DeORFanizing Candida albicans Genes using Coexpression.
DeORFanizing Candida albicans Genes using Coexpression.
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DOI：
10.1128/msphere.01245-20
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发表时间：
2021-01-20
期刊：
影响因子：
4.8
通讯作者：
O'Meara MJ
中科院分区：
文献类型：
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作者：
O'Meara TR;O'Meara MJ
白色念珠菌是人类常见且致命的真菌病原体，但该生物体的基因组包含许多功能未知的基因。通过确定基因功能，我们可以帮助识别必需基因、新的毒力因子或新的耐药性调节因子，从而为抗真菌药物的开发提供新的靶点。非模式生物（例如常见的机会性真菌病原体白色念珠菌）中开放阅读框的功能表征可能是劳动密集型的。为了应对这一挑战，我们根据 NCBI 序列读取存档中 18 项大规模研究的 853 次 RNA 测序运行收集的数据，为白色念珠菌建立了一个全面且公正的共表达网络，我们将其称为 CalCEN。回顾起来，CalCEN 对已知基因功能注释具有高度预测性，并且可以通过直系同源性与酿酒酵母中的序列相似性和相互作用网络协同组合，以提高基因功能预测的准确性。为了前瞻性地证明共表达网络在白色念珠菌中的实用性，我们预测了未注释的开放阅读框（ORF）的功能，并将 CCJ1 鉴定为白色念珠菌中的新型细胞周期调节因子。这项研究为未来白色念珠菌基因功能的系统生物学分析提供了工具。我们在 http://github.com/momeara/CalCEN 上提供了用于构建和分析共表达网络和 CalCEN 本身的计算管道。重要性白色念珠菌是人类常见且致命的真菌病原体，但该生物体的基因组包含许多功能未知的基因。通过确定基因功能，我们可以帮助识别必需基因、新的毒力因子或新的耐药性调节因子，从而为抗真菌药物的开发提供新的靶点。在这里，我们利用大规模 RNA 测序 (RNAseq) 研究的信息，生成了一个稳健且能够预测基因功能的白色念珠菌共表达网络 (CalCEN)。我们展示了该网络在回顾性和前瞻性测试中的实用性，并使用 CalCEN 来预测 C4_06590W/CCJ1 在细胞周期中的作用。该工具将有助于更好地表征病原酵母中注释不足的基因。
Candida albicans is a common and deadly fungal pathogen of humans, yet the genome of this organism contains many genes of unknown function. By determining gene function, we can help identify essential genes, new virulence factors, or new regulators of drug resistance, and thereby give new targets for antifungal development. Functional characterization of open reading frames in nonmodel organisms, such as the common opportunistic fungal pathogen Candida albicans, can be labor-intensive. To meet this challenge, we built a comprehensive and unbiased coexpression network for C. albicans, which we call CalCEN, from data collected from 853 RNA sequencing runs from 18 large-scale studies deposited in the NCBI Sequence Read Archive. Retrospectively, CalCEN is highly predictive of known gene function annotations and can be synergistically combined with sequence similarity and interaction networks in Saccharomyces cerevisiae through orthology for additional accuracy in gene function prediction. To prospectively demonstrate the utility of the coexpression network in C. albicans, we predicted the function of underannotated open reading frames (ORFs) and identified CCJ1 as a novel cell cycle regulator in C. albicans. This study provides a tool for future systems biology analyses of gene function in C. albicans. We provide a computational pipeline for building and analyzing the coexpression network and CalCEN itself at http://github.com/momeara/CalCEN. IMPORTANCE Candida albicans is a common and deadly fungal pathogen of humans, yet the genome of this organism contains many genes of unknown function. By determining gene function, we can help identify essential genes, new virulence factors, or new regulators of drug resistance, and thereby give new targets for antifungal development. Here, we use information from large-scale RNA sequencing (RNAseq) studies and generate a C. albicans coexpression network (CalCEN) that is robust and able to predict gene function. We demonstrate the utility of this network in both retrospective and prospective testing and use CalCEN to predict a role for C4_06590W/CCJ1 in cell cycle. This tool will allow for a better characterization of underannotated genes in pathogenic yeasts.