G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Numerous techniques have been developed to follow the progress of DNA replication through the S phase of the cell cycle. Most of these techniques have ...

Pooled shRNA Screen for Reactivation of MeCP2 on the Inactive X Chromosome

Forward genetic screens using reporter genes inserted into the heterochromatin have been extensively used to investigate mechanisms of epigenetic control ...

Fred Hutchinson Cancer Research Center

Proper regulation of protein levels is essential for health, and abnormal levels of proteins are hallmarks of many diseases. A number of studies have recently shown that messenger RNA levels vary among individuals of a species and that genetic linkage analysis can be used to identify quantitative trait loci that influence these levels. By contrast, little is known about the genetic basis of variation in protein levels in genetically diverse populations, in large part because techniques for large-scale measurements of protein abundance lag far behind those for measuring transcript abundance. Here we describe a label-free, mass spectrometry-based approach to measuring protein levels in total unfractionated cellular proteins, and we apply this approach to elucidate the genetic basis of variation in protein abundance in a cross between two diverse strains of yeast. Loci that influenced protein abundance differed from those that influenced transcript levels, emphasizing the importance of direct analysis of the proteome.

Genetic basis of proteome variation in yeast.

Networks of co-regulated transcripts in genetically diverse populations have been studied extensively, but little is known about the degree to which these networks cause similar co-variation at the protein level. We quantified 354 proteins in a genetically diverse population of yeast segregants, which allowed for the first time construction of a coherent protein co-variation matrix. We identified tightly co-regulated groups of 36 and 93 proteins that were made up predominantly of genes involved in ribosome biogenesis and amino acid metabolism, respectively. Even though the ribosomal genes were tightly co-regulated at both the protein and transcript levels, genetic regulation of proteins was entirely distinct from that of transcripts, and almost no genes in this network showed a significant correlation between protein and transcript levels. This result calls into question the widely held belief that in yeast, as opposed to higher eukaryotes, ribosomal protein levels are regulated primarily by regulating transcript levels. Furthermore, although genetic regulation of the amino acid network was more similar for proteins and transcripts, regression analysis demonstrated that even here, proteins vary predominantly as a result of non-transcriptional variation. We also found that cis regulation, which is common in the transcriptome, is rare at the level of the proteome. We conclude that most inter-individual variation in levels of these particular high abundance proteins in this genetically diverse population is not caused by variation of their underlying transcripts.

Genetic variation shapes protein networks mainly through non-transcriptional mechanisms.

Despite immense interest in the proteome as a source of biomarkers in cancer, mass spectrometry has yet to yield a clinically useful protein biomarker for tumor classification. To explore the potential of a particular class of mass spectrometry-based quantitation approaches, label-free alignment of liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) data sets, for the identification of biomarkers for acute leukemias, we asked whether a label-free alignment algorithm could distinguish known classes of leukemias on the basis of their proteomes. This approach to quantitation involves (1) computational alignment of MS1 peptide peaks across large numbers of samples; (2) measurement of the relative abundance of peptides across samples by integrating the area under the curve of the MS1 peaks; and (3) assignment of peptide IDs to those quantified peptide peaks on the basis of the corresponding MS2 spectra. We extracted proteins from blasts derived from four patients with acute myeloid leukemia (AML, acute leukemia of myeloid lineage) and five patients with acute lymphoid leukemia (ALL, acute leukemia of lymphoid lineage). Mobilized CD34+ cells purified from peripheral blood of six healthy donors and mononuclear cells (MNC) from the peripheral blood of two healthy donors were used as healthy controls. Proteins were analyzed by LC-MS/MS and quantified with a label-free alignment-based algorithm developed in our laboratory. Unsupervised hierarchical clustering of blinded samples separated the samples according to their known biological characteristics, with each sample group forming a discrete cluster. The four proteins best able to distinguish CD34+, AML, and ALL were all either known biomarkers or proteins whose biological functions are consistent with their ability to distinguish these classes. We conclude that alignment-based label-free quantitation of LC-MS/MS data sets can, at least in some cases, robustly distinguish known classes of leukemias, thus opening the possibility that large scale studies using such algorithms can lead to the identification of clinically useful biomarkers.

