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1.  Dynamic cyanobacterial response to hydration and dehydration in a desert biological soil crust 
The ISME Journal  2013;7(11):2178-2191.
Biological soil crusts (BSCs) cover extensive portions of the earth's deserts. In order to survive desiccation cycles and utilize short periods of activity during infrequent precipitation, crust microorganisms must rely on the unique capabilities of vegetative cells to enter a dormant state and be poised for rapid resuscitation upon wetting. To elucidate the key events involved in the exit from dormancy, we performed a wetting experiment of a BSC and followed the response of the dominant cyanobacterium, Microcoleus vaginatus, in situ using a whole-genome transcriptional time course that included two diel cycles. Immediate, but transient, induction of DNA repair and regulatory genes signaled the hydration event. Recovery of photosynthesis occurred within 1 h, accompanied by upregulation of anabolic pathways. Onset of desiccation was characterized by the induction of genes for oxidative and photo-oxidative stress responses, osmotic stress response and the synthesis of C and N storage polymers. Early expression of genes for the production of exopolysaccharides, additional storage molecules and genes for membrane unsaturation occurred before drying and hints at preparedness for desiccation. We also observed signatures of preparation for future precipitation, notably the expression of genes for anaplerotic reactions in drying crusts, and the stable maintenance of mRNA through dormancy. These data shed light on possible synchronization between this cyanobacterium and its environment, and provides key mechanistic insights into its metabolism in situ that may be used to predict its response to climate, and or, land-use driven perturbations.
doi:10.1038/ismej.2013.83
PMCID: PMC3806265  PMID: 23739051
biological soil crust; desiccation survival; dormancy; Microcoleus vaginatus; pulsed-activity event; resuscitation
2.  The COMBREX Project: Design, Methodology, and Initial Results 
Anton, Brian P. | Chang, Yi-Chien | Brown, Peter | Choi, Han-Pil | Faller, Lina L. | Guleria, Jyotsna | Hu, Zhenjun | Klitgord, Niels | Levy-Moonshine, Ami | Maksad, Almaz | Mazumdar, Varun | McGettrick, Mark | Osmani, Lais | Pokrzywa, Revonda | Rachlin, John | Swaminathan, Rajeswari | Allen, Benjamin | Housman, Genevieve | Monahan, Caitlin | Rochussen, Krista | Tao, Kevin | Bhagwat, Ashok S. | Brenner, Steven E. | Columbus, Linda | de Crécy-Lagard, Valérie | Ferguson, Donald | Fomenkov, Alexey | Gadda, Giovanni | Morgan, Richard D. | Osterman, Andrei L. | Rodionov, Dmitry A. | Rodionova, Irina A. | Rudd, Kenneth E. | Söll, Dieter | Spain, James | Xu, Shuang-yong | Bateman, Alex | Blumenthal, Robert M. | Bollinger, J. Martin | Chang, Woo-Suk | Ferrer, Manuel | Friedberg, Iddo | Galperin, Michael Y. | Gobeill, Julien | Haft, Daniel | Hunt, John | Karp, Peter | Klimke, William | Krebs, Carsten | Macelis, Dana | Madupu, Ramana | Martin, Maria J. | Miller, Jeffrey H. | O'Donovan, Claire | Palsson, Bernhard | Ruch, Patrick | Setterdahl, Aaron | Sutton, Granger | Tate, John | Yakunin, Alexander | Tchigvintsev, Dmitri | Plata, Germán | Hu, Jie | Greiner, Russell | Horn, David | Sjölander, Kimmen | Salzberg, Steven L. | Vitkup, Dennis | Letovsky, Stanley | Segrè, Daniel | DeLisi, Charles | Roberts, Richard J. | Steffen, Martin | Kasif, Simon
PLoS Biology  2013;11(8):e1001638.
Experimental data exists for only a vanishingly small fraction of sequenced microbial genes. This community page discusses the progress made by the COMBREX project to address this important issue using both computational and experimental resources.
doi:10.1371/journal.pbio.1001638
PMCID: PMC3754883  PMID: 24013487
3.  The Ability of Flux Balance Analysis to Predict Evolution of Central Metabolism Scales with the Initial Distance to the Optimum 
PLoS Computational Biology  2013;9(6):e1003091.
