During the fall of 2021, I took a special topics course as part of my graduate program titled “R for Biologists”. In it I had the opportunity to dig into the world of bulk RNA-seq analysis. Below I describe my personal project.

Introduction

As bacterivores, C. elegans feed on bacteria that grow on rotting vegetation. Historically, a strain of Escherichia coli, OP50, was chosen as the standard laboratory diet. While most experimental studies use OP50 as the primary food source, the number of bacterial diets used to propagate C. elegans has expanded in recent years. Another laboratory E. coli strain that is routinely used is HB101. Although both of these bacterial diets support growth and development, the nutritional composition of each is unique, and when fed to C. elegans, differentially impacts several traits. Research has indicated that animals fed HB101 develop faster and are larger than those fed OP50 [1,2,3]. Unsurprisingly, these diet-induced growth effects are also accompanied by global changes in gene expression [4]. Characterizing the expression changes of C. elegans fed differing strains of bacteria expands the understanding of diet effects at the molecular level.

The data

Changes in diet can have profound effects on gene expression. Previously, researchers have analyzed the transcriptional response of animals fed differing diets [4]. C. elegans populations were cultured for five days with either E. coli OP50 or E. coli HB101. Two replicates for each diet were included, resulting in four total experimental populations. Total RNA was then extracted from these populations, and samples were sequenced using an Illumina NextSeq 500 sequencing platform. Raw FASTQ sequence files were processed with a bioinformatics pipeline. Another recent study also collected expression data of C. elegans fed multiple bacterial diets [5]. In this study, animals were grown on plates seeded with E.coli (OP50, HT115, and HB101) for 48 hours. RNA was extracted, samples were sequenced, and read counts were reported in triplicate.

Objective

I plan to study the publicly available raw FASTQ files during this course. My goal is to successfully analyze these data through all steps of an RNAseq bioinformatics pipeline. I have considerable experience using R for data manipulation and statistical analysis but I have no experience handling sequencing data. Through this project I hope to gain a better understanding of how sequencing data can be processed and studied using command line and R. From this analysis, I expect to identify differentially expressed genes when comparing E. coli OP50 and HB101 diets. I expect many of these genes to play a role in metabolic pathways.

Ultimately, my goal is to gain experience handling and analyzing sequencing data.

See final project page here.

References

1. So S, Miyahara K, Ohshima Y. Control of body size in C. elegans dependent on food and insulin/IGF-1 signal: Body size control in C. elegans. Genes Cells. 2011;16: 639–651.
2. Soukas AA, Kane EA, Carr CE, Melo JA, Ruvkun G. Rictor/TORC2 regulates fat metabolism, feeding, growth, and life span in Caenorhabditis elegans. Genes Dev. 2009;23: 496–511.
3. Avery L, Shtonda BB. Food transport in the C. elegans pharynx. J Exp Biol. 2003;206: 2441–2457.
4. Schumacker ST, Chidester CAM, Enke RA, Marcello MR. RNA sequencing dataset characterizing transcriptomic responses to dietary changes in Caenorhabditis elegans. Data Brief. 2019;25: 104006.
5. Stuhr NL, Curran SP. Bacterial diets differentially alter lifespan and health span trajectories in C. elegans, Commun Biol. 2020;653