EMPathways2: Estimation of Enzyme Expression and Metabolic Pathway Activity Using RNA-Seq Reads.

In this chapter, we outline an approach to analyzing metatranscriptomic data, focusing on the assessment of differential enzyme expression and metabolic pathway activities using a novel bioinformatics software tool, EMPathways2. The analysis pipeline commences with raw data originating from a sequencer and concludes with an output of enzyme expressions and an estimate of metabolic pathway activities. The initial step involves aligning specific transcriptomes assembled from RNA-Seq data using Bowtie2 and acquiring gene expression data with IsoEM2. Subsequently, the pipeline proceeds to quality assessment and preprocessing of the input data, ensuring accurate estimates of enzymes and their differential regulation. Upon completion of the preprocessing stage, EMPathways2 is employed to decipher the intricate relationships between genes, enzymes, and pathways. An online repository containing sample data has been made available, alongside custom Python scripts designed to modify the output of the programs within the pipeline for diverse downstream analyses. This chapter highlights the technical aspects and practical applications of using EMPathways2, which facilitates the advancement of transcriptome data analysis and contributes to a deeper understanding of the complex regulatory mechanisms underlying living systems.

Estimating Enzyme Expression and Metabolic Pathway Activity in Borreliella-Infected and Uninfected Mice.

Evaluating changes in metabolic pathway activity is essential for studying disease mechanisms and developing new treatments, with significant benefits extending to human health. Here, we propose EMPathways2, a maximum likelihood pipeline that is based on the expectation-maximization algorithm, which is capable of evaluating enzyme expression and metabolic pathway activity level. We first estimate enzyme expression from RNA-seq data that is used for simultaneous estimation of pathway activity levels using enzyme participation levels in each pathway. We implement the novel pipeline to RNA-seq data from several groups of mice, which provides a deeper look at the biochemical changes occurring as a result of bacterial infection, disease, and immune response. Our results show that estimated enzyme expression, pathway activity levels, and enzyme participation levels in each pathway are robust and stable across all samples. Estimated activity levels of a significant number of metabolic pathways strongly correlate with the infected and uninfected status of the respective rodent types.