Metabolic response of porcine colon explants to in vitro infection by <Emphasis Type="Italic">Brachyspira hyodysenteriae</Emphasis>: a leap into disease pathophysiology

Introduction

Swine dysentery caused by Brachyspira hyodysenteriae is a production limiting disease in pig farming. Currently antimicrobial therapy is the only treatment and control method available.

Objective

The aim of this study was to characterize the metabolic response of porcine colon explants to infection by B. hyodysenteriae.

Methods

Porcine colon explants exposed to B. hyodysenteriae were analyzed for histopathological, metabolic and pro-inflammatory gene expression changes.

Results

Significant epithelial necrosis, increased levels of l-citrulline and IL-1α were observed on explants infected with B. hyodysenteriae.

Conclusions

The spirochete induces necrosis in vitro likely through an inflammatory process mediated by IL-1α and NO.

     

Detecting complexes from edge-weighted PPI networks via genes expression analysis

Background

Identifying complexes from PPI networks has become a key problem to elucidate protein functions and identify signal and biological processes in a cell. Proteins binding as complexes are important roles of life activity. Accurate determination of complexes in PPI networks is crucial for understanding principles of cellular organization.

Results

We propose a novel method to identify complexes on PPI networks, based on different co-expression information. First, we use Markov Cluster Algorithm with an edge-weighting scheme to calculate complexes on PPI networks. Then, we propose some significant features, such as graph information and gene expression analysis, to filter and modify complexes predicted by Markov Cluster Algorithm. To evaluate our method, we test on two experimental yeast PPI networks.

Conclusions

On DIP network, our method has Precision and F-Measure values of 0.6004 and 0.5528. On MIPS network, our method has F-Measure and S _n values of 0.3774 and 0.3453. Comparing to existing methods, our method improves Precision value by at least 0.1752, F-Measure value by at least 0.0448, S _n value by at least 0.0771. Experiments show that our method achieves better results than some state-of-the-art methods for identifying complexes on PPI networks, with the prediction quality improved in terms of evaluation criteria.

     

Boolean genetic network model for the control of <Emphasis Type="Italic">C. elegans</Emphasis> early embryonic cell cycles

Background

In Caenorhabditis elegans early embryo, cell cycles only have two phases: DNA synthesis and mitosis, which are different from the typical 4-phase cell cycle. Modeling this cell-cycle process into network can fill up the gap in C. elegans cell-cycle study and provide a thorough understanding on the cell-cycle regulations and progressions at the network level.

Methods

In this paper, C. elegans early embryonic cell-cycle network has been constructed based on the knowledge of key regulators and their interactions from literature studies. A discrete dynamical Boolean model has been applied in computer simulations to study dynamical properties of this network. The cell-cycle network is compared with random networks and tested under several perturbations to analyze its robustness. To investigate whether our proposed network could explain biological experiment results, we have also compared the network simulation results with gene knock down experiment data.

Results

With the Boolean model, this study showed that the cell-cycle network was stable with a set of attractors (fixed points). A biological pathway was observed in the simulation, which corresponded to a whole cell-cycle progression. The C. elegans network was significantly robust when compared with random networks of the same size because there were less attractors and larger basins than random networks. Moreover, the network was also robust under perturbations with no significant change of the basin size. In addition, the smaller number of attractors and the shorter biological pathway from gene knock down network simulation interpreted the shorter cell-cycle lengths in mutant from the RNAi gene knock down experiment data. Hence, we demonstrated that the results in network simulation could be verified by the RNAi gene knock down experiment data.

Conclusions

A C. elegans early embryonic cell cycles network was constructed and its properties were analyzed and compared with those of random networks. Computer simulation results provided biologically meaningful interpretations of RNAi gene knock down experiment data.

     

Iridoid and phenylethanoid/phenylpropanoid metabolite profiles of <Emphasis Type="Italic">Scrophularia</Emphasis> and <Emphasis Type="Italic">Verbascum</Emphasis> species used medicinally in North America

Introduction

Botanicals containing iridoid and phenylethanoid/phenylpropanoid glycosides are used worldwide for the treatment of inflammatory musculoskeletal conditions that are primary causes of human years lived with disability, such as arthritis and lower back pain.

Objectives

We report the analysis of candidate anti-inflammatory metabolites of several endemic Scrophularia species and Verbascum thapsus used medicinally by peoples of North America.

Methods

Leaves, stems, and roots were analyzed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and partial least squares-discriminant analysis (PLS-DA) was performed in MetaboAnalyst 3.0 after processing the datasets in Progenesis QI.

