A new approach to isolating siderophore-producing actinobacteria

Aims:  This study was conducted to investigate the application of 2,2′‐dipyridyl as a new approach to isolating siderophore‐producing actinobacteria. Methods and Results:  Isolation of actinobacteria from soil was conducted by a soil dilution plate technique using starch‐casein agar. Iron starvation was fostered by the incorporation of the iron chelator 2,2′‐dipyridyl in the isolation medium. Pretreatment of the samples at an elevated temperature (40°C) ensured that the majority of nonsporulating bacteria were excluded. The survivors of this treatment were largely actinobacteria. Of the viable cultures grown in the presence of 2,2′‐dipyridyl, more than 78–88% (average of three separate studies) were reported to produce siderophore‐like compounds compared to 13–18% (average of three separate studies) when grown on the basic media in the absence of the chelating agent. The most prolific producers as assessed by the chrome azurol sulphate (CAS) assay were further characterized and found to belong to the genus Streptomyces. Conclusions:  Selective pressure using 2,2′‐dipyridyl as an iron‐chelating agent in starch‐casein media increased the isolation of siderophore‐producing actinobacteria compared to the unamended medium. Significance and Impact of the Study:  The study described represents a new approach to the isolation of siderophore‐producing actinobacteria using a novel procedure that places a selection on cell population based upon the incorporation of a chelating agent in the medium.     

A broad-host-range plasmid for isolating mobile genetic elements in gram-negative bacteria

Plasmid pGBG1 was constructed to isolate mobile genetic elements in a wide variety of gram-negative bacteria. The mutation target, carried on a broad-host-range vector, allows positive selection for tetracycline resistance. In tests using several gram-negative bacteria we could detect transposition events of either insertion sequences or transposons. A new insertion sequence (IS) element was identified in Ralstonia eutropha.     

SIMPLE approach for isolating mutants expressing fimbriae

Genomes of members of the family Enterobacteriaceae contain large repertoires of putative fimbrial operons. Since many of these operons are poorly expressed in vitro, a convenient method for inducing elaboration of the encoded fimbriae would greatly facilitate their functional characterization. Here we describe a new technique for identifying fimbriated bacteria from a library of transposon mutants by screening with immunomagnetic particles for ligand expression (SIMPLE). The SIMPLE method was applied to identify the T-POP mutants of Salmonella enterica serotype Typhimurium carrying on their surfaces filaments composed of PefA, the major subunit product of a fimbrial operon (pef) that is not expressed during growth in Luria-Bertani broth. Four such mutants were identified from a library of 24,000 mutants, each of which carried a T-POP insertion within the hns gene, which encodes a global silencer of horizontally acquired genes. Our data suggest that the SIMPLE method is an effective approach for isolating fimbriated bacteria, which can be readily applied to fimbrial operons identified by whole-genome sequencing.     

A genetic algorithm approach to optimization of asynchronous automatic assembly systems

This paper presents the application of genetic algorithms to the performance optimization of asynchronous automatic assembly systems (AAS). These stochastic systems are subject to blocking and starvation effects that make complete analytic performance modeling difficult. Therefore, this paper extends genetic algorithms to stochastic systems. The performance of the genetic algorithm is measured through comparison with the results of stochastic quasi-gradient (SQM) methods to the same AAS. The genetic algorithm performs reasonably well in obtaining good solutions (as compared with results of SQM) in this stochastic optimization example, even though genetic algorithms were designed for application to deterministic systems. However, the genetic algorithm's performance does not appear to be superior to SQM.     

A novel and rapid approach to isolating functional ryanodine receptors

Conventional methods of isolating and reconstituting ryanodine receptors (RyRs) from native membranes into proteoliposomes take a minimum of 2 days to complete. We have developed an alternative strategy that can be used to isolate and reconstitute functional RyRs in just 3 h with a similar degree of purification. RyRs isolated by this method display characteristic functional behaviour as assessed by radioligand binding and single channel analyses.     

A method for benchmarking genetic screens reveals a predominant mitochondrial bias

We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome‐wide CRISPR screen data and methods for analyzing them. FLEX provides a quantitative measurement of the functional information captured by a given gene‐pair dataset and a means to explore the diversity of functions captured by the input dataset. We apply FLEX to analyze data from the diverse cell line screens generated by the DepMap project. We identify a predominant mitochondria‐associated signal within co‐essentiality networks derived from these data and explore the basis of this signal. Our analysis and time‐resolved CRISPR screens in a single cell line suggest that the variable phenotypes associated with mitochondria genes across cells may reflect screen dynamics and protein stability effects rather than genetic dependencies. We characterize this functional bias and demonstrate its relevance for interpreting differential hits in any CRISPR screening context. More generally, we demonstrate the utility of the FLEX pipeline for performing robust comparative evaluations of CRISPR screens or methods for processing them.     

Microbial combinatorics: a simplified approach for isolating insecticidal bacteria

Bacteria can control pest insects that damage food crops, vector diseases and defoliate trees. Conventionally, isolation of these bacteria has been from soil and sporadically from dead insects. A simplified approach for isolating insecticidal bacteria from soil using the target insect as the selective agent was employed in this study. Instead of isolating single strains of bacteria from soil and testing each individual strain for insect toxicity, mixtures of bacteria present in each soil sample were tested together directly for toxicity using Manduca sexta (Linnaeus) (Lepidoptera: Sphingidae) as a model insect. Thirty-five soil suspensions or bacterial suspensions of the 40 suspensions tested killed at least one M. sexta larva. All but one bacterial culture isolated from dead larvae and retested for toxicity, killed at least one M. sexta larva. Nineteen bacterial strains isolated from larvae killed in the first test, were identical to the bacteria fed to the retested larvae. Of the 19 strains isolated, 14 were identified by 16S rDNA sequencing as belonging to the Bacillus cereus group including three strains that formed crystals that were identified as B. thuringiensis. Of the three other spore-forming strains, two were identified as psychrotrophic B. weihenstephanensis and the third as Lysinibacillus fusiformis. Two others were identified as Enterococcus faecalis. This approach, microbial combinatorics, reduces the number of insects necessary for toxicity screening and associated time and resources compared to conventional methods that first isolate bacteria and then individually test for toxicity as well as a means of discovery of new pathogens using the insect as the selective agent.     

