DNA microarray technology for the microbiologist: an overview

Confirmation of Glucose Transporters through Targeted Mutagenesis and Transcriptional Analysis in Clostridium acetobutylicum

The solvent-producing bacterium Clostridium acetobutylicum is able to grow on a variety of carbohydrates. The main hexose transport system is the phosphoenolpyruvate-dependent phosphotransferase system (PTS). When the gene glcG that encodes the glucose transporter was inactivated, the resulting mutant glcG::int(1224) grew as well as the wild type, yet its glucose consumption was reduced by 17% in a batch fermentation. Transcriptomics analysis of the phosphate-limited continuous cultures showed that the cellobiose transporter GlcCE was highly up-regulated in the mutant glcG::int(1224). The glcCE mutation did not affect growth and even consumed slightly more glucose during solventogenesis growth compared to wild type, indicating that GlcG is the primary glucose-specific PTS. Poor growth of the double mutant glcG::int(1224)-glcCE::int(193) further revealed that GlcCE was the secondary glucose PTS and that there must be other PTSs capable of glucose uptake. The observations obtained in this study provided a promising foundation to understand glucose transport in C. acetobutylicum.

Advancement in Salmonella Detection Methods: From Conventional to Electrochemical-Based Sensing Detection

Large-scale food-borne outbreaks caused by Salmonella are rarely seen nowadays, thanks to the advanced nature of the medical system. However, small, localised outbreaks in certain regions still exist and could possess a huge threat to the public health if eradication measure is not initiated. This review discusses the progress of Salmonella detection approaches covering their basic principles, characteristics, applications, and performances. Conventional Salmonella detection is usually performed using a culture-based method, which is time-consuming, labour intensive, and unsuitable for on-site testing and high-throughput analysis. To date, there are many detection methods with a unique detection system available for Salmonella detection utilising immunological-based techniques, molecular-based techniques, mass spectrometry, spectroscopy, optical phenotyping, and biosensor methods. The electrochemical biosensor has growing interest in Salmonella detection mainly due to its excellent sensitivity, rapidity, and portability. The use of a highly specific bioreceptor, such as aptamers, and the application of nanomaterials are contributing factors to these excellent characteristics. Furthermore, insight on the types of biorecognition elements, the principles of electrochemical transduction elements, and the miniaturisation potential of electrochemical biosensors are discussed.

The Application of High-Throughput Technologies for the Study of Microbiome and Cancer

Human gut microbiome research, especially gut microbiome, has been developing at a considerable pace over the last decades, driven by a rapid technological advancement. The emergence of high-throughput technologies, such as genomics, transcriptomics, and others, has afforded the generation of large volumes of data, and in relation to specific pathologies such as different cancer types. The current review identifies high-throughput technologies as they have been implemented in the study of microbiome and cancer. Four main thematic areas have emerged: the characterization of microbial diversity and composition, microbial functional analyses, biomarker prediction, and, lastly, potential therapeutic applications. The majority of studies identified focus on the microbiome diversity characterization, which is reaching technological maturity, while the remaining three thematic areas could be described as emerging.

Microbial detection and identification methods: Bench top assays to omics approaches

Rapid detection of foodborne pathogens, spoilage microbes, and other biological contaminants in complex food matrices is essential to maintain food quality and ensure consumer safety. Traditional methods involve culturing microbes using a range of nonselective and selective enrichment methods, followed by biochemical confirmation among others. The time-to-detection is a key limitation when testing foods, particularly those with short shelf lives, such as fresh meat, fish, dairy products, and vegetables. Some recent detection methods developed include the use of spectroscopic techniques, such as matrix-assisted laser desorption ionization-time of flight along with hyperspectral imaging protocols.This review presents a comprehensive overview comparing insights into the principles, characteristics, and applications of newer and emerging techniques methods applied to the detection and identification of microbes in food matrices, to more traditional benchtop approaches. The content has been developed to provide specialist scientists a broad view of bacterial identification methods available in terms of their benefits and limitations, which may be useful in the development of future experimental design. The case is also made for incorporating some of these emerging methods into the mainstream, for example, underutilized potential of spectroscopic techniques and hyperspectral imaging.

DNA-based microarray studies in visceral leishmaniasis: identification of biomarkers for diagnostic, prognostic and drug target for treatment

Visceral leishmaniasis (VL) is one of the major infectious diseases affecting the poorest regions of the world. Current therapy is not very much satisfactory. The alarming rise of drug resistance and the unavailability of an effective vaccine against VL urges research towards identifying new targets or biomarkers for its effective treatment. New technology developments offer some fresh hope in its diagnosis, treatment, and control. DNA microarray approach is now broadly used in parasitology research to facilitate the thoughtful of mechanisms of disease and identification of drug targets and biomarkers for diagnostic and therapeutic development. An electronic search on "VL" and "Microarray" was conducted in Medline and Scopus and papers published in the English mentioning use of DNA microarray on VL were selected and read to write this paper review. Functional analysis and interpretation of microarray results remain very challenging due to the inherent nature of experimental workflows, access, cost, and complexity of data obtained. We have explained and emphasized the use of curate knowledge of microarray in the case of VL for the identification of therapeutic target and biomarker and their selection/implementation in clinical use.

