A cloud-based resource for genome coordinate-based exploration and large-scale analysis of chromosome aberrations and gene fusions in cancer.

Cytogenetic analysis provides important information on the genetic mechanisms of cancer. The Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer (Mitelman DB) is the largest catalog of acquired chromosome aberrations, presently comprising >70 000 cases across multiple cancer types. Although this resource has enabled the identification of chromosome abnormalities leading to specific cancers and cancer mechanisms, a large-scale, systematic analysis of these aberrations and their downstream implications has been difficult due to the lack of a standard, automated mapping from aberrations to genomic coordinates. We previously introduced CytoConverter as a tool that automates such conversions. CytoConverter has now been updated with improved interpretation of karyotypes and has been integrated with the Mitelman DB, providing a comprehensive mapping of the 70 000+ cases to genomic coordinates, as well as visualization of the frequencies of chromosomal gains and losses. Importantly, all CytoConverter-generated genomic coordinates are publicly available in Google BigQuery, a cloud-based data warehouse, facilitating data exploration and integration with other datasets hosted by the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC) Resource. We demonstrate the use of BigQuery for integrative analysis of Mitelman DB with other cancer datasets, including a comparison of the frequency of imbalances identified in Mitelman DB cases with those found in The Cancer Genome Atlas (TCGA) copy number datasets. This solution provides opportunities to leverage the power of cloud computing for low-cost, scalable, and integrated analysis of chromosome aberrations and gene fusions in cancer.

Network analysis of transcriptomics expands regulatory landscapes in Synechococcus sp. PCC 7002.

Cyanobacterial regulation of gene expression must contend with a genome organization that lacks apparent functional context, as the majority of cellular processes and metabolic pathways are encoded by genes found at disparate locations across the genome and relatively few transcription factors exist. In this study, global transcript abundance data from the model cyanobacterium Synechococcus sp. PCC 7002 grown under 42 different conditions was analyzed using Context-Likelihood of Relatedness (CLR). The resulting network, organized into 11 modules, provided insight into transcriptional network topology as well as grouping genes by function and linking their response to specific environmental variables. When used in conjunction with genome sequences, the network allowed identification and expansion of novel potential targets of both DNA binding proteins and sRNA regulators. These results offer a new perspective into the multi-level regulation that governs cellular adaptations of the fast-growing physiologically robust cyanobacterium Synechococcus sp. PCC 7002 to changing environmental variables. It also provides a methodological high-throughput approach to studying multi-scale regulatory mechanisms that operate in cyanobacteria. Finally, it provides valuable context for integrating systems-level data to enhance gene grouping based on annotated function, especially in organisms where traditional context analyses cannot be implemented due to lack of operon-based functional organization.

Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction.

Metabolic network modeling of microbial communities provides an in-depth understanding of community-wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high-quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community-level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph-heterotroph consortium that was used to provide data needed for a community-level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources. J. Cell. Physiol. 231: 2339-2345, 2016. © 2016 Wiley Periodicals, Inc.

The Toxoplasma gondii cyst wall protein CST1 is critical for cyst wall integrity and promotes bradyzoite persistence.

Toxoplasma gondii infects up to one third of the world's population. A key to the success of T. gondii as a parasite is its ability to persist for the life of its host as bradyzoites within tissue cysts. The glycosylated cyst wall is the key structural feature that facilitates persistence and oral transmission of this parasite. Because most of the antibodies and reagents that recognize the cyst wall recognize carbohydrates, identification of the components of the cyst wall has been technically challenging. We have identified CST1 (TGME49_064660) as a 250 kDa SRS (SAG1 related sequence) domain protein with a large mucin-like domain. CST1 is responsible for the Dolichos biflorus Agglutinin (DBA) lectin binding characteristic of T. gondii cysts. Deletion of CST1 results in reduced cyst number and a fragile brain cyst phenotype characterized by a thinning and disruption of the underlying region of the cyst wall. These defects are reversed by complementation of CST1. Additional complementation experiments demonstrate that the CST1-mucin domain is necessary for the formation of a normal cyst wall structure, the ability of the cyst to resist mechanical stress, and binding of DBA to the cyst wall. RNA-seq transcriptome analysis demonstrated dysregulation of bradyzoite genes within the various cst1 mutants. These results indicate that CST1 functions as a key structural component that confers essential sturdiness to the T. gondii tissue cyst critical for persistence of bradyzoite forms.

