Phenotype Microarray Data Research Articles

Reconstruction and analyses of genome-scale halomonas metabolic network yield a highly efficient PHA production

In pursuit of reliable and efficient industrial microbes, this study integrates cutting-edge systems biology tools with Halomonas bluephagenesis TD01, a robust halophilic bacterium. We generated the complete and annotated circular genome sequence for this model organism, constructed and meticulously curated a genome-scale metabolic network, achieving striking 86.32% agreement with Biolog Phenotype Microarray data and visualize the network via an interactive Electron/Thrift server architecture. We then analyzed the genome-scale network using vertex sampling analysis (VSA) and found that productions of biomass, polyhydroxyalkanoates (PHA), citrate, acetate, and pyruvate are mutually competing. Recognizing the dynamic nature of H. bluephagenesis TD01, we further developed and implemented the hyper-cube-shrink-analysis (HCSA) framework to predict effects of nutrient availabilities and metabolic reactions in the model on biomass and PHA accumulation. We then, based on the analysis results, proposed and validate multi-step feeding strategies tailored to different fermentation stages. This integrated approach yielded remarkable results, with fermentation culminating in a cell dry weight of 100.4 g/L and 70% PHA content, surpassing previous benchmarks. Our findings exemplify the powerful potential of system-level tools in the design and optimization of industrial microorganisms, paving the way for more efficient and sustainable bio-based processes.

PhenoMetaboDiff: R Package for Analysis and Visualization of Phenotype Microarray Data.

PhenoMetaboDiff is a novel R package for computational analysis and visualization of data generated by Biolog Phenotype Mammalian Microarrays (PM-Ms). These arrays measure the energy production of mammalian cells in different metabolic environments, assess the metabolic activity of cells exposed to various drugs or energy sources, and compare the metabolic profiles of cells from individuals affected by specific disorders versus healthy controls. PhenoMetaboDiff has several modules that facilitate statistical analysis by sample comparisons using non-parametric Mann-Whitney U-test, the integration of the OPM package (an R package for analysing OmniLog® phenotype microarray data) for robust file conversion, and calculation of slope and area under the curve (AUC). In addition, the built-in visualization allows specific wells to be visualized in selected pathways for a particular time slice. Compared to the standard OPM package, the features developed in PhenoMetaboDiff assess metabolic profiles by employing statistical tests and visualize the dynamic nature of the energy production in several conditions. Examples of how this package can be used are demonstrated for several rare disease conditions. The incorporation of a graphical user interface expands the utility of this program to both expert and novice users of R. PhenoMetaboDiff makes the deployment of the cutting-edge Biolog system available to any researcher.

High-quality genome-scale metabolic network reconstruction of probiotic bacterium Escherichia coli Nissle 1917

BackgroundEscherichia coli Nissle 1917 (EcN) is a probiotic bacterium used to treat various gastrointestinal diseases. EcN is increasingly being used as a chassis for the engineering of advanced microbiome therapeutics. To aid in future engineering efforts, our aim was to construct an updated metabolic model of EcN with extended secondary metabolite representation.ResultsAn updated high-quality genome-scale metabolic model of EcN, iHM1533, was developed based on comparison with 55 E. coli/Shigella reference GEMs and manual curation, including expanded secondary metabolite pathways (enterobactin, salmochelins, aerobactin, yersiniabactin, and colibactin). The model was validated and improved using phenotype microarray data, resulting in an 82.3% accuracy in predicting growth phenotypes on various nutrition sources. Flux variability analysis with previously published 13C fluxomics data validated prediction of the internal central carbon fluxes. A standardised test suite called Memote assessed the quality of iHM1533 to have an overall score of 89%. The model was applied by using constraint-based flux analysis to predict targets for optimisation of secondary metabolite production. Modelling predicted design targets from across amino acid metabolism, carbon metabolism, and other subsystems that are common or unique for influencing the production of various secondary metabolites.ConclusioniHM1533 represents a well-annotated metabolic model of EcN with extended secondary metabolite representation. Phenotype characterisation and the iHM1533 model provide a better understanding of the metabolic capabilities of EcN and will help future metabolic engineering efforts.