The most powerful genome-scale framework to model metabolism, flux balance analysis (FBA), is an evolutionary optimality model. It hypothesizes selection upon a proposed optimality criterion in order to predict the set of internal fluxes that would maximize fitness. Here we present a direct test of the optimality assumption underlying FBA by comparing the central metabolic fluxes predicted by multiple criteria to changes measurable by a 13C-labeling method for experimentally-evolved strains. We considered datasets for three Escherichia coli evolution experiments that varied in their length, consistency of environment, and initial optimality. For ten populations that were evolved for 50,000 generations in glucose minimal medium, we observed modest changes in relative fluxes that led to small, but significant decreases in optimality and increased the distance to the predicted optimal flux distribution. In contrast, seven populations evolved on the poor substrate lactate for 900 generations collectively became more optimal and had flux distributions that moved toward predictions. For three pairs of central metabolic knockouts evolved on glucose for 600–800 generations, there was a balance between cases where optimality and flux patterns moved toward or away from FBA predictions. Despite this variation in predictability of changes in central metabolism, two generalities emerged. First, improved growth largely derived from evolved increases in the rate of substrate use. Second, FBA predictions bore out well for the two experiments initiated with ancestors with relatively sub-optimal yield, whereas those begun already quite optimal tended to move somewhat away from predictions. These findings suggest that the tradeoff between rate and yield is surprisingly modest. The observed positive correlation between rate and yield when adaptation initiated further from the optimum resulted in the ability of FBA to use stoichiometric constraints to predict the evolution of metabolism despite selection for rate.
Author Summary
The most common method of modeling genome-scale metabolism, flux balance analysis, involves using known stoichiometry to define feasible metabolic states and then choosing between these states by proposing that evolution has selected a metabolic flux that optimizes fitness. But does evolution optimize metabolism, and if so, what component of metabolism equates to fitness? We directly tested the underlying assumption of stoichiometric optimality by comparing predicted flux distributions with changes in fluxes that occurred following experimental evolution. Across three experiments ranging in length from a few hundred to fifty thousand generations, we found that substrate uptake – an input to the model – always increased, but supposed optimality criteria such as yield only increased sometimes. Despite this, there was a clear trend. Highly optimal ancestors evolved slightly lower yield in the course of increasing the overall rate, whereas more sub-optimal strains were able to increase both. These results suggest that flux balance analysis is capable of predicting either the initial metabolic behavior of strains or how they will evolve, but not both.
doi:10.1371/journal.pcbi.1003091
PMCID: PMC3688462  PMID: 23818838
4.  Deep Sequencing of the Oral Microbiome Reveals Signatures of Periodontal Disease 
PLoS ONE  2012;7(6):e37919.
The oral microbiome, the complex ecosystem of microbes inhabiting the human mouth, harbors several thousands of bacterial types. The proliferation of pathogenic bacteria within the mouth gives rise to periodontitis, an inflammatory disease known to also constitute a risk factor for cardiovascular disease. While much is known about individual species associated with pathogenesis, the system-level mechanisms underlying the transition from health to disease are still poorly understood. Through the sequencing of the 16S rRNA gene and of whole community DNA we provide a glimpse at the global genetic, metabolic, and ecological changes associated with periodontitis in 15 subgingival plaque samples, four from each of two periodontitis patients, and the remaining samples from three healthy individuals. We also demonstrate the power of whole-metagenome sequencing approaches in characterizing the genomes of key players in the oral microbiome, including an unculturable TM7 organism. We reveal the disease microbiome to be enriched in virulence factors, and adapted to a parasitic lifestyle that takes advantage of the disrupted host homeostasis. Furthermore, diseased samples share a common structure that was not found in completely healthy samples, suggesting that the disease state may occupy a narrow region within the space of possible configurations of the oral microbiome. Our pilot study demonstrates the power of high-throughput sequencing as a tool for understanding the role of the oral microbiome in periodontal disease. Despite a modest level of sequencing (∼2 lanes Illumina 76 bp PE) and high human DNA contamination (up to ∼90%) we were able to partially reconstruct several oral microbes and to preliminarily characterize some systems-level differences between the healthy and diseased oral microbiomes.
doi:10.1371/journal.pone.0037919
PMCID: PMC3366996  PMID: 22675498
5.  Detection of transcriptional triggers in the dynamics of microbial growth: application to the respiratorily versatile bacterium Shewanella oneidensis 
Nucleic Acids Research  2012;40(15):7132-7149.