Results

Comparison of the datasets revealed significant and differential accumulation of iridoid and phenylethanoid/phenylpropanoid glycosides in the tissues of the endemic Scrophularia species and Verbascum thapsus.

Conclusions

Our investigation identified several species of pharmacological interest as good sources for harpagoside and other important anti-inflammatory metabolites.

     

<Emphasis Type="Italic">massPix</Emphasis>: an R package for annotation and interpretation of mass spectrometry imaging data for lipidomics

Introduction

Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools.

Objectives

We have developed massPix—an R package for analysing and interpreting data from MSI of lipids in tissue.

Methods

massPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries.

Results

Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering.

Conclusion

massPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications.

     

Comparative analysis of metabolic network of pathogens

Background

Metabolic networks are complex and system of highly connected chemical reactions and hence it needs a system level computational approach to identify the genotype- phenotype relationship. The study of essential genes and reactions and synthetic lethality of genes and reactions plays a crucial role in explaining functional links between genes and gene function predictions.

Methods

Flux balance analysis (FBA) has been developed as a powerful method for the in silico analyses of metabolic networks. In this study, we present the comparative analysis of the genomic scale metabolic networks of the four microorganisms i.e. Salmonella typhimurium, Mycobacterium tuberculosis, Staphylococcus aureus, and Helicobacter pylori. The fluxes of all reaction were obtained and the growth rate of the organism was calculated by setting the biomass reaction as the objective function.

Results & Conclusions

The average lethality fraction of all the four organisms studied ranged from 0.2 to 0.6. It was also observed that there are very few metabolites which are highly connected. Those metabolites that are highly connected are supposed to be the ‘global players’ similar to the hub protein in the protein–protein interaction network.

     

Multi-component gene network design as a survival strategy in diverse environments

Background

Gene-environment interactions are often mediated though gene networks in which gene expression products interact with other network components to dictate network activity levels, which in turn determines the fitness of the host cell in specific environments. Even though a gene network is the right context for studying gene-environment interactions, we have little understanding on how systematic genetic perturbations affects fitness in the context of a gene network.

Results

Here we examine the effect of combinatorial gene dosage alterations on gene network activity and cellular fitness. Using the galactose utilization pathway as a model network in diploid yeast, we reduce the copy number of four regulatory genes (GAL2, GAL3, GAL4, GAL80) from two to one, and measure the activity of the perturbed networks. We integrate these results with competitive fitness measurements made in six different rationally-designed environments containing different galactose concentrations representing the natural induction spectrum of the galactose network. In the lowest galactose environment, we find a nonlinear relationship between gene expression and fitness while high galactose environments lead to a linear relationship between the two with a saturation regime reached at a sufficiently high galactose concentration. We further uncover environment-specific relevance of the different network components for dictating the relationship between the network activity and organismal fitness, indicating that none of the network components are redundant.

Conclusions

These results provide experimental support to the hypothesis that dynamic changes in the environment throughout natural evolution is key to structuring natural gene networks in a multi-component fashion, which robustly provides protection against population extinction in different environments.

     

Study on the Clinical Features and Prognosis of <Emphasis Type="Italic">Penicilliosis marneffei</Emphasis> Without Human Immunodeficiency Virus Infection

Objective

To improve the diagnosis and treatment of Penicilliosis marneffei without human immunodeficiency virus infection.

Methods

Analyze and review the clinical features, diagnosis and treatment of six cases of P. marneffei without human immunodeficiency virus infection at The First Affiliated Hospital of Fujian Medical University.

Results

Two cases were diagnosed in the ENT Department, three cases in the respiratory department and one case in the dermatological department. Penicillium marneffei infection was confirmed by sputum culture, blood culture and tissue biopsy. After definite diagnosis, one refused further treatment, and others showed significant improvement.

Conclusion

Penicilliosis marneffei is insidious onset and easy to be escaped and misdiagnosed. To achieve early diagnosis and appropriate treatment, doubtful cases should be alerted for the diagnoses as P. marneffei.

     

Measuring phenotype-phenotype similarity through the interactome

Background

Recently, measuring phenotype similarity began to play an important role in disease diagnosis. Researchers have begun to pay attention to develop phenotype similarity measurement. However, existing methods ignore the interactions between phenotype-associated proteins, which may lead to inaccurate phenotype similarity.

Results

We proposed a network-based method PhenoNet to calculate the similarity between phenotypes. We localized phenotypes in the network and calculated the similarity between phenotype-associated modules by modeling both the inter- and intra-similarity.