A new approach for detecting visibility bias for the fixed-width transect method

Four road counts and four series of daily road counts of 28.4% sampling intensity were carried out at Wildlife Ranching and Research Ltd (WRR), Athi River, Kenya, between December 1966 and July 1987 to test the applicability of the method to the WRR environment. The results, however, exhibited high variation. Difficulties were encountered when applying the Anderson & Pospahala (1970) method to detect the existence of visibility bias in the distribution of Thomson's gazelle. An alternative approach, based on the regression of group size on the right angle sighting distance was applied to detect the existence of visibility bias.     

A wavelet optimization approach for ECG signal classification

Wavelets have proved particularly effective for extracting discriminative features in ECG signal classification. In this paper, we show that wavelet performances in terms of classification accuracy can be pushed further by customizing them for the considered classification task. A novel approach for generating the wavelet that best represents the ECG beats in terms of discrimination capability is proposed. It makes use of the polyphase representation of the wavelet filter bank and formulates the design problem within a particle swarm optimization (PSO) framework. Experimental results conducted on the benchmark MIT/BIH arrhythmia database with the state-of-the-art support vector machine (SVM) classifier confirm the superiority in terms of classification accuracy and stability of the proposed method over standard wavelets (i.e., Daubechies and Symlet wavelets).     

A genetic ensemble approach for gene-gene interaction identification

Background  

It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging.     

Balanced codon usage optimizes eukaryotic translational efficiency

Cellular efficiency in protein translation is an important fitness determinant in rapidly growing organisms. It is widely believed that synonymous codons are translated with unequal speeds and that translational efficiency is maximized by the exclusive use of rapidly translated codons. Here we estimate the in vivo translational speeds of all sense codons from the budding yeast Saccharomyces cerevisiae. Surprisingly, preferentially used codons are not translated faster than unpreferred ones. We hypothesize that this phenomenon is a result of codon usage in proportion to cognate tRNA concentrations, the optimal strategy in enhancing translational efficiency under tRNA shortage. Our predicted codon-tRNA balance is indeed observed from all model eukaryotes examined, and its impact on translational efficiency is further validated experimentally. Our study reveals a previously unsuspected mechanism by which unequal codon usage increases translational efficiency, demonstrates widespread natural selection for translational efficiency, and offers new strategies to improve synthetic biology.     

A pseudolikelihood approach to correcting for ascertainment bias in family studies.

The Cannings and Thompson approach to correcting for ascertainment bias in family studies is extended to settings with multiple ascertainment. The extension is based on maximizing a pseudolikelihood. Two approaches to computing standard errors for the maximum pseudolikelihood estimate are described. One is especially simple to compute, while the other is more generally applicable. Simulation experiments suggest that the standard-error computations can be quite accurate.     

A combined steepest descent and genetic algorithm (SD/GA) approach for the optimization of solvation parameters

An accurate solvation model is essential for computer modeling of protein folding and other biomolecular self-assembly processes. Compared to explicit solvent models, implicit solvent models, such as the Poisson-Boltzmann (PB) with solvent accessible surface area model (PB/SA), offer a much faster speed—the most compelling reason for the popularity of these implicit solvent models. Since these implicit solvent models typically use empirical parameters, such as atomic radii and the surface tensions, an optimal fit of these parameters is crucial for the final accuracy of properties such as solvation free energy and folding free energy. In this paper, we proposed a combined approach, namely SD/GA, which takes the advantage of both local optimization with the steepest descent (SD), and global optimization with the genetic algorithm (GA), for parameters optimization in multi-dimensional space. The SD/GA method is then applied to the optimization of solvation parameters in the non-polar cavity term of the PB/SA model. The results show that the newly optimized parameters from SD/GA not only increase the accuracy in the solvation free energies for ～200 organic molecules, but also significantly improve the free energy landscape of a β-hairpin folding. The current SD/GA method can be readily applied to other multi-dimensional parameter space optimization as well.     

A nonalkaline method for isolating sequencing-ready plasmids

We describe a simple method of isolating plasmid DNA directly from Escherichia coli culture medium by addition of lithium acetate and Sodium dodecyl sulphate, followed by centrifugation and alcohol precipitation. The plasmid is sufficiently pure that it can be used in many enzyme-based reactions, including DNA sequencing and restriction analysis. Chromosomal DNA contamination is significantly reduced by pretreatment of the culture with DNase I, suggesting that much of the contaminant is associated with permeable dead cells. Chromosomal DNA contaminant can also be selectively denatured without damage to the supercoiled plasmid by alkaline denaturation in an arginine buffer or heat treatment in the presence of urea or N,N-dimethylformamide.     

A combinatorial optimization approach for diverse motif finding applications

Background  

Discovering approximately repeated patterns, or motifs, in biological sequences is an important and widely-studied problem in computational molecular biology. Most frequently, motif finding applications arise when identifying shared regulatory signals within DNA sequences or shared functional and structural elements within protein sequences. Due to the diversity of contexts in which motif finding is applied, several variations of the problem are commonly studied.