Analysing Microbial Community Composition through Amplicon Sequencing: From Sampling to Hypothesis Testing

Microbial ecology as a scientific field is fundamentally driven by technological advance. The past decade's revolution in DNA sequencing cost and throughput has made it possible for most research groups to map microbial community composition in environments of interest. However, the computational and statistical methodology required to analyse this kind of data is often not part of the biologist training. In this review, we give a historical perspective on the use of sequencing data in microbial ecology and restate the current need for this method; but also highlight the major caveats with standard practices for handling these data, from sample collection and library preparation to statistical analysis. Further, we outline the main new analytical tools that have been developed in the past few years to bypass these caveats, as well as highlight the major requirements of common statistical practices and the extent to which they are applicable to microbial data. Besides delving into the meaning of select alpha- and beta-diversity measures, we give special consideration to techniques for finding the main drivers of community dissimilarity and for interaction network construction. While every project design has specific needs, this review should serve as a starting point for considering what options are available.

Array Analysis Manager-An automated DNA microarray analysis tool simplifying microarray data filtering, bias recognition, normalization, and expression analysis

Desoxyribonucleic acid (DNA) microarray experiments generate big datasets. To successfully harness the potential information within, multiple filtering, normalization, and analysis methods need to be applied. An in-depth knowledge of underlying physical, chemical, and statistical processes is crucial to the success of this analysis. However, due to the interdisciplinarity of DNA microarray applications and experimenter backgrounds, the published analyses differ greatly, for example, in methodology. This severely limits the comprehensibility and comparability among studies and research fields. In this work, we present a novel end-user software, developed to automatically filter, normalize, and analyze two-channel microarray experiment data. It enables the user to analyze single chip, dye-swap, and loop experiments with an extended dynamic intensity range using a multiscan approach. Furthermore, to our knowledge, this is the first analysis software solution, that can account for photobleaching, automatically detected by an artificial neural network. The user gets feedback on the effectiveness of each applied normalization regarding bias minimization. Standardized methods for expression analysis are included as well as the possibility to export the results in the Gene Expression Omnibus (GEO) format. This software was designed to simplify the microarray analysis process and help the experimenter to make educated decisions about the analysis process to contribute to reproducibility and comparability.

Optimization of Cyanine Dye Stability and Analysis of FRET Interaction on DNA Microarrays

The application of DNA microarrays for high throughput analysis of genetic regulation is often limited by the fluorophores used as markers. The implementation of multi-scan techniques is limited by the fluorophores' susceptibility to photobleaching when exposed to the scanner laser light. This paper presents combined mechanical and chemical strategies which enhance the photostability of cyanine 3 and cyanine 5 as part of solid state DNA microarrays. These strategies are based on scanning the microarrays while the hybridized DNA is still in an aqueous solution with the presence of a reductive/oxidative system (ROXS). Furthermore, the experimental setup allows for the analysis and eventual normalization of Förster-resonance-energy-transfer (FRET) interaction of cyanine-3/cyanine-5 dye combinations on the microarray. These findings constitute a step towards standardization of microarray experiments and analysis and may help to increase the comparability of microarray experiment results between labs.

Living Cell Microarrays: An Overview of Concepts

Living cell microarrays are a highly efficient cellular screening system. Due to the low number of cells required per spot, cell microarrays enable the use of primary and stem cells and provide resolution close to the single-cell level. Apart from a variety of conventional static designs, microfluidic microarray systems have also been established. An alternative format is a microarray consisting of three-dimensional cell constructs ranging from cell spheroids to cells encapsulated in hydrogel. These systems provide an in vivo-like microenvironment and are preferably used for the investigation of cellular physiology, cytotoxicity, and drug screening. Thus, many different high-tech microarray platforms are currently available. Disadvantages of many systems include their high cost, the requirement of specialized equipment for their manufacture, and the poor comparability of results between different platforms. In this article, we provide an overview of static, microfluidic, and 3D cell microarrays. In addition, we describe a simple method for the printing of living cell microarrays on modified microscope glass slides using standard DNA microarray equipment available in most laboratories. Applications in research and diagnostics are discussed, e.g., the selective and sensitive detection of biomarkers. Finally, we highlight current limitations and the future prospects of living cell microarrays.

The Impact of Photobleaching on Microarray Analysis

DNA-Microarrays have become a potent technology for high-throughput analysis of genetic regulation. However, the wide dynamic range of signal intensities of fluorophore-based microarrays exceeds the dynamic range of a single array scan by far, thus limiting the key benefit of microarray technology: parallelization. The implementation of multi-scan techniques represents a promising approach to overcome these limitations. These techniques are, in turn, limited by the fluorophores’ susceptibility to photobleaching when exposed to the scanner’s laser light. In this paper the photobleaching characteristics of cyanine-3 and cyanine-5 as part of solid state DNA microarrays are studied. The effects of initial fluorophore intensity as well as laser scanner dependent variables such as the photomultiplier tube’s voltage on bleaching and imaging are investigated. The resulting data is used to develop a model capable of simulating the expected degree of signal intensity reduction caused by photobleaching for each fluorophore individually, allowing for the removal of photobleaching-induced, systematic bias in multi-scan procedures. Single-scan applications also benefit as they rely on pre-scans to determine the optimal scanner settings. These findings constitute a step towards standardization of microarray experiments and analysis and may help to increase the lab-to-lab comparability of microarray experiment results.

Systems biology approaches to defining transcription regulatory networks in halophilic archaea

To survive complex and changing environmental conditions, microorganisms use gene regulatory networks (GRNs) composed of interacting regulatory transcription factors (TFs) to control the timing and magnitude of gene expression. Genome-wide datasets; such as transcriptomics and protein-DNA interactions; and experiments such as high throughput growth curves; facilitate the construction of GRNs and provide insight into TF interactions occurring under stress. Systems biology approaches integrate these datasets into models of GRN architecture as well as statistical and/or dynamical models to understand the function of networks occurring in cells. Previously, these types of studies have focused on traditional model organisms (e.g. Escherichia coli, yeast). However, recent advances in archaeal genetics and other tools have enabled a systems approach to understanding GRNs in these relatively less studied archaeal model organisms. In this report, we outline a systems biology workflow for generating and integrating data focusing on the TF regulator. We discuss experimental design, outline the process of data collection, and provide the tools required to produce high confidence regulons for the TFs of interest. We provide a case study as an example of this workflow, describing the construction of a GRN centered on multi-TF coordinate control of gene expression governing the oxidative stress response in the hypersaline-adapted archaeon Halobacterium salinarum.

