Aneuploidy, referring here to genome contents characterized by abnormal numbers of chromosomes, has been associated with developmental defects, cancer, and adaptive evolution in experimental organisms1–9. However, it remains unresolved how aneuploidy impacts gene expression and whether aneuploidy could directly bring phenotypic variation and improved fitness over that of euploid counterparts. In this work, we designed a novel scheme to generate, through random meiotic segregation, 38 stable and fully isogenic aneuploid yeast strains with distinct karyotypes and genome contents between 1N and 3N without involving any genetic selection. Through phenotypic profiling under various growth conditions or in the presence of a panel of chemotherapeutic or antifungal drugs, we found that aneuploid strains exhibited diverse growth phenotypes, and some aneuploid strains grew better than euploid control strains under conditions suboptimal for the latter. Using quantitative mass spectrometry-based proteomics, we show that the levels of protein expression largely scale with chromosome copy numbers, following the same trend observed for the transcriptome. These results provide strong evidence that aneuploidy directly impacts gene expression at both the transcriptome and proteome levels and can generate significant phenotypic variation that could bring about fitness gains under diverse conditions. Our findings suggest that the fitness ranking between euploid and aneuploid cells is context- and karyotype-dependent, providing the basis for the notion that aneuploidy can directly underlie phenotypic evolution and cellular adaptation.

SUMMARYThe vertebral column is a conserved anatomical structure that defines the vertebrate phylum. The periodic or segmental pattern of the vertebral column is established early in development when the vertebral precursors, the somites, are rhythmically produced from presomitic mesoderm (PSM). This rhythmic activity is controlled by a segmentation clock that is associated with the periodic transcription of cyclic genes in the PSM. Comparison of the mouse, chicken and zebrafish PSM oscillatory transcriptomes revealed networks of 40 to 100 cyclic genes mostly involved in Notch, Wnt and FGF signaling pathways. However, despite this conserved signaling oscillation, the identity of individual cyclic genes mostly differed between the three species, indicating a surprising evolutionary plasticity of the segmentation networks. the GeneChip Eukaryotic Small Sample Target Labeling Assay version II, and the second cycle of the Two-Cycle Eukaryotic Target Labeling Assay, as described in the Affymetrix GeneChip Expression Analysis Technical Manual rev5. Amplified RNA samples were hybridized to Affymetrix GeneChip Mouse Genome 430 2.0 arrays. For chicken and zebrafish, total RNA was extracted from PSM pieces using Trizol. PSM pieces contained ~4000 chicken cells or ~400 zebrafish cells. The mRNA of each sample was amplified following a two-cycle linear amplification protocol as described in the Two-Cycle Eukaryotic Target Labeling Assay in the Affymetrix GeneChip Expression Analysis Technical Manual rev5. Briefly, cDNA was synthesized from total RNA using T7 linked to oligo(dT) primers. After second-strand synthesis, cRNA was in vitro transcribed using unlabeled ribonucleotides. Upon a second round of cDNA synthesis using random priming, cRNA was made with biotin-labeled CTP and UTP, yielding 35-135 g of biotinylated cRNA for chicken and 3-6 g for zebrafish. RNA quality was checked after the second round of amplification by measuring the A 260 /A 280 ratio (which ranged from 1.8 to 2.1) and the fragment size with the Agilent Bioanalyser (mean size >1 kb). Five micrograms of RNA from good quality samples was fragmented to less than 200 bp, hybridized to Affymetrix GeneChip chicken genome arrays or GeneChip zebrafish genome arrays according to manufacturer's instructions and scanned with a GeneArray scanner (Agilent G2500A) at the Microarray Core Facility of the Stowers Institute for Medical Research. The array readout was processed using Affymetrix Microarray Suite (MAS) 5.0 and scaled by adjusting the average intensity of each array to a target intensity of 150. The quality control parameters from the Affymetrix array reports were within acceptable limits and highly similar between the arrays. The raw data are available on ArrayExpress with accession E-MTAB-406. Non-biological variance of the microarrays was controlled by two methods: the R package affyPLM (Bolstad et al., 2005) using the normalized unscaled standard error (NUSE) and relative expression (RLE) functions; and principal component analysis (PCA) using Partek ...