Conclusions

PhenoNet was evaluated on two independent evaluation datasets: gene ontology and gene expression data. The result shows that PhenoNet performs better than the state-of-art methods on all evaluation tests.

     

Analysis of the main active ingredients and bioactivities of essential oil from <Emphasis Type="Italic">Osmanthus fragrans</Emphasis> Var. <Emphasis Type="Italic">thunbergii</Emphasis> using a complex network approach

Background

Osmanthus fragrans has been used as folk medicine for thousands of years. The extracts of Osmanthus fragrans flowers were reported to have various bioactivities including free radical scavenging, anti-inflammation, neuroprotection and antitumor effects. However, there is still lack of knowledge about its essential oil.

Methods

In this work, we analyzed the chemical composition of the essential oil from Osmanthus fragrans var. thunbergii by GC-MS. A complex network approach was applied to investigate the interrelationships between the ingredients, target proteins, and related pathways for the essential oil. Statistical characteristics of the networks were further studied to explore the main active ingredients and potential bioactivities of O. fragrans var. thunbergii essential oil.

Results

A total of 44 ingredients were selected from the chemical composition of O. fragrans var. thunbergii essential oil, and that 191 potential target proteins together with 70 pathways were collected for these compounds. An ingredient-target-pathway network was constructed based on these data and showed scale-free property as well as power-law degree distribution. Eugenol and geraniol were screened as main active ingredients with much higher degree values. Potential neuroprotective and anti-tumor effect of the essential oil were also found. A core subnetwork was extracted from the ingredient-target-pathway network, and indicated that eugenol and geraniol contributed most to the neuroprotection of this essential oil. Furthermore, a pathway-based protein association network was built and exhibited small-world property. MAPK1 and MAPK3 were considered as key proteins with highest scores of centrality indices, which might play an important role in the anti-tumor effect of the essential oil.

Conclusions

This work predicted the main active ingredients and bioactivities of O. fragrans var. thunbergii essential oil, which would benefit the development and utilization of Osmanthus fragrans flowers. The application of complex network theory was proved to be effective in bioactivities studies of essential oil. Moreover, it provides a novel strategy for exploring the molecular mechanisms of traditional medicines.

     

Development of a novel engineered antibody targeting <Emphasis Type="Italic">Neisseria</Emphasis> species

Objectives

A Neissaria bacterial pilus sugar, bacillosamine, was synthesized and, for the first time, used as a probe to screen a single-chain variable fragment (scFv).

Results

Four Neisseria, Neisseria gonorrhoeae, Neisseria meningitidis, Neisseria sicca and Neisseria subflava, and two negative controls, Streptococcus pneumoniae and Escherichia coli, were tested through ELISA, immunostaining and gold nanoparticle immunological assay. All results indicated that the selected scFv is feasible for the specific detection of Neisseria species via the recognition of bacillosamine.

Conclusions

The recombinant scFv could detect Neisseria strains at 10⁶ CFU/ml.

     

Model-based reconstruction of synthetic promoter library in <Emphasis Type="Italic">Corynebacterium glutamicum</Emphasis>

Objective

To develop an efficient synthetic promoter library for fine-tuned expression of target genes in Corynebacterium glutamicum.

Results

A synthetic promoter library for C. glutamicum was developed based on conserved sequences of the ??10 and ??35 regions. The synthetic promoter library covered a wide range of strengths, ranging from 1 to 193% of the tac promoter. 68 promoters were selected and sequenced for correlation analysis between promoter sequence and strength with a statistical model. A new promoter library was further reconstructed with improved promoter strength and coverage based on the results of correlation analysis. Tandem promoter P70 was finally constructed with increased strength by 121% over the tac promoter. The promoter library developed in this study showed a great potential for applications in metabolic engineering and synthetic biology for the optimization of metabolic networks.

Conclusions

To the best of our knowledge, this is the first reconstruction of synthetic promoter library based on statistical analysis of C. glutamicum.

     

The bacterial community associated with rose-scented geranium (<Emphasis Type="Italic">Pelargonium graveolens</Emphasis>) leaves responds to anthracnose symptoms

Background

The fungus Colletotrichum is a plant pathogen that causes the anthracnose disease, resulting in huge losses in various crops including the rose-scented geranium (Pelargonium graveolens). Although the bacterial community associated with plants has an important role in the establishment of plant diseases, little is known about what happens in P. graveolens.

Aims

To increase the knowledge about the bacterial community associated with P. graveolens and its relationship with anthracnose disease symptoms.