The highly conserved MraZ protein is a transcriptional regulator in Escherichia coli.

The mraZ and mraW genes are highly conserved in bacteria, both in sequence and in their position at the head of the division and cell wall (dcw) gene cluster. Located directly upstream of the mraZ gene, the Pmra promoter drives the transcription of mraZ and mraW, as well as many essential cell division and cell wall genes, but no regulator of Pmra has been found to date. Although MraZ has structural similarity to the AbrB transition state regulator and the MazE antitoxin and MraW is known to methylate the 16S rRNA, mraZ and mraW null mutants have no detectable phenotypes. Here we show that overproduction of Escherichia coli MraZ inhibited cell division and was lethal in rich medium at high induction levels and in minimal medium at low induction levels. Co-overproduction of MraW suppressed MraZ toxicity, and loss of MraW enhanced MraZ toxicity, suggesting that MraZ and MraW have antagonistic functions. MraZ-green fluorescent protein localized to the nucleoid, suggesting that it binds DNA. Consistent with this idea, purified MraZ directly bound a region of DNA containing three direct repeats between Pmra and the mraZ gene. Excess MraZ reduced the expression of an mraZ-lacZ reporter, suggesting that MraZ acts as a repressor of Pmra, whereas a DNA-binding mutant form of MraZ failed to repress expression. Transcriptome sequencing (RNA-seq) analysis suggested that MraZ also regulates the expression of genes outside the dcw cluster. In support of this, purified MraZ could directly bind to a putative operator site upstream of mioC, one of the repressed genes identified by RNA-seq.

HTS384 NCI60: The Next Phase of the NCI60 Screen.

The NCI60 human tumor cell line screen has been in operation as a service to the cancer research community for over 30 years. The screen operated with 96-well plates, a 2-day exposure period to test agents, and, following cell fixation, a visible absorbance endpoint by the protein-staining dye sulforhodamine B. Here, we describe the next phase of this important cancer research tool, the HTS384 NCI60 screen. While the cell lines remain the same, the updated screen is performed with 384-well plates, a 3-day exposure period to test agents, and a luminescent endpoint to measure cell viability based upon cellular ATP content. In this study, a library of 1003 FDA-approved and investigational small molecule anticancer agents was screened by the two NCI60 assays. The datasets were compared with a focus on targeted agents with at least six representatives in the library. For many agents, including inhibitors of EGFR, BRAF, MEK, ERK, and PI3K, the patterns of GI50 values were very similar between the screens with strong correlations between those patterns within the dataset from each screen. However, for some groups of targeted agents, including mTOR, BET bromodomain, and NAMPRTase inhibitors, there were limited or no correlations between the two datasets, although the patterns of GI50 values and correlations between those patterns within each dataset were apparent. Beginning in January 2024, the HTS384 NCI60 screen became the free screening service of the National Cancer Institute to facilitate drug discovery by the cancer research community.

An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics.

BACKGROUND: Bioinformatics researchers are now confronted with analysis of ultra large-scale data sets, a problem that will only increase at an alarming rate in coming years. Recent developments in open source software, that is, the Hadoop project and associated software, provide a foundation for scaling to petabyte scale data warehouses on Linux clusters, providing fault-tolerant parallelized analysis on such data using a programming style named MapReduce. DESCRIPTION: An overview is given of the current usage within the bioinformatics community of Hadoop, a top-level Apache Software Foundation project, and of associated open source software projects. The concepts behind Hadoop and the associated HBase project are defined, and current bioinformatics software that employ Hadoop is described. The focus is on next-generation sequencing, as the leading application area to date. CONCLUSIONS: Hadoop and the MapReduce programming paradigm already have a substantial base in the bioinformatics community, especially in the field of next-generation sequencing analysis, and such use is increasing. This is due to the cost-effectiveness of Hadoop-based analysis on commodity Linux clusters, and in the cloud via data upload to cloud vendors who have implemented Hadoop/HBase; and due to the effectiveness and ease-of-use of the MapReduce method in parallelization of many data analysis algorithms.