Identifying Multiple Potential Metabolic Cycles in Time-Series from Biolog Experiments.

Biolog Phenotype Microarray (PM) is a technology allowing simultaneous screening of the metabolic behaviour of bacteria under a large number of different conditions. Bacteria may often undergo several cycles of metabolic activity during a Biolog experiment. We introduce a novel algorithm to identify these metabolic cycles in PM experimental data, thus increasing the potential of PM technology in microbiology. Our method is based on a statistical decomposition of the time-series measurements into a set of growth models. We show that the method is robust to measurement noise and captures accurately the biologically relevant signals from the data. Our implementation is made freely available as a part of an R package for PM data analysis and can be found at www.helsinki.fi/bsg/software/Biolog_Decomposition.

Proposal of a type strain for Frankia alni (Woronin 1866) Von Tubeuf 1895, emended description of Frankia alni, and recognition of Frankia casuarinae sp. nov. and Frankia elaeagni sp. nov.

Before the establishment of pure cultures, the species Frankia alni, 'Frankia casuarinae' and 'Frankia elaeagni' were proposed to encompass all causal agents of the nitrogen-fixing root nodules of dicotyledonous plants from the genera Alnus, Casuarina or Elaeagnus. The sole Frankia species with a validly published name, the type species F. alni, was described by Woronin (1866) as present in the root of alder. Until now no type strain has been designated for F. alni, even though the absence of a type strain has seriously inhibited the application of modern taxonomic methods to the genus Frankia. Thus, we propose that strain ACN14aT, isolated in pure culture from Alnus viridis ssp. crispa with morphological properties matching the original description of F. alni, be recognized as the type strain of this species according to Rule 18f of the International Code of Nomenclature of Bacteria. We compared ACN14aT to two strains, CcI3T and BMG5.12T, isolated from Casuarina cunninghamiana and Elaeagnus angustifolia, respectively, based on chemotaxonomy, phenotype microarray data and molecular data retrieved from genome sequences. All three tested strains grew as branched hyphae, produced vesicles and multilocular sporangia containing non-motile spores and metabolized short fatty acids, TCA-cycle intermediates and carbohydrates. Chemotaxonomically, the three strains were indistinguishable with respect to phospholipids (phosphatidylinositol, diphosphatidylglycerol, glycophospholipids and phosphatidylglycerol) and cell-sugar composition (glucose, mannose, ribose, rhamnose, galactose and xylose, with the latter two being diagnostic for the genus). The major fatty acids identified in all three strains were iso-C16 : 0, C17 : 1ω8c, C15 : 0, C17 : 0 and C16 : 0. ACN14aT and BMG5.12T also shared C15 : 1ω6c, while C18 : 1ω9c was found to be unique to BMG5.12T. The major menaquinones identified in all three novel type strains were MK-9(H8), MK-9(H6) and MK-9(H4). MK-9(H2) was shared by ACN14aT and BMG5.12T, while MK-10(H4) and MK-8(H4) were only found in BMG5.12T. Analysis of 16S rRNA gene sequences showed 98.1-98.9 % identity between strains ACN14aT, CcI3T and BMG5.12T. Digital DNA-DNA hybridization values between the three type strains were well below 70 %. These results confirm the separation of the strains into three distinct species, Frankia alni, Frankia casuarinae sp. nov. and Frankia elaeagni sp. nov. Thus, we propose ACN14aT (=DSM 45986T=CECT 9034T), CcI3T (=DSM 45818T=CECT 9043T) and BMG5.12T (=DSM 46783T=CECT 9031T) as the respective type strains.

A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters.

