Extensive in vivo metabolite-protein interactions revealed by large-scale systematic analyses.

Natural small compounds comprise most cellular molecules and bind proteins as substrates, products, cofactors, and ligands. However, a large-scale investigation of in vivo protein-small metabolite interactions has not been performed. We developed a mass spectrometry assay for the large-scale identification of in vivo protein-hydrophobic small metabolite interactions in yeast and analyzed compounds that bind ergosterol biosynthetic proteins and protein kinases. Many of these proteins bind small metabolites; a few interactions were previously known, but the vast majority are new. Importantly, many key regulatory proteins such as protein kinases bind metabolites. Ergosterol was found to bind many proteins and may function as a general regulator. It is required for the activity of Ypk1, a mammalian AKT/SGK kinase homolog. Our study defines potential key regulatory steps in lipid biosynthetic pathways and suggests that small metabolites may play a more general role as regulators of protein activity and function than previously appreciated.

Publisher Correction: Shared strategies for ß-lactam catabolism in the soil microbiome.

In the version of the article originally published, the x axis of the graph in Fig. 4d was incorrectly labeled as "Retention time (min)". It should read "Reaction time (min)". The 'deceased' footnote was also formatted incorrectly when published. The footnote text itself should include the name of co-author Tara A. Gianoulis in addition to the previous link to her name in the author list through footnote number 10. The errors have been corrected in the HTML and PDF versions of the article.

Shared strategies for ß-lactam catabolism in the soil microbiome.

The soil microbiome can produce, resist, or degrade antibiotics and even catabolize them. While resistance genes are widely distributed in the soil, there is a dearth of knowledge concerning antibiotic catabolism. Here we describe a pathway for penicillin catabolism in four isolates. Genomic and transcriptomic sequencing revealed ß-lactamase, amidase, and phenylacetic acid catabolon upregulation. Knocking out part of the phenylacetic acid catabolon or an apparent penicillin utilization operon (put) resulted in loss of penicillin catabolism in one isolate. A hydrolase from the put operon was found to degrade in vitro benzylpenicilloic acid, the ß-lactamase penicillin product. To test the generality of this strategy, an Escherichia coli strain was engineered to co-express a ß-lactamase and a penicillin amidase or the put operon, enabling it to grow using penicillin or benzylpenicilloic acid, respectively. Elucidation of additional pathways may allow bioremediation of antibiotic-contaminated soils and discovery of antibiotic-remodeling enzymes with industrial utility.

Genomic analysis of the hydrocarbon-producing, cellulolytic, endophytic fungus Ascocoryne sarcoides.

The microbial conversion of solid cellulosic biomass to liquid biofuels may provide a renewable energy source for transportation fuels. Endophytes represent a promising group of organisms, as they are a mostly untapped reservoir of metabolic diversity. They are often able to degrade cellulose, and they can produce an extraordinary diversity of metabolites. The filamentous fungal endophyte Ascocoryne sarcoides was shown to produce potential-biofuel metabolites when grown on a cellulose-based medium; however, the genetic pathways needed for this production are unknown and the lack of genetic tools makes traditional reverse genetics difficult. We present the genomic characterization of A. sarcoides and use transcriptomic and metabolomic data to describe the genes involved in cellulose degradation and to provide hypotheses for the biofuel production pathways. In total, almost 80 biosynthetic clusters were identified, including several previously found only in plants. Additionally, many transcriptionally active regions outside of genes showed condition-specific expression, offering more evidence for the role of long non-coding RNA in gene regulation. This is one of the highest quality fungal genomes and, to our knowledge, the only thoroughly annotated and transcriptionally profiled fungal endophyte genome currently available. The analyses and datasets contribute to the study of cellulose degradation and biofuel production and provide the genomic foundation for the study of a model endophyte system.

Regulatory variation within and between species.

Understanding how individuals differ from one another and from closely related species is a fundamental problem in biology. Recent evidence suggests that much of the variation both within and between species is due to differential gene regulation. Here we review differential gene regulation focusing on evolutionary-developmental (evo-devo) biology, global comparison of genomic sequences, whole-genome gene expression, and transcription factor (TF) binding profiles. We also explore the relationship between divergence rate of regulatory sequences, coding sequences, and TF binding events using several different measures and discuss their implications in the context of evolution of regulatory networks. Finally, we discuss the current status and future challenges in relating regulatory variation to the divergence across and within species.

