Computational Strategies for the Identification of a Transcriptional Biomarker Panel to Sense Cellular Growth States in Bacillus subtilis.
Computational Strategies for the Identification of a Transcriptional Biomarker Panel to Sense Cellular Growth States in Bacillus subtilis.
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DOI：
10.3390/s21072436
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发表时间：
2021-04-01
期刊：
影响因子：
--
通讯作者：
Bacardit J
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文献类型：
--
作者：
Huang Y;Smith W;Harwood C;Wipat A;Bacardit J
生物技术工业的目标是能够识别可能导致细菌群体中次优或异常生长的有害细胞状态。我们目前对不同环境处理如何调节基因调控并带来生理适应的知识是有限的，因此很难确定导致其影响的机制。使用微阵列或RNA-seq等技术揭示的基因表达模式可以提供指示细菌生理状态的不同基因调控状态的有用生物标志物。希望仅具有几个关键基因作为生物标志物，以通过为诸如定量RT-PCR和扩增子组的方法开辟道路来降低确定转录状态的成本。在本文中，我们使用无监督机器学习从条件依赖性转录组数据构建转录景观模型，从中我们确定了10个具有差异化基因表达谱并与不同细胞生长状态相关的样本集群。使用迭代特征消除策略，我们确定了10个生物标志物基因的最小面板，其在预测聚类分配中实现了100%的交叉验证准确度。此外，我们设计并评估了各种数据处理策略，以确保我们的方法能够生成有意义的转录景观模型，捕获相关的生物过程。总的来说，本研究中引入的计算策略有助于识别一组详细的相关细胞生长状态，以及如何使用减少的生物标志物面板来感测它们。
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.
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影响因子：
64.8
作者：
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通讯作者：
Shendure, Jay
影响因子：
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作者：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1093/bioinformatics/btp101
发表时间：
2009-04-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Bindea G;Mlecnik B;Hackl H;Charoentong P;Tosolini M;Kirilovsky A;Fridman WH;Pagès F;Trajanoski Z;Galon J
通讯作者：
Galon J