Review of micro/nanotechnologies for microbial biosensors

A microbial biosensor is an analytical device with a biologically integrated transducer that generates a measurable signal indicating the analyte concentration. This method is ideally suited for the analysis of extracellular chemicals and the environment, and for metabolic sensory regulation. Although microbial biosensors show promise for application in various detection fields, some limitations still remain such as poor selectivity, low sensitivity, and impractical portability. To overcome such limitations, microbial biosensors have been integrated with many recently developed micro/nanotechnologies and applied to a wide range of detection purposes. This review article discusses micro/nanotechnologies that have been integrated with microbial biosensors and summarizes recent advances and the applications achieved through such novel integration. Future perspectives on the combination of micro/nanotechnologies and microbial biosensors will be discussed, and the necessary developments and improvements will be strategically deliberated.

Nucleic Acid-based approaches for detection of viral hepatitis

CONTEXT

To determining suitable nucleic acid diagnostics for individual viral hepatitis agent, an extensive search using related keywords was done in major medical library and data were collected, categorized, and summarized in different sections.

RESULTS

Various types of molecular biology tools can be used to detect and quantify viral genomic elements and analyze the sequences. These molecular assays are proper technologies for rapidly detecting viral agents with high accuracy, high sensitivity, and high specificity. Nonetheless, the application of each diagnostic method is completely dependent on viral agent.

CONCLUSIONS

Despite rapidity, automation, accuracy, cost-effectiveness, high sensitivity, and high specificity of molecular techniques, each type of molecular technology has its own advantages and disadvantages.

Polyphenols from olive mill waste affect biofilm formation and motility in Escherichia coli K-12

Olive mill wastes are sources of phenolic compounds with a wide array of biological activities, including antimicrobial effects. A potential option for bioremediation to overcome ecological problems is the reutilization of these natural compounds in food production. The aim of this work was to gain a better understanding of the antimicrobial mode of action of a phenols extract from olive vegetation water (PEOVW) at molecular level by studying Escherichia coli as a model microorganism. Genome-wide transcriptional analysis was performed on E. coli K-12 exposed to PEOVW. The repression of genes for flagellar synthesis and the involvement of genes linked to biofilm formation and stress response were observed. Sub-inhibitory concentrations of PEOVW significantly decreased biofilm formation, swarming and swimming motility, thus confirming the gene expression data. This study provides interesting insights on the molecular action of PEOVW on E. coli K-12. Given these anti-biofilm properties and considering that biofilm formation is a serious problem for the food industry and human health, PEOVW has proved to be a high-value natural product.

Effect of Target Length on Specificity and Sensitivity of Oligonucleotide Microarrays: A Comparison between Dendrimer and Modified PCR based Labelling Methods

DNA microarrays are widely used as end point detectors for gene expression analysis. Several methods have been developed for target labelling to enable quantification but without taking target length into consideration. Here we highlight the importance of choosing the optimum target length that would ensure specificity without compromising sensitivity of the assay. For this, eight plasmids that are identical to each other except for a closely related 23 bp unique reporter (UR) sequence were used to examine the hybridization efficiency for these URs. Targets of various lengths were generated and labelled as follows: full length and 330 bases transcripts using a dendrimer labelling method, 120 bp amplicons by the modified PCR end labelling method and synthetic labelled targets of 33 bases. This report also shows the advantages of using the modified PCR method over other labelling methods in generating labelled amplicons of the desired lengths to maximize hybridization efficiency.

Microarray technology for analysis of microRNA expression in renal biopsies of lupus nephritis patients

Systemic lupus erythematosus (SLE) is a complex autoimmune disease, which predominantly occurs in females and is characterized by autoantibody production against a host of nuclear self-antigens and deposition of proinflammatory immune complexes in the organs including kidney glomeruli. MicroRNAs are small noncoding intracellular RNAs that modulate gene expression at the posttranslational level. Microarray technology is in widespread use for analysis of microRNA (miRNA) gene expression because of its flexibility and accurate high throughput. RNA microarray technology is based on nucleic acid hybridization between a mixture of labeled RNA targets and their corresponding complementary probes. This article offers a technological overview of microarray technology for analysis of microRNA gene expression in kidney biopsies from SLE patients.

Differential reconstructed gene interaction networks for deriving toxicity threshold in chemical risk assessment

Background

Pathway alterations reflected as changes in gene expression regulation and gene interaction can result from cellular exposure to toxicants. Such information is often used to elucidate toxicological modes of action. From a risk assessment perspective, alterations in biological pathways are a rich resource for setting toxicant thresholds, which may be more sensitive and mechanism-informed than traditional toxicity endpoints. Here we developed a novel differential networks (DNs) approach to connect pathway perturbation with toxicity threshold setting.

Methods

Our DNs approach consists of 6 steps: time-series gene expression data collection, identification of altered genes, gene interaction network reconstruction, differential edge inference, mapping of genes with differential edges to pathways, and establishment of causal relationships between chemical concentration and perturbed pathways. A one-sample Gaussian process model and a linear regression model were used to identify genes that exhibited significant profile changes across an entire time course and between treatments, respectively. Interaction networks of differentially expressed (DE) genes were reconstructed for different treatments using a state space model and then compared to infer differential edges/interactions. DE genes possessing differential edges were mapped to biological pathways in databases such as KEGG pathways.