Network inference algorithms elucidate Nrf2 regulation of mouse lung oxidative stress.

A variety of cardiovascular, neurological, and neoplastic conditions have been associated with oxidative stress, i.e., conditions under which levels of reactive oxygen species (ROS) are elevated over significant periods. Nuclear factor erythroid 2-related factor (Nrf2) regulates the transcription of several gene products involved in the protective response to oxidative stress. The transcriptional regulatory and signaling relationships linking gene products involved in the response to oxidative stress are, currently, only partially resolved. Microarray data constitute RNA abundance measures representing gene expression patterns. In some cases, these patterns can identify the molecular interactions of gene products. They can be, in effect, proxies for protein-protein and protein-DNA interactions. Traditional techniques used for clustering coregulated genes on high-throughput gene arrays are rarely capable of distinguishing between direct transcriptional regulatory interactions and indirect ones. In this study, newly developed information-theoretic algorithms that employ the concept of mutual information were used: the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Context Likelihood of Relatedness (CLR). These algorithms captured dependencies in the gene expression profiles of the mouse lung, allowing the regulatory effect of Nrf2 in response to oxidative stress to be determined more precisely. In addition, a characterization of promoter sequences of Nrf2 regulatory targets was conducted using a Support Vector Machine classification algorithm to corroborate ARACNE and CLR predictions. Inferred networks were analyzed, compared, and integrated using the Collective Analysis of Biological Interaction Networks (CABIN) plug-in of Cytoscape. Using the two network inference algorithms and one machine learning algorithm, a number of both previously known and novel targets of Nrf2 transcriptional activation were identified. Genes predicted as novel Nrf2 targets include Atf1, Srxn1, Prnp, Sod2, Als2, Nfkbib, and Ppp1r15b. Furthermore, microarray and quantitative RT-PCR experiments following cigarette-smoke-induced oxidative stress in Nrf2(+/+) and Nrf2(-/-) mouse lung affirmed many of the predictions made. Several new potential feed-forward regulatory loops involving Nrf2, Nqo1, Srxn1, Prdx1, Als2, Atf1, Sod1, and Park7 were predicted. This work shows the promise of network inference algorithms operating on high-throughput gene expression data in identifying transcriptional regulatory and other signaling relationships implicated in mammalian disease.

Gene set analyses for interpreting microarray experiments on prokaryotic organisms.

BACKGROUND: Despite the widespread usage of DNA microarrays, questions remain about how best to interpret the wealth of gene-by-gene transcriptional levels that they measure. Recently, methods have been proposed which use biologically defined sets of genes in interpretation, instead of examining results gene-by-gene. Despite a serious limitation, a method based on Fisher's exact test remains one of the few plausible options for gene set analysis when an experiment has few replicates, as is typically the case for prokaryotes. RESULTS: We extend five methods of gene set analysis from use on experiments with multiple replicates, for use on experiments with few replicates. We then use simulated and real data to compare these methods with each other and with the Fisher's exact test (FET) method. As a result of the simulation we find that a method named MAXMEAN-NR, maintains the nominal rate of false positive findings (type I error rate) while offering good statistical power and robustness to a variety of gene set distributions for set sizes of at least 10. Other methods (ABSSUM-NR or SUM-NR) are shown to be powerful for set sizes less than 10. Analysis of three sets of experimental data shows similar results. Furthermore, the MAXMEAN-NR method is shown to be able to detect biologically relevant sets as significant, when other methods (including FET) cannot. We also find that the popular GSEA-NR method performs poorly when compared to MAXMEAN-NR. CONCLUSION: MAXMEAN-NR is a method of gene set analysis for experiments with few replicates, as is common for prokaryotes. Results of simulation and real data analysis suggest that the MAXMEAN-NR method offers increased robustness and biological relevance of findings as compared to FET and other methods, while maintaining the nominal type I error rate.

SEBINI: Software Environment for BIological Network Inference.

