Global transcription regulation of RK2 plasmids: a case study in the combined use of dynamical mathematical models and statistical inference for integration of experimental data and hypothesis exploration

<p>Abstract</p> <p>Background</p> <p>IncP-1 plasmids are broad host range plasmids that have been found in clinical and environmental bacteria. They often carry genes for antibiotic resistance or catabolic pathways. The archetypal IncP-1 plasmid RK2 is a well-characterized biological system, with a fully sequenced and annotated genome and wide range of experimental measurements. Its central control operon, encoding two global regulators KorA and KorB, is a natural example of a negatively self-regulated operon. To increase our understanding of the regulation of this operon, we have constructed a dynamical mathematical model using Ordinary Differential Equations, and employed a Bayesian inference scheme, Markov Chain Monte Carlo (MCMC) using the Metropolis-Hastings algorithm, as a way of integrating experimental measurements and a priori knowledge. We also compared MCMC and Metabolic Control Analysis (MCA) as approaches for determining the sensitivity of model parameters.</p> <p>Results</p> <p>We identified two distinct sets of parameter values, with different biological interpretations, that fit and explain the experimental data. This allowed us to highlight the proportion of repressor protein as dimers as a key experimental measurement defining the dynamics of the system. Analysis of joint posterior distributions led to the identification of correlations between parameters for protein synthesis and partial repression by KorA or KorB dimers, indicating the necessary use of joint posteriors for correct parameter estimation. Using MCA, we demonstrated that the system is highly sensitive to the growth rate but insensitive to repressor monomerization rates in their selected value regions; the latter outcome was also confirmed by MCMC. Finally, by examining a series of different model refinements for partial repression by KorA or KorB dimers alone, we showed that a model including partial repression by KorA and KorB was most compatible with existing experimental data.</p> <p>Conclusions</p> <p>We have demonstrated that the combination of dynamical mathematical models with Bayesian inference is valuable in integrating diverse experimental data and identifying key determinants and parameters for the IncP-1 central control operon. Moreover, we have shown that Bayesian inference and MCA are complementary methods for identification of sensitive parameters. We propose that this demonstrates generic value in applying this combination of approaches to systems biology dynamical modelling.</p

Correlation analysis of the transcriptome of growing leaves with mature leaf parameters in a maize RIL population

Background: To sustain the global requirements for food and renewable resources, unraveling the molecular networks underlying plant growth is becoming pivotal. Although several approaches to identify genes and networks involved in final organ size have been proven successful, our understanding remains fragmentary. Results: Here, we assessed variation in 103 lines of the Zea mays B73xH99 RIL population for a set of final leaf size and whole shoot traits at the seedling stage, complemented with measurements capturing growth dynamics, and cellular measurements. Most traits correlated well with the size of the division zone, implying that the molecular basis of final leaf size is already defined in dividing cells of growing leaves. Therefore, we searched for association between the transcriptional variation in dividing cells of the growing leaf and final leaf size and seedling biomass, allowing us to identify genes and processes correlated with the specific traits. A number of these genes have a known function in leaf development. Additionally, we illustrated that two independent mechanisms contribute to final leaf size, maximal growth rate and the duration of growth. Conclusions: Untangling complex traits such as leaf size by applying in-depth phenotyping allows us to define the relative contributions of the components and their mutual associations, facilitating dissection of the biological processes and regulatory networks underneath

Selection of organisms for the co-evolution-based study of protein interactions

<p>Abstract</p> <p>Background</p> <p>The prediction and study of protein interactions and functional relationships based on similarity of phylogenetic trees, exemplified by the <it>mirrortree </it>and related methodologies, is being widely used. Although dependence between the performance of these methods and the set of organisms used to build the trees was suspected, so far nobody assessed it in an exhaustive way, and, in general, previous works used as many organisms as possible. In this work we asses the effect of using different sets of organism (chosen according with various phylogenetic criteria) on the performance of this methodology in detecting protein interactions of different nature.</p> <p>Results</p> <p>We show that the performance of three <it>mirrortree</it>-related methodologies depends on the set of organisms used for building the trees, and it is not always directly related to the number of organisms in a simple way. Certain subsets of organisms seem to be more suitable for the predictions of certain types of interactions. This relationship between type of interaction and optimal set of organism for detecting them makes sense in the light of the phylogenetic distribution of the organisms and the nature of the interactions.</p> <p>Conclusions</p> <p>In order to obtain an optimal performance when predicting protein interactions, it is recommended to use different sets of organisms depending on the available computational resources and data, as well as the type of interactions of interest.</p

Effects of Central Injection of Anti-LPS Antibody and Blockade of TLR4 on GnRH/LH Secretion during Immunological Stress in Anestrous Ewes

The present study was designed to examine the effect of intracerebroventricular (icv) administration of antilipopolysaccharide (LPS) antibody and blockade of Toll-like receptor 4 (TLR4) during immune stress induced by intravenous (iv) LPS injection on the gonadotropin-releasing hormone/luteinizing hormone (GnRH/LH) secretion in anestrous ewes. Injection of anti-LPS antibody and TLR4 blockade significantly (P < 0.01) reduced the LPS dependent lowering amount of GnRH mRNA in the median eminence (ME). Moreover, blockade of TLR4 caused restoration of LH-β transcription in the anterior pituitary decreased by the immune stress. However, there was no effect of this treatment on reduced LH release. The results of our study showed that the blockade of TLR4 receptor in the hypothalamus is not sufficient to unblock the release of LH suppressed by the immune/inflammatory challenges. This suggests that during inflammation the LH secretion could be inhibited directly at the pituitary level by peripheral factors such as proinflammatory cytokines and circulating endotoxin as well

