Genome-scale gene/reaction essentiality and synthetic lethality analysis

Synthetic lethals are to pairs of non-essential genes whose simultaneous deletion prohibits growth. One can extend the concept of synthetic lethality by considering gene groups of increasing size where only the simultaneous elimination of all genes is lethal, whereas individual gene deletions are not. We developed optimization-based procedures for the exhaustive and targeted enumeration of multi-gene (and by extension multi-reaction) lethals for genome-scale metabolic models. Specifically, these approaches are applied to iAF1260, the latest model of Escherichia coli, leading to the complete identification of all double and triple gene and reaction synthetic lethals as well as the targeted identification of quadruples and some higher-order ones. Graph representations of these synthetic lethals reveal a variety of motifs ranging from hub-like to highly connected subgraphs providing a birds-eye view of the avenues available for redirecting metabolism and uncovering complex patterns of gene utilization and interdependence. The procedure also enables the use of falsely predicted synthetic lethals for metabolic model curation. By analyzing the functional classifications of the genes involved in synthetic lethals, we reveal surprising connections within and across clusters of orthologous group functional classifications

The AlgZR Two-Component System Recalibrates the RsmAYZ Posttranscriptional Regulatory System To Inhibit Expression of the Pseudomonas aeruginosa Type III Secretion System

Pseudomonas aeruginosa causes chronic airway infections in cystic fibrosis (CF) patients. A classic feature of CF airway isolates is the mucoid phenotype. Mucoidy arises through mutation of the mucA anti-sigma factor and subsequent activation of the AlgU regulon. Inactivation of mucA also results in reduced expression of the Vfr transcription factor. Vfr regulates several important virulence factors, including a type III secretion system (T3SS). In the present study, we report that ExsA expression, the master regulator of T3SS gene expression, is further reduced in mucA mutants through a Vfr-independent mechanism involving the RsmAYZ regulatory system. RsmA is an RNA binding protein required for T3SS gene expression. Genetic experiments suggest that the AlgZR two-component system, part of the AlgU regulon, inhibits ExsA expression by increasing the expression of RsmY and RsmZ, two small noncoding RNAs that sequester RsmA from target mRNAs. Epistasis analyses revealed that increasing the concentration of free RsmA, through either rsmYZ deletion or increased RsmA expression, partially restored T3SS gene expression in the mucA mutant. Furthermore, increasing RsmA availability in combination with Vfr complementation fully restored T3SS expression. Recalibration of the RsmAYZ system by AlgZR, however, did not alter the expression of other selected RsmA-dependent targets. We account for this observation by showing that ExsA expression is more sensitive to changes in free RsmA than other members of the RsmA regulon. Together, these data indicate that recalibration of the RsmAYZ system partially accounts for reduced T3SS gene expression in mucA mutants

Optimisation and investment analysis of two biomass-to-heat supply chain structures

As oil prices have risen dramatically lately, many people explore alternative ways of heating their residences and businesses in order to reduce the respective cost. One of the options usually considered nowadays is biomass, especially in rural areas with significant local biomass availability. This work focuses on comparing two different biomass energy exploitation systems, aiming to provide heat to a specific number of customers at a specific cost. The first system explored is producing pellets from biomass and distributing them to the final customers for use in domestic pellet boilers. The second option is building a centralised co-generation (CHP) unit that will generate electricity and heat. Electricity will be fed to the grid, whereas heat will be distributed to the customers via a district heating network. The biomass source examined is agricultural residues and the model is applied to a case study region in Greece. The analysis is performed from the viewpoint of the potential investor. Several design characteristics of both systems are optimised. In both cases the whole biomass-to-energy supply chain is modelled, both upstream and downstream of the pelleting/CHP units. The results of the case study show that both options have positive financial yield, with the pelleting plant having higher yield. However, the sensitivity analysis reveals that the pelleting plant yield is much more sensitive than that of the CHP plant, therefore constituting a riskier investment. The model presented may be used as a decision support system for potential investors willing to engage in the biomass energy field

Metabolic Adaptation of Ralstonia solanacearum during Plant Infection: A Methionine Biosynthesis Case Study

MetE and MetH are two distinct enzymes that catalyze a similar biochemical reaction during the last step of methionine biosynthesis, MetH being a cobalamin-dependent enzyme whereas MetE activity is cobalamin-independent. In this work, we show that the last step of methionine synthesis in the plant pathogen Ralstonia solanacearum is under the transcriptional control of the master pathogenicity regulator HrpG. This control is exerted essentially on metE expression through the intermediate regulator MetR. Expression of metE is strongly and specifically induced in the presence of plant cells in a hrpG- and metR-dependent manner. metE and metR mutants are not auxotrophic for methionine and not affected for growth inside the plant but produce significantly reduced disease symptoms on tomato whereas disruption of metH has no impact on pathogenicity. The finding that the pathogen preferentially induces metE expression rather than metH in the presence of plant cells is indicative of a probable metabolic adaptation to physiological host conditions since this induction of metE occurs in an environment in which cobalamin, the required co-factor for MetH, is absent. It also shows that MetE and MetH are not functionally redundant and are deployed during specific stages of the bacteria lifecycle, the expression of metE and metH being controlled by multiple and distinct signals