Proteomic classification of acute leukemias by alignment-based quantitation of LC-MS/MS data sets.

Aging and longevity are complex traits influenced by genetic and environmental factors. To identify quantitative trait loci (QTLs) that control replicative lifespan, we employed an outbred Saccharomyces cerevisiae model, generated by crossing a vineyard and a laboratory strain. The predominant QTL mapped to the rDNA, with the vineyard rDNA conferring a lifespan increase of 41%. The lifespan extension was independent of Sir2 and Fob1, but depended on a polymorphism in the rDNA origin of replication from the vineyard strain that reduced origin activation relative to the laboratory origin. Strains carrying vineyard rDNA origins have increased capacity for replication initiation at weak plasmid and genomic origins, suggesting that inability to complete genome replication presents a major impediment to replicative lifespan. Calorie restriction, a conserved mediator of lifespan extension that is also independent of Sir2 and Fob1, reduces rDNA origin firing in both laboratory and vineyard rDNA. Our results are consistent with the possibility that calorie restriction, similarly to the vineyard rDNA polymorphism, modulates replicative lifespan through control of rDNA origin activation, which in turn affects genome replication dynamics.

A natural polymorphism in rDNA replication origins links origin activation with calorie restriction and lifespan.

Replication gaps that persist into mitosis likely represent important threats to genome stability, but experimental identification of these gaps has proved challenging. We have developed a technique that allows us to explore the dynamics by which genome replication is completed before mitosis. Using this approach, we demonstrate that excessive allocation of replication resources to origins within repetitive regions, induced by SIR2 deletion, leads to persistent replication gaps and genome instability. Conversely, the weakening of replication origins in repetitive regions suppresses these gaps. Given known age- and cancer-associated changes in chromatin accessibility at repetitive sequences, we suggest that replication gaps resulting from misallocation of replication resources underlie age- and disease-associated genome instability.

SIR2 suppresses replication gaps and genome instability by balancing replication between repetitive and unique sequences.

Rett syndrome (RS) is a debilitating neurological disorder affecting mostly girls with heterozygous mutations in the gene encoding the methyl-CpG-binding protein MeCP2 on the X chromosome. Because restoration of MeCP2 expression in a mouse model reverses neurologic deficits in adult animals, reactivation of the wild-type copy of MeCP2 on the inactive X chromosome (Xi) presents a therapeutic opportunity in RS. To identify genes involved in MeCP2 silencing, we screened a library of 60,000 shRNAs using a cell line with a MeCP2 reporter on the Xi and found 30 genes clustered in seven functional groups. More than half encoded proteins with known enzymatic activity, and six were members of the bone morphogenetic protein (BMP)/TGF-β pathway. shRNAs directed against each of these six genes down-regulated X-inactive specific transcript (XIST), a key player in X-chromosome inactivation that encodes an RNA that coats the silent X chromosome, and modulation of regulators of this pathway both in cell culture and in mice demonstrated robust regulation of XIST. Moreover, we show that Rnf12, an X-encoded ubiquitin ligase important for initiation of X-chromosome inactivation and XIST transcription in ES cells, also plays a role in maintenance of the inactive state through regulation of BMP/TGF-β signaling. Our results identify pharmacologically suitable targets for reactivation of MeCP2 on the Xi and a genetic circuitry that maintains XIST expression and X-chromosome inactivation in differentiated cells.

Eric J. Foss

Division of Clinical Research,
Clinical Research Division

Fred Hutchinson Cancer Research Center

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Pooled shRNA Screen for Reactivation of MeCP2 on the Inactive X Chromosome

Eric J. Foss

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Fred Hutchinson Cancer Research Center

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Pooled shRNA Screen for Reactivation of MeCP2 on the Inactive X Chromosome

Division of Clinical Research,
Clinical Research Division