Biolog phenotype microarrays (PMs) enable simultaneous, high throughput analysis of cell cultures in different environments. The output is high-density time-course data showing redox curves (approximating growth) for each experimental condition. The software provided with the Omnilog incubator/reader summarizes each time-course as a single datum, so most of the information is not used. However, the time courses can be extremely varied and often contain detailed qualitative (shape of curve) and quantitative (values of parameters) information. We present a novel, Bayesian approach to estimating parameters from Phenotype Microarray data, fitting growth models using Markov Chain Monte Carlo (MCMC) methods to enable high throughput estimation of important information, including length of lag phase, maximal "growth" rate and maximum output. We find that the Baranyi model for microbial growth is useful for fitting Biolog data. Moreover, we introduce a new growth model that allows for diauxic growth with a lag phase, which is particularly useful where Phenotype Microarrays have been applied to cells grown in complex mixtures of substrates, for example in industrial or biotechnological applications, such as worts in brewing. Our approach provides more useful information from Biolog data than existing, competing methods, and allows for valuable comparisons between data series and across different models.

Construction and analysis of a genome-scale metabolic network for Bacillus licheniformis WX-02

We constructed the genome-scale metabolic network of Bacillus licheniformis (B. licheniformis) WX-02 by combining genomic annotation, high-throughput phenotype microarray (PM) experiments and literature-based metabolic information. The accuracy of the metabolic network was assessed by an OmniLog PM experiment. The final metabolic model iWX1009 contains 1009 genes, 1141 metabolites and 1762 reactions, and the predicted metabolic phenotypes showed an agreement rate of 76.8% with experimental PM data. In addition, key metabolic features such as growth yield, utilization of different substrates and essential genes were identified by flux balance analysis. A total of 195 essential genes were predicted from LB medium, among which 149 were verified with the experimental essential gene set of B. subtilis 168. With the removal of 5 reactions from the network, pathways for poly-γ-glutamic acid (γ-PGA) synthesis were optimized and the γ-PGA yield reached 83.8 mmol/h. Furthermore, the important metabolites and pathways related to γ-PGA synthesis and bacterium growth were comprehensively analyzed. The present study provides valuable clues for exploring the metabolisms and metabolic regulation of γ-PGA synthesis in B. licheniformis WX-02.

Genome and Phenotype Microarray Analyses of Rhodococcus sp. BCP1 and Rhodococcus opacus R7: Genetic Determinants and Metabolic Abilities with Environmental Relevance.

In this paper comparative genome and phenotype microarray analyses of Rhodococcus sp. BCP1 and Rhodococcus opacus R7 were performed. Rhodococcus sp. BCP1 was selected for its ability to grow on short-chain n-alkanes and R. opacus R7 was isolated for its ability to grow on naphthalene and on o-xylene. Results of genome comparison, including BCP1, R7, along with other Rhodococcus reference strains, showed that at least 30% of the genome of each strain presented unique sequences and only 50% of the predicted proteome was shared. To associate genomic features with metabolic capabilities of BCP1 and R7 strains, hundreds of different growth conditions were tested through Phenotype Microarray, by using Biolog plates and plates manually prepared with additional xenobiotic compounds. Around one-third of the surveyed carbon sources was utilized by both strains although R7 generally showed higher metabolic activity values compared to BCP1. Moreover, R7 showed broader range of nitrogen and sulphur sources. Phenotype Microarray data were combined with genomic analysis to genetically support the metabolic features of the two strains. The genome analysis allowed to identify some gene clusters involved in the metabolism of the main tested xenobiotic compounds. Results show that R7 contains multiple genes for the degradation of a large set of aromatic and PAHs compounds, while a lower variability in terms of genes predicted to be involved in aromatic degradation was found in BCP1. This genetic feature can be related to the strong genetic pressure exerted by the two different environment from which the two strains were isolated. According to this, in the BCP1 genome the smo gene cluster involved in the short-chain n-alkanes degradation, is included in one of the unique regions and it is not conserved in the Rhodococcus strains compared in this work. Data obtained underline the great potential of these two Rhodococcus spp. strains for biodegradation and environmental decontamination processes.

Phenotype MicroArrays as a complementary tool to next generation sequencing for characterization of tree endophytes.