Homologs of the small RNA SgrS are broadly distributed in enteric bacteria but have diverged in size and sequence

Sugar phosphate stress in Escherichia coli is sensed and managed by the transcriptional regulator SgrR and the small RNA (sRNA) SgrS. SgrS is a dual function RNA that performs base pairing-dependent regulation of mRNA targets and encodes a small protein, SgrT. Homologs of SgrR were analyzed for gene synteny and inter-homolog identity to identify those that are likely to be functionally analogous. These 22 SgrR homologs were used to manually locate adjacent sRNAs functionally analogous to SgrS. SgrS homologs shared little sequence identity with E. coli SgrS, but most shared several structural features. The most conserved feature of SgrS homologs was the base pairing region while the most variable feature was the sgrT-coding sequence. Analyses of predicted interactions between SgrS:ptsG mRNA pairs in different organisms revealed interesting differences in the patterns of base pairing interactions. RNA pairs with more interrupted regions of complementarity had a higher proportion of G:C base pairs than those with longer contiguous stretches of complementarity. The identification of this set of homologous sRNAs and their targets sets the stage for future studies to further elucidate the molecular requirements for regulation by SgrS

Query Large Scale Microarray Compendium Datasets Using a Model-Based Bayesian Approach with Variable Selection

In microarray gene expression data analysis, it is often of interest to identify genes that share similar expression profiles with a particular gene such as a key regulatory protein. Multiple studies have been conducted using various correlation measures to identify co-expressed genes. While working well for small datasets, the heterogeneity introduced from increased sample size inevitably reduces the sensitivity and specificity of these approaches. This is because most co-expression relationships do not extend to all experimental conditions. With the rapid increase in the size of microarray datasets, identifying functionally related genes from large and diverse microarray gene expression datasets is a key challenge. We develop a model-based gene expression query algorithm built under the Bayesian model selection framework. It is capable of detecting co-expression profiles under a subset of samples/experimental conditions. In addition, it allows linearly transformed expression patterns to be recognized and is robust against sporadic outliers in the data. Both features are critically important for increasing the power of identifying co-expressed genes in large scale gene expression datasets. Our simulation studies suggest that this method outperforms existing correlation coefficients or mutual information-based query tools. When we apply this new method to the Escherichia coli microarray compendium data, it identifies a majority of known regulons as well as novel potential target genes of numerous key transcription factors

Genetic regulation of glycogen biosynthesis in Escherichia coli : In vivo effects of the catabolite repression and stringent response systems in glg gene expression

The synthesis of two of the Escherichia coli glycogen biosynthetic enzymes, ADPglucose pyrophosphorylase ( glg C) and glycogen synthase ( glg A) was activated by the addition of 5 m M cyclic AMP (cAMP) to maxicells; synthesis of glycogen branching enzyme ( glg B) was unaffected. β -Galactosidase activity expressed from a gene fusion, φ(glg C- lac Z), was approximately five-fold higher in a cya + versus an isogenic cya − strain of E. coli . Addition of cAMP restored β -galactosidase in the cya − strain. The expression of φ(glg C‘−’ lac Z) encoded β -galactosidase activity in a series of spo T mutants exhibited an apparent exponential relationship to intracellular guanosine 5′-diphosphate 3′-diphosphate (ppGpp) levels. These results provide evidence for the control of glycogen biosynthesis in vivo by cAMP and ppGpp at the level of gene expression, and identify a region of DNA required for the control. The φ(glg C‘−’ lac Z) encoded β -galactosidase activity was also elevated three-to five-fold in strain AC70R1, which contains a transacting mutation ( glg Q) that affects the levels of the glycogen biosynthetic enzymes and glg C transcripts.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41337/1/284_2005_Article_BF02091831.pd

Modulation of Bacterial Type III Secretion System by a Spermidine Transporter Dependent Signaling Pathway

10.1371/journal.pone.0001291PLoS ONE212

Genomic mining of prokaryotic repressors for orthogonal logic gates

Genetic circuits perform computational operations based on interactions between freely diffusing molecules within a cell. When transcription factors are combined to build a circuit, unintended interactions can disrupt its function. Here, we apply 'part mining' to build a library of 73 TetR-family repressors gleaned from prokaryotic genomes. The operators of a subset were determined using an in vitro method, and this information was used to build synthetic promoters. The promoters and repressors were screened for cross-reactions. Of these, 16 were identified that both strongly repress their cognate promoter (5- to 207-fold) and exhibit minimal interactions with other promoters. Each repressor-promoter pair was converted to a NOT gate and characterized. Used as a set of 16 NOT/NOR gates, there are >10[superscript 54] circuits that could be built by changing the pattern of input and output promoters. This represents a large set of compatible gates that can be used to construct user-defined circuits.United States. Air Force Office of Scientific Research (Award FA9550-11-C-0028)American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowship (32 CFR 168a)United States. Defense Advanced Research Projects Agency. Chronical of Lineage Indicative of Origins (N66001-12-C-4016)United States. Office of Naval Research (N00014-13-1-0074)National Institutes of Health (U.S.) (GM095765)National Science Foundation (U.S.). Synthetic Biology Engineering Research Center (SA5284-11210