This collection encompasses studies exploring the role of gut microbiota, omics analyses, and biomarkers in various disease states, primarily focusing on COVID-19, inflammatory bowel disease (IBD), and asthma. The research highlights long-term gut microbiome alterations following mild COVID-19 recovery, identifying bacterial and fungal taxa indicative of persistent immune and metabolic impacts. Another theme addresses the gut microbial dysbiosis in IBD, employing individualized microbial network analyses to predict response to therapeutic treatments for Crohn’s disease and ulcerative colitis. Additional studies present associations between gut microbiota and plasma metabolites influencing asthma risk via Mendelian randomization, emphasizing causative microbial factors. Several investigations also examine predictive biomarkers and molecular signatures distinguishable across disease severity phases of COVID-19, notably through multi-omics integrative network analyses that highlight critical inflammation-related biosignatures. Collectively, these articles underscore the importance of microbiome-host interactions, omics tools, and biomarkers in disease stratification, prognosis, and personalized therapeutic approaches, demonstrating significant biochemical and microbial biomarkers suited for clinical stratification and management.
The field of ecological microbiology has increasingly recognized the critical role of microbiota in various ecosystems and host niches, such as soil and the human gut. These microbial consortia interact with each other, other life forms, and the environment, significantly influencing functionality and health. In the context of accelerated ecological degradation and its impacts on human health, understanding these interactions has become paramount. Traditional knowledge systems have long acknowledged the holistic potential of ecosystems to achieve homeostasis through synergistic interactions. However, the lack of detailed mechanistic knowledge and reliance on low-throughput sampling and non-systemic research methodologies have limited our understanding. This gap underscores the need for identifying community-level biomarkers and developing evidence-based intervention strategies to aid in ecosystem restoration.
This research topic aims to highlight the available technological methodologies and modeling approaches to study the effects of the microbiome at multiple scales in complex ecosystems. By applying these advanced techniques to large datasets from well-designed experiments, researchers can identify assemblages of molecules and species and their interactions, which contribute to specific phenotypes, whether related to health or disease. Technological advancements at the molecular level, such as Next Generation Sequencing (NGS) techniques, including 16S, whole-genome shotgun (WGS), metatranscriptomics, metaproteomics, and metabolomics, enable comprehensive community-level profiling. Model-guided supervised and unsupervised data integration approaches can then interpret the relative importance of individual -omics datasets to the phenotype of interest, identifying phenotype-associated molecular signatures and inferring phenotypically-relevant modules at a systemic level.
To gather further insights into the multi-scale systems and ecological approaches to investigate the role of the microbiota in different niches, we welcome articles addressing, but not limited to, the following themes:
- Single or multi-omics based profiling of the community microbiota in any biological niche, with or without functional interpretation and/or modeling.
- Mechanistic studies using computational and/or experimental approaches to infer modes of action (such as metabolic cross-feeding) of the microbiota and/or microbiota-derived molecules whose expression levels correlate with the phenotypes of interest.
- Network-based representation of phenotype-associated microbiota or microbiota-derived molecules and their analysis to identify critical hubs/nodes.
- Integrative studies exploring the role of microbiota-host interactions by combining molecular -omics datasets profiling microbiota compositions/activity and host responses.
This collection encompasses studies exploring the role of gut microbiota, omics analyses, and biomarkers in various disease states, primarily focusing on COVID-19, inflammatory bowel disease (IBD), and asthma. The research highlights long-term gut microbiome alterations following mild COVID-19 recovery, identifying bacterial and fungal taxa indicative of persistent immune and metabolic impacts. Another theme addresses the gut microbial dysbiosis in IBD, employing individualized microbial network analyses to predict response to therapeutic treatments for Crohn’s disease and ulcerative colitis. Additional studies present associations between gut microbiota and plasma metabolites influencing asthma risk via Mendelian randomization, emphasizing causative microbial factors. Several investigations also examine predictive biomarkers and molecular signatures distinguishable across disease severity phases of COVID-19, notably through multi-omics integrative network analyses that highlight critical inflammation-related biosignatures. Collectively, these articles underscore the importance of microbiome-host interactions, omics tools, and biomarkers in disease stratification, prognosis, and personalized therapeutic approaches, demonstrating significant biochemical and microbial biomarkers suited for clinical stratification and management.
The field of ecological microbiology has increasingly recognized the critical role of microbiota in various ecosystems and host niches, such as soil and the human gut. These microbial consortia interact with each other, other life forms, and the environment, significantly influencing functionality and health. In the context of accelerated ecological degradation and its impacts on human health, understanding these interactions has become paramount. Traditional knowledge systems have long acknowledged the holistic potential of ecosystems to achieve homeostasis through synergistic interactions. However, the lack of detailed mechanistic knowledge and reliance on low-throughput sampling and non-systemic research methodologies have limited our understanding. This gap underscores the need for identifying community-level biomarkers and developing evidence-based intervention strategies to aid in ecosystem restoration.
This research topic aims to highlight the available technological methodologies and modeling approaches to study the effects of the microbiome at multiple scales in complex ecosystems. By applying these advanced techniques to large datasets from well-designed experiments, researchers can identify assemblages of molecules and species and their interactions, which contribute to specific phenotypes, whether related to health or disease. Technological advancements at the molecular level, such as Next Generation Sequencing (NGS) techniques, including 16S, whole-genome shotgun (WGS), metatranscriptomics, metaproteomics, and metabolomics, enable comprehensive community-level profiling. Model-guided supervised and unsupervised data integration approaches can then interpret the relative importance of individual -omics datasets to the phenotype of interest, identifying phenotype-associated molecular signatures and inferring phenotypically-relevant modules at a systemic level.
To gather further insights into the multi-scale systems and ecological approaches to investigate the role of the microbiota in different niches, we welcome articles addressing, but not limited to, the following themes:
- Single or multi-omics based profiling of the community microbiota in any biological niche, with or without functional interpretation and/or modeling.
- Mechanistic studies using computational and/or experimental approaches to infer modes of action (such as metabolic cross-feeding) of the microbiota and/or microbiota-derived molecules whose expression levels correlate with the phenotypes of interest.
- Network-based representation of phenotype-associated microbiota or microbiota-derived molecules and their analysis to identify critical hubs/nodes.
- Integrative studies exploring the role of microbiota-host interactions by combining molecular -omics datasets profiling microbiota compositions/activity and host responses.