Results

Using the DNs approach, we analyzed a time-series Escherichia coli live cell gene expression dataset consisting of 4 treatments (control, 10, 100, 1000 mg/L naphthenic acids, NAs) and 18 time points. Through comparison of reconstructed networks and construction of differential networks, 80 genes were identified as DE genes with a significant number of differential edges, and 22 KEGG pathways were altered in a concentration-dependent manner. Some of these pathways were perturbed to a degree as high as 70% even at the lowest exposure concentration, implying a high sensitivity of our DNs approach.

Conclusions

Findings from this proof-of-concept study suggest that our approach has a great potential in providing a novel and sensitive tool for threshold setting in chemical risk assessment. In future work, we plan to analyze more time-series datasets with a full spectrum of concentrations and sufficient replications per treatment. The pathway alteration-derived thresholds will also be compared with those derived from apical endpoints such as cell growth rate.

Explaining microbial phenotypes on a genomic scale: GWAS for microbes

There is an increasing availability of complete or draft genome sequences for microbial organisms. These data form a potentially valuable resource for genotype-phenotype association and gene function prediction, provided that phenotypes are consistently annotated for all the sequenced strains. In this review, we address the requirements for successful gene-trait matching. We outline a basic protocol for microbial functional genomics, including genome assembly, annotation of genotypes (including single nucleotide polymorphisms, orthologous groups and prophages), data pre-processing, genotype-phenotype association, visualization and interpretation of results. The methodologies for association described herein can be applied to other data types, opening up possibilities to analyze transcriptome-phenotype associations, and correlate microbial population structure or activity, as measured by metagenomics, to environmental parameters.

Use of functional gene arrays for elucidating in situ biodegradation

Microarrays have revolutionized the study of microbiology by providing a high-throughput method for examining thousands of genes with a single test and overcome the limitations of many culture-independent approaches. Functional gene arrays (FGA) probe a wide range of genes involved in a variety of functions of interest to microbial ecology (e.g., carbon degradation, N fixation, metal resistance) from many different microorganisms, cultured and uncultured. The most comprehensive FGA to date is the GeoChip array, which targets tens of thousands of genes involved in the geochemical cycling of carbon, nitrogen, phosphorus, and sulfur, metal resistance and reduction, energy processing, antibiotic resistance and contaminant degradation as well as phylogenetic information (gyrB). Since the development of GeoChips, many studies have been performed using this FGA and have shown it to be a powerful tool for rapid, sensitive, and specific examination of microbial communities in a high-throughput manner. As such, the GeoChip is well-suited for linking geochemical processes with microbial community function and structure. This technology has been used successfully to examine microbial communities before, during, and after in situ bioremediation at a variety of contaminated sites. These studies have expanded our understanding of biodegradation and bioremediation processes and the associated microorganisms and environmental conditions responsible. This review provides an overview of FGA development with a focus on the GeoChip and highlights specific GeoChip studies involving in situ bioremediation.

Principal methods for isolation and identification of soil microbial communities

Soil microbial populations play crucial role in soil properties and influence below-ground ecosystem processes. Microbial composition and functioning changes the soil quality through decomposition of organic matter, recycling of nutrients, and biological control of parasites of plants. Moreover, the discovery that soil microbes may translate into benefits for biotechnology, management of agricultural, forest, and natural ecosystems, biodegradation of pollutants, and waste treatment systems maximized the need of scientists for the isolation and their characterization. Operations such as the production of antibiotics and enzymic activities from microorganisms of soil constitute objectives of industry in her effort to cope with the increase of population of earth and disturbance of environment and may ameliorate the effects of global climate change. In the past decades, new biochemical and molecular techniques have been developed in our effort to identify and classify soil bacteria. The goal of measuring the soil microbial diversity is difficult because of the limited knowledge about bacteria species and classification through families and orders. Molecular techniques extend our knowledge about microbial diversity and help the taxonomy of species. Measuring and monitoring soil microbial communities can lead us to better understanding of their composition and function in many ecosystem processes.

Detecting unknown sequences with DNA microarrays: explorative probe design strategies

Designing environmental DNA microarrays that can be used to survey the extreme diversity of microorganisms existing in nature, represents a stimulating challenge in the field of molecular ecology. Indeed, recent efforts in metagenomics have produced a substantial amount of sequence information from various ecosystems, and will continue to accumulate large amounts of sequence data given the qualitative and quantitative improvements in the next-generation sequencing methods. It is now possible to take advantage of these data to develop comprehensive microarrays by using explorative probe design strategies. Such strategies anticipate genetic variations and thus are able to detect known and unknown sequences in environmental samples. In this review, we provide a detailed overview of the probe design strategies currently available to construct both phylogenetic and functional DNA microarrays, with emphasis on those permitting the selection of such explorative probes. Furthermore, exploration of complex environments requires particular attention on probe sensitivity and specificity criteria. Finally, these innovative probe design approaches require exploiting newly available high-density microarray formats.

