Person:

Springer, Michael

In eukaryotic cells, most mRNAs are exported from the nucleus by the transcription export (TREX) complex, which is loaded onto mRNAs after their splicing and capping. We have studied in mammalian cells the nuclear export of mRNAs that code for secretory proteins, which are targeted to the endoplasmic reticulum membrane by hydrophobic signal sequences. The mRNAs were injected into the nucleus or synthesized from injected or transfected DNA, and their export was followed by fluorescent in situ hybridization. We made the surprising observation that the signal sequence coding region (SSCR) can serve as a nuclear export signal of an mRNA that lacks an intron or functional cap. Even the export of an intron-containing natural mRNA was enhanced by its SSCR. Like conventional export, the SSCR-dependent pathway required the factor TAP, but depletion of the TREX components had only moderate effects. The SSCR export signal appears to be characterized in vertebrates by a low content of adenines, as demonstrated by genome-wide sequence analysis and by the inhibitory effect of silent adenine mutations in SSCRs. The discovery of an SSCR-mediated pathway explains the previously noted amino acid bias in signal sequences and suggests a link between nuclear export and membrane targeting of mRNAs.

Background: The evolution of gene expression is a challenging problem in evolutionary biology, for which accurate, well-calibrated measurements and methods are crucial. Results: We quantified gene expression with whole-transcriptome sequencing in four diploid, prototrophic strains of Saccharomyces species grown under the same condition to investigate the evolution of gene expression. We found that variation in expression is gene-dependent with large variations in each gene's expression between replicates of the same species. This confounds the identification of genes differentially expressed across species. To address this, we developed a statistical approach to establish significance bounds for inter-species differential expression in RNA-Seq data based on the variance measured across biological replicates. This metric estimates the combined effects of technical and environmental variance, as well as Poisson sampling noise by isolating each component. Despite a paucity of large expression changes, we found a strong correlation between the variance of gene expression change and species divergence ((R^2) = 0.90). Conclusion: We provide an improved methodology for measuring gene expression changes in evolutionary diverged species using RNA Seq, where experimental artifacts can mimic evolutionary effects.

Gene copy number variation has been discovered in humans, between related species, and in different cancer tissues, but it is unclear how much of this genomic-level variation leads to changes in the level of protein abundance. To address this, we eliminated one of the two genomic copies of 730 different genes in Saccharomyces cerevisiae and asked how often a 50% reduction in gene dosage leads to a 50% reduction in protein level. For at least 80% of genes tested, and under several environmental conditions, it does: protein levels in the heterozygous strain are close to 50% of wild type. For < 5% of the genes tested, the protein levels in the heterozygote are maintained at nearly wild-type levels. These experiments show that protein levels are not, in general, directly monitored and adjusted to a desired level. Combined with fitness data, this implies that proteins are expressed at levels higher than necessary for survival.

mRNA synthesis, processing, and destruction involve a complex series of molecular steps that are incompletely understood. Because the RNA intermediates in each of these steps have finite lifetimes, extensive mechanistic and dynamical information is encoded in total cellular RNA. Here we report the development of SnapShot-Seq, a set of computational methods that allow the determination of in vivo rates of pre-mRNA synthesis, splicing, intron degradation, and mRNA decay from a single RNA-Seq snapshot of total cellular RNA. SnapShot-Seq can detect in vivo changes in the rates of specific steps of splicing, and it provides genome-wide estimates of pre-mRNA synthesis rates comparable to those obtained via labeling of newly synthesized RNA. We used SnapShot-Seq to investigate the origins of the intrinsic bimodality of metazoan gene expression levels, and our results suggest that this bimodality is partly due to spillover of transcriptional activation from highly expressed genes to their poorly expressed neighbors. SnapShot-Seq dramatically expands the information obtainable from a standard RNA-Seq experiment.

The past decade has seen a rapid increase in the ability of biologists to collect large amounts of data. It is therefore vital that research biologists acquire the necessary skills during their training to visualize, analyze, and interpret such data. To begin to meet this need, we have developed a “boot camp” in quantitative methods for biology graduate students at Harvard Medical School. The goal of this short, intensive course is to enable students to use computational tools to visualize and analyze data, to strengthen their computational thinking skills, and to simulate and thus extend their intuition about the behavior of complex biological systems. The boot camp teaches basic programming using biological examples from statistics, image processing, and data analysis. This integrative approach to teaching programming and quantitative reasoning motivates students’ engagement by demonstrating the relevance of these skills to their work in life science laboratories. Students also have the opportunity to analyze their own data or explore a topic of interest in more detail. The class is taught with a mixture of short lectures, Socratic discussion, and in-class exercises. Students spend approximately 40% of their class time working through both short and long problems. A high instructor-to-student ratio allows students to get assistance or additional challenges when needed, thus enhancing the experience for students at all levels of mastery. Data collected from end-of-course surveys from the last five offerings of the course (between 2012 and 2014) show that students report high learning gains and feel that the course prepares them for solving quantitative and computational problems they will encounter in their research. We outline our course here which, together with the course materials freely available online under a Creative Commons License, should help to facilitate similar efforts by others.

