Bacterial infections continue to pose a significant threat to global public health, despite advancements in prevention, treatment, and control measures. The dynamic interplay between host and pathogen is pivotal in shaping the course of bacterial diseases. In this process, bacterial pathogens employ a range of strategies to invade hosts, evade immune defenses, and propagate infection. Key to their success are virulence factors, including secreted toxins and effector proteins, which manipulate host cellular processes and establish favorable environments for replication. The host's innate immune system serves as the frontline defense, detecting pathogens through pattern recognition receptors and activating inflammatory pathways to combat bacterial invasion. Recent studies have focused on dissecting these complex interactions at the molecular, cellular, and systemic levels, uncovering key determinants influencing infection outcomes. However, there remains a need for a deeper understanding of these mechanisms to identify novel therapeutic targets and develop effective vaccines, thereby mitigating the impact of bacterial infections on global health.
This research topic aims to unravel the intricate dynamics of host-pathogen interactions in bacterial infections, leveraging insights from omics and machine learning technologies. Specifically, we seek to identify and characterize bacterial virulence factors and host immune proteins critical for infection outcomes; pinpoint pivotal biomarkers and signaling cascades driving pathogenic processes, facilitating the discovery of novel therapeutic targets; and translate research findings into clinically relevant insights and diagnostic tools to develop precision medicine approaches tailored to individual patients and specific bacterial infections.
To gather further insights into the molecular mechanisms driving bacterial pathogenesis and host immune responses, we welcome articles addressing, but not limited to, the following themes:
- Exploration of underlying mechanisms of bacterial pathogenesis and host immune response by integrating multi-omics data (genomics, transcriptomics, proteomics, metabolomics, macrobiotics).
- Development of computational algorithms and tools for identifying bacterial virulence factors and their interactions with host proteins using machine learning approaches.
- Development of predictive frameworks for forecasting infection severity, disease progression, and treatment responses.
- Identification of host factors and pathways contributing to immune response using omics approaches.
- Identification of biomarkers by integrating omics and machine learning approaches.
Bacterial infections continue to pose a significant threat to global public health, despite advancements in prevention, treatment, and control measures. The dynamic interplay between host and pathogen is pivotal in shaping the course of bacterial diseases. In this process, bacterial pathogens employ a range of strategies to invade hosts, evade immune defenses, and propagate infection. Key to their success are virulence factors, including secreted toxins and effector proteins, which manipulate host cellular processes and establish favorable environments for replication. The host's innate immune system serves as the frontline defense, detecting pathogens through pattern recognition receptors and activating inflammatory pathways to combat bacterial invasion. Recent studies have focused on dissecting these complex interactions at the molecular, cellular, and systemic levels, uncovering key determinants influencing infection outcomes. However, there remains a need for a deeper understanding of these mechanisms to identify novel therapeutic targets and develop effective vaccines, thereby mitigating the impact of bacterial infections on global health.
This research topic aims to unravel the intricate dynamics of host-pathogen interactions in bacterial infections, leveraging insights from omics and machine learning technologies. Specifically, we seek to identify and characterize bacterial virulence factors and host immune proteins critical for infection outcomes; pinpoint pivotal biomarkers and signaling cascades driving pathogenic processes, facilitating the discovery of novel therapeutic targets; and translate research findings into clinically relevant insights and diagnostic tools to develop precision medicine approaches tailored to individual patients and specific bacterial infections.
To gather further insights into the molecular mechanisms driving bacterial pathogenesis and host immune responses, we welcome articles addressing, but not limited to, the following themes:
- Exploration of underlying mechanisms of bacterial pathogenesis and host immune response by integrating multi-omics data (genomics, transcriptomics, proteomics, metabolomics, macrobiotics).
- Development of computational algorithms and tools for identifying bacterial virulence factors and their interactions with host proteins using machine learning approaches.
- Development of predictive frameworks for forecasting infection severity, disease progression, and treatment responses.
- Identification of host factors and pathways contributing to immune response using omics approaches.
- Identification of biomarkers by integrating omics and machine learning approaches.