Microbial whole-cell arrays

The coming of age of whole‐cell biosensors, combined with the continuing advances in array technologies, has prepared the ground for the next step in the evolution of both disciplines – the whole‐cell array. In the present review, we highlight the state‐of‐the‐art in the different disciplines essential for a functional bacterial array. These include the genetic engineering of the biological components, their immobilization in different polymers, technologies for live cell deposition and patterning on different types of solid surfaces, and cellular viability maintenance. Also reviewed are the types of signals emitted by the reporter cell arrays, some of the transduction methodologies for reading these signals and the mathematical approaches proposed for their analysis. Finally, we review some of the potential applications for bacterial cell arrays, and list the future needs for their maturation: a richer arsenal of high‐performance reporter strains, better methodologies for their incorporation into hardware platforms, design of appropriate detection circuits, the continuing development of dedicated algorithms for multiplex signal analysis and – most importantly – enhanced long‐term maintenance of viability and activity on the fabricated biochips.

Proteomic and transcriptomic elucidation of the mutant ralstonia eutropha G+1 with regard to glucose utilization

By taking advantage of the available genome sequence of Ralstonia eutropha H16, glucose uptake in the UV-generated glucose-utilizing mutant R. eutropha G(+)1 was investigated by transcriptomic and proteomic analyses. Data revealed clear evidence that glucose is transported by a usually N-acetylglucosamine-specific phosphotransferase system (PTS)-type transport system, which in this mutant is probably overexpressed due to a derepression of the encoding nag operon by an identified insertion mutation in gene H16_A0310 (nagR). Furthermore, a missense mutation in nagE (membrane component EIICB), which yields a substitution of an alanine by threonine in NagE and may additionally increase glucose uptake, was identified. Phosphorylation of glucose is subsequently mediated by NagF (cytosolic PTS component EIIA-HPr-EI) or glucokinase (GlK), respectively. The inability of the defined deletion mutant R. eutropha G(+)1 ΔnagFEC to utilize glucose strongly confirms this finding. In addition, secondary effects of glucose, which is now intracellularly available as a carbon source, on the metabolism of the mutant cells in the stationary growth phase occurred: intracellular glucose degradation is stimulated by the stronger expression of enzymes involved in the 2-keto-3-deoxygluconate 6-phosphate (KDPG) pathway and in subsequent reactions yielding pyruvate. The intermediate phosphoenolpyruvate (PEP) in turn supports further glucose uptake by the Nag PTS. Pyruvate is then decarboxylated by the pyruvate dehydrogenase multienzyme complex to acetyl coenzyme A (acetyl-CoA), which is directed to poly(3-hydroxybutyrate). The polyester is then synthesized to a greater extent, as also indicated by the upregulation of various enzymes of poly-β-hydroxybutyrate (PHB) metabolism. The larger amounts of NADPH required for PHB synthesis are delivered by significantly increased quantities of proton-translocating NAD(P) transhydrogenases. The current study successfully combined transcriptomic and proteomic investigations to unravel the phenotype of this hitherto-undefined glucose-utilizing mutant.

A miniaturized spatial temperature gradient capillary electrophoresis system with radiative heating and automated sample introduction for DNA mutation detection

A miniaturized spatial temperature gradient CE system with automated sample introduction for DNA mutation detection was established. Continuous electrokinetic sample injection was achieved by combining an automated slotted-vial array sample introduction device to the spatial temperature gradient CE system. The temperature gradient was produced by a radiative heating system with a single graphite block heater, and the stability of the temperature gradient was investigated. The temperature variation of each measure point was 0.12-0.21% RSD (n=7) within 6 h. A 14-cm Teflon AF-coated silica capillary was used both as the separation channel and as the liquid-core waveguide tube of fluorescence signal. Under a temperature gradient from 54.8 to 59.5°C, a low range control mutation standard (209 bp) was separated within 4 min with only 5.6 nL sample consumption. Automated continuous sample introducing and changing were realized with a carryover of 3.3%. Utility of the system was further demonstrated by detecting K-ras gene mutations in paraffin tissue sections from two colorectal cancer patients.

Next-generation sequencing techniques for eukaryotic microorganisms: sequencing-based solutions to biological problems

Over the past 5 years, large-scale sequencing has been revolutionized by the development of several so-called next-generation sequencing (NGS) technologies. These have drastically increased the number of bases obtained per sequencing run while at the same time decreasing the costs per base. Compared to Sanger sequencing, NGS technologies yield shorter read lengths; however, despite this drawback, they have greatly facilitated genome sequencing, first for prokaryotic genomes and within the last year also for eukaryotic ones. This advance was possible due to a concomitant development of software that allows the de novo assembly of draft genomes from large numbers of short reads. In addition, NGS can be used for metagenomics studies as well as for the detection of sequence variations within individual genomes, e.g., single-nucleotide polymorphisms (SNPs), insertions/deletions (indels), or structural variants. Furthermore, NGS technologies have quickly been adopted for other high-throughput studies that were previously performed mostly by hybridization-based methods like microarrays. This includes the use of NGS for transcriptomics (RNA-seq) or the genome-wide analysis of DNA/protein interactions (ChIP-seq). This review provides an overview of NGS technologies that are currently available and the bioinformatics analyses that are necessary to obtain information from the flood of sequencing data as well as applications of NGS to address biological questions in eukaryotic microorganisms.