The capacity of microorganisms to respond to variable external conditions requires a coordination of environment-sensing mechanisms and decision-making regulatory circuits. Here, we seek to understand the interplay between these two processes by combining high-throughput measurement of time-dependent mRNA profiles with a novel computational approach that searches for key genetic triggers of transcriptional changes. Our approach helped us understand the regulatory strategies of a respiratorily versatile bacterium with promising bioenergy and bioremediation applications, Shewanella oneidensis, in minimal and rich media. By comparing expression profiles across these two conditions, we unveiled components of the transcriptional program that depend mainly on the growth phase. Conversely, by integrating our time-dependent data with a previously available large compendium of static perturbation responses, we identified transcriptional changes that cannot be explained solely by internal network dynamics, but are rather triggered by specific genes acting as key mediators of an environment-dependent response. These transcriptional triggers include known and novel regulators that respond to carbon, nitrogen and oxygen limitation. Our analysis suggests a sequence of physiological responses, including a coupling between nitrogen depletion and glycogen storage, partially recapitulated through dynamic flux balance analysis, and experimentally confirmed by metabolite measurements. Our approach is broadly applicable to other systems.
doi:10.1093/nar/gks467
PMCID: PMC3424579  PMID: 22638572
6.  Host-pathogen interactome mapping for HTLV-1 and -2 retroviruses 
Retrovirology  2012;9:26.
Background
Human T-cell leukemia virus type 1 (HTLV-1) and type 2 both target T lymphocytes, yet induce radically different phenotypic outcomes. HTLV-1 is a causative agent of Adult T-cell leukemia (ATL), whereas HTLV-2, highly similar to HTLV-1, causes no known overt disease. HTLV gene products are engaged in a dynamic struggle of activating and antagonistic interactions with host cells. Investigations focused on one or a few genes have identified several human factors interacting with HTLV viral proteins. Most of the available interaction data concern the highly investigated HTLV-1 Tax protein. Identifying shared and distinct host-pathogen protein interaction profiles for these two viruses would enlighten how they exploit distinctive or common strategies to subvert cellular pathways toward disease progression.
Results
We employ a scalable methodology for the systematic mapping and comparison of pathogen-host protein interactions that includes stringent yeast two-hybrid screening and systematic retest, as well as two independent validations through an additional protein interaction detection method and a functional transactivation assay. The final data set contained 166 interactions between 10 viral proteins and 122 human proteins. Among the 166 interactions identified, 87 and 79 involved HTLV-1 and HTLV-2 -encoded proteins, respectively. Targets for HTLV-1 and HTLV-2 proteins implicate a diverse set of cellular processes including the ubiquitin-proteasome system, the apoptosis, different cancer pathways and the Notch signaling pathway.
Conclusions
This study constitutes a first pass, with homogeneous data, at comparative analysis of host targets for HTLV-1 and -2 retroviruses, complements currently existing data for formulation of systems biology models of retroviral induced diseases and presents new insights on biological pathways involved in retroviral infection.
doi:10.1186/1742-4690-9-26
PMCID: PMC3351729  PMID: 22458338
HTLV; Interactome; Retrovirus; ORFeome; Tax; HBZ
7.  Empirically-controlled mapping of the Caenorhabditis elegans protein-protein interactome network 
Nature methods  2009;6(1):47-54.
To provide accurate biological hypotheses and inform upon global properties of cellular networks, systematic identification of protein–protein interactions has to meet high-quality standards. We present an expanded Caenorhabditis elegans protein-protein interaction network, or “interactome” map derived from testing a matrix of ~ 10,000 × ~ 10,000 proteins using a highly specific high-throughput yeast two-hybrid system. Through a new quality control empirical framework, We show that the resulting dataset (Worm Interactome 2007 or WI-2007) is similar in quality to low-throughput data curated from the literature. Previous interaction datasets have been filtered and integrated with WI-2007 to generate a high confidence consolidated map (Worm Interactome version 8 or WI8). This work allows us to estimate the size of the worm interactome at ~ 116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationship features between genes or proteins.
PMCID: PMC3057923  PMID: 19123269
8.  COMBREX: a project to accelerate the functional annotation of prokaryotic genomes 
Nucleic Acids Research  2010;39(Database issue):D11-D14.
COMBREX (http://combrex.bu.edu) is a project to increase the speed of the functional annotation of new bacterial and archaeal genomes. It consists of a database of functional predictions produced by computational biologists and a mechanism for experimental biochemists to bid for the validation of those predictions. Small grants are available to support successful bids.
doi:10.1093/nar/gkq1168
PMCID: PMC3013729  PMID: 21097892
9.  Environments that Induce Synthetic Microbial Ecosystems 
PLoS Computational Biology  2010;6(11):e1001002.
