Department of Biological Sciences, California State University Sacramento, Sacramento, CA, 95819, USA.
Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G 2E9, Canada.
Am J Bot. 2019 Feb;106(2):280-291. doi: 10.1002/ajb2.1239. Epub 2019 Feb 19.
Studies of gene expression and polyploidy are typically restricted to characterizing differences in transcript concentration. Using diploid and autotetraploid Tolmiea, we present an integrated approach for cross-ploidy comparisons that account for differences in transcriptome size and cell density and make multiple comparisons of transcript abundance.
We use RNA spike-in standards in concert with cell size and density to identify and correct for differences in transcriptome size and compare levels of gene expression across multiple scales: per transcriptome, per cell, and per biomass.
In total, ~17% of all loci were identified as differentially expressed (DEGs) between the diploid and autopolyploid species. The per-transcriptome normalization, the method researchers typically use, captured the fewest DEGs (58% of total DEGs) and failed to detect any DEGs not found by the alternative normalizations. When transcript abundance was normalized per biomass and per cell, ~66% and ~82% of the total DEGs were recovered, respectively. The discrepancy between per-transcriptome and per-cell recovery of DEGs occurs because per-transcriptome normalizations are concentration-based and therefore blind to differences in transcriptome size.
While each normalization enables valid comparisons at biologically relevant scales, a holistic comparison of multiple normalizations provides additional explanatory power not available from any single approach. Notably, autotetraploid loci tend to conserve diploid-like transcript abundance per biomass through increased gene expression per cell, and these loci are enriched for photosynthesis-related functions.
基因表达和多倍体的研究通常仅限于描述转录物浓度的差异。本研究使用二倍体和同源四倍体 Tolmiea,提出了一种综合的跨倍性比较方法,该方法考虑了转录组大小和细胞密度的差异,并对转录丰度进行了多次比较。
我们使用 RNA Spike-in 标准,结合细胞大小和密度,识别和校正转录组大小的差异,并在多个尺度上比较基因表达水平:每个转录组、每个细胞和每个生物量。
在二倍体和同源四倍体物种之间,总共约有 17%的所有基因座被鉴定为差异表达(DEGs)。每个转录组的归一化,是研究人员通常使用的方法,仅捕获了最少的 DEGs(总 DEGs 的 58%),并且无法检测到任何其他归一化方法未发现的 DEGs。当根据生物量和细胞进行转录丰度归一化时,分别恢复了约 66%和 82%的总 DEGs。每个转录组和每个细胞恢复 DEGs 的差异是因为每个转录组的归一化是基于浓度的,因此无法检测到转录组大小的差异。
虽然每种归一化方法都可以在生物学相关的尺度上进行有效比较,但对多种归一化方法的综合比较提供了任何单一方法都无法提供的额外解释力。值得注意的是,同源四倍体基因座倾向于通过每个细胞的基因表达增加来保持与二倍体类似的生物量转录丰度,并且这些基因座富含与光合作用相关的功能。