Rapid environmental changes impact the global distribution and abundance of species, highlighting the urgency to understand and predict how populations will respond. The analysis of differentially expressed genes has elucidated areas of the genome involved in adaptive divergence to past and present environmental change. Such studies however have been hampered by large numbers of differentially expressed genes and limited knowledge of how these genes work in conjunction with each other. Recent methods (broadly termed “pathway analyses”) have emerged that aim to group genes that behave in a coordinated fashion to a factor of interest. These methods aid in functional annotation and uncovering biological pathways, thereby collapsing complex datasets into more manageable units, providing more nuanced understandings of both the organism‐level effects of modified gene expression, and the targets of adaptive divergence. Here, we reanalyze a dataset that investigated temperature‐induced changes in gene expression in marine‐adapted and freshwater‐adapted threespine stickleback (Gasterosteus aculeatus), using Weighted Gene Co‐expression Network Analysis (WGCNA) with PANTHER Gene Ontology (GO)‐Slim overrepresentation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Six modules exhibited a conserved response and six a divergent response between marine and freshwater stickleback when acclimated to 7°C or 22°C. One divergent module showed freshwater‐specific response to temperature, and the remaining divergent modules showed differences in height of reaction norms. PPARAa, a transcription factor that regulates fatty acid metabolism and has been implicated in adaptive divergence, was located in a module that had higher expression at 7°C and in freshwater stickleback. This updated methodology revealed patterns that were not found in the original publication. Although such methods hold promise toward predicting population response to environmental stressors, many limitations remain, particularly with regard to module expression representation, database resources, and cross‐database integration.

中文翻译：

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基因表达途径的功能和局限性有助于预测人群对环境应激源的反应。

快速的环境变化影响着物种的全球分布和丰富度，突显了理解和预测种群反应的紧迫性。差异表达基因的分析已经阐明了基因组中与过去和现在环境变化的适应性分化有关的区域。但是，由于大量差异表达的基因以及这些基因如何相互协同工作的知识有限，这些研究受到了阻碍。最近出现了一些方法（被广泛称为“途径分析”），这些方法旨在将以协调方式表现的基因归类到感兴趣的因素。这些方法有助于功能注释和发现生物途径，从而将复杂的数据集折叠成更易于管理的单元，对修饰基因表达的生物体水平影响和适应性差异的靶标提供更细致入微的理解。在这里，我们重新分析了一个数据集，该数据集研究了温度诱导的海洋适应和淡水适应性三脊背棘回中基因表达的变化（Gasterosteus叶树），使用加权的基因共表达网络分析（WGCNA）与PANTHER基因本体论（GO）-Slim比例过高和京都基因与基因组百科（KEGG）通路分析。当适应7°C或22°C时，六个模块在海洋和淡水棘背动物之间表现出保守的响应，而六个则表现出不同的响应。一个发散模块显示特定于淡水的温度响应，其余的发散模块显示反应规范高度的差异。PPARAa，调节脂肪酸代谢的转录因子已经参与了适应性发散，它位于一个模块中，该模块在7°C和淡水棘背鱼中具有较高的表达。这种更新的方法揭示了原始出版物中没有的模式。尽管此类方法有望预测人口对环境压力的响应，但仍然存在许多局限性，尤其是在模块表达式表示，数据库资源和跨数据库集成方面。