UNLABELLED: The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment and evaluation of algorithms used to reconstruct the structure of biological regulatory and interaction networks. SEBINI can be used to compare and train network inference methods on artificial networks and simulated gene expression perturbation data. It also allows the analysis within the same framework of experimental high-throughput expression data using the suite of (trained) inference methods; hence SEBINI should be useful to software developers wishing to evaluate, compare, refine or combine inference techniques, and to bioinformaticians analyzing experimental data. SEBINI provides a platform that aids in more accurate reconstruction of biological networks, with less effort, in less time. AVAILABILITY: A demonstration website is located at https://www.emsl.pnl.gov/NIT/NIT.html. The Java source code and PostgreSQL database schema are available freely for non-commercial use.

Modeling framework for isotopic labeling of heteronuclear moieties.

BACKGROUND: Isotopic labeling is an analytic technique that is used to track the movement of isotopes through reaction networks. In general, the applicability of isotopic labeling techniques is limited to the investigation of reaction networks that consider homonuclear moieties, whose atoms are of one tracer element with two isotopes, distinguished by the presence of one additional neutron. RESULTS: This article presents a reformulation of the modeling framework for isotopic labeling, generalized to arbitrarily large, heteronuclear moieties, arbitrary numbers of isotopic tracer elements, and arbitrary numbers of isotopes per element, distinguished by arbitrary numbers of additional neutrons. CONCLUSIONS: With this work, it is now possible to simulate the isotopic labeling states of metabolites in completely arbitrary biochemical reaction networks.

Predicting Species-Resolved Macronutrient Acquisition during Succession in a Model Phototrophic Biofilm Using an Integrated 'Omics Approach.

The principles governing acquisition and interspecies exchange of nutrients in microbial communities and how those exchanges impact community productivity are poorly understood. Here, we examine energy and macronutrient acquisition in unicyanobacterial consortia for which species-resolved genome information exists for all members, allowing us to use multi-omic approaches to predict species' abilities to acquire resources and examine expression of resource-acquisition genes during succession. Metabolic reconstruction indicated that a majority of heterotrophic community members lacked the genes required to directly acquire the inorganic nutrients provided in culture medium, suggesting high metabolic interdependency. The sole primary producer in consortium UCC-O, cyanobacterium Phormidium sp. OSCR, displayed declining expression of energy harvest, carbon fixation, and nitrate and sulfate reduction proteins but sharply increasing phosphate transporter expression over 28 days. Most heterotrophic members likewise exhibited signs of phosphorus starvation during succession. Though similar in their responses to phosphorus limitation, heterotrophs displayed species-specific expression of nitrogen acquisition genes. These results suggest niche partitioning around nitrogen sources may structure the community when organisms directly compete for limited phosphate. Such niche complementarity around nitrogen sources may increase community diversity and productivity in phosphate-limited phototrophic communities.

Development of the Minimum Information Specification for In Situ Hybridization and Immunohistochemistry Experiments (MISFISHIE).

We describe the creation process of the Minimum Information Specification for In Situ Hybridization and Immunohistochemistry Experiments (MISFISHIE). Modeled after the existing minimum information specification for microarray data, we created a new specification for gene expression localization experiments, initially to facilitate data sharing within a consortium. After successful use within the consortium, the specification was circulated to members of the wider biomedical research community for comment and refinement. After a period of acquiring many new suggested requirements, it was necessary to enter a final phase of excluding those requirements that were deemed inappropriate as a minimum requirement for all experiments. The full specification will soon be published as a version 1.0 proposal to the community, upon which a more full discussion must take place so that the final specification may be achieved with the involvement of the whole community.

Brain in situ hybridization maps as a source for reverse-engineering transcriptional regulatory networks: Alzheimer's disease insights.

