A digital twin of the infant microbiome to predict neurodevelopmental deficits

The role of secondary genomes in neurodevelopment and co-evolutionary dynamics

This chapter examines how human biology and microbial "secondary genomes" have co-evolved to shape neurodevelopment through the gut-brain axis. Microbial communities generate metabolites that cross blood-brain and placental barriers, influencing synaptogenesis, immune responses, and neural circuit formation. Simultaneously, Human Accelerated Regions (HARs) and Endogenous Retroviruses (ERVs) modulate gene expression and immune pathways, determining which microbes thrive in the gut and impacting brain maturation. These factors converge to form a dynamic host-microbe dialogue with significant consequences for neurodevelopmental disorders (NDD), including autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and schizophrenia. Building on evolutionary perspectives, the chapter elucidates how genetic and immune mechanisms orchestrate beneficial and pathological host-microbe interactions in early brain development. It then explores therapeutic strategies, such as probiotics, prebiotics, fecal microbiota transplantation, and CRISPR-driven microbial engineering, targeting gut dysbiosis to mitigate or prevent neurodevelopmental dysfunctions. Furthermore, innovative organ-on-chip models reveal mechanistic insights under physiologically relevant conditions, offering a translational bridge between in vitro experiments and clinical applications. As the field continues to evolve, this work underscores the translational potential of manipulating the microbiome to optimize neurological outcomes. It enriches our understanding of the intricate evolutionary interplay between host genomes and the microbial world.

Gut Microbiota Dysbiosis: Pathogenesis, Diseases, Prevention, and Therapy

Dysbiosis refers to the disruption of the gut microbiota balance and is the pathological basis of various diseases. The main pathogenic mechanisms include impaired intestinal mucosal barrier function, inflammation activation, immune dysregulation, and metabolic abnormalities. These mechanisms involve dysfunctions in the gut-brain axis, gut-liver axis, and others to cause broader effects. Although the association between diseases caused by dysbiosis has been extensively studied, many questions remain regarding the specific pathogenic mechanisms and treatment strategies. This review begins by examining the causes of gut microbiota dysbiosis and summarizes the potential mechanisms of representative diseases caused by microbiota imbalance. It integrates clinical evidence to explore preventive and therapeutic strategies targeting gut microbiota dysregulation, emphasizing the importance of understanding gut microbiota dysbiosis. Finally, we summarized the development of artificial intelligence (AI) in the gut microbiota research and suggested that it will play a critical role in future studies on gut dysbiosis. The research combining multiomics technologies and AI will further uncover the complex mechanisms of gut microbiota dysbiosis. It will drive the development of personalized treatment strategies.

Digital twins, synthetic patient data, and in-silico trials: can they empower paediatric clinical trials?

Randomised controlled trials are the gold standard to assess the effectiveness and safety of clinical interventions; however, many paediatric trials are discontinued early due to challenges in patient enrolment. Hence, most paediatric clinical trials suffer from lack of adequate power. Additionally, trials are expensive and might expose patients to unproven therapies. Alternatives to overcome these issues using virtual patient data-namely, digital twins, synthetic patient data, and in-silico trials-are now possible due to rapid advances in digital health-care tools and interventions. However, such digital innovations have been rarely used in paediatric trials. In this Viewpoint, we propose using virtual patient data to empower paediatric trials. The use of virtual patient data has the advantages of decreased exposure of children to potentially ineffective or risky interventions, shorter trial durations leading to more rapid ascertainment of safety and effectiveness of interventions, and faster drug approvals. Use of virtual patient data could lead to more personalised treatment options with low costs and could result in faster clinical implementation of interventions in children. However, ethical and regulatory concerns, including replacing humans with digital data, data privacy, and security should be addressed and the safety and sustainability of digital data innovation ensured before virtual patient data are adopted widely.

Mathematical Modeling and Artificial Intelligence to Explore Connections Between Glaucoma and the Gut Microbiome

Background and Objectives: Glaucoma is a major cause of irreversible blindness, with primary open-angle glaucoma (POAG) being the most prevalent form. While elevated intraocular pressure (IOP) is a well-known risk factor for POAG, emerging evidence suggests that the human gut microbiome may also play a role in the disease. This review synthesizes current findings on the relationship between gut microbiome and glaucoma, with a focus on mathematical modeling and artificial intelligence (AI) approaches to uncover key insights. Materials and Methods: A comprehensive literature search was conducted using PubMed and Google Scholar, covering studies from its inception to 1 August 2024. Selected studies included basic science, observational research, and those incorporating mathematical-related models. Results: Traditional statistical and machine learning approaches, such as random forest regression and Mendelian randomization, have identified associations between specific microbiota and POAG features. These findings highlight the potential of AI to explore complex, nonlinear interactions in the gut-eye axis. However, limitations include variability in study designs and a lack of integrative, mechanistic models. Conclusions: Preliminary evidence supports the existence of a gut-eye axis influencing POAG disease. Combining data-driven and mechanism-driven models with AI could identify therapeutic targets and novel biomarkers. Future research should prioritize longitudinal studies in diverse populations and integrate physiological data to improve model accuracy and clinical relevance. Furthermore, physics-based models could deepen our mechanistic understanding of the gut-eye axis in glaucoma, advancing beyond associative findings to actionable insights.