The development of a DNA microarray-based assay for the characterization of commercially formulated microbial products

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

Comparative assessment of next-generation sequencing, denaturing gradient gel electrophoresis, clonal restriction fragment length polymorphism and cloning-sequencing as methods for characterizing commercial microbial consortia

Characterization of commercial microbial consortia products for human and environmental health risk assessment is a major challenge for regulatory agencies. As a means to develop an approach to assess the potential environmental risk of these products, research was conducted to compare four genomics methods for characterizing bacterial communities; (i) Denaturing Gradient Gel Electrophoresis (DGGE), (ii) Clonal-Restriction Fragment Length Polymorphism (C/RFLP), (iii) partial 16S rDNA amplification, cloning followed by Sanger sequencing (PRACS) and (iv) Next-Generation Sequencing (NGS) based on Ion Torrent technology. A commercially available microbial consortium, marketed as a remediation agent for degrading petroleum hydrocarbon contamination in soil and water, was assessed. The bacterial composition of the commercial microbial product was characterized using the above four methods. PCR amplification of 16S rDNA was performed targeting the variable region V6 for DGGE, C/RFLP and PRACS and V5 for Ion Torrent sequencing. Ion Torrent technology was shown to be a promising tool for initial screening by detecting the majority of bacteria in the consortium that were also detected by DGGE, C/RFLP and PRACS. Additionally, Ion Torrent sequencing detected some of the bacteria that were claimed to be in the product, while three other methods failed to detect these specific bacteria. However, the relative proportions of the microbial composition detected by Ion Torrent were found to be different from DGGE, C/RFLP and PRACS, which gave comparable results across these three methods. The discrepancy of the Ion Torrent results may be due to the short read length generated by this technique and the targeting of different variable regions on the 16S rRNA gene used in this study. Arcobacter spp. a potential pathogenic bacteria was detected in the product by all methods, which was further confirmed using genus and species-specific PCR, RFLP and DNA-based sequence analyses. However, the viability of Arcobacter spp. was not confirmed. This study suggests that a combination of two or more methods may be required to ascertain the microbial constituents of a commercial microbial consortium reliably and for the presence of potentially human pathogenic contaminants.

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.

Bacterial gene expression at low temperatures

Under suboptimal environmental conditions such as low temperatures, many bacteria have an extended lag phase, altered cell structures, and composition such as a less fluid (more rigid) and leaky cytoplasmic membrane. As a result, cells may die, enter into a starvation mode of metabolism or a physiologically viable but non-culturable (VBNC) state. In the latter state, the amount of gene expression per cell is virtually undetectable. In this article, gene expression under (suboptimal) low temperature conditions in non-psychrophilic environmental bacteria is examined. The pros and cons of some of the molecular methodologies for gene expression analysis are also discussed.

Development of a DNA microarray for enterococcal species, virulence, and antibiotic resistance gene determinations among isolates from poultry

A DNA microarray (Enteroarray) was designed with probes targeting four species-specific taxonomic identifiers to discriminate among 18 different enterococcal species, while other probes were designed to identify 18 virulence factors and 174 antibiotic resistance genes. In total, 262 genes were utilized for rapid species identification of enterococcal isolates, while characterizing their virulence potential through the simultaneous identification of endogenous antibiotic resistance and virulence genes. Enterococcal isolates from broiler chicken farms were initially identified by using the API 20 Strep system, and the results were compared to those obtained with the taxonomic genes atpA, recA, pheS, and ddl represented on our microarray. Among the 171 isolates studied, five different enterococcal species were identified by using the API 20 Strep system: Enterococcus faecium, E. faecalis, E. durans, E. gallinarum, and E. avium. The Enteroarray detected the same species as API 20 Strep, as well as two more: E. casseliflavus and E. hirae. Species comparisons resulted in 15% (27 isolates) disagreement between the two methods among the five API 20 Strep identifiable species and 24% (42 isolates) disagreement when considering the seven Enteroarray identified species. The species specificity of key antibiotic and virulence genes identified by the Enteroarray were consistent with the literature adding further robustness to the redundant taxonomic probe data. Sequencing of the cpn60 gene further confirmed the complete accuracy of the microarray results. The new Enteroarray should prove to be a useful tool to accurately genotype strains of enterococci and assess their virulence potential.