Microarray data have been a valuable resource for identifying transcriptional regulatory relationships among genes. As an example, brain region-specific transcriptional regulatory events have the potential of providing etiological insights into Alzheimer Disease (AD). However, there is often a paucity of suitable brain-region specific expression data obtained via microarrays or other high throughput means. The Allen Brain Atlas in situ hybridization (ISH) data sets (Jones et al., 2009) represent a potentially valuable alternative source of high-throughput brain region-specific gene expression data for such purposes. In this study, Allen Brain Atlas mouse ISH data in the hippocampal fields were extracted, focusing on 508 genes relevant to neurodegeneration. Transcriptional regulatory networks were learned using three high-performing network inference algorithms. Only 17% of regulatory edges from a network reverse-engineered based on brain region-specific ISH data were also found in a network constructed upon gene expression correlations in mouse whole brain microarrays, thus showing the specificity of gene expression within brain sub-regions. Furthermore, the ISH data-based networks were used to identify instructive transcriptional regulatory relationships. Ncor2, Sp3 and Usf2 form a unique three-party regulatory motif, potentially affecting memory formation pathways. Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2). Further, Nfe2l1, Egr1 and Usf2 are sensitive to dietary factors and could be among links between dietary influences and genes in the AD etiology. Thus, this approach of harnessing brain region-specific ISH data represents a rare opportunity for gleaning unique etiological insights for diseases such as AD.

Identification of Differential Gene Expression Patterns after Acute Exposure to High and Low Doses of Low-LET Ionizing Radiation in a Reconstituted Human Skin Tissue.

In this study we utilized a systems biology approach to identify dose- (0.1, 2.0 and 10 Gy) and time- (3 and 8 h) dependent responses to acute ionizing radiation exposure in a complex tissue, reconstituted human skin. The low dose used here (0.1 Gy) falls within the range of certain medical diagnostic procedures. Of the two higher doses used, 2.0 Gy is typically administered for radiotherapy, while 10 Gy is lethal. Because exposure to any of these doses is possible after an intentional or accidental radiation events, biomarkers are needed to rapidly and accurately triage potentially exposed individuals. Here, tissue samples were acutely exposed to X-ray-generated low-linear-energy transfer (LET) ionizing radiation, and direct RNA sequencing (RNA-seq) was used to quantify altered transcripts. The time points used for this study aid in assessing early responses to exposure, when key signaling pathways and biomarkers can be identified, which precede and regulate later phenotypic alterations that occur at high doses, including cell death. We determined that a total of 1,701 genes expressed were significantly affected by high-dose radiation, with the majority of genes affected at 10 Gy. Expression levels of a group of 29 genes, including GDF15, BBC3, PPM1D, FDXR, GADD45A, MDM2, CDKN1A, TP53INP1, CYCSP27, SESN1, SESN2, PCNA and AEN, were similarly altered at both 2 and 10 Gy, but not 0.1 Gy, at both time points. A much larger group of upregulated genes, including those involved in inflammatory responses, was significantly altered only after 10 Gy irradiation. At high doses, downregulated genes were associated with cell cycle regulation and exhibited an apparent linear response between 2 and 10 Gy. While only a few genes were significantly affected by 0.1 Gy irradiation, using stringent statistical filters, groups of related genes regulating cell cycle progression and inflammatory responses consistently exhibited opposite trends in their regulation compared to high-dose irradiated groups. Differential regulation of PLK1 signaling at low- and high-dose irradiation was confirmed using qRT-PCR. These results indicate that some alterations in gene expression are qualitatively different at low and high doses of ionizing radiation in this model system. They also highlight potential biomarkers for radiation exposure that may precede the development of overt physiological symptoms in exposed individuals.

Reconstruction of the Regulatory Network for Bacillus subtilis and Reconciliation with Gene Expression Data.

We introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of Bacillus subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs, and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, we reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches, and small regulatory RNAs. Overall, regulatory information is included in the model for ∼2500 of the ∼4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same "ON" and "OFF" gene expression profiles across multiple samples of experimental data. We show how ARs for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how ARs can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental conditions, gaining insights into novel biology.

Cells Respond to Distinct Nanoparticle Properties with Multiple Strategies As Revealed by Single-Cell RNA-Seq.