Impact of multiple beta-ketothiolase deletion mutations in Ralstonia eutropha H16 on the composition of 3-mercaptopropionic acid-containing copolymers

beta-Ketothiolases catalyze the first step of poly(3-hydroxybutyrate) [poly(3HB)] synthesis in bacteria by condensing two molecules of acetyl coenzyme A (acetyl-CoA) to acetoacetyl-CoA. Analyses of the genome sequence of Ralstonia eutropha H16 revealed 15 isoenzymes of PhaA in this bacterium. In this study, we generated knockout mutants of various phaA homologues to investigate their role in and contributions to poly(3HB) metabolism and to suppress biosynthesis of 3HB-CoA for obtaining enhanced molar 3-mercaptopriopionate (3MP) contents in poly(3HB-co-3MP) copolymers when cells were grown on gluconate plus 3-mercaptopropionate or 3,3'-dithiodipropionate. In silico sequence analysis of PhaA homologues, transcriptome data, and other aspects recommended the homologues phaA, bktB, H16_A1713/H16_B1771, H16_A1528, H16_B1369, H16_B0381, and H16_A0170 for further analysis. Single- and multiple-deletion mutants were generated to investigate the influence of these beta-ketothiolases on growth and polymer accumulation. The deletion of single genes resulted in no significant differences from the wild type regarding growth and polymer accumulation during cultivation on gluconate or gluconate plus 3MP. Deletion of phaA plus bktB (H16Delta2 mutant) resulted in approximately 30% less polymer accumulation than in the wild type. Deletion of H16_A1713/H16_B1771, H16_A1528, H16_B0381, and H16_B1369 in addition to phaA and bktB gave no differences in comparison to the H16Delta2 mutant. In contrast, deletion of H16_A0170 additionally to phaA and bktB yielded a mutant which accumulated about 30% poly(3HB) (wt/wt of the cell dry weight [CDW]). Although we were not able to suppress poly(3HB) biosynthesis completely, the copolymer compositions could be altered significantly with a lowered percentage ratio of 3HB constituents (from 85 to 52 mol%) and an increased percentage ratio of 3MP constituents (from 15 to 48 mol%), respectively. In this study, we demonstrated that PhaA, BktB, and H16_A0170 are majorly involved in poly(3HB) synthesis in R. eutropha H16. A fourth beta-ketothiolase or a combination of several of the other beta-ketothiolases contributed to a maximum of only 30% (wt/wt of CDW) of the remaining (co)polymer.

Genome-wide transcriptome analyses of the ‘Knallgas’ bacterium Ralstonia eutropha H16 with regard to polyhydroxyalkanoate metabolism

Ralstonia eutropha H16 is probably the best-studied ‘Knallgas’ bacterium and producer of poly(3-hydroxybutyrate) (PHB). Genome-wide transcriptome analyses were employed to detect genes that are differentially transcribed during PHB biosynthesis. For this purpose, four transcriptomes from different growth phases of the wild-type H16 and of the two PHB-negative mutants PHB−4 and ΔphaC1 were compared: (i) cells from the exponential growth phase with cells that were in transition to stationary growth phase, and (ii) cells from the transition phase with cells from the stationary growth phase of R. eutropha H16, as well as (iii) cells from the transition phase of R. eutropha H16 with those from the transition phase of R. eutropha PHB−4 and (iv) cells from the transition phase of R. eutropha ΔphaC1 with those from the transition phase of R. eutropha PHB−4. Among a large number of genes exhibiting significant changes in transcription level, several genes within the functional class of lipid metabolism were detected. In strain H16, phaP3, accC2, fabZ, fabG and H16_A3307 exhibited a decreased transcription level in the stationary growth phase compared with the transition phase, whereas phaP1, H16_A3311, phaZ2 and phaZ6 were found to be induced in the stationary growth phase. Compared with PHB−4, we found that phaA, phaB1, paaH1, H16_A3307, phaP3, accC2 and fabG were induced in the wild-type, and phaP1, phaP4, phaZ2 and phaZ6 exhibited an elevated transcription level in PHB−4. In strain ΔphaC1, phaA and phaB1 were highly induced compared with PHB−4. Additionally, the results of this study suggest that mutant strain PHB−4 is defective in PHB biosynthesis and fatty acid metabolism. A significant downregulation of the two cbb operons in mutant strain PHB−4 was observed. The putative polyhydroxyalkanoate (PHA) synthase phaC2 identified in strain H16 was further investigated by several functional analyses. Mutant PHB−4 could be phenotypically complemented by expression of phaC2 from a plasmid; on the other hand, in the mutant H16ΔphaC1, no PHA production was observed. PhaC2 activity could not be detected in any experiment.

Review of microarray studies for host-intracellular pathogen interactions

Obligate intracellular bacteria are privileged soldiers on the battlefield that represent host-pathogen interactions. Microarrays are a powerful technology that can increase our knowledge about how bacteria respond to and interact with their hosts. This review summarizes the limitations inherent to host-pathogen interaction studies and essential strategies to improve microarray investigations of intracellular bacteria. We have compiled the comparative genomic and gene expression analyses of obligate intracellular bacteria currently available from microarrays. In this review we explore ways in which microarrays can be used to identify polymorphisms in different obligate intracellular bacteria such as Coxiella burnetii, Chlamydia trachomatis, Ehrlichia chaffeensis, Rickettsia prowazekii and Tropheryma whipplei. These microarray studies reveal that, while genomic content is highly conserved in obligate intracellular bacteria, genetic polymorphisms can potentially occur to increase bacterial pathogenesis. Additionally, changes in the gene expression of C. trachomatis throughout its life cycle, as well as changes in the gene expression profile of the pathogens R. prowazekii, Rickettsia rickettsii, Rickettsia typhi, T. whipplei and C. trachomatis following environmental changes, are discussed. Finally, an in vivo model of Rickettsia conorii within the skin is discussed. The gene expression analyses highlight the capacity of obligate intracellular bacteria to adapt to environmental changes and potentially to thwart the host response.

