Partial correlation network analysis identifies coordinated gene expression within a regional cluster of COPD genome-wide association signals.

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2024-10-17 eCollection Date: 2024-10-01 DOI:10.1371/journal.pcbi.1011079
Michele Gentili, Kimberly Glass, Enrico Maiorino, Brian D Hobbs, Zhonghui Xu, Peter J Castaldi, Michael H Cho, Craig P Hersh, Dandi Qiao, Jarrett D Morrow, Vincent J Carey, John Platig, Edwin K Silverman
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Abstract

Chronic obstructive pulmonary disease (COPD) is a complex disease influenced by well-established environmental exposures (most notably, cigarette smoking) and incompletely defined genetic factors. The chromosome 4q region harbors multiple genetic risk loci for COPD, including signals near HHIP, FAM13A, GSTCD, TET2, and BTC. Leveraging RNA-Seq data from lung tissue in COPD cases and controls, we estimated the co-expression network for genes in the 4q region bounded by HHIP and BTC (~70MB), through partial correlations informed by protein-protein interactions. We identified several co-expressed gene pairs based on partial correlations, including NPNT-HHIP, BTC-NPNT and FAM13A-TET2, which were replicated in independent lung tissue cohorts. Upon clustering the co-expression network, we observed that four genes previously associated to COPD: BTC, HHIP, NPNT and PPM1K appeared in the same network community. Finally, we discovered a sub-network of genes differentially co-expressed between COPD vs controls (including FAM13A, PPA2, PPM1K and TET2). Many of these genes were previously implicated in cell-based knock-out experiments, including the knocking out of SPP1 which belongs to the same genomic region and could be a potential local key regulatory gene. These analyses identify chromosome 4q as a region enriched for COPD genetic susceptibility and differential co-expression.

部分相关网络分析确定了慢性阻塞性肺病全基因组关联信号区域集群中的协调基因表达。
慢性阻塞性肺病(COPD)是一种复杂的疾病,受已确定的环境暴露(最明显的是吸烟)和未完全确定的遗传因素的影响。4q 染色体区域存在多个慢性阻塞性肺病遗传风险位点,包括 HHIP、FAM13A、GSTCD、TET2 和 BTC 附近的信号。利用慢性阻塞性肺病病例和对照组肺组织的 RNA-Seq 数据,我们通过蛋白质-蛋白质相互作用的部分相关性,估算了以 HHIP 和 BTC 为界的 4q 区域(约 70MB)内基因的共表达网络。我们根据部分相关性确定了几个共表达基因对,包括 NPNT-HHIP、BTC-NPNT 和 FAM13A-TET2,这些基因对在独立的肺组织队列中得到了重复。在对共表达网络进行聚类后,我们发现之前与慢性阻塞性肺病相关的四个基因:BTC、HHIP、NPNT 和 PPM1K 出现在同一个网络群落中。最后,我们发现了慢性阻塞性肺病与对照组之间存在差异共表达的基因子网络(包括 FAM13A、PPA2、PPM1K 和 TET2)。其中许多基因以前在基于细胞的基因敲除实验中被发现,包括敲除 SPP1,该基因属于同一基因组区域,可能是潜在的局部关键调控基因。这些分析确定了 4q 染色体是 COPD 遗传易感性和差异共表达的富集区。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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