A DNA array based assay for the characterization of microbial community in raw milk

An oligonucleotide array based on PCR method containing a combination of probes for taxonomic markers and species-specific virulence genes was developed for the simultaneous identification of 14 Lactic Acid Bacteria (LAB) and food-borne pathogenic bacteria in raw milk. The hypervariable regions V3 and V6 of 16S, together with a variable region of 23S rRNA genes and several genes specific for virulence factors were selected. Universal primers and multiplex PCR were used for the rapid differentiation of bacterial species and low concentration of specific pathogenic and spoilage bacteria were detected in milk. The dominant species such as Streptococcus thermophilus, Enterococcus faecalis, Lactococcus lactis, andLeuconostoc lactis were identified by indirect-labelling reactions based upon incorporation of amino-allyl dUTPs. The results regarding food-borne pathogens detection showed highly specific hybridisation patterns with the genomic DNA from Campylobacter jejuni, Escherichia coli, Salmonella thyphimurium, Listeria monocytogenes and Yersinia enterocolitica. A clear differentiation of dominant species present in a complex microbial community such as raw milk was achieved by the application of short oligonucleotide probes which discriminate sequences differing by few nucleotides.

A short-oligonucleotide microarray that allows improved detection of gastrointestinal tract microbial communities

Background

The human gastrointestinal (GI) tract contains a diverse collection of bacteria, most of which are unculturable by conventional microbiological methods. Increasingly molecular profiling techniques are being employed to examine this complex microbial community. The purpose of this study was to develop a microarray technique based on 16S ribosomal gene sequences for rapidly monitoring the microbial population of the GI tract.

Results

We have developed a culture-independent, semi-quantitative, rapid method for detection of gut bacterial populations based on 16S rDNA probes using a DNA microarray. We compared the performance of microarrays based on long (40- and 50-mer) and short (16–21-mer) oligonucleotides. Short oligonucleotides consistently gave higher specificity. Optimal DNA amplification and labelling, hybridisation and washing conditions were determined using a probe with an increasing number of nucleotide mismatches, identifying the minimum number of nucleotides needed to distinguish between perfect and mismatch probes. An independent PCR-based control was used to normalise different hybridisation results, and to make comparisons between different samples, greatly improving the detection of changes in the gut bacterial population. The sensitivity of the microarray was determined to be 8.8 × 10⁴ bacterial cells g^-1 faecal sample, which is more sensitive than a number of existing profiling methods. The short oligonucleotide microarray was used to compare the faecal flora from healthy individuals and a patient suffering from Ulcerative Colitis (UC) during the active and remission states. Differences were identified in the bacterial profiles between healthy individuals and a UC patient. These variations were verified by Denaturing Gradient Gel Electrophoresis (DGGE) and DNA sequencing.

Conclusion

In this study we demonstrate the design, testing and application of a highly sensitive, short oligonucleotide community microarray. Our approach allows the rapid discrimination of bacteria inhabiting the human GI tract, at taxonomic levels ranging from species to the superkingdom bacteria. The optimised protocol is available at: . It offers a high throughput method for studying the dynamics of the bacterial population over time and between individuals.

Characterization of formulated microbial products by denaturing gradient gel electrophoresis, total cellular fatty acid analysis, and DNA microarray analysis

Two commercial products, Biotize and Cycle, containing bacteria as an active ingredient were characterized for species identification and batch-to-batch variation by denaturing gradient gel electrophoresis (DGGE), total cellular fatty acid analysis (FAA), and a taxonomic DNA microarray. DGGE was useful at assessing the stability of consortia in different batches, and cluster analysis differentiated each batch even when only slight differences in species composition were observed. DGGE, FAA, and DNA microarray results indicated little batch-to-batch variation in Biotize and some batch variation in Cycle. The 3 methods agreed well with species identification in Biotize but generated conflicting results in the species composition of Cycle. This multi-method approach was useful in determining if the observed bacterial species present in the products matched the expected species composition.

Investigation of the microbial community in a microbiological additive used in a manure composting process

The objectives of this study were to investigate the fate of microorganisms by using cultivation methods as well as DNA analyses in a commercial microbiological additive (MA) in the course of the composting. Almost all the predominant species in the microbial succession during composting process determined by polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) were in disagreement with those determined by the clone library method. None of the microbial species in the composting stages corresponded to the microorganisms identified in the MA either by the cultivation method or DNA analysis. The results in regard to predominant microorganisms of the MA detected from the liquid medium by the PCR-DGGE did not correspond with those detected from the MA itself and composting processes. Although no evidence was found that predominant species in the MA itself dominate in the composting process, predominant species diversity in the MA itself was markedly changed after culturing at different thermophilic temperatures. These results suggested that cultivable microorganisms in the MA did not become predominant in the composting process: however, some microorganisms that are detected from the MA itself by the DNA analysis may act effectively in the composting process.

