Adam M. Deutschbauer

Functional profiling of the Saccharomyces cerevisiae genome

Determining the effect of gene deletion is a fundamental approach to understanding gene function. Conventional genetic screens exhibit biases, and genes contributing to a phenotype are often missed. We systematically constructed a nearly complete collection of gene-deletion mutants (96% of annotated open reading frames, or ORFs) of the yeast Saccharomyces cerevisiae. DNA sequences dubbed ‘molecular bar codes’ uniquely identify each strain, enabling their growth to be analysed in parallel and the fitness contribution of each gene to be quantitatively assessed by hybridization to high-density oligonucleotide arrays. We show that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment. Less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal growth in four of the tested conditions. Our results validate the yeast gene-deletion collection as a valuable resource for functional genomics.

Systematic screen for human disease genes in yeast

High similarity between yeast and human mitochondria allows functional genomic study of Saccharomyces cerevisiae to be used to identify human genes involved in disease. So far, 102 heritable disorders have been attributed to defects in a quarter of the known nuclear-encoded mitochondrial proteins in humans. Many mitochondrial diseases remain unexplained, however, in part because only 40–60% of the presumed 700–1,000 proteins involved in mitochondrial function and biogenesis have been identified. Here we apply a systematic functional screen using the pre-existing whole-genome pool of yeast deletion mutants to identify mitochondrial proteins. Three million measurements of strain fitness identified 466 genes whose deletions impaired mitochondrial respiration, of which 265 were new. Our approach gave higher selection than other systematic approaches, including fivefold greater selection than gene expression analysis. To apply these advantages to human disorders involving mitochondria, human orthologs were identified and linked to heritable diseases using genomic map positions.

Quantitative trait loci mapped to single-nucleotide resolution in yeast.

Identifying the genetic variation underlying quantitative trait loci remains problematic. Consequently, our molecular understanding of genetically complex, quantitative traits is limited. To address this issue directly, we mapped three quantitative trait loci that control yeast sporulation efficiency to single-nucleotide resolution in a noncoding regulatory region (RME1) and to two missense mutations (TAO3 and MKT1). For each quantitative trait locus, the responsible polymorphism is rare among a diverse set of 13 yeast strains, suggestive of genetic heterogeneity in the control of yeast sporulation. Additionally, under optimal conditions, we reconstituted ∼92% of the sporulation efficiency difference between the two genetically distinct parents by engineering three nucleotide changes in the appropriate yeast genome. Our results provide the highest resolution to date of the molecular basis of a quantitative trait, showing that the interaction of a few genetic variants can have a profound phenotypic effect.

Parallel phenotypic analysis of sporulation and postgermination growth in Saccharomyces cerevisiae

We have quantitatively monitored the sporulation and germination efficiencies of ≈4,200 yeast deletion strains in parallel by using a molecular bar coding strategy. In a single study, we doubled the number of genes functionally implicated in sporulation to ≈400, identifying both positive and negative regulators. Our set of 261 sporulation-deficient genes illustrates the importance of autophagy, carbon utilization, and transcriptional machinery during sporulation. These general cellular factors are more likely to exhibit fitness defects when deleted and less likely to be transcriptionally regulated than sporulation-specific genes. Our postgermination screening assay identified recombination/chromosome segregation genes, aneuploid strains, and possible germination-specific factors. Finally, our results facilitate a genome-wide comparison of expression pattern and mutant phenotype for a developmental process and suggest that 16% of genes differentially expressed during sporulation confer altered efficiency of spore production or defective postgermination growth when disrupted.

Interactive XCMS Online: Simplifying Advanced Metabolomic Data Processing and Subsequent Statistical Analyses

XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, “control” versus “disease” experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.