There is an increasing need to calibrate microbial community profiles obtained through next generation sequencing (NGS) with relevant taxonomic identities of the microbes, and to further associate these identities with phenotypic attributes. Phenotype MicroArray (PM) techniques provide a semi-high throughput assay for characterization and monitoring the microbial cellular phenotypes. Here, we present detailed descriptions of two different PM protocols used in our recent studies on fungal endophytes of forest trees, and highlight the benefits and limitations of this technique. We found that the PM approach enables effective screening of substrate utilization by endophytes. However, the technical limitations are multifaceted and the interpretation of the PM data challenging. For the best result, we recommend that the growth conditions for the fungi are carefully standardized. In addition, rigorous replication and control strategies should be employed whether using pre-configured, commercial microwell-plates or in-house designed PM plates for targeted substrate analyses. With these precautions, the PM technique is a valuable tool to characterize the metabolic capabilities of individual endophyte isolates, or successional endophyte communities identified by NGS, allowing a functional interpretation of the taxonomic data. Thus, PM approaches can provide valuable complementary information for NGS studies of fungal endophytes in forest trees.

Phenotypic profiling of Scedosporium aurantiacum, an opportunistic pathogen colonizing human lungs.

Genotyping studies of Australian Scedosporium isolates have revealed the strong prevalence of a recently described species: Scedosporium aurantiacum. In addition to occurring in the environment, this fungus is also known to colonise the respiratory tracts of cystic fibrosis (CF) patients. A high throughput Phenotype Microarray (PM) analysis using 94 assorted substrates (sugars, amino acids, hexose-acids and carboxylic acids) was carried out for four isolates exhibiting different levels of virulence, determined using a Galleria mellonella infection model. A significant difference was observed in the substrate utilisation patterns of strains displaying differential virulence. For example, certain sugars such as sucrose (saccharose) were utilised only by low virulence strains whereas some sugar derivatives such as D-turanose promoted respiration only in the more virulent strains. Strains with a higher level of virulence also displayed flexibility and metabolic adaptability at two different temperature conditions tested (28 and 37°C). Phenotype microarray data were integrated with the whole-genome sequence data of S. aurantiacum to reconstruct a pathway map for the metabolism of selected substrates to further elucidate differences between the strains.

Novel R pipeline for analyzing Biolog Phenotypic MicroArray data.

Data produced by Biolog Phenotype MicroArrays are longitudinal measurements of cells’ respiration on distinct substrates. We introduce a three-step pipeline to analyze phenotypic microarray data with novel procedures for grouping, normalization and effect identification. Grouping and normalization are standard problems in the analysis of phenotype microarrays defined as categorizing bacterial responses into active and non-active, and removing systematic errors from the experimental data, respectively. We expand existing solutions by introducing an important assumption that active and non-active bacteria manifest completely different metabolism and thus should be treated separately. Effect identification, in turn, provides new insights into detecting differing respiration patterns between experimental conditions, e.g. between different combinations of strains and temperatures, as not only the main effects but also their interactions can be evaluated. In the effect identification, the multilevel data are effectively processed by a hierarchical model in the Bayesian framework. The pipeline is tested on a data set of 12 phenotypic plates with bacterium Yersinia enterocolitica. Our pipeline is implemented in R language on the top of opm R package and is freely available for research purposes.

From pangenome to panphenome and back.

The ability to relate genomic differences in bacterial species to their variability in expressed phenotypes is one of the most challenging tasks in today's biology. Such task is of paramount importance towards the understanding of biotechnologically relevant pathways and possibly for their manipulation. Fundamental prerequisites are the genome-wide reconstruction of metabolic pathways and a comprehensive measurement of cellular phenotypes. Cellular pathways can be reliably reconstructed using the KEGG database, while the OmniLog™ Phenotype Microarray (PM) technology may be used to measure nearly 2,000 growth conditions over time. However, few computational tools that can directly link PM data with the gene(s) of interest followed by the extraction of information on gene-phenotype correlation are available. In this chapter the use of the DuctApe software suite is presented, which allows the joint analysis of bacterial genomic and phenomic data, highlighting those pathways and reactions most probably associated with phenotypic variability. A case study on four Sinorhizobium meliloti strains is presented; more example datasets are available online.

