, and challenges in real-time analysis and data sharing. Together, these contributions will provide a comprehensive overview of current advances and future directions in pathogen surveillance.

Dr. Eleonora Cella
Guest Editor

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Keywords

  • molecular epidemiology
  • pathogen surveillance
  • genomic epidemiology
  • bioinformatics
  • infectious disease monitoring

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Published Papers (2 papers)

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Research

17 pages, 6220 KB  
Article
Molecular Epidemiology of Klebsiella pneumoniae Based on Whole-Genome Sequencing Among Hospitalized Patients in Huzhou, China: A 6-Year Surveillance Study, 2020–2025
by Lei Ji, Peng Zhang, Yunfeng Zha, Fenfen Dong and Wei Yan
Microorganisms 2026, 14(7), 1498; https://doi.org/10.3390/microorganisms14071498 - 9 Jul 2026
Viewed by 611
Abstract
Klebsiella pneumoniae has become a critical global public health threat due to the rapid spread of hypervirulent and multidrug-resistant clones. A total of 205 non-duplicate K. pneumoniae isolates were collected from seven hospitals in Huzhou, China, between January 2020 and December 2025. Antimicrobial [...] Read more.
Klebsiella pneumoniae has become a critical global public health threat due to the rapid spread of hypervirulent and multidrug-resistant clones. A total of 205 non-duplicate K. pneumoniae isolates were collected from seven hospitals in Huzhou, China, between January 2020 and December 2025. Antimicrobial susceptibility testing, the string test, whole-genome sequencing, and bioinformatics analysis were applied to determine resistance phenotypes, virulence features, multilocus sequence types (STs), capsular types, plasmid replicons, and phylogenetic relationships. Of the 205 isolates, 180 (87.80%) were identified as hypervirulent K. pneumoniae (hvKP), with ST23-K1 and ST86-K2 being the dominant clones. This unusually high hvKP detection rate may be attributable to our study population composition, with patients aged ≥ 60 years accounting for 77.08% of all subjects. High resistance rates were observed for ampicillin (73.66%), tetracycline (27.32%), and trimethoprim–sulfamethoxazole (25.85%). Eleven isolates carried blaKPC-2 and one harbored mcr-1, displaying corresponding drug-resistant phenotypes. The isolates exhibited high genetic diversity with 62 STs, 25 capsular types, and 38 plasmid replicons. Co-occurrence of virulence and resistance determinants was commonly detected. Five genetic lineages (A to E) were classified among all isolates, and no epidemiological outbreak clusters were detected in our study. The hvKP is highly endemic in Huzhou, while local K. pneumoniae isolates show high resistance to conventional antibiotics but remain largely susceptible to last-resort antimicrobials. The coexistence of hypervirulence and antimicrobial resistance underscores the urgent need for continuous genomic surveillance to curb the dissemination of high-risk clones. Full article
(This article belongs to the Special Issue Molecular Epidemiology and Bioinformatics in Pathogen Surveillance)
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22 pages, 3370 KB  
Article
Phylogenetic Analyses of RdRp Region and VP1 Gene in Human Norovirus Genotype GII.17[P17] Variants
by Fuminori Mizukoshi, Yen Hai Doan, Asumi Hirata-Saito, Hiroyuki Tsukagoshi, Takumi Motoya, Ryusuke Kimura, Tomoko Takahashi, Yuriko Hayashi, Yuki Matsushima, Kei Miyakawa, Naomi Sakon, Kenji Sadamasu, Kazuhisa Yoshimura, Nobuhiro Saruki, Yoshiyuki Suzuki, Masashi Uema, Kosuke Murakami, Kazuhiko Katayama, Akihide Ryo, Tsutomu Kageyama and Hirokazu Kimuraadd Show full author list remove Hide full author list
Microorganisms 2026, 14(4), 770; https://doi.org/10.3390/microorganisms14040770 - 28 Mar 2026
Cited by 1 | Viewed by 1210
Abstract
In this study, we investigated the long-term evolutionary dynamics of human norovirus GII.17[P17] using the RNA-dependent RNA polymerase (RdRp) region and the VP1 capsid gene, integrating phylogenetics, time-scaled inference, phylodynamics, and structure-based analyses. Maximum-likelihood phylogenies of both genomic regions consistently resolved [...] Read more.
In this study, we investigated the long-term evolutionary dynamics of human norovirus GII.17[P17] using the RNA-dependent RNA polymerase (RdRp) region and the VP1 capsid gene, integrating phylogenetics, time-scaled inference, phylodynamics, and structure-based analyses. Maximum-likelihood phylogenies of both genomic regions consistently resolved four major clades (Clades 1–4). VP1 patristic-distance distributions indicated higher within-clade diversity in the phylogenetically basal Clades 1 and 3, whereas Clades 2 and 4 showed lower diversity, consistent with recent demographic expansion. Similarity-plot analysis identified pronounced variability in the VP1 P2 domain, while the S and P1 domains remained comparatively conserved, supporting P2 as the primary hotspot of diversification. Bayesian time-scaled analyses estimated the most recent common ancestor around 1993 (VP1) and 2000 (RdRp) and revealed two major lineages (Clade 1/2 and Clade 3/4), with the split between Clades 3 and 4 occurring around 2016–2017. Bayesian skyline plots showed a marked increase in effective population size after 2013, and substitution-rate estimates indicated faster evolution in VP1 than in RdRp, with higher VP1 rates in the Clade 3/4 lineage than in Clade 1/2. Capsid dimer modeling further mapped high-confidence conformational B-cell epitopes and positively selected residues predominantly to the distal surface of P2, with broadly conserved spatial patterns across clades. Compared with the Clade 1 reference (Kawasaki323), Clade 2 accumulated numerous P2 substitutions, whereas Clades 3 and 4 retained fewer changes and remained closer to Clade 1 at the amino-acid level. Together, these results suggest lineage turnover within GII.17[P17] driven by constrained diversification at the P2 surface, potentially contributing to the recent predominance of the Clade 3/4 lineage. Full article
(This article belongs to the Special Issue Molecular Epidemiology and Bioinformatics in Pathogen Surveillance)
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