Search

Heritable variation in gene expression provides a critical bridge between differences in genome sequence and the biology of many traits, including common human diseases. However, the sources of most regulatory genetic variation remain unknown. Here, we used transcriptome profiling in 1,012 yeast segregants to map the genetic basis of variation in gene expression with high statistical power. We identified expression quantitative trait loci (eQTL) that together account for over 70% of the total genetic contribution to variation in mRNA levels, allowing us to examine the sources of regulatory variation comprehensively. We found that variation in the expression of a typical gene has a complex genetic architecture involving multiple eQTL. We also detected hundreds of eQTL pairs with significant non-additive interactions in an unbiased genome-wide scan. Although most genes were influenced by a local eQTL located close to the gene, most expression variation arose from distant,trans-acting eQTL located far from their target genes. Nearly all distant eQTL clustered at 102 “hotspot” locations, some of which influenced the expression of thousands of genes. Hotspot regions were enriched for transcription factor genes and altered expression of their target genes though both direct and indirect mechanisms. Many local eQTL had no detectable effects on the expression of other genes intrans. These results reveal the complexity of genetic influences on transcriptome variation in unprecedented depth and detail.

Heritable variation in gene expression forms a crucial bridge between genomic variation and the biology of many traits. However, most expression quantitative trait loci (eQTLs) remain unidentified. We mapped eQTLs by transcriptome sequencing in 1012 yeast segregants. The resulting eQTLs accounted for over 70% of the heritability of mRNA levels, allowing comprehensive dissection of regulatory variation. Most genes had multiple eQTLs. Most expression variation arose from trans-acting eQTLs distant from their target genes. Nearly all trans-eQTLs clustered at 102 hotspot locations, some of which influenced the expression of thousands of genes. Fine-mapped hotspot regions were enriched for transcription factor genes. While most genes had a local eQTL, most of these had no detectable effects on the expression of other genes in trans. Hundreds of non-additive genetic interactions accounted for small fractions of expression variation. These results reveal the complexity of genetic influences on transcriptome variation in unprecedented depth and detail., eLife digest Every individual’s genome is unique, with variations in the DNA sequence at many thousands of points. Each difference is a change in one or more ‘letters’ of the DNA code. Some of these DNA letter variations have consequences for the way the individual looks or behaves. They can influence these traits either by changing the sequence of a protein encoded by a gene; or by changing when, where or how much a gene is active. Studying how individual differences in the DNA influence gene activity requires a very large amount of data on many individuals within a species. Only recently have such large datasets become available. These have made it possible to study these regulatory differences in unprecedented detail. Albert, Bloom et al. set out to map as many regulatory genetic variants as possible in budding yeast – a popular model organism used in many branches of science. The approach involved measuring how active every gene in the genome was, and which genetic variants influenced whether each gene’s activity was turned up or down, in more than 1,000 different strains of yeast. Thousands of regions of the DNA turned out to influence regulation of genes. The analysis revealed that almost every gene is influenced by a complex set of regulatory regions all over the genome. Some hotspot regions were found to influence thousands of genes at once. The findings provide the most complete set of data for studying the effects of variation in DNA sequence on genetic regulation in any species, and can act as a model for researchers to carry out similar experiments in other species. Ultimately, these results could help understand exactly how differences in genome sequence help to make individuals unique.

Understanding the functional effects of DNA sequence variants is of critical importance for studies of basic biology, evolution, and medical genetics; however, measuring these effects in a high-throughput manner is a major challenge. One promising avenue is precise editing with the CRISPR-Cas9 system, which allows for generation of DNA double-strand breaks (DSBs) at genomic sites matching the targeting sequence of a guide RNA (gRNA). Recent studies have used CRISPR libraries to generate many frameshift mutations genome wide through faulty repair of CRISPR-directed breaks by nonhomologous end joining (NHEJ) 1 . Here, we developed a CRISPR-library-based approach for highly efficient and precise genome-wide variant engineering. We used our method to examine the functional consequences of premature-termination codons (PTCs) at different locations within all annotated essential genes in yeast. We found that most PTCs were highly deleterious unless they occurred close to the 3' end of the gene and did not affect an annotated protein domain. Unexpectedly, we discovered that some putatively essential genes are dispensable, whereas others have large dispensable regions. This approach can be used to profile the effects of large classes of variants in a high-throughput manner.

Direct measurement of functional effects of DNA sequence variants throughout a genome is a major challenge. We developed a method that uses CRISPR/Cas9 to engineer many specific variants of interest in parallel in the budding yeastSaccharomyces cerevisiae, and to screen them for functional effects. We used the method to examine the functional consequences of premature termination codons (PTCs) at different locations within all annotated essential genes in yeast. We found that most PTCs were highly deleterious unless they occurred close to the C-terminal end and did not interrupt an annotated protein domain. Surprisingly, we discovered that some putatively essential genes are dispensable, while others have large dispensable regions. This approach can be used to profile the effects of large classes of variants in a high-throughput manner.

The shift toward condition-based monitoring is a key area of research for many military, industrial, and commercial customers who want to lower the overall operating costs of capital equipment and general facilities. Assessing the health of rotating systems such as gearboxes, bearings, pumps and other actuation systems often rely on the need for continuous monitoring to capture transient signals that are evidence of events that could cause (i.e. cavitation), or be the result of (i.e. spalling), damage within a system. In some applications this can be accomplished using line powered analyzers, however for wide-spread monitoring, the use of small-scale embedded electronic systems are more desirable. In such cases the method for powering the electronics becomes a significant design factor. This work presents a multi-source energy harvesting approach meant to provide a robust power source for embedded electronics, capturing energy from vibration, thermal and light sources to operate a low-power sensor node. This paper presents the general design philosophy behind the multi-source harvesting circuit, and how it can be extended from powering electronics developed for periodic monitoring to sensing equipment capable of providing continuous condition-based monitoring.