Novel insight into antimicrobial resistance and sensitivity phenotypes associated to qac and norA genotypes in Staphylococcus aureus

Staphylococcus aureus strains harboring QacA, QacB, QacC, QacG transporters and norA promoter up-regulating mutations were characterized by phenotype microarray (PM), standard methods for susceptibility testing, and ethidium bromide efflux assays, in order to increase knowledge on phenotypes associated to efflux pumps and their substrates. PM data and standard susceptibility testing lead to the identification of new potential efflux targets, such as guanidine hydrochloride or 8-hydroxyquinoline for QacA and QacC pumps, respectively. The identification of compounds to which the presence of efflux pumps induced increased susceptibility opens new perspectives for potential adjunct anti-resistance treatment (i.e. strains bearing QacB transporters showed increased susceptibility to thioridazine, amitriptyline and orphenadrine). Although the tested isolates were characterized by high degree of heterogeneity, a hallmark of clinical isolates, direct ethidium bromide efflux assays were effective in highlighting differences in efflux efficiency among strains. These data add to characterization of substrate specificity in the different classes of staphylococcal multidrug efflux systems conferring specific substrate profiles and efflux features to each of them.

DuctApe: A suite for the analysis and correlation of genomic and OmniLog™ Phenotype Microarray data

Addressing the functionality of genomes is one of the most important and challenging tasks of today's biology. In particular the ability to link genotypes to corresponding phenotypes is of interest in the reconstruction and biotechnological manipulation of metabolic pathways. Over the last years, the OmniLog™ Phenotype Microarray (PM) technology has been used to address many specific issues related to the metabolic functionality of microorganisms. However, computational tools that could directly link PM data with the gene(s) of interest followed by the extraction of information on gene–phenotype correlation are still missing.Here we present DuctApe, a suite that allows the analysis of both genomic sequences and PM data, to find metabolic differences among PM experiments and to correlate them with KEGG pathways and gene presence/absence patterns. As example, an application of the program to four bacterial datasets is presented.The source code and tutorials are available at http://combogenomics.github.io/DuctApe/.

Opm: an R package for analysing OmniLog® phenotype microarray data

opm is an R package designed to analyse multidimensional OmniLog® phenotype microarray (PM) data. opm provides management, visualization and statistical analysis of PM data, including curve-parameter estimation and discretization, dedicated and customizable plots, metadata management, automated generation of textual and tabular reports, mapping of substrates to databases, batch conversion of files and export to phylogenetic software in the YAML markup language. opm is distributed under the GPL through the Comprehensive R Archive Network (http://cran.r-project.org/package=opm) along with a comprehensive manual and a user-friendly tutorial. Further information may be found at http://www.dsmz.de/research/microorganisms/projects/. johannes.sikorski@dsmz.de.

Elucidating the Regulon of Multidrug Resistance Regulator RarA in Klebsiella pneumoniae

RarA is an AraC-type regulator in Klebsiella pneumoniae, which, when overexpressed, confers a low-level multidrug-resistant (MDR) phenotype linked to the upregulation of both the acrAB and oqxAB efflux genes. Increased rarA expression has also been shown to be integral in the development of tigecycline resistance in the absence of ramA in K. pneumoniae. Given its phenotypic role in MDR, microarray analyses were performed to determine the RarA regulon. Transcriptome analysis was undertaken using strains Ecl8ΔrarA/pACrarA-2 (rarA-expressing construct) and Ecl8ΔrarA/pACYC184 (vector-only control) using bespoke microarray slides consisting of probes derived from the genomic sequences of K. pneumoniae MGH 78578 (NC_009648.1) and Kp342 (NC_011283.1). Our results show that rarA overexpression resulted in the differential expression of 66 genes (42 upregulated and 24 downregulated). Under the COG (clusters of orthologous groups) functional classification, the majority of affected genes belonged to the category of cell envelope biogenesis and posttranslational modification, along with genes encoding the previously uncharacterized transport proteins (e.g., KPN_03141, sdaCB, and leuE) and the porin OmpF. However, genes associated with energy production and conversion and amino acid transport/metabolism (e.g., nuoA, narJ, and proWX) were found to be downregulated. Biolog phenotype analyses demonstrated that rarA overexpression confers enhanced growth of the overexpresser in the presence of several antibiotic classes (i.e., beta-lactams and fluoroquinolones), the antifungal/antiprotozoal compound clioquinol, disinfectants (8-hydroxyquinoline), protein synthesis inhibitors (i.e., minocycline and puromycin), membrane biogenesis agents (polymyxin B and amitriptyline), DNA synthesis (furaltadone), and the cytokinesis inhibitor (sanguinarine). Both our transcriptome and phenotypic microarray data support and extend the role of RarA in the MDR phenotype of K. pneumoniae.