Sister-chromatid cohesion mediated by the alternative RF-CCtf18/Dcc1/Ctf8, the helicase Chl1 and the polymerase-alpha-associated protein Ctf4 is essential for chromatid disjunction during meiosis II

Cohesion between sister chromatids mediated by a multisubunit complex called cohesin is established during DNA replication and is essential for the orderly segregation of chromatids during anaphase. In budding yeast, a specialized replication factor C called RF-CCtf18/Dcc1/Ctf8 and the DNA-polymerase-α-associated protein Ctf4 are required to maintain sister-chromatid cohesion in cells arrested for long periods in mitosis. We show here that CTF8, CTF4 and a helicase encoded by CHL1 are required for efficient sister chromatid cohesion in unperturbed mitotic cells, and provide evidence that Chl1 functions during S-phase. We also show that, in contrast to mitosis, RF-CCtf18/Dcc1/Cft8, Ctf4 and Chl1 are essential for chromosome segregation during meiosis and for the viability of meiotic products. Our finding that cells deleted for CTF8, CTF4 or CHL1 undergo massive meiosis II non-disjunction suggests that the second meiotic division is particularly sensitive to cohesion defects. Using a functional as well as a cytological assay, we demonstrate that CTF8, CHL1 and CTF4 are essential for cohesion between sister centromeres during meiosis but dispensable for cohesins association with centromeric DNA. Our finding that mutants in fission yeast ctf18 and dcc1 have similar defects suggests that the involvement of the alternative RF-CCtf18/Dcc1/Ctf8 complex in sister chromatid cohesion might be highly conserved.

Evidence-based annotation of gene function in Shewanella oneidensis MR-1 using genome-wide fitness profiling across 121 conditions.

Most genes in bacteria are experimentally uncharacterized and cannot be annotated with a specific function. Given the great diversity of bacteria and the ease of genome sequencing, high-throughput approaches to identify gene function experimentally are needed. Here, we use pools of tagged transposon mutants in the metal-reducing bacterium Shewanella oneidensis MR-1 to probe the mutant fitness of 3,355 genes in 121 diverse conditions including different growth substrates, alternative electron acceptors, stresses, and motility. We find that 2,350 genes have a pattern of fitness that is significantly different from random and 1,230 of these genes (37% of our total assayed genes) have enough signal to show strong biological correlations. We find that genes in all functional categories have phenotypes, including hundreds of hypotheticals, and that potentially redundant genes (over 50% amino acid identity to another gene in the genome) are also likely to have distinct phenotypes. Using fitness patterns, we were able to propose specific molecular functions for 40 genes or operons that lacked specific annotations or had incomplete annotations. In one example, we demonstrate that the previously hypothetical gene SO_3749 encodes a functional acetylornithine deacetylase, thus filling a missing step in S. oneidensis metabolism. Additionally, we demonstrate that the orphan histidine kinase SO_2742 and orphan response regulator SO_2648 form a signal transduction pathway that activates expression of acetyl-CoA synthase and is required for S. oneidensis to grow on acetate as a carbon source. Lastly, we demonstrate that gene expression and mutant fitness are poorly correlated and that mutant fitness generates more confident predictions of gene function than does gene expression. The approach described here can be applied generally to create large-scale gene-phenotype maps for evidence-based annotation of gene function in prokaryotes.

Rapid Quantification of Mutant Fitness in Diverse Bacteria by Sequencing Randomly Bar-Coded Transposons