BackgroundConsiderable progress has been made in our understanding of sex determination and dosage compensation mechanisms in model organisms such as C. elegans, Drosophila and M. musculus. Strikingly, the mechanism involved in sex determination and dosage compensation are very different among these three model organisms. Birds present yet another situation where the heterogametic sex is the female. Sex determination is still poorly understood in birds and few key determinants have so far been identified. In contrast to most other species, dosage compensation of bird sex chromosomal genes appears rather ineffective.ResultsBy comparing microarrays from microdissected primitive streak from single chicken embryos, we identified a large number of genes differentially expressed between male and female embryos at a very early stage (Hamburger and Hamilton stage 4), long before any sexual differentiation occurs. Most of these genes are located on the Z chromosome, which indicates that dosage compensation is ineffective in early chicken embryos. Gene ontology analyses, using an enhanced annotation tool for Affymetrix probesets of the chicken genome developed in our laboratory (called Manteia), show that among these male-biased genes found on the Z chromosome, more than 20 genes play a role in sex differentiation.ConclusionsThese results corroborate previous studies demonstrating the rather inefficient dosage compensation for Z chromosome in birds and show that this sexual dimorphism in gene regulation is observed long before the onset of sexual differentiation. These data also suggest a potential role of non-compensated Z-linked genes in somatic sex differentiation in birds.

Despite the continued analysis of HDAC inhibitors in clinical trials, the heterogeneous nature of the protein complexes they target limits our understanding of the beneficial and off-target effects associated with their application. Among the many HDAC protein complexes found within the cell, Sin3 complexes are conserved from yeast to humans and likely play important roles as regulators of transcriptional activity. The presence of two Sin3 paralogs in humans, SIN3A and SIN3B, may result in a heterogeneous population of Sin3 complexes and contributes to our poor understanding of the functional attributes of these complexes. Here, we profile the interaction networks of SIN3A and SIN3B to gain insight into complex composition and organization. In accordance with existing data, we show that Sin3 paralog identity influences complex composition. Additionally, chemical crosslinking mass spectrometry identifies domains that mediate interactions between Sin3 proteins and binding partners. The characterization of rare SIN3B proteoforms provides additional evidence for the existence of conserved and divergent elements within human Sin3 proteins. Together, these findings shed light on both the shared and divergent properties of human Sin3 proteins and highlight the heterogeneous nature of the complexes they organize.

While genome-wide gene expression data are generated at an increasing rate, the repertoire of approaches for pattern discovery in these data is still limited. Identifying subtle patterns of interest in large amounts of data (tens of thousands of profiles) associated with a certain level of noise remains a challenge. A microarray time series was recently generated to study the transcriptional program of the mouse segmentation clock, a biological oscillator associated with the periodic formation of the segments of the body axis. A method related to Fourier analysis, the Lomb-Scargle periodogram, was used to detect periodic profiles in the dataset, leading to the identification of a novel set of cyclic genes associated with the segmentation clock. Here, we applied to the same microarray time series dataset four distinct mathematical methods to identify significant patterns in gene expression profiles. These methods are called: Phase consistency, Address reduction, Cyclohedron test and Stable persistence, and are based on different conceptual frameworks that are either hypothesis- or data-driven. Some of the methods, unlike Fourier transforms, are not dependent on the assumption of periodicity of the pattern of interest. Remarkably, these methods identified blindly the expression profiles of known cyclic genes as the most significant patterns in the dataset. Many candidate genes predicted by more than one approach appeared to be true positive cyclic genes and will be of particular interest for future research. In addition, these methods predicted novel candidate cyclic genes that were consistent with previous biological knowledge and experimental validation in mouse embryos. Our results demonstrate the utility of these novel pattern detection strategies, notably for detection of periodic profiles, and suggest that combining several distinct mathematical approaches to analyze microarray datasets is a valuable strategy for identifying genes that exhibit novel, interesting transcriptional patterns.