Integrative Analysis of Nontargeted LC-HRMS and High-Throughput Metabarcoding Data for Aquatic Environmental Studies...

Area of Science:

• Environmental chemistry
• Ecotoxicology
• Bioinformatics

Background:

• High-throughput analytical techniques enable novel data integration.
• Nontarget screening (NTS) with LC-HRMS is vital for analyzing organic micropollutants.
• Biological indicators provide complementary ecosystem health data.

Purpose of the Study:

• To develop a multivariate data processing framework for integrating LC-HRMS NTS and metabarcoding data.
• To evaluate the temporal impact of treated wastewater (TWW) on an aquatic ecosystem using data fusion.
• To understand the interplay between chemical and biological changes in a perturbed environment.

Main Methods:

• Nontarget screening (NTS) using liquid chromatography-high-resolution mass spectrometry (LC-HRMS).
• Region of interest-multivariate curve resolution-alternating least-squares (MCR-ALS) for data compression (ROIMCR).
• ANOVA-simultaneous component analysis with structural learning and integrative decomposition (SLIDE-ASCA) for data fusion.

Main Results:

• Temporal variability (74.6%) was a larger driver of ecosystem changes than the treatment effect.
• Both temporal and treatment factors contributed to shared variation (41%).
• The integrated approach revealed associations between chemical and biological markers.

Conclusions:

• Data fusion enhances the interpretability of complex environmental data.
• The SLIDE-ASCA approach accurately represents interactions between chemical and biological data.
• This framework provides deeper insights into wastewater impacts on aquatic ecosystems.