Interactions between microbial species are sometimes mediated by the exchange of small molecules, secreted by one species and metabolized by another. Both one-way (commensal) and two-way (mutualistic) interactions may contribute to complex networks of interdependencies. Understanding these interactions constitutes an open challenge in microbial ecology, with applications ranging from the human microbiome to environmental sustainability. In parallel to natural communities, it is possible to explore interactions in artificial microbial ecosystems, e.g. pairs of genetically engineered mutualistic strains. Here we computationally generate artificial microbial ecosystems without re-engineering the microbes themselves, but rather by predicting their growth on appropriately designed media. We use genome-scale stoichiometric models of metabolism to identify media that can sustain growth for a pair of species, but fail to do so for one or both individual species, thereby inducing putative symbiotic interactions. We first tested our approach on two previously studied mutualistic pairs, and on a pair of highly curated model organisms, showing that our algorithms successfully recapitulate known interactions, robustly predict new ones, and provide novel insight on exchanged molecules. We then applied our method to all possible pairs of seven microbial species, and found that it is always possible to identify putative media that induce commensalism or mutualism. Our analysis also suggests that symbiotic interactions may arise more readily through environmental fluctuations than genetic modifications. We envision that our approach will help generate microbe-microbe interaction maps useful for understanding microbial consortia dynamics and evolution, and for exploring the full potential of natural metabolic pathways for metabolic engineering applications.
Author Summary
Microbial metabolism affects biogeochemical cycles and human health. In most natural environments, multiple microbial species interact with each other, forming complex ecosystems whose properties are poorly understood. In an effort to understand inter-microbial interactions, and to explore new metabolic engineering avenues, researchers have started building artificial microbial ecosystems, e.g. pairs of genetically engineered strains that require each other for survival. Here we computationally explore the possibility of creating artificial microbial ecosystems without re-engineering the microbes themselves, but rather by manipulating the environment in which they grow. Specifically, using the framework of flux balance analysis, we predict environments in which either one or both microbes in a pair would not be able to grow without the other, inducing commensal (one-way) or mutualistic (two-way) interactions, respectively. Our algorithms can successfully recapitulate known inter-microbial interactions, and predict millions of new ones across any pair amongst different microbial species. Surprisingly, we find that it is always possible to identify conditions that induce mutualistic or commensal interactions between any two species. Hence, our method should help in mapping naturally occurring microbe-microbe interactions, and in engineering new ones through a novel, environment-driven branch of synthetic ecology.
doi:10.1371/journal.pcbi.1001002
PMCID: PMC2987903  PMID: 21124952
10.  A Genome-Wide Gene Function Prediction Resource for Drosophila melanogaster 
PLoS ONE  2010;5(8):e12139.
Predicting gene functions by integrating large-scale biological data remains a challenge for systems biology. Here we present a resource for Drosophila melanogaster gene function predictions. We trained function-specific classifiers to optimize the influence of different biological datasets for each functional category. Our model predicted GO terms and KEGG pathway memberships for Drosophila melanogaster genes with high accuracy, as affirmed by cross-validation, supporting literature evidence, and large-scale RNAi screens. The resulting resource of prioritized associations between Drosophila genes and their potential functions offers a guide for experimental investigations.
doi:10.1371/journal.pone.0012139
PMCID: PMC2920829  PMID: 20711346
11.  An empirical framework for binary interactome mapping 
Nature methods  2008;6(1):83-90.
Several attempts have been made at systematically mapping protein-protein interaction, or “interactome” networks. However, it remains difficult to assess the quality and coverage of existing datasets. We describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human are superior in precision to literature-curated interactions supported by only a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains ~130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the human genome project, estimates of protein interaction data quality and interactome size are critical to establish the magnitude of the task of comprehensive human interactome mapping and to illuminate a path towards this goal.
doi:10.1038/nmeth.1280
PMCID: PMC2872561  PMID: 19060904
12.  Edgetic perturbation models of human inherited disorders 
Cellular functions are mediated through complex systems of macromolecules and metabolites linked through biochemical and physical interactions, represented in interactome models as ‘nodes' and ‘edges', respectively. Better understanding of genotype-to-phenotype relationships in human disease will require modeling of how disease-causing mutations affect systems or interactome properties. Here we investigate how perturbations of interactome networks may differ between complete loss of gene products (‘node removal') and interaction-specific or edge-specific (‘edgetic') alterations. Global computational analyses of ∼50 000 known causative mutations in human Mendelian disorders revealed clear separations of mutations probably corresponding to those of node removal versus edgetic perturbations. Experimental characterization of mutant alleles in various disorders identified diverse edgetic interaction profiles of mutant proteins, which correlated with distinct structural properties of disease proteins and disease mechanisms. Edgetic perturbations seem to confer distinct functional consequences from node removal because a large fraction of cases in which a single gene is linked to multiple disorders can be modeled by distinguishing edgetic network perturbations. Edgetic network perturbation models might improve both the understanding of dissemination of disease alleles in human populations and the development of molecular therapeutic strategies.
doi:10.1038/msb.2009.80
PMCID: PMC2795474  PMID: 19888216
binary protein interaction; genotype-to-phenotype relationships; human Mendelian disorders; network perturbation
13.  A protein domain-based interactome network for C. elegans early embryogenesis 
Cell  2008;134(3):534-545.