The impact of distinct nanoparticle (NP) properties on cellular response and ultimately human health is unclear. This gap is partially due to experimental difficulties in achieving uniform NP loads in the studied cells, creating heterogeneous populations with some cells "overloaded" while other cells are loaded with few or no NPs. Yet gene expression studies have been conducted in the population as a whole, identifying generic responses, while missing unique responses due to signal averaging across many cells, each carrying different loads. Here, we applied single-cell RNA-Seq to alveolar epithelial cells carrying defined loads of aminated or carboxylated quantum dots (QDs), showing higher or lower toxicity, respectively. Interestingly, cells carrying lower loads responded with multiple strategies, mostly with up-regulated processes, which were nonetheless coherent and unique to each QD type. In contrast, cells carrying higher loads responded more uniformly, with mostly down-regulated processes that were shared across QD types. Strategies unique to aminated QDs showed strong up-regulation of stress responses, coupled in some cases with regulation of cell cycle, protein synthesis, and organelle activities. In contrast, strategies unique to carboxylated QDs showed up-regulation of DNA repair and RNA activities and decreased regulation of cell division, coupled in some cases with up-regulation of stress responses and ATP-related functions. Together, our studies suggest scenarios where higher NP loads lock cells into uniform responses, mostly shutdown of cellular processes, whereas lower loads allow for unique responses to each NP type that are more diversified proactive defenses or repairs of the NP insults.

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation.

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets, Atomic Regulons (ARs), represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here, we describe an approach for inferring ARs that leverages large-scale expression data sets, gene context, and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: Hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB, showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness (CLR) analysis, finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs, it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms, we computed ARs for Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus, each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance, but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms, comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain.

Genomic expansion of domain archaea highlights roles for organisms from new phyla in anaerobic carbon cycling.

BACKGROUND: Archaea represent a significant fraction of Earth's biodiversity, yet they remain much less well understood than Bacteria. Gene surveys, a few metagenomic studies, and some single-cell sequencing projects have revealed numerous little-studied archaeal phyla. Certain lineages appear to branch deeply and may be part of a major phylum radiation. The structure of this radiation and the physiology of the organisms remain almost unknown. RESULTS: We used genome-resolved metagenomic analyses to investigate the diversity, genomes sizes, metabolic capacities, and potential roles of Archaea in terrestrial subsurface biogeochemical cycles. We sequenced DNA from complex sediment and planktonic consortia from an aquifer adjacent to the Colorado River (USA) and reconstructed the first complete genomes for Archaea using cultivation-independent methods. To provide taxonomic context, we analyzed an additional 151 newly sampled archaeal sequences. We resolved two new phyla within a major, apparently deep-branching group of phyla (a superphylum). The organisms have small genomes, and metabolic predictions indicate that their primary contributions to Earth's biogeochemical cycles involve carbon and hydrogen metabolism, probably associated with symbiotic and/or fermentation-based lifestyles. CONCLUSIONS: The results dramatically expand genomic sampling of the domain Archaea and clarify taxonomic designations within a major superphylum. This study, in combination with recently published work on bacterial phyla lacking cultivated representatives, reveals a fascinating phenomenon of major radiations of organisms with small genomes, novel proteome composition, and strong interdependence in both domains.

Distinct strains of Toxoplasma gondii feature divergent transcriptomes regardless of developmental stage.

Using high through-put RNA sequencing, we assayed the transcriptomes of three different strains of Toxoplasma gondii representing three common genotypes under both in vitro tachyzoite and in vitro bradyzoite-inducing alkaline stress culture conditions. Strikingly, the differences in transcriptional profiles between the strains, RH, PLK, and CTG, is much greater than differences between tachyzoites and alkaline stressed in vitro bradyzoites. With an FDR of 10%, we identified 241 genes differentially expressed between CTG tachyzoites and in vitro bradyzoites, including 5 putative AP2 transcription factors. We also observed a close association between cell cycle regulated genes and differentiation. By Gene Set Enrichment Analysis (GSEA), there are a number of KEGG pathways associated with the in vitro bradyzoite transcriptomes of PLK and CTG, including pyrimidine metabolism and DNA replication. These functions are likely associated with cell-cycle arrest. When comparing mRNA levels between strains, we identified 1,526 genes that were differentially expressed regardless of culture-condition as well as 846 differentially expressed only in bradyzoites and 542 differentially expressed only in tachyzoites between at least two strains. Using GSEA, we identified that ribosomal proteins were expressed at significantly higher levels in the CTG strain than in either the RH or PLK strains. This association holds true regardless of life cycle stage.