Computational Strategies for the Identification of a Transcriptional Biomarker Panel to Sense Cellular Growth States in Bacillus subtilis.

Yiming Huang,Colin Harwood,Wendy Smith,Anil Wipat,Jaume Bacardit

doi:10.3390/s21072436

Abstract

A goal of the biotechnology industry is to be able to recognise detrimental cellular states that may lead to suboptimal or anomalous growth in a bacterial population. Our current knowledge of how different environmental treatments modulate gene regulation and bring about physiology adaptations is limited, and hence it is difficult to determine the mechanisms that lead to their effects. Patterns of gene expression, revealed using technologies such as microarrays or RNA-seq, can provide useful biomarkers of different gene regulatory states indicative of a bacterium’s physiological status. It is desirable to have only a few key genes as the biomarkers to reduce the costs of determining the transcriptional state by opening the way for methods such as quantitative RT-PCR and amplicon panels. In this paper, we used unsupervised machine learning to construct a transcriptional landscape model from condition-dependent transcriptome data, from which we have identified 10 clusters of samples with differentiated gene expression profiles and linked to different cellular growth states. Using an iterative feature elimination strategy, we identified a minimal panel of 10 biomarker genes that achieved 100% cross-validation accuracy in predicting the cluster assignment. Moreover, we designed and evaluated a variety of data processing strategies to ensure our methods were able to generate meaningful transcriptional landscape models, capturing relevant biological processes. Overall, the computational strategies introduced in this study facilitate the identification of a detailed set of relevant cellular growth states, and how to sense them using a reduced biomarker panel.

Highlights

The majority of free-living, single-celled bacteria inhabit environments that are constantly changing
We identified 10 clusters representing distinct changes in the transcriptional state dergoing similar transcriptional changes were positioned close to each other
Making the most effective use of high throughput transcriptomics data collected under a wide range of conditions, our research deploys data-driven approaches to enhance the global understanding of transcriptional shifts in response to different conditions and to quantitatively extract a few key genes as the transcriptional biomarkers for sensing diverse cellular growth states presented in this transcriptomics data

Summary

Introduction

The majority of free-living, single-celled bacteria inhabit environments that are constantly changing. As a result, they are subject to periods of stress that are often multifactorial in nature. They are subject to periods of stress that are often multifactorial in nature In response to these environmental changes, bacteria have evolved a range of interacting regulatory circuits that bring about metabolic and physiological adaptations that help to mitigate against the damage resulting from these stresses [1,2,3,4,5,6,7,8]. Knowledge of how the global bacterial gene expression profile (i.e., the transcriptome) changes in response to nutrient and environmental stresses is key to recognising when a population of cells encounters conditions that are detrimental to growth and product synthesis

Methods

Results

Conclusion