Diverse roles and interactions of the SWI/SNF chromatin remodeling complex revealed using global approaches.

A systems understanding of nuclear organization and events is critical for determining how cells divide, differentiate, and respond to stimuli and for identifying the causes of diseases. Chromatin remodeling complexes such as SWI/SNF have been implicated in a wide variety of cellular processes including gene expression, nuclear organization, centromere function, and chromosomal stability, and mutations in SWI/SNF components have been linked to several types of cancer. To better understand the biological processes in which chromatin remodeling proteins participate, we globally mapped binding regions for several components of the SWI/SNF complex throughout the human genome using ChIP-Seq. SWI/SNF components were found to lie near regulatory elements integral to transcription (e.g. 5' ends, RNA Polymerases II and III, and enhancers) as well as regions critical for chromosome organization (e.g. CTCF, lamins, and DNA replication origins). Interestingly we also find that certain configurations of SWI/SNF subunits are associated with transcripts that have higher levels of expression, whereas other configurations of SWI/SNF factors are associated with transcripts that have lower levels of expression. To further elucidate the association of SWI/SNF subunits with each other as well as with other nuclear proteins, we also analyzed SWI/SNF immunoprecipitated complexes by mass spectrometry. Individual SWI/SNF factors are associated with their own family members, as well as with cellular constituents such as nuclear matrix proteins, key transcription factors, and centromere components, implying a ubiquitous role in gene regulation and nuclear function. We find an overrepresentation of both SWI/SNF-associated regions and proteins in cell cycle and chromosome organization. Taken together the results from our ChIP and immunoprecipitation experiments suggest that SWI/SNF facilitates gene regulation and genome function more broadly and through a greater diversity of interactions than previously appreciated.

Analysis of membrane proteins in metagenomics: networks of correlated environmental features and protein families.

Recent metagenomics studies have begun to sample the genomic diversity among disparate habitats and relate this variation to features of the environment. Membrane proteins are an intuitive, but thus far overlooked, choice in this type of analysis as they directly interact with the environment, receiving signals from the outside and transporting nutrients. Using global ocean sampling (GOS) data, we found nearly approximately 900,000 membrane proteins in large-scale metagenomic sequence, approximately a fifth of which are completely novel, suggesting a large space of hitherto unexplored protein diversity. Using GPS coordinates for the GOS sites, we extracted additional environmental features via interpolation from the World Ocean Database, the National Center for Ecological Analysis and Synthesis, and empirical models of dust occurrence. This allowed us to study membrane protein variation in terms of natural features, such as phosphate and nitrate concentrations, and also in terms of human impacts, such as pollution and climate change. We show that there is widespread variation in membrane protein content across marine sites, which is correlated with changes in both oceanographic variables and human factors. Furthermore, using these data, we developed an approach, protein families and environment features network (PEN), to quantify and visualize the correlations. PEN identifies small groups of covarying environmental features and membrane protein families, which we call "bimodules." Using this approach, we find that the affinity of phosphate transporters is related to the concentration of phosphate and that the occurrence of iron transporters is connected to the amount of shipping, pollution, and iron-containing dust.

RSEQtools: a modular framework to analyze RNA-Seq data using compact, anonymized data summaries.

SUMMARY: The advent of next-generation sequencing for functional genomics has given rise to quantities of sequence information that are often so large that they are difficult to handle. Moreover, sequence reads from a specific individual can contain sufficient information to potentially identify and genetically characterize that person, raising privacy concerns. In order to address these issues, we have developed the Mapped Read Format (MRF), a compact data summary format for both short and long read alignments that enables the anonymization of confidential sequence information, while allowing one to still carry out many functional genomics studies. We have developed a suite of tools (RSEQtools) that use this format for the analysis of RNA-Seq experiments. These tools consist of a set of modules that perform common tasks such as calculating gene expression values, generating signal tracks of mapped reads and segmenting that signal into actively transcribed regions. Moreover, the tools can readily be used to build customizable RNA-Seq workflows. In addition to the anonymization afforded by MRF, this format also facilitates the decoupling of the alignment of reads from downstream analyses. AVAILABILITY AND IMPLEMENTATION: RSEQtools is implemented in C and the source code is available at http://rseqtools.gersteinlab.org/.

Quantifying environmental adaptation of metabolic pathways in metagenomics.