Methods

Quantitative PCR and high-throughput sequencing were combined to determine the presence of the fungus Colletotrichum and to reveal the bacterial communities associated with different plant parts – root, stem and leaf – and in the rhizosphere and bulk soil, and also to determine the respective bacterial communities associated with P. graveolens leaves symptomatic and asymptomatic for anthracnose disease.

Results

The fungus Colletotrichum was detected in all plant parts and in the surrounding soil. Bacterial communities varied spatially in plants, and the disease symptoms also influenced the composition of the bacterial community. Abundances of operational taxonomic units (OTUs) assigned to the phylum Actinobacteria and to the genus Streptococcus were greatly increased in asymptomatic leaves.

Conclusions

The bacterial community associated to geranium leaves responds to anthracnose symptoms.

     

Seroprevalence of <Emphasis Type="Italic">Borrelia burgdorferi</Emphasis> sensu lato and <Emphasis Type="Italic">Anaplasma phagocytophilum</Emphasis> in Danish horses

Background

Borrelia burgdorferi sensu lato and Anaplasma phagocytophilum are able to infect horses. However, the extend to which Danish horses are infected and seroconvert due to these two bacteria is unknown. The aim of the present study was to evaluate the seroprevalence of B. burgdorferi sensu lato and A. phagocytophilum in Danish horses.

Methods

A total of 390 blood samples collected from all major regions of Denmark and with a geographical distribution corresponding to the density of the Danish horse population were analyzed. All samples were examined for the presence of antibodies against B. burgdorferi sensu lato and A. phagocytophilum by the use of the SNAP^®4DX ^® ELISA test.

Results

Overall, 29.0% of the horses were seropositive for B. burgdorferi sensu lato whereas 22.3% were seropositive for A. phagocytophilum.

Conclusions

Antibodies against B burgdorferi sensu lato and A. phagocytophilum are commonly found among Danish horses thus showing that Danish horses are frequently infected by these organisms.

     

Plant growth-promoting bacterium <Emphasis Type="Italic">Pseudomonas fluorescens</Emphasis> FR1 secrets a novel type of extracellular polyhydroxybutyrate polymerase involved in abiotic stress response in plants

Objectives

Identification of novel microbial factors contributing to plant protection against abiotic stress.

Results

The genome of plant growth-promoting bacterium Pseudomonas fluorescens FR1 contains a short mobile element encoding a novel type of extracellular polyhydroxybutyrate (PHB) polymerase (PhbC) associated with a type I secretion system. Genetic analysis using a phbC mutant strain and plants showed that this novel extracellular enzyme is related to the PHB production in planta and suggests that PHB could be a beneficial microbial compound synthesized during plant adaptation to cold stress.

Conclusion

Extracellular PhbC can be used as a new tool for improve crop production under abiotic stress.

     

Production of caffeoylmalic acid from glucose in engineered <Emphasis Type="Italic">Escherichia coli</Emphasis>

Objectives

To achieve biosynthesis of caffeoylmalic acid from glucose in engineered Escherichia coli.

Results

We constructed the biosynthetic pathway of caffeoylmalic acid in E. coli by co-expression of heterologous genes RgTAL, HpaBC, At4CL2 and HCT2. To enhance the production of caffeoylmalic acid, we optimized the tyrosine metabolic pathway of E. coli to increase the supply of the substrate caffeic acid. Consequently, an E. coli–E. coli co-culture system was used for the efficient production of caffeoylmalic acid. The final titer of caffeoylmalic acid reached 570.1 mg/L.

Conclusions

Microbial production of caffeoylmalic acid using glucose has application potential. In addition, microbial co-culture is an efficient tool for producing caffeic acid esters.

     

Synthesis of rosmarinic acid analogues in <Emphasis Type="Italic">Escherichia coli</Emphasis>

Objectives

To produce rosmarinic acid analogues in the recombinant Escherichia coli BLRA1, harboring a 4-coumarate: CoA ligase from Arabidopsis thaliana (At4CL) and a rosmarinic acid synthase from Coleus blumei (CbRAS).

Results

Incubation of the recombinant E. coli strain BLRA1 with exogenously supplied phenyllactic acid (PL) and analogues as acceptor substrates, and coumaric acid and analogues as donor substrates led to production of 18 compounds, including 13 unnatural RA analogues.

Conclusion

This work demonstrates the viability of synthesizing a broad range of rosmarinic acid analogues in E. coli, and sheds new light on the substrate specificity of CbRAS.