Integrating Multi-omics Data to Understand Complex Isolate and Microbiome Systems

Increasingly multi-omics data is becoming more accessible for the study of a wide range of complex biological systems. Today, terabase-scale metagenomes can be readily obtained from soil microbiome systems, while the instruments and protocols surrounding the collection of metabolomic and proteomic data are constantly improving. Yet analysis methods still struggle to annotate these individual datasets, let alone combine them to discover new biological principles. For example, one of the great challenges associated with the use and interpretation of metabolomics data is the large portion of observed peaks that cannot be readily associated with known biochemical compounds. With the lack of clear identities for peaks, and with many identified peaks lacking known pathways, analysis is often limited to correlations alone. In this talk, we will discuss tools and workflows that have been integrated into the DOE Systems-biology Knowledgebase (KBase) to permit the combination of metabolomic and genomic data to present mechanistic explanations for correlations observed in microbiome systems. For example, we will show how exometabolite data from an isolate of Pseudomonas led to an explanation for the widely conserved correlation observed between betaine and pseudomonas in many environments (see Figure 1). We will also explore how these tools are leading to improved annotations of metabolomic data, improved understanding of spontaneous chemistry, and overall a dramatically improved capacity to produce mechanistic explanations for the metabolites observed in biological systems ranging from the JCVI minimal genome to large-scale soil microbiomes.

Bio

Dr. Christopher Henry is a scientist at Argonne National Laboratory and co-PI of the DOE Systems Biology Knowledgebase (KBase) project. He is an expert in genome and metagenome annotation, metabolic model reconstruction, and the application of metabolic models to integrate multi-omics data. He has developed numerous frameworks and tools for building models from genomic and metagenomic data and refining models to better match experimental observations, working with systems ranging from cultured isolates to large-scale soil microbiomes.