The development of new methodology for determination of vincristine (VCR) in human serum using LC-MS/MS-based method for medical diagnostics

In this article, we have presented the development and validation of a rapid and sensitive reversed-phase liquid chromatography with tandem mass spectrometry (LC-MS/MS) method for the determination of vincristine (VCR) in patient serum samples. Chromatographic separation was achieved on a Kinetex(®) (Singapore) column using a mobile phase consisting of 25 mM acetic acid and 0.3% formic acid (A) and methanol (B) in a gradient elution mode at a flow rate of 0.3 mL/min. The VCR and internal standard (vinblastine) were monitored using the multiple reaction monitoring mode under positive electrospray ionization. The lower limit of quantification (LLOQ) was 0.67 ng/mL, and the upper limit of quantification (ULOQ) was 250 ng/mL for VCR. The calculated values of LOD and LOQ for VCR were 0.075 and 0.228 ng/mL, respectively. The calibration curve was linear over the VCR concentration range of 1.0–250 ng/mL in serum. The intra- and inter-day precision and precision were within the generally accepted criteria for the bioanalytical method (<15%). The method was successfully applied to the analysis of serum samples in clinical practice

Combined large-scale phenotyping and transcriptomics in maize reveals a robust growth regulatory network

Leaves are vital organs for biomass and seed production because of their role in the generation of metabolic energy and organic compounds. A better understanding of the molecular networks underlying leaf development is crucial to sustain global requirements for food and renewable energy. Here, we combined transcriptome profiling of proliferative leaf tissue with indepth phenotyping of the fourth leaf at later stages of development in 197 recombinant inbred lines of two different maize (Zea mays) populations. Previously, correlation analysis in a classical biparental mapping population identified 1,740 genes correlated with at least one of 14 traits. Here, we extended these results with data from a multiparent advanced generation intercross population. As expected, the phenotypic variability was found to be larger in the latter population than in the biparental population, although general conclusions on the correlations among the traits are comparable. Data integration from the two diverse populations allowed us to identify a set of 226 genes that are robustly associated with diverse leaf traits. This set of genes is enriched for transcriptional regulators and genes involved in protein synthesis and cell wall metabolism. In order to investigate the molecular network context of the candidate gene set, we integrated our data with publicly available functional genomics data and identified a growth regulatory network of 185 genes. Our results illustrate the power of combining in-depth phenotyping with transcriptomics in mapping populations to dissect the genetic control of complex traits and present a set of candidate genes for use in biomass improvement

Using single‐plant‐omics in the field to link maize genes to functions and phenotypes

Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene-phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory-generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field-generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab-field gap

The reduction in maize leaf growth under mild drought affects the transition between cell division and cell expansion and cannot be restored by elevated gibberellic acid levels

Growth is characterized by the interplay between cell division and cell expansion, two processes that occur separated along the growth zone at the maize leaf. To gain further insight into the transition between cell division and cell expansion, conditions were investigated in which the position of this transition zone was positively or negatively affected. High levels of gibberellic acid (GA) in plants overexpressing the GA biosynthesis gene GA20-OXIDASE (GA20OX-1(OE)) shifted the transition zone more distally, whereas mild drought, which is associated with lowered GA biosynthesis, resulted in a more basal positioning. However, the increased levels of GA in the GA20OX-1(OE) line were insufficient to convey tolerance to the mild drought treatment, indicating that another mechanism in addition to lowered GA levels is restricting growth during drought. Transcriptome analysis with high spatial resolution indicated that mild drought specifically induces a reprogramming of transcriptional regulation in the division zone. 'Leaf Growth Viewer' was developed as an online searchable tool containing the high-resolution data

Parasitic nematodes of the genus Syphacia Seurat, 1916 infecting Cricetidae in the British Isles: The enigmatic status of Syphacia nigeriana

Oxyurid nematodes (Syphacia spp.) from bank (Myodes glareolus) and field/common (Microtus spp.) voles, from disparate geographical sites in the British Isles, were examined morphologically and genetically. The genetic signatures of 118 new isolates are provided, based primarily on the rDNA internal transcribed spacers (ITS1-5.8S-ITS2) region and for representative isolates also on the small subunit 18S rDNA region and cytochrome c oxidase subunit 1 (cox-1) gene locus. Genetic data on worms recovered from Microtus spp. from the European mainland and from other rodent genera from the Palaearctic, North America and West Africa are also included. We test historical hypotheses indicating that S. nigeriana is a generalist species, infecting a range of different rodent genera. Our results establish that S. nigeriana is a parasite of both bank and field voles in the British Isles. An identical genotype was also recorded from Hubert's multimammate mouse (Mastomys huberti) from Senegal, but Mastomys spp. from West Africa were additionally parasitized by a related, although genetically distinct Syphacia species. We found no evidence for S. petrusewiczi in voles from the British Isles but isolates from Russia and North America were genetically distinct and formed their own separate deep branch in maximum likelihood molecular phylogenetic trees