Volume 07 · Issue 05 · 2025

Interactions among zoonotic pathogens play a critical role in shaping disease transmission, severity, and public health responses. However, the mechanisms and population-level consequences of these interactions remain underexplored in current modelling frameworks. This review aims to synthesize emerging evidence and address key scientific challenges in understanding how pathogen interactions influence transmission dynamics and mathematical modelling, with a focus on zoonotic and other cocirculating pathogens. In this review, we synthesize current evidence on synergistic, antagonistic, and neutral interactions between zoonotic and other cocirculating pathogens. We explore the underlying mechanisms of these interactions, such as transmission enhancement, immune modulation, and resource competition, at both the individual and population levels. We further review mathematical models to illustrate how these interaction features, such as transmission pathways, coinfection histories, cross-immunity, and superspreading potential, could be incorporated into epidemiological frameworks to increase our understanding of the community transmission of infections. Particular attention is given to the challenges of parameter estimation, incomplete surveillance data, and the difficulty of modelling interactions across scales and pathogen types. Understanding and modelling these interactions is essential for predicting outbreak trajectories, designing effective vaccination strategies, and improving early-warning systems. We conclude by calling for enhanced integration of empirical data and mechanistic modelling, especially in the context of emerging zoonoses and postpandemic preparedness. This review provides a structured perspective to support future interdisciplinary efforts aimed at managing cocirculating pathogens and mitigating their public health impact.

Artificial intelligence (AI)-driven de novo protein design is revolutionizing synthetic biology by facilitating the first-principle rational engineering of protein-based functional modules unbound by known structural templates and evolutionary constraints, enabling a diverse range of applications. Expressing these novel, structurally unprecedented proteins within cellular systems inherently adds complexity to their functional unpredictability. Robust biosafety and bioethics evaluations are therefore required to address potential risks such as immune reactions, cellular pathway disruptions, and environmental persistence. We systematically analyse the computational frameworks underpinning this revolution and highlight the capability of de novo proteins to act as a modular toolkit for synthetic biology. Looking forward, we envision integrating closed-loop validation with multi-omics profiling for comprehensive risk assessments along with a hierarchical design framework for advancing the future of synthetic biology - from the creation of tailored de novo functional protein modules and structure-guided rational genetic circuits design to the development of full-synthetic cellular systems, thereby establishing a scalable path from protein design to system-level implementation.

Global infectious disease prevention faces escalating challenges due to the continual emergence of novel pathogens and rapid viral mutations. Synthetic biology has revolutionized this field by enabling precise diagnostics, innovative vaccine platforms, and targeted therapeutics, yet it simultaneously raises concerns regarding dual-use potential, biosafety, and ethical governance. This systematic review (2015-2025, PubMed, Web of Science, Scopus) focuses on CRISPR-based diagnostics, synthetic vaccines, and engineered probiotics. CRISPR/Cas systems such as DETECTR (Cas12a) and SHERLOCK (Cas13a) demonstrate high sensitivity and rapid pathogen detection (e.g., SARS-CoV-2, Ebola), but their misuse could enhance pathogen virulence or enable bioweapon development. mRNA and viral vector vaccines offer flexible and rapid responses to emerging infections but encounter limitations in molecular stability, delivery system toxicity, and ecological safety. Engineered probiotics, designed as "living therapeutics," can detect pathogens and modulate immune responses, yet pose potential risks of horizontal gene transfer and host-specific variability. Overall, while synthetic biology provides transformative tools for infectious disease control, it necessitates robust global regulatory frameworks, standardized biosafety practices, and ethical oversight to ensure responsible and sustainable application.

Large language models (LLMs) have emerged as transformative tools in infectious disease research, offering unprecedented capabilities in analyzing biological sequences. This review summarizes three primary types of biological LLMs, including protein language models, genomic language models, and multimodal models, highlighting their architectures and applications. These models are revolutionizing key areas such as pathogen identification, evolutionary surveillance, host-pathogen prediction, and therapeutic development by enabling the interpretation of complex genomic and proteomic data at an unparalleled scale. While recent advancements are remarkable, challenges persist in data quality, long-context processing, model interpretability, and biosafety considerations. Understanding the potential and limitations of LLMs is crucial for leveraging them effectively in infectious disease research while ensuring responsible development and deployment.