Basic concepts of microarrays and potential applications in clinical microbiology

The introduction of in vitro nucleic acid amplification techniques, led by real-time PCR, into the clinical microbiology laboratory has transformed the laboratory detection of viruses and select bacterial pathogens. However, the progression of the molecular diagnostic revolution currently relies on the ability to efficiently and accurately offer multiplex detection and characterization for a variety of infectious disease pathogens. Microarray analysis has the capability to offer robust multiplex detection but has just started to enter the diagnostic microbiology laboratory. Multiple microarray platforms exist, including printed double-stranded DNA and oligonucleotide arrays, in situ-synthesized arrays, high-density bead arrays, electronic microarrays, and suspension bead arrays. One aim of this paper is to review microarray technology, highlighting technical differences between them and each platform's advantages and disadvantages. Although the use of microarrays to generate gene expression data has become routine, applications pertinent to clinical microbiology continue to rapidly expand. This review highlights uses of microarray technology that impact diagnostic microbiology, including the detection and identification of pathogens, determination of antimicrobial resistance, epidemiological strain typing, and analysis of microbial infections using host genomic expression and polymorphism profiles.

Microbial cell arrays

The coming of age of whole-cell biosensors, combined with the continuing advances in array technologies, has prepared the ground for the next step in the evolution of both disciplines - the whole cell array. In the present chapter, we highlight the state-of-the-art in the different disciplines essential for a functional bacterial array. These include the genetic engineering of the biological components, their immobilization in different polymers, technologies for live cell deposition and patterning on different types of solid surfaces, and cellular viability maintenance. Also reviewed are the types of signals emitted by the reporter cell arrays, some of the transduction methodologies for reading these signals, and the mathematical approaches proposed for their analysis. Finally, we review some of the potential applications for bacterial cell arrays, and list the future needs for their maturation: a richer arsenal of high-performance reporter strains, better methodologies for their incorporation into hardware platforms, design of appropriate detection circuits, the continuing development of dedicated algorithms for multiplex signal analysis, and - most importantly - enhanced long term maintenance of viability and activity on the fabricated biochips.

New methods to analyse microarray data that partially lack a reference signal

Background

Microarray-based Comparative Genomic Hybridisation (CGH) has been used to assess genetic variability between bacterial strains. Crucial for interpretation of microarray data is the availability of a reference to compare signal intensities to reliably determine presence or divergence each DNA fragment. However, the production of a good reference becomes unfeasible when microarrays are based on pan-genomes.

When only a single strain is used as a reference for a multistrain array, the accessory gene pool will be partially represented by reference DNA, although these genes represent the genomic repertoire that can explain differences in virulence, pathogenicity or transmissibility between strains. The lack of a reference makes interpretation of the data for these genes difficult and, if the test signal is low, they are often deleted from the analysis. We aimed to develop novel methods to determine the presence or divergence of genes in a Staphylococcus aureus multistrain PCR product microarray-based CGH approach for which reference DNA was not available for some probes.

Results

In this study we have developed 6 new methods to predict divergence and presence of all genes spotted on a multistrain Staphylococcus aureus DNA microarray, published previously, including those gene spots that lack reference signals. When considering specificity and PPV (i.e. the false-positive rate) as the most important criteria for evaluating these methods, the method that defined gene presence based on a signal at least twice as high as the background and higher than the reference signal (method 4) had the best test characteristics. For this method specificity was 100% and 82% for MRSA252 (compared to the GACK method) and all spots (compared to sequence data), respectively, and PPV were 100% and 76% for MRSA252 (compared to the GACK method) and all spots (compared to sequence data), respectively.

Conclusion

A definition of gene presence based on signal at least twice as high as the background and higher than the reference signal (method 4) had the best test characteristics, allowing the analysis of 6-17% more of the genes not present in the reference strain. This method is recommended to analyse microarray data that partially lack a reference signal.

Gene expression profiling in the rhesus macaque: methodology, annotation and data interpretation

Gene microarray analyses represent potentially effective means for high-throughput gene expression profiling in non-human primates. In the companion article, we emphasize effective experimental design based on the in vivo physiology of the rhesus macaque, whereas this article emphasizes considerations for gene annotation and data interpretation using gene microarray platforms from Affymetrix. Initial annotation of the rhesus genome array was based on Affymetrix human GeneChips. However, annotation revisions improve the precision with which rhesus transcripts are identified. Annotation of the rhesus GeneChip is under continuous revision with large percentages of probesets under multiple annotation systems having undergone multiple reassignments between March 2007 and November 2008. It is also important to consider that quantitation and comparison of gene expression levels across multiple chips requires appropriate normalization. External corroboration of microarray results using PCR-based methodology also requires validation of appropriate internal reference genes for normalization of expression values. Many tools are now freely available to aid investigators with microarray normalization and selection of internal reference genes to be used for independent corroboration of microarray results.

Insect symbioses: a case study of past, present, and future fungus-growing ant research

Fungus-growing ants (Attini: Formicidae) engage in an obligate mutualism with fungi they cultivate for food. Although biologists have been fascinated with fungus-growing ants since the resurgence of natural history in the modern era, the early stages of research focused mainly on the foraging behavior of the leaf-cutters (the most derived attine lineage). Indeed, the discovery that the ants actually use leaf fragments to manure a fungus did not come until the 1800s. More recently, three additional microbial symbionts have been described, including specialized microfungal parasites of the ant's fungus garden, antibiotic-producing actinobacteria that help protect the fungus garden from the parasite, and a black yeast that parasitizes the ant-actinobacteria mutualism. The fungus-growing ant symbiosis serves as a particularly useful model system for studying insect-microbe symbioses, because, to date, it contains four well-characterized microbial symbionts, including mutualists and parasites that encompass micro-fungi, macro-fungi, yeasts, and bacteria. Here, we discuss approaches for studying insect-microbe symbioses, using the attine ant-microbial symbiosis as our framework. We draw attention to particular challenges in the field of symbiosis, including the establishment of symbiotic associations and symbiont function. Finally, we discuss future directions in insect-microbe research, with particular focus on applying recent advances in DNA sequencing technologies.