Recently, approaches have been developed to sample the genetic content of heterogeneous environments (metagenomics). However, by what means these sequences link distinct environmental conditions with specific biological processes is not well understood. Thus, a major challenge is how the usage of particular pathways and subnetworks reflects the adaptation of microbial communities across environments and habitats-i.e., how network dynamics relates to environmental features. Previous research has treated environments as discrete, somewhat simplified classes (e.g., terrestrial vs. marine), and searched for obvious metabolic differences among them (i.e., treating the analysis as a typical classification problem). However, environmental differences result from combinations of many factors, which often vary only slightly. Therefore, we introduce an approach that employs correlation and regression to relate multiple, continuously varying factors defining an environment to the extent of particular microbial pathways present in a geographic site. Moreover, rather than looking only at individual correlations (one-to-one), we adapted canonical correlation analysis and related techniques to define an ensemble of weighted pathways that maximally covaries with a combination of environmental variables (many-to-many), which we term a metabolic footprint. Applied to available aquatic datasets, we identified footprints predictive of their environment that can potentially be used as biosensors. For example, we show a strong multivariate correlation between the energy-conversion strategies of a community and multiple environmental gradients (e.g., temperature). Moreover, we identified covariation in amino acid transport and cofactor synthesis, suggesting that limiting amounts of cofactor can (partially) explain increased import of amino acids in nutrient-limited conditions.

Comparing classical pathways and modern networks: towards the development of an edge ontology.

Pathways are integral to systems biology. Their classical representation has proven useful but is inconsistent in the meaning assigned to each arrow (or edge) and inadvertently implies the isolation of one pathway from another. Conversely, modern high-throughput (HTP) experiments offer standardized networks that facilitate topological calculations. Combining these perspectives, classical pathways can be embedded within large-scale networks and thus demonstrate the crosstalk between them. As more diverse types of HTP data become available, both perspectives can be effectively merged, embedding pathways simultaneously in multiple networks. However, the original problem still remains - the current edge representation is inadequate to accurately convey all the information in pathways. Therefore, we suggest that a standardized and well-defined edge ontology is necessary and propose a prototype as a starting point for reaching this goal.

Volatile organic compound production by organisms in the genus Ascocoryne and a re-evaluation of myco-diesel production by NRRL 50072.

The Patagonian fungal endophyte NRRL 50072 is reported to produce a variety of medium-chain and highly branched volatile organic compounds (VOCs) that have been highlighted for their potential as fuel alternatives and are collectively termed myco-diesel. To assess the novelty of this observation, we determined the extent to which ten closely related Ascocoryne strains from commercial culture collections possess similar VOC production capability. DNA sequencing established a high genetic similarity between NRRL 50072 and each Ascocoryne isolate, consistent with its reassignment as Ascocoryne sarcoides. The Ascocoryne strains did not produce highly branched medium-chain-length alkanes, and efforts to reproduce the branched alkane production of NRRL 50072 were unsuccessful. However, we confirmed the production of 30 other products and expanded the list of VOCs for NRRL 50072 and members of the genus Ascocoryne. VOCs detected from the cultures consisted of short- and medium-chain alkenes, ketones, esters and alcohols and several sesquiterpenes. Ascocoryne strains NRRL 50072 and CBS 309.71 produced a more diverse range of volatiles than the other isolates tested. CBS 309.71 also showed enhanced production compared with other strains when grown on cellulose agar. Collectively, the members of the genus Ascocoryne demonstrated production of over 100 individual compounds, with a third of the short- and medium-chain compounds also produced when cultures were grown on a cellulose substrate. This comparative production analysis could facilitate future studies to identify and manipulate the biosynthetic machinery responsible for production of individual VOCs, including several that have a potential application as biofuels.

Draft Genome Sequences of Three ß-Lactam-Catabolizing Soil Proteobacteria.

Most antibiotics are derived from the soil, but their catabolism there, which is necessary to close the antibiotic carbon cycle, remains uncharacterized. We report the first draft genome sequences of soil Proteobacteria identified for subsisting solely on ß-lactams as their carbon sources. The genomes encode multiple ß-lactamases, although their antibiotic catabolic pathways remain enigmatic.

Integration of curated databases to identify genotype-phenotype associations.

