The study of human microbiomes has yielded insights into basic science, and applied therapeutics are emerging. However, conflicting definitions of what microbiomes are and how they affect the health of the “host” are less understood. A major impediment towards systematic design, discovery, and implementation of targeted microbiome therapeutics is the continued reliance on taxonomic indicators to define microbiomes in health and disease. Such reliance often confounds analyses, potentially suggesting associations where there are none, and conversely failing to identify significant, causal relationships. This review article discusses recent discoveries pointing towards a molecular understanding of microbiome “dysbiosis” and away from a purely taxonomic approach. We highlight the growing role of systems biological principles in the complex interrelationships between the gut microbiome and host cells, and review current approaches commonly used in targeted microbiome therapeutics, including fecal microbial transplant, bacteriophage therapies, and the use of metabolic toxins to selectively eliminate specific taxa from dysbiotic microbiomes. These approaches, however, remain wholly or partially dependent on the bacterial taxa involved in dysbiosis, and therefore may not capitalize fully on many therapeutic opportunities presented at the bioactive molecular level. New technologies capable of addressing microbiome-associated diseases as molecular problems, if solved, will open possibilities of new classes and categories of targeted microbiome therapeutics aimed, in principle, at all dysbiosis-driven disorders.

Background: Norovirus is a leading cause of epidemic acute gastroenteritis worldwide, associated with significant morbidity, mortality, and economic loss. Despite its global impact, no licensed vaccine is currently available, and vaccine development remains challenging. Methods: We explored avian immunoglobulin Y (IgY) antibodies as a low-cost countermeasure against norovirus infection. We generated recombinant protruding (P) domain proteins from the capsid protein (VP1) of noroviruses, representing two GII.4 variants and the GII.6 genotype. These were combined into a single immunogen to immunize laying hens to produce norovirus VP1-specific IgY antibodies. Results: Immunization of laying hens with the P domain proteins elicited high-titer (>1:450,000) P domain-specific IgY antibodies. The yolk-derived IgY effectively inhibited binding of various norovirus P particles to their histo-blood group antigen ligands, with 50% blocking titers (BT50) up to 1:8533 against homotypic GII.4 and 1:667 against heterotypic G1.1 Norwalk virus P particles. Importantly, the IgY neutralized replication of GII.4 norovirus in the human intestinal enteroid (HIE) system at a high titer of over 1:2500, equivalent to 0.70 µg/mL of total IgY. We also produced norovirus shell (S) domain proteins and corresponding IgY antibodies, which neutralized GII.4 norovirus replication in the HIE model at a titer of ~1:800, equivalent to 2.98 µg/mL of total IgY. This provides the first evidence that the S domain contains neutralizing epitopes. Conclusions: Our findings support the potential of IgY targeting norovirus P or S domains as a scalable, cost-effective strategy for preventing norovirus infection and disease.

Background/Objectives: Pediatric obstructive sleep apnea (OSA) is a complex disorder with a variable presentation and often challenging diagnostic testing. The history and physical examination in pediatric OSA frequently differ from those in adults. The treatment options are multifaceted and must be tailored to the individual patient. Artificial intelligence (AI) modalities currently employed in pediatric sleep medicine face several important limitations: modality fragmentation, lack of explainability, and limited semantic integration. Method: Our team proposes a new vision for AI and pediatric sleep medicine. This platform is based on a knowledge graph (KG) framework integrating structured and unstructured data to enable reasoning, personalization, and clinical decision support. Results: This framework represents a conceptual architecture; it has not yet been empirically implemented, and the use cases described herein are illustrative of its intended capabilities. Components of the infrastructure developed for similar applications have been successfully implemented. The quantitative feasibility pilot KG represented 100% multimodal data with >90% semantic completeness. Conclusions: Fully realized and deployed into the clinical space, this pediatric OSA KG system will enhance tertiary care programs and help project tertiary-level pediatric care into underserved regions.