Microarray experiments to uncover Toll-like receptor function

This chapter is intended as a handbook for anyone interested in using microarrays to study Toll-like receptor (TLR) function or any other biological question. Although microarray technology has developed into a standard tool at many laboratories disposal, most of the actual microarray processing is done by core facilities using highly specialized equipment. This chapter only briefly describes these methods in principle and instead focus on the parts that investigators themselves can influence, such as the experimental design, RNA isolation, statistical analysis, cluster analysis, data visualization, and biological interpretation.

DNA microarrays: an introduction to the technology

DNA microarrays allow the comprehensive genetic analysis of an organism or a sample. They are based on probes, which are immobilized in an ordered two-dimensional pattern on substrates, such as nylon membranes or glass slides. Probes are either spotted cDNAs or oligonucleotides and are designed to be specific for an organism, a gene, a genetic variant (mutation or polymorphism), or intergenic regions. Thus, they can be used for example for genotyping, expression analysis, or studies of protein-DNA interactions, and in the biomedical field they allow the detection of pathogens, antibiotic resistances, gene mutations and polymorphisms, and pathogenic states and can guide therapy. Microarrays, which cover the whole genome of an organism, are as well available as those which are focussed on genes related to a certain diagnostic application.

Stripping custom microRNA microarrays and the lessons learned about probe-slide interactions

Microarrays have been used extensively in gene expression profiling and genotyping studies. To reduce the high cost and enhance the consistency of microarray experiments, it is often desirable to strip and reuse microarray slides. Our genome-wide analysis of microRNA expression involves the hybridization of fluorescently labeled nucleic acids to custom-made, spotted DNA microarrays based on GAPSII-coated slides. We describe here a simple and effective method to regenerate such custom microarrays that uses a very low-salt buffer to remove labeled nucleic acids from microarrays. Slides can be stripped and reused multiple times without significantly compromising data quality. Moreover, our analyses of the performance of regenerated slides identifies parameters that influence the attachment of oligonucleotide probes to GAPSII slides, shedding light on the interactions between DNA and the microarray surface and suggesting ways in which to improve the design of oligonucleotide probes.

Potentials and limitations of molecular diagnostic methods in food safety

Molecular methods allow the detection of pathogen nucleic acids (DNA and RNA) and, therefore, the detection of contamination in food is carried out with high selectivity and rapidity. In the last 2 decades molecular methods have accompanied traditional diagnostic methods in routine pathogen detection, and might replace them in the upcoming future. In this review the implementation in diagnostics of four of the most used molecular techniques (PCR, NASBA, microarray, LDR) are described and compared, highlighting advantages and limitations of each of them. Drawbacks of molecular methods with regard to traditional ones and the difficulties encountered in pathogen detection from food or clinical specimen are also discussed. Moreover, criteria for the choice of the target sequence for a secure detection and classification of pathogens and possible developments in molecular diagnostics are also proposed.

Gene set analyses for interpreting microarray experiments on prokaryotic organisms

BACKGROUND

Despite the widespread usage of DNA microarrays, questions remain about how best to interpret the wealth of gene-by-gene transcriptional levels that they measure. Recently, methods have been proposed which use biologically defined sets of genes in interpretation, instead of examining results gene-by-gene. Despite a serious limitation, a method based on Fisher's exact test remains one of the few plausible options for gene set analysis when an experiment has few replicates, as is typically the case for prokaryotes.

RESULTS

We extend five methods of gene set analysis from use on experiments with multiple replicates, for use on experiments with few replicates. We then use simulated and real data to compare these methods with each other and with the Fisher's exact test (FET) method. As a result of the simulation we find that a method named MAXMEAN-NR, maintains the nominal rate of false positive findings (type I error rate) while offering good statistical power and robustness to a variety of gene set distributions for set sizes of at least 10. Other methods (ABSSUM-NR or SUM-NR) are shown to be powerful for set sizes less than 10. Analysis of three sets of experimental data shows similar results. Furthermore, the MAXMEAN-NR method is shown to be able to detect biologically relevant sets as significant, when other methods (including FET) cannot. We also find that the popular GSEA-NR method performs poorly when compared to MAXMEAN-NR.

CONCLUSION

MAXMEAN-NR is a method of gene set analysis for experiments with few replicates, as is common for prokaryotes. Results of simulation and real data analysis suggest that the MAXMEAN-NR method offers increased robustness and biological relevance of findings as compared to FET and other methods, while maintaining the nominal type I error rate.

OligoArrayDb: pangenomic oligonucleotide microarray probe sets database

OligoArrayDb is a comprehensive database containing pangenomic oligonucleotide microarray probe sets designed for most of the sequenced genomes that are not covered by commercial catalog arrays. The availability of probe sequences, associated with custom microarray fabrication services offered by many companies and cores presents the unequalled possibility to perform microarray experiments on most of the sequenced organisms. OligoArrayDb contains more than 2.8 probes per gene in average for more than 600 organisms, mostly archaea and bacteria strains available from public database. On average, 98% of the annotated genes have at least one probe which is predicted to be specific to its intended target in >94% of the cases. OligoArrayDb is weekly updated as new sequenced genomes become available. Probe sequences, in addition to a comprehensive set of annotations can be downloaded from this database. OligoArrayDb is publicly accessible online at http://berry.engin.umich.edu/oligoarraydb.