Characterization of the MSMEG_2631 Gene ( mmp ) Encoding a Multidrug and Toxic Compound Extrusion (MATE) Family Protein in Mycobacterium smegmatis and Exploration of Its Polyspecific Nature Using Biolog Phenotype MicroArray

In Mycobacterium, multidrug efflux pumps can be associated with intrinsic drug resistance. Comparison of putative mycobacterial transport genes revealed a single annotated open reading frame (ORF) for a multidrug and toxic compound extrusion (MATE) family efflux pump in all sequenced mycobacteria except Mycobacterium leprae. Since MATE efflux pumps function as multidrug efflux pumps by conferring resistance to structurally diverse antibiotics and DNA-damaging chemicals, we studied this gene (MSMEG_2631) in M. smegmatis mc(2)155 and determined that it encodes a MATE efflux system that contributes to intrinsic resistance of Mycobacterium. We propose that the MSMEG_2631 gene be named mmp, for mycobacterial MATE protein. Biolog Phenotype MicroArray data indicated that mmp deletion increased susceptibility for phleomycin, bleomycin, capreomycin, amikacin, kanamycin, cetylpyridinium chloride, and several sulfa drugs. MSMEG_2619 (efpA) and MSMEG_3563 mask the effect of mmp deletion due to overlapping efflux capabilities. We present evidence that mmp is a part of an MSMEG_2626-2628-2629-2630-2631 operon regulated by a strong constitutive promoter, initiated from a single transcription start site. All together, our results show that M. smegmatis constitutively encodes an Na(+)-dependent MATE multidrug efflux pump from mmp in an operon with putative genes encoding proteins for apparently unrelated functions.

Tools to kill: genome of one of the most destructive plant pathogenic fungi Macrophomina phaseolina.

BackgroundMacrophomina phaseolina is one of the most destructive necrotrophic fungal pathogens that infect more than 500 plant species throughout the world. It can grow rapidly in infected plants and subsequently produces a large amount of sclerotia that plugs the vessels, resulting in wilting of the plant.ResultsWe sequenced and assembled ~49 Mb into 15 super-scaffolds covering 92.83% of the M. phaseolina genome. We predict 14,249 open reading frames (ORFs) of which 9,934 are validated by the transcriptome. This phytopathogen has an abundance of secreted oxidases, peroxidases, and hydrolytic enzymes for degrading cell wall polysaccharides and lignocelluloses to penetrate into the host tissue. To overcome the host plant defense response, M. phaseolina encodes a significant number of P450s, MFS type membrane transporters, glycosidases, transposases, and secondary metabolites in comparison to all sequenced ascomycete species. A strikingly distinct set of carbohydrate esterases (CE) are present in M. phaseolina, with the CE9 and CE10 families remarkably higher than any other fungi. The phenotypic microarray data indicates that M. phaseolina can adapt to a wide range of osmotic and pH environments. As a broad host range pathogen, M. phaseolina possesses a large number of pathogen-host interaction genes including those for adhesion, signal transduction, cell wall breakdown, purine biosynthesis, and potent mycotoxin patulin.ConclusionsThe M. phaseolina genome provides a framework of the infection process at the cytological and molecular level which uses a diverse arsenal of enzymatic and toxin tools to destroy the host plants. Further understanding of the M. phaseolina genome-based plant-pathogen interactions will be instrumental in designing rational strategies for disease control, essential to ensuring global agricultural crop production and security.