Summary
Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or “interactome” networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based interactome network for proteins involved in C. elegans early embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete interactome network. This interactome modeling strategy revealed new insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms.
doi:10.1016/j.cell.2008.07.009
PMCID: PMC2596478  PMID: 18692475
14.  Novel insights into RNAi off-target effects using C. elegans paralogs 
BMC Genomics  2007;8:106.
Background
In the few years since its discovery, RNAi has turned into a very powerful tool for the study of gene function by allowing post-transcriptional gene silencing. The RNAi mechanism, which is based on the introduction of a double-stranded RNA (dsRNA) trigger whose sequence is similar to that of the targeted messenger RNA (mRNA), is subject to off-target cross-reaction.
Results
We use a novel strategy based on phenotypic analysis of paralogs and predict that, in Caenorhabditis elegans, off-target effects occur when an mRNA sequence shares more than 95% identity over 40 nucleotides with the dsRNA. Interestingly, our results suggest that the minimum length necessary of a high-similarity stretch between a dsRNA and its target in order to observe an efficient RNAi effect varies from 30 to 50 nucleotides rather than 22 nucleotides, which is the length of siRNAs in C. elegans.
Conclusion
Our predictive methods would improve the design of dsRNA and ultimately the use of RNAi as a therapeutic tool upon experimental verification.
doi:10.1186/1471-2164-8-106
PMCID: PMC1868761  PMID: 17445269
15.  Intrinsic Disorder Is a Common Feature of Hub Proteins from Four Eukaryotic Interactomes 
PLoS Computational Biology  2006;2(8):e100.
Recent proteome-wide screening approaches have provided a wealth of information about interacting proteins in various organisms. To test for a potential association between protein connectivity and the amount of predicted structural disorder, the disorder propensities of proteins with various numbers of interacting partners from four eukaryotic organisms (Caenorhabditis elegans, Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens) were investigated. The results of PONDR VL-XT disorder analysis show that for all four studied organisms, hub proteins, defined here as those that interact with ≥10 partners, are significantly more disordered than end proteins, defined here as those that interact with just one partner. The proportion of predicted disordered residues, the average disorder score, and the number of predicted disordered regions of various lengths were higher overall in hubs than in ends. A binary classification of hubs and ends into ordered and disordered subclasses using the consensus prediction method showed a significant enrichment of wholly disordered proteins and a significant depletion of wholly ordered proteins in hubs relative to ends in worm, fly, and human. The functional annotation of yeast hubs and ends using GO categories and the correlation of these annotations with disorder predictions demonstrate that proteins with regulation, transcription, and development annotations are enriched in disorder, whereas proteins with catalytic activity, transport, and membrane localization annotations are depleted in disorder. The results of this study demonstrate that intrinsic structural disorder is a distinctive and common characteristic of eukaryotic hub proteins, and that disorder may serve as a determinant of protein interactivity.
Synopsis
From the formulation of Emil Fisher's lock-and-key hypothesis in 1894 until the early 1990s, a dominating and widely accepted concept in molecular biology was the protein structure–function paradigm. According to this concept, a protein can perform its biological function(s) only after folding into a specific rigid 3-D structure. Only recently has the validity of this structure–function paradigm been seriously challenged, primarily through the wealth of counterexamples that have gradually accumulated over the past 15 years. These counterexamples demonstrated that many proteins exist in a natively unfolded (or intrinsically disordered) state, and function without a prerequisite stably folded structure. In many cases, the lack of structure is required for biological function. Previous results have implicated intrinsic disorder as having an important role in protein interactions. The authors generalize this notion by comparing interaction networks from four eukaryotic organisms: yeast, worm, fly, and human. They have found that within these networks the proteins that interact with multiple protein partners (network hubs) are significantly more disordered than proteins that interact with a single protein partner (network ends). The results of this study demonstrate that intrinsic structural disorder is a distinctive and common characteristic of hub proteins, and that disorder may serve as a determinant of protein interactivity.
doi:10.1371/journal.pcbi.0020100
PMCID: PMC1526461  PMID: 16884331

Results 1-15 (15)