Safe laboratory processing requires mitigating risks from the release of pathogens into the environment through generated waste streams. This study evaluated the inactivation kinetics of bacteriophage MS2 as a surrogate for infectious viruses in liquid waste produced from total nucleic acid extractions of wastewater. The goal was to determine a waste handling protocol that ensures sufficient viral infectivity loss (i.e., inactivation) for safe disposal. Liquid waste was generated using a viral total nucleic acid extraction kit (Wizard^® Enviro Total Nucleic Acid Kit, Promega, The United States of America) containing guanidinium chloride, isopropanol, ethanol, and other residual reagents. MS2 phage was artificially added into liquid waste, and inactivation was monitored over 24 h using double agar layer plaque assays. A one-phase exponential decay model was applied to estimate the time required for safe disposal, showing MS2 inactivation followed an exponential decay pattern, achieving a predicted 6-log₁₀ reduction at an average of 2.41 h (145 min), with a 95 % confidence interval of 1.34 h (80 min) to 4.05 h (243 min). However, only the 24-hour holding time was observed to significantly exceed the 6-log₁₀ reduction threshold, supporting its recommendation as a conservative and practical holding time after which the waste can be safely disposed of as chemical solvent waste without additional decontamination measures such as autoclaving, as viral infectivity is reduced by at least 6-log₁₀.

With the improvement of transportation and the rise of tourism on the Qinghai-Xizang Plateau, the scope of human activities has continuously expanded, increasing opportunities for contact with wildlife, also exacerbating the outbreak rate of zoonotic emerging infectious diseases. Currently, research on the gut microbiota of wildlife, especially Marmota himalayana (M. himalayana), which are reservoir hosts for plague, is scarce. In this study, we investigated the composition, function, and regional variations of the gut microbiota in M. himalayana based on the metagenomic sequencing of 45 fecal samples from the Sanjiangyuan National Nature Reserve in Qinghai Province. The results indicated that at the phylum level, the dominant bacterial phyla in the gut microbiota of the M. himalayana were Firmicutes, Bacteroidota, and Proteobacteria, collectively accounting for 74.16 % of the community. At the genus level, the top three most abundant genera were Alistipes (11.86 % ± 1.56 %), Bacteroides (6.68 % ± 0.95 %), and Clostridium (4.92 % ± 1.04 %). Kyoto encyclopedia of genes and genomes (KEGG) database annotation results showed that the most enriched functional categories of the marmot gut microbiota were metabolism, genetic information processing (GIP), and environmental information processing (EIP). These active functions played a crucial role in food digestion, nutrient absorption, metabolic balance maintenance, and pathogen defense, aiding the marmot in better adapting to the extreme environment of the Qinghai-Xizang Plateau. The study provided critical insights into host-microbe interactions, highlighting the role of microbiota in the survival and conservation of endangered species in unique habitats.

Diarrhea is currently a prominent global public health issue. This study evaluated recent trends in the global burden of diarrhea and projected future changes over the next decade. Using the diarrhea data from the global burden of disease (GBD) 2021, this study assessed the temporal trends using Joinpoint regression and explored the impact of different factors using age-period-cohort modeling. Decomposition analysis identified drivers of disease burden changes, and the Bayesian age-period-cohort (BAPC) model predicted future trends. Additionally, health inequalities were measured by the inequality slope index and the concentration index. Results show a downward trend in the global burden of diarrhea since 1990. Age-period-cohort analysis suggests that the risk of incidence decreases with age until the age of 20, but increases with age after the age of 60. Risk of death from diarrhea was highest in children aged 0-4 and also increasesd after the age of 60. Decomposition identified population growth as the primary driver of burden changes, and BAPC projections indicated that the burden of diarrhea will continue declining. However, significant inequalities persist, with lower sociodemographic index (SDI) countries bearing a disproportionately high burden, although these gaps have decreased over time. The conclusion highlights that children under 5 and adults over 60 face the highest risks of diarrhea incidence and death. More attention should be paid to these populations, and effective public health policies should be implemented.

As a single-stranded ribonucleic acid (RNA) virus, the replication, transcription, and interactions with host cells of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on a complex network of RNA-RNA interactions. Investigating local RNA-RNA interactions is crucial for elucidating how viruses regulate their own functions and respond to host immune responses. This study aims to explore the application of machine learning techniques in analyzing and predicting RNA-RNA interactions within the coronavirus genome. Using virion RNA in situ conformation sequencing technology(vRIC-Seq) data and advanced computational models, we evaluated potential interactions between viral RNA fragments. By employing a variety of traditional machine learning algorithms, including traditional One-hot coding, Word2Vec models, a number of different neural network architectures, and the RNAErnie language modeling framework, we achieved significant predictive accuracy in determining the presence or absence of interactions. Furthermore, this approach provides a novel framework for investigating RNA-RNA interactions in other viral systems, thereby opening new avenues for the development of targeted therapeutic strategies against viral infections. The integration of computational models substantially enhances our comprehension of complex biological processes and represents a promising trajectory for future virology research. The source codes and models are freely available at https://github.com/VV1025/RNA-language-models.