Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ.

Patient blood samples are invaluable in clinical omics databases, yet current methodologies often fail to fully uncover the molecular mechanisms driving patient pathology. While genome-scale metabolic models (GEMs) show promise in systems medicine by integrating various omics data, having only exometabolomic data remains a limiting factor. To address this gap, we introduce a comprehensive pipeline integrating GEMs with patient plasma metabolome. This pipeline constructs case-specific GEMs using literature-based and patient-specific metabolomic data. Novel computational methods, including adaptive sampling and an in-house developed algorithm for the rational exploration of the sampled space of solutions, enhance integration accuracy while improving computational performance. Model characterization involves task analysis in combination with clustering methods to identify critical cellular functions. The new pipeline was applied to a cohort of trauma patients to investigate shock-induced endotheliopathy using patient plasma metabolome data. By analyzing endothelial cell metabolism comprehensively, the pipeline identified critical therapeutic targets and biomarkers that can potentially contribute to the development of therapeutic strategies. Our study demonstrates the efficacy of integrating patient plasma metabolome data into computational models to analyze endothelial cell metabolism in disease contexts. This approach offers a deeper understanding of metabolic dysregulations and provides insights into diseases with metabolic components and potential treatments.

Passive Sampler Technology for Viral Detection in Wastewater-Based Surveillance: Current State and Nanomaterial Opportunities.

Although wastewater-based surveillance (WBS) is an efficient community-wide surveillance tool, its implementation for pathogen surveillance remains limited by ineffective sample treatment procedures, as the complex composition of wastewater often interferes with biomarker recovery. Moreover, current sampling protocols based on grab samples are susceptible to fluctuant biomarker concentrations and may increase operative costs, often rendering such systems inaccessible to communities in low-to-middle-income countries (LMICs). As a response, passive samplers have emerged as a way to make wastewater sampling more efficient and obtain more reliable, consistent data. Therefore, this study aims to review recent developments in passive sampling technologies to provide researchers with the tools to develop novel passive sampling strategies. Although promising advances in the development of nanostructured passive samplers have been reported, optimization remains a significant area of opportunity for researchers in the area, as methods for flexible, robust adsorption and recovery of viral genetic materials would greatly improve the efficacy of WBS systems while making them more accessible for communities worldwide.

Biosensors for the detection of disease outbreaks through wastewater-based epidemiology.

Wastewater-Based Epidemiology (WBE) is a novel community-wide monitoring tool that provides comprehensive real-time data of the public and environmental health status and can contribute to public health interventions, including those related to infectious disease outbreaks (e.g., the ongoing COVID-19 pandemic). Nonetheless, municipalities without centralized laboratories are likely still not able to process WBE samples. Biosensors are a potentially cost-effective solution to monitor the development of diseases through WBE to prevent local outbreaks. This review discusses the economic and technical feasibility of eighteen recently developed biosensors for the detection and monitoring of infectious disease agents in wastewater, prospecting the prevention of future pandemics.