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

Lookup NU author(s): Dr Yiming Huang, Dr Wendy Smith, Professor Colin Harwood, Professor Anil Wipat, Professor Jaume Bacardit



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2021 by the authors. Licensee MDPI, Basel, Switzerland.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.

Publication metadata

Author(s): Huang Y, Smith W, Harwood C, Wipat A, Bacardit J

Publication type: Article

Publication status: Published

Journal: Sensors

Year: 2021

Volume: 21

Issue: 7

Online publication date: 01/04/2021

Acceptance date: 30/03/2021

Date deposited: 12/04/2021

ISSN (electronic): 1424-8220

Publisher: MDPI AG


DOI: 10.3390/s21072436


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