Pseudomonas putida NBRIC19 dihydrolipoamide succinyltransferase (SucB) gene controls degradation of toxic allelochemicals produced by Parthenium hysterophorus

The aim of this study was to identify the gene responsible for degradation of toxic allelochemicals of Parthenium by generating Tn5-induced mutant of Pseudomonas putida NBRIC19. Furthermore, the study characterizes the mutant at physiological, biochemical and molecular level that helped in understanding the mechanisms of reducing the allelopathic inhibition of Parthenium by Ps. putida NBRIC19. Tn5 mutant S-74.3 showing inability to degrade toxic allelochemicals was selected after screening 22 000 transconjugants. Tn5 flanking SucB gene (dihydrolipoamide succinyltransferase) of Ps. putida NBRIC19 was found to be responsible for the degradation of toxic allelochemicals that also affected biofilm formation, chemotaxis and alginate production under toxic environment of allelochemicals. Phenotypic microarray data revealed that the respiratory activity of Ps. putida NBRIC19 and S-74.3 differed on 47 substrates including amino acids, carboxylic acids, peptides and some chemical inhibitors. Study revealed that SucB gene regulates processes either directly or indirectly in Ps. putida NBRIC19, which on inactivation made the mutant less compatible for tolerating stress. This work provides the first evidence for a functional role of Ps. putida SucB gene in degradation of toxic allelochemicals of Parthenium that lead to reversal of plant growth inhibition by these toxic allelochemicals. The investigation also revealed interesting features about the involvement of microbes in plant-plant allelopathic interactions.

Cronobacter gen. nov., a new genus to accommodate the biogroups of Enterobacter sakazakii, and proposal of Cronobacter sakazakii gen. nov., comb. nov., Cronobacter malonaticus sp. nov., Cronobacter turicensis sp. nov., Cronobacter muytjensii sp. nov., Cronobacter dublinensis sp. nov., Cronobacter genomospecies 1, and of three subspecies, Cronobacter dublinensis subsp. dublinensis subsp. nov., Cronobacter dublinensis subsp. lausannensis subsp. nov. and Cronobacter dublinensis

[Enterobacter] sakazakii is an opportunistic pathogen that can cause infections in neonates. This study further clarifies the taxonomy of isolates described as [E.] sakazakii and completes the formal description of the proposed reclassification of these organisms as novel species and subspecies within a proposed novel genus, Cronobacter gen. nov. [E.] sakazakii was first defined in 1980, however recent polyphasic taxonomic analysis has determined that this group of organisms consists of several genomospecies. In this study, the phenotypic descriptions of the proposed novel species are expanded using Biotype 100 and Biolog Phenotype MicroArray data. Further DNA-DNA hybridization experiments showed that malonate-positive strains within the [E.] sakazakii genomospecies represent a distinct species, not a subspecies. DNA-DNA hybridizations also determined that phenotypically different strains within the proposed species, Cronobacter dublinensis sp. nov., belong to the same species and can be considered as novel subspecies. Based on these analyses, the following alternative classifications are proposed: Cronobacter sakazakii gen. nov., comb. nov. [type strain ATCC 29544(T) (=NCTC 11467(T))]; Cronobacter malonaticus sp. nov. [type strain CDC 1058-77(T) (=LMG 23826(T)=DSM 18702(T))]; Cronobacter turicensis sp. nov. [type strain z3032(T) (=LMG 23827(T)=DSM 18703(T))]; Cronobacter muytjensii sp. nov. [type strain ATCC 51329(T) (=CIP 103581(T))]; Cronobacter dublinensis sp. nov. [type strain DES187(T) (=LMG 23823(T)=DSM 18705(T))]; Cronobacter dublinensis subsp. dublinensis subsp. nov. [type strain DES187(T) (=LMG 23823(T)=DSM 18705(T))]; Cronobacter dublinensis subsp. lausannensis subsp. nov. [type strain E515(T) (=LMG 23824=DSM 18706(T))], and Cronobacter dublinensis subsp. lactaridi subsp. nov. [type strain E464(T) (=LMG 23825(T)=DSM 18707(T))].