Previous studies of laboratory strains of budding yeast had shown that when gene copy number is altered experimentally, RNA levels generally scale accordingly. This is true when the copy number of individual genes or entire chromosomes is altered. In a recent study, Hose et al. (2015) reported that this tight correlation between gene copy number and RNA levels is not observed in recently isolated wild Saccharomyces cerevisiae variants. To understand the origins of this proposed difference in gene expression regulation between natural variants and laboratory strains of S. cerevisiae, we evaluated the karyotype and gene expression studies performed by Hose et al. on wild S. cerevisiae strains. In contrast to the results of Hose et al., our reexamination of their data revealed a tight correlation between gene copy number and gene expression. We conclude that widespread dosage compensation occurs neither in laboratory strains nor in natural variants of S. cerevisiae. DOI: http://dx.doi.org/10.7554/eLife.10996.001

Metabolism underlies many important cellular decisions, such as the decisions to proliferate and differentiate, and defects in metabolic signaling can lead to disease and aging. In addition, metabolic heterogeneity can have biological consequences, such as differences in outcomes and drug susceptibilities in cancer and antibiotic treatments. Many approaches exist for characterizing the metabolic state of a population of cells, but technologies for measuring metabolism at the single cell level are in the preliminary stages and are limited. Here, we describe novel analysis methodologies that can be applied to established experimental methods to measure metabolic variability within a population. We use mass spectrometry to analyze amino acid composition in cells grown in a mixture of 12C- and 13C-labeled sugars; these measurements allow us to quantify the variability in sugar usage and thereby infer information about the behavior of cells within the population. The methodologies described here can be applied to a large range of metabolites and macromolecules and therefore have the potential for broad applications.

In structured environments, antibiotic producing microorganisms can gain a selective advantage by inhibiting nearby competing species1. However, despite their genetic potential2,3, natural isolates often make only small amounts of antibiotics, and laboratory evolution can lead to loss rather than enhancement of antibiotic production4. Here we show that, due to competition with antibiotic resistant cheater cells, increased levels of antibiotic production can actually decrease the selective advantage to producers. Competing fluorescently-labeled Escherichia coli colicin producers with non-producing resistant and sensitive strains on solid media, we found that while producer colonies can greatly benefit from the inhibition of nearby sensitive colonies, this benefit is shared with resistant colonies growing in their vicinity. A simple model, which accounts for such local competitive and inhibitory interactions, suggests that the advantage of producers varies non-monotonically with the amount of production. Indeed, experimentally varying the amount of production shows a peak in selection for producers, reflecting a trade-off between benefit gained by inhibiting sensitive competitors and loss due to an increased contribution to resistant cheater colonies. These results help explain the low level of antibiotic production observed for natural species, and can help direct laboratory evolution experiments selecting for increased or novel production of antibiotics.

BioNumbers (http://www.bionumbers.hms.harvard.edu) is a database of key numbers in molecular and cell biology—the quantitative properties of biological systems of interest to computational, systems and molecular cell biologists. Contents of the database range from cell sizes to metabolite concentrations, from reaction rates to generation times, from genome sizes to the number of mitochondria in a cell. While always of importance to biologists, having numbers in hand is becoming increasingly critical for experimenting, modeling, and analyzing biological systems. BioNumbers was motivated by an appreciation of how long it can take to find even the simplest number in the vast biological literature. All numbers are taken directly from a literature source and that reference is provided with the number. BioNumbers is designed to be highly searchable and queries can be performed by keywords or browsed by menus. BioNumbers is a collaborative community platform where registered users can add content and make comments on existing data. All new entries and commentary are curated to maintain high quality. Here we describe the database characteristics and implementation, demonstrate its use, and discuss future directions for its development.

In nature, microbes often need to "decide" which of several available nutrients to utilize, a choice that depends on a cell’s inherent preference and external nutrient levels. While natural environments can have mixtures of different nutrients, phenotypic variation in microbes’ decisions of which nutrient to utilize is poorly studied. Here, we quantified differences in the concentration of glucose and galactose required to induce galactose-responsive (GAL) genes across 36 wild S. cerevisiae strains. Using bulk segregant analysis, we found that a locus containing the galactose sensor GAL3 was associated with differences in GAL signaling in eight different crosses. Using allele replacements, we confirmed that GAL3 is the major driver of GAL induction variation, and that GAL3 allelic variation alone can explain as much as 90% of the variation in GAL induction in a cross. The GAL3 variants we found modulate the diauxic lag, a selectable trait. These results suggest that ecological constraints on the galactose pathway may have led to variation in a single protein, allowing cells to quantitatively tune their response to nutrient changes in the environment.