ABSTRACT Transposon mutagenesis with next-generation sequencing (TnSeq) is a powerful approach to annotate gene function in bacteria, but existing protocols for TnSeq require laborious preparation of every sample before sequencing. Thus, the existing protocols are not amenable to the throughput necessary to identify phenotypes and functions for the majority of genes in diverse bacteria. Here, we present a method, random bar code transposon-site sequencing (RB-TnSeq), which increases the throughput of mutant fitness profiling by incorporating random DNA bar codes into Tn5 and mariner transposons and by using bar code sequencing (BarSeq) to assay mutant fitness. RB-TnSeq can be used with any transposon, and TnSeq is performed once per organism instead of once per sample. Each BarSeq assay requires only a simple PCR, and 48 to 96 samples can be sequenced on one lane of an Illumina HiSeq system. We demonstrate the reproducibility and biological significance of RB-TnSeq with Escherichia coli, Phaeobacter inhibens, Pseudomonas stutzeri, Shewanella amazonensis, and Shewanella oneidensis. To demonstrate the increased throughput of RB-TnSeq, we performed 387 successful genome-wide mutant fitness assays representing 130 different bacterium-carbon source combinations and identified 5,196 genes with significant phenotypes across the five bacteria. In P. inhibens, we used our mutant fitness data to identify genes important for the utilization of diverse carbon substrates, including a putative d-mannose isomerase that is required for mannitol catabolism. RB-TnSeq will enable the cost-effective functional annotation of diverse bacteria using mutant fitness profiling. IMPORTANCE A large challenge in microbiology is the functional assessment of the millions of uncharacterized genes identified by genome sequencing. Transposon mutagenesis coupled to next-generation sequencing (TnSeq) is a powerful approach to assign phenotypes and functions to genes. However, the current strategies for TnSeq are too laborious to be applied to hundreds of experimental conditions across multiple bacteria. Here, we describe an approach, random bar code transposon-site sequencing (RB-TnSeq), which greatly simplifies the measurement of gene fitness by using bar code sequencing (BarSeq) to monitor the abundance of mutants. We performed 387 genome-wide fitness assays across five bacteria and identified phenotypes for over 5,000 genes. RB-TnSeq can be applied to diverse bacteria and is a powerful tool to annotate uncharacterized genes using phenotype data. A large challenge in microbiology is the functional assessment of the millions of uncharacterized genes identified by genome sequencing. Transposon mutagenesis coupled to next-generation sequencing (TnSeq) is a powerful approach to assign phenotypes and functions to genes. However, the current strategies for TnSeq are too laborious to be applied to hundreds of experimental conditions across multiple bacteria. Here, we describe an approach, random bar code transposon-site sequencing (RB-TnSeq), which greatly simplifies the measurement of gene fitness by using bar code sequencing (BarSeq) to monitor the abundance of mutants. We performed 387 genome-wide fitness assays across five bacteria and identified phenotypes for over 5,000 genes. RB-TnSeq can be applied to diverse bacteria and is a powerful tool to annotate uncharacterized genes using phenotype data.

Indirect and suboptimal control of gene expression is widespread in bacteria

Gene regulation in bacteria is usually described as an adaptive response to an environmental change so that genes are expressed when they are required. We instead propose that most genes are under indirect control: their expression responds to signal(s) that are not directly related to the genes’ function. Indirect control should perform poorly in artificial conditions, and we show that gene regulation is often maladaptive in the laboratory. In Shewanella oneidensis MR‐1, 24% of genes are detrimental to fitness in some conditions, and detrimental genes tend to be highly expressed instead of being repressed when not needed. In diverse bacteria, there is little correlation between when genes are important for optimal growth or fitness and when those genes are upregulated. Two common types of indirect control are constitutive expression and regulation by growth rate; these occur for genes with diverse functions and often seem to be suboptimal. Because genes that have closely related functions can have dissimilar expression patterns, regulation may be suboptimal in the wild as well as in the laboratory.

The Epigenomic Landscape of Prokaryotes

DNA methylation acts in concert with restriction enzymes to protect the integrity of prokaryotic genomes. Studies in a limited number of organisms suggest that methylation also contributes to prokaryotic genome regulation, but the prevalence and properties of such non-restriction-associated methylation systems remain poorly understood. Here, we used single molecule, real-time sequencing to map DNA modifications including m6A, m4C, and m5C across the genomes of 230 diverse bacterial and archaeal species. We observed DNA methylation in nearly all (93%) organisms examined, and identified a total of 834 distinct reproducibly methylated motifs. This data enabled annotation of the DNA binding specificities of 620 DNA Methyltransferases (MTases), doubling known specificities for previously hard to study Type I, IIG and III MTases, and revealing their extraordinary diversity. Strikingly, 48% of organisms harbor active Type II MTases with no apparent cognate restriction enzyme. These active ‘orphan’ MTases are present in diverse bacterial and archaeal phyla and show motif specificities and methylation patterns consistent with functions in gene regulation and DNA replication. Our results reveal the pervasive presence of DNA methylation throughout the prokaryotic kingdoms, as well as the diversity of sequence specificities and potential functions of DNA methylation systems.