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通过差异网络分析对反义调控进行大规模研究。

Large scale study of anti-sense regulation by differential network analysis.

作者信息

Legeay Marc, Aubourg Sébastien, Renou Jean-Pierre, Duval Béatrice

机构信息

LERIA, Université d'Angers, 2 bd Lavoisier, Angers, 49045, France.

IRHS, Agrocampus-Ouest, INRA, Université d'Angers, SFR 4207 QuaSaV, Beaucouzé, 49071, France.

出版信息

BMC Syst Biol. 2018 Nov 20;12(Suppl 5):95. doi: 10.1186/s12918-018-0613-7.

Abstract

BACKGROUND

Systems biology aims to analyse regulation mechanisms into the cell. By mapping interactions observed in different situations, differential network analysis has shown its power to reveal specific cellular responses or specific dysfunctional regulations. In this work, we propose to explore on a large scale the role of natural anti-sense transcription on gene regulation mechanisms, and we focus our study on apple (Malus domestica) in the context of fruit ripening in cold storage.

RESULTS

We present a differential functional analysis of the sense and anti-sense transcriptomic data that reveals functional terms linked to the ripening process. To develop our differential network analysis, we introduce our inference method of an Extended Core Network; this method is inspired by C3NET, but extends the notion of significant interactions. By comparing two extended core networks, one inferred with sense data and the other one inferred with sense and anti-sense data, our differential analysis is first performed on a local view and reveals AS-impacted genes, genes that have important interactions impacted by anti-sense transcription. The motifs surrounding AS-impacted genes gather transcripts with functions mostly consistent with the biological context of the data used and the method allows us to identify new actors involved in ripening and cold acclimation pathways and to decipher their interactions. Then from a more global view, we compute minimal sub-networks that connect the AS-impacted genes using Steiner trees. Those Steiner trees allow us to study the rewiring of the AS-impacted genes in the network with anti-sense actors.

CONCLUSION

Anti-sense transcription is usually ignored in transcriptomic studies. The large-scale differential analysis of apple data that we propose reveals that anti-sense regulation may have an important impact in several cellular stress response mechanisms. Our data mining process enables to highlight specific interactions that deserve further experimental investigations.

摘要

背景

系统生物学旨在分析细胞内的调控机制。通过绘制在不同情况下观察到的相互作用,差异网络分析已显示出其揭示特定细胞反应或特定功能失调调控的能力。在这项工作中,我们提议大规模探索天然反义转录在基因调控机制中的作用,并将研究重点放在冷藏果实成熟背景下的苹果(苹果属)上。

结果

我们对有义链和反义链转录组数据进行了差异功能分析,揭示了与成熟过程相关的功能术语。为了开展我们的差异网络分析,我们引入了扩展核心网络的推理方法;该方法受C3NET启发,但扩展了显著相互作用的概念。通过比较两个扩展核心网络,一个由有义链数据推断得出,另一个由有义链和反义链数据推断得出,我们首先在局部视角上进行差异分析,揭示出受反义转录影响的基因,即那些具有受反义转录影响的重要相互作用的基因。受反义转录影响的基因周围的基序聚集了功能大多与所用数据的生物学背景一致的转录本,该方法使我们能够识别参与成熟和冷驯化途径的新因子,并解读它们的相互作用。然后从更全局的视角,我们使用斯坦纳树计算连接受反义转录影响的基因的最小子网。这些斯坦纳树使我们能够研究网络中受反义转录影响的基因与反义因子的重新连接情况。

结论

在转录组学研究中,反义转录通常被忽视。我们提出的对苹果数据的大规模差异分析表明,反义调控可能在几种细胞应激反应机制中具有重要影响。我们的数据挖掘过程能够突出值得进一步实验研究的特定相互作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae46/6245689/512b44de5546/12918_2018_613_Fig1_HTML.jpg

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