BACKGROUND: The ability to rapidly characterize an unknown microorganism is critical in both responding to infectious disease and biodefense. To do this, we need some way of anticipating an organism's phenotype based on the molecules encoded by its genome. However, the link between molecular composition (i.e. genotype) and phenotype for microbes is not obvious. While there have been several studies that address this challenge, none have yet proposed a large-scale method integrating curated biological information. Here we utilize a systematic approach to discover genotype-phenotype associations that combines phenotypic information from a biomedical informatics database, GIDEON, with the molecular information contained in National Center for Biotechnology Information's Clusters of Orthologous Groups database (NCBI COGs). RESULTS: Integrating the information in the two databases, we are able to correlate the presence or absence of a given protein in a microbe with its phenotype as measured by certain morphological characteristics or survival in a particular growth media. With a 0.8 correlation score threshold, 66% of the associations found were confirmed by the literature and at a 0.9 correlation threshold, 86% were positively verified. CONCLUSION: Our results suggest possible phenotypic manifestations for proteins biochemically associated with sugar metabolism and electron transport. Moreover, we believe our approach can be extended to linking pathogenic phenotypes with functionally related proteins.

The CRIT framework for identifying cross patterns in systems biology and application to chemogenomics.

Biological data is often tabular but finding statistically valid connections between entities in a sequence of tables can be problematic--for example, connecting particular entities in a drug property table to gene properties in a second table, using a third table associating genes with drugs. Here we present an approach (CRIT) to find connections such as these and show how it can be applied in a variety of genomic contexts including chemogenomics data.

Precise manipulation of chromosomes in vivo enables genome-wide codon replacement.

We present genome engineering technologies that are capable of fundamentally reengineering genomes from the nucleotide to the megabase scale. We used multiplex automated genome engineering (MAGE) to site-specifically replace all 314 TAG stop codons with synonymous TAA codons in parallel across 32 Escherichia coli strains. This approach allowed us to measure individual recombination frequencies, confirm viability for each modification, and identify associated phenotypes. We developed hierarchical conjugative assembly genome engineering (CAGE) to merge these sets of codon modifications into genomes with 80 precise changes, which demonstrate that these synonymous codon substitutions can be combined into higher-order strains without synthetic lethal effects. Our methods treat the chromosome as both an editable and an evolvable template, permitting the exploration of vast genetic landscapes.

New insights into Acinetobacter baumannii pathogenesis revealed by high-density pyrosequencing and transposon mutagenesis.

Acinetobacter baumannii has emerged as an important and problematic human pathogen as it is the causative agent of several types of infections including pneumonia, meningitis, septicemia, and urinary tract infections. We explored the pathogenic content of this harmful pathogen using a combination of DNA sequencing and insertional mutagenesis. The genome of this organism was sequenced using a strategy involving high-density pyrosequencing, a novel, rapid method of high-throughput sequencing. Excluding the rDNA repeats, the assembled genome is 3,976,746 base pairs (bp) and has 3830 ORFs. A significant fraction of ORFs (17.2%) are located in 28 putative alien islands, indicating that the genome has acquired a large amount of foreign DNA. Consistent with its role in pathogenesis, a remarkable number of the islands (16) contain genes implicated in virulence, indicating the organism devotes a considerable portion of its genes to pathogenesis. The largest island contains elements homologous to the Legionella/Coxiella Type IV secretion apparatus. Type IV secretion systems have been demonstrated to be important for virulence in other organisms and thus are likely to help mediate pathogenesis of A. baumannii. Insertional mutagenesis generated avirulent isolates of A. baumannii and verified that six of the islands contain virulence genes, including two novel islands containing genes that lacked homology with others in the databases. The DNA sequencing approach described in this study allows the rapid elucidation of the DNA sequence of any microbe and, when combined with genetic screens, can identify many novel genes important for microbial pathogenesis.

Divergence of transcription factor binding sites across related yeast species.

Characterization of interspecies differences in gene regulation is crucial for understanding the molecular basis of both phenotypic diversity and evolution. By means of chromatin immunoprecipitation and DNA microarray analysis, the divergence in the binding sites of the pseudohyphal regulators Ste12 and Tec1 was determined in the yeasts Saccharomyces cerevisiae, S. mikatae, and S. bayanus under pseudohyphal conditions. We have shown that most of these sites have diverged across these species, far exceeding the interspecies variation in orthologous genes. A group of Ste12 targets was shown to be bound only in S. mikatae and S. bayanus under pseudohyphal conditions. Many of these genes are targets of Ste12 during mating in S. cerevisiae, indicating that specialization between the two pathways has occurred in this species. Transcription factor binding sites have therefore diverged substantially faster than ortholog content. Thus, gene regulation resulting from transcription factor binding is likely to be a major cause of divergence between related species.