A perspective on microarrays: current applications, pitfalls, and potential uses

With advances in robotics, computational capabilities, and the fabrication of high quality glass slides coinciding with increased genomic information being available on public databases, microarray technology is increasingly being used in laboratories around the world. In fact, fields as varied as: toxicology, evolutionary biology, drug development and production, disease characterization, diagnostics development, cellular physiology and stress responses, and forensics have benefiting from its use. However, for many researchers not familiar with microarrays, current articles and reviews often address neither the fundamental principles behind the technology nor the proper designing of experiments. Although, microarray technology is relatively simple, conceptually, its practice does require careful planning and detailed understanding of the limitations inherently present. Without these considerations, it can be exceedingly difficult to ascertain valuable information from microarray data. Therefore, this text aims to outline key features in microarray technology, paying particular attention to current applications as outlined in recent publications, experimental design, statistical methods, and potential uses. Furthermore, this review is not meant to be comprehensive, but rather substantive; highlighting important concepts and detailing steps necessary to conduct and interpret microarray experiments. Collectively, the information included in this text will highlight the versatility of microarray technology and provide a glimpse of what the future may hold.

A fuzzy distance metric for measuring the dissimilarity of planar chromatographic profiles with application to denaturing gradient gel electrophoresis data from human skin microbes: demonstration of an individual and gender-based fingerprint

A newly devised fuzzy metric for measuring the dissimilarity between two planar chromatographic profiles is proposed in this paper. It does not require an accurately assigned sample-feature matrix and can cope with slight imprecision of the positional information. This makes it very suitable for 1-D techniques which do not have a second spectroscopic dimension to aid variable assignment. The usefulness of this metric has been demonstrated on a large data set consisting of nearly 400 samples from Denaturing Gradient Gel Electrophoresis (DGGE) analysis of microbes on human skin. The pattern revealed by this dissimilarity metric was compared with the one represented by a sample-feature matrix and highly consistent results were obtained. Several pattern recognition techniques have been applied on the dissimilarity matrix based on this dissimilarity metric. According to rank analysis, within-individual variation is significantly less than between-individual variation, suggesting a unique individual microbial fingerprint. Principal Coordinates Analysis (PCO) suggests that there is a considerable separation between genders. These results suggest that there are specific microbial colonies characteristic of individuals.

Hybridization of genomic DNA to microarrays: a challenge for the analysis of environmental samples

The use of DNA microarrays for detection and identification of bacteria and genes of interest from various environments (e.g. soil, sediment, water column...) is a major challenge for microbiologists working on functional diversity. So far, most of the genomic methods that have been described rely on the use of taxonomic markers (such as 16S rRNA) that can be easily amplified by PCR prior to hybridization on microarrays. However, taxonomical markers are not always informative on the functions present in these bacteria. Moreover, genes for which sequence database is limited or that lack any conserved regions will be difficult to amplify and thus to detect in unknown samples. Furthermore, PCR amplification often introduces biases that lead to inaccurate analysis of microbial communities. An alternative solution to overcome these strong limitations is to use genomic DNA (gDNA) as target for hybridisation, without prior PCR amplification. Though hybridization of gDNA is already used for comparative genome hybridization or sequencing by hybridization, yet to the high cost of tiling strategies and important data filtering, its adaptation for use in environmental research poses great challenges in terms of specificity, sensitivity and reproducibility of hybridization. Considering the very faint number of publications that have described hybridization of gDNA to microarrays for environmental applications, we confront in this review the different approaches that have been developed so far, and propose alternative strategies that may contribute to improve the development of microarrays for studying the microbial genetic structure and composition of samples of high environmental and ecological value.

Microbial gene expression in soil: methods, applications and challenges

About 99% of soil microorganisms are unculturable. However, advances in molecular biology techniques allow for the analysis of living microorganisms. With the advent of new technologies and the optimization of previous methods, various approaches to studying gene expression are expanding the field of microbiology and molecular biology. Methods used for RNA extraction, DNA microarrays, real-time PCR, competitive RT-PCR, stable isotope probing and the use of reporter genes provide methods for detecