About this Research Topic
Most ecosystems and host niches, be it the soil or the gut, harbor a consortium of microbial populations termed as the microbiota which by virtue of its interactions among itself, with other kingdoms of life and the environment, plays a key role in imparting functionality and health. In the current era of accelerated ecological degradation and its subsequent impacts on human health and livelihoods, the importance of the microbiota cannot be highlighted enough. Although traditional and pre-modern knowledge systems are perceived to be able to recognize the holistic potential of ecosystems to achieve homeostasis by means of synergistic interactions, the lack of detailed mechanistic knowledge is a drawback. The use of low-throughput sampling and non-systemic research methodologies in traditional approaches reduce the feature space and exclude contextuality thus adding to limitations. Consequently, identifying community level biomarkers and evidence-based intervention strategies to aid restoration efforts in ecosystems stands as a challenge.
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. Such approaches when applied to large datasets generated from well-designed experiments, help identify assemblages of molecules and species and the interactions among/between them which contribute to the manifestation of particular phenotypes - be it health or disease. Technological advancements at the molecular level include Next Generation Sequencing (NGS) techniques ranging from 16S, whole-genome shotgun (WGS), metatranscriptomics, metaproteomics, metabolomics etc. which can be used for community level profiling. Subsequently, various model-guided supervised and unsupervised data integration approaches can be used to interpret the relative importance of the individual -omics datasets to the phenotype of interest. Such data integration approaches also help identify a set of phenotype-associated molecular signatures from across different -omics data types thus enabling the inference of phenotypically-relevant modules at a systemic level. Reference databases compiled for specific niches, diseases, ecotypes etc. as well as signaling/metabolic pathways can then be used to interpret the functional relevance of the multi-omics signatures associated with the phenotypes of interest. Qualitative representations of ecological-scale molecular interactions can serve as templates for quantitative and predictive modeling.
We welcome submissions on themes including, but not limited to:
- 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 actions (such as metabolic cross feeding etc) 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 thereof 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 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. Such approaches when applied to large datasets generated from well-designed experiments, help identify assemblages of molecules and species and the interactions among/between them which contribute to the manifestation of particular phenotypes - be it health or disease. Technological advancements at the molecular level include Next Generation Sequencing (NGS) techniques ranging from 16S, whole-genome shotgun (WGS), metatranscriptomics, metaproteomics, metabolomics etc. which can be used for community level profiling. Subsequently, various model-guided supervised and unsupervised data integration approaches can be used to interpret the relative importance of the individual -omics datasets to the phenotype of interest. Such data integration approaches also help identify a set of phenotype-associated molecular signatures from across different -omics data types thus enabling the inference of phenotypically-relevant modules at a systemic level. Reference databases compiled for specific niches, diseases, ecotypes etc. as well as signaling/metabolic pathways can then be used to interpret the functional relevance of the multi-omics signatures associated with the phenotypes of interest. Qualitative representations of ecological-scale molecular interactions can serve as templates for quantitative and predictive modeling.
We welcome submissions on themes including, but not limited to:
- 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 actions (such as metabolic cross feeding etc) 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 thereof 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.
Keywords: Microbiota, Inter-/intra-species interactions, Systems ecology, Health, Restoration
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
