Co-Metabolism and Co-Expression Networks of Hydrocarbon-Degrading Estuarine Microbiomes

🧠 Background & Motivation

Hydrocarbon degradation in natural ecosystems is rarely an isolated function. In estuarine microbiomes, it is embedded within broader metabolic frameworks that support energy conservation, redox balance, and carbon flow. Understanding these linked processes is essential for explaining how microbial communities respond to environmental gradients and sustain ecosystem function in dynamic coastal systems.

This project investigates hydrocarbon-degrading microbial populations in two Mid-Atlantic estuaries using a genome-resolved, multi-omics framework. The goal is to move beyond simply identifying hydrocarbon degradation genes and instead ask how these organisms are metabolically structured, what energy systems co-occur with degradation, and which pathways are actively expressed together under natural environmental conditions.

This work is part of a broader collaborative project on estuarine hydrocarbon-degrading microbiomes, and I am actively mentoring Dinuka in the main project while contributing to the genome-resolved metabolic and co-expression analyses.

🎯 Research Questions & Objectives

*📌 Study 1: Metabolic Potential of Hydrocarbon-Degrading MAGs

• Which additional metabolic functions are encoded in MAGs that contain hydrocarbon degradation genes?
• Do hydrocarbon degraders share a common metabolic blueprint, or do they differ across taxa?

*📌 Study 2: Co-Energy Metabolism

• Which energy generation and conservation pathways co-occur with hydrocarbon degradation in the same genome?
• How do these pathways vary across redox niches in estuarine environments?

*📌 Study 3: Co-Metabolism and Co-Expression

• Which metabolic pathways are actively expressed together with hydrocarbon degradation genes?
• Can co-expression patterns reveal distinct ecological strategies among hydrocarbon-degrading taxa?

👨‍🔬 My Role

My contribution focuses on genome-resolved metabolism, computational workflow development, and mentoring, including:

• Mentoring Dinuka as part of the broader hydrocarbon degrader project
• Identifying hydrocarbon-degrading MAGs based on key marker genes
• Building workflows to assess metabolic potential, co-energy metabolism, and co-metabolism
• Integrating metagenomic and metatranscriptomic data at the MAG level
• Developing R-based visualization pipelines for heatmaps and bipartite networks
• Interpreting co-expression and metabolic integration patterns across taxa and samples

🧬 Analytical Framework

This project addresses three related but distinct questions:

1. Metabolic Potential
   What additional metabolic capabilities are encoded in hydrocarbon-degrading MAGs?
   → Based on gene presence/absence

2. Co-Energy Metabolism
   Which energy pathways co-occur with hydrocarbon degradation?
   → Based on energy-related marker genes

3. Co-Metabolism
   Which metabolic pathways are actively expressed together in the same genome?
   → Based on gene expression patterns from metatranscriptomes

Together, these analyses connect what is possible to what is actively used.

🛠 Methods & Tools

*Genome-Resolved Analysis

• MAG screening for hydrocarbon degradation markers (e.g., alkB, bssA, assA)
• Protein prediction using Prodigal
• Marker gene annotation using DIAMOND
• Genome-level metabolic potential summarization

*Metabolic Marker Framework

Curated marker proteins were used to detect pathways related to:

• Aerobic and anaerobic respiration
• Sulfur cycling
• Nitrogen cycling
• Carbon fixation
• Phototrophy
• Fermentation
• Central carbon metabolism

*Expression Analysis

• MAG-level gene expression profiling from metatranscriptomic datasets
• Category-level organization of genes into:
  • Hydrocarbon degradation
  • C1 metabolism
  • Complex carbon degradation
  • Fermentation
  • Central carbon metabolism

*Visualization & Statistics

• R
• pheatmap
• igraph
• ggraph
• Custom scripts for category standardization, matrix generation, and network construction

🧩 Challenges & Solutions**

Challenge 1: Distinguishing metabolic potential from active co-metabolism
Solution: Separated genomic presence/absence analyses from metatranscriptomic expression-based analyses, allowing clearer interpretation of encoded versus expressed functions.

Challenge 2: Inconsistent category naming and complex expression matrices
Solution: Developed standardized data-cleaning and recoding steps to harmonize pathway categories before heatmap and network construction.

Challenge 3: Hidden functional differences among hydrocarbon degraders
Solution: Used MAG-resolved analyses rather than bulk community summaries, revealing taxon-specific metabolic integration and ecological strategies.

🌊 Key Insights

• Hydrocarbon degradation is not a standalone process but is embedded within broader metabolic and energetic frameworks
• Hydrocarbon-degrading MAGs differ substantially in their associated respiration, sulfur, nitrogen, and carbon pathways
• Co-metabolism patterns suggest distinct ecological strategies among taxa, including flexible versus highly integrated metabolic lifestyles
• Genome-resolved approaches reveal structure and function that would be masked in bulk metagenomic or metatranscriptomic analyses

🌍 Perspective

This project advances our understanding of how hydrocarbon-degrading microorganisms function in estuarine ecosystems shaped by environmental gradients and anthropogenic inputs. By linking metabolic potential, co-energy pathways, and active co-metabolism, the work provides a more complete view of the ecological roles of hydrocarbon degraders in coastal biogeochemistry.

More broadly, this framework demonstrates how genome-resolved metagenomics and metatranscriptomics can be used to uncover hidden metabolic structure in environmental microbiomes, with relevance to functional ecology, pollution response, and ecosystem resilience.

Outcome

Patabandige, D. L. J., John, J., Ortiz, M., & Campbell, B. J. (2026). Environmental gradients shape the hydrocarbon-degrading microbiome in two Mid-Atlantic bays. Submitted (ISME Communications). Preprint available upon request

Reference

Dr. Barbara J. Campbell, 📧 bcampb7@clemson.edu

Dinuka Lakmali Jayasuriya Patabandige PhD Student, Clemson University 📧 djayasu@g.clemson.edu

Read More at https://jojyjohn28.github.io/blog/genome-resolved-metabolism-co-metabolism/