Dissecting Gene Expression Changes Accompanying a Ploidy-Based Phenotypic Switch

Abstract

Aneuploidy, a state in which the chromosome number deviates from a multiple of the haploid count, significantly impacts human health. The phenotypic consequences of aneuploidy are believed to arise from gene expression changes associated with the altered copy number of genes on the aneuploid chromosomes. To dissect the mechanisms underlying altered gene expression in aneuploids, we used RNA-seq to measure transcript abundance in colonies of the haploid Saccharomyces cerevisiae strain F45 and two aneuploid derivatives harboring disomies of chromosomes XV and XVI. F45 colonies display complex “fluffy” morphologies, while the disomic colonies are smooth, resembling laboratory strains. Our two disomes displayed similar transcriptional profiles, a phenomenon not driven by their shared smooth colony morphology nor simply by their karyotype. Surprisingly, the environmental stress response (ESR) was induced in F45, relative to the two disomes. We also identified genes whose expression reflected a nonlinear interaction between the copy number of a transcriptional regulatory gene on chromosome XVI, DIG1, and the copy number of other chromosome XVI genes. DIG1 and the remaining chromosome XVI genes also demonstrated distinct contributions to the effect of the chromosome XVI disome on ESR gene expression. Expression changes in aneuploids appear to reflect a mixture of effects shared between different aneuploidies and effects unique to perturbing the copy number of particular chromosomes, including nonlinear copy number interactions between genes. The balance between these two phenomena is likely to be genotype- and environment-specific.

Spatial, climate, and ploidy factors drive genomic diversity and resilience in the widespread grassThemeda triandra

“…Further, increased performance could be due to differential levels of expression between ploidy landraces (e.g. Cromie et al , 2017; Wang et al , 2018; Liqin et al , 2019), and be partially dependent on different epigenetic patterns (Nagymihály et al , 2017). However, quantitative measures are needed to determine how differential expression between diploid and tetraploid landraces may affect their ability to persist in their optimal climates.…”

Long Range Order and Short Range Disorder in Saccharomyces cerevisiae Biofilm

“…We analysed the whole transcriptome of wildtype and 6 overexpressed strains (DIG1, SAN1, TOS8, ROF1, SFL1, HEK2), with 4 replicates for each condition, of biofilm modulators in the yeast Saccharomyces cerevisiae strain F45, adapted from Cromie et al [25,26]. The selected genes are known to regulate the fluffy colony morphology of the yeast strain studied.…”

“…Experimental data was obtained from datasets publicly available on the Gene Expression Omnibus Database (https://www.ncbi.nlm.nih.gov/geo/), under accession numbers GSE98079 and GSE85843 [25,26]. The available FPKM expression data was used and reprocessed into TPM expression units for this study [27], such as the TPM value of the i th gene is the ratio between the FPKM value of the i th gene and the sum of the FPKM values of all n genes, multiplied by a factor for 10 6 :…”

“…In this paper, to investigate the yeast Saccharomyces cerevisiae biofilm phase transition, we adopted information theory related statistical approaches to analyse the transcriptome-wide RNA-seq expressions in wildtype and 6 overexpressed gene mutants (DIG1, SAN1, TOS8, ROF1, SFL1, HEK2) [25,26]. Our analyses include scatter plots, distribution fitting, Pearson correlation, principal components analysis, hierarchical clustering and gene ontology (GO) enrichment.…”

Transcriptional Profiling of Biofilm Regulators Identified by an Overexpression Screen in Saccharomyces cerevisiae

“…Read-pair alignment for RNA-seq data was carried out against the S288c reference (R64-1-1), with the FASTA and GFF files extended to include noncoding RNAs (ncRNAs) and genes present in F45, but absent in S288c, as described in Cromie et al (2017) , using Bowtie2 (version 2.1.0) ( Langmead and Salzberg 2012 ) with the parameters [-N 1 -I 50 -X 450 -p 6–reorder -x -S] and allowing one mismatch per read. For each strain, read alignments were converted to gene counts using featureCounts (version 1.4.0) in the Subread package ( Liao et al 2014 ), with the parameters [-a -o -t gene –g ID –s 2 -T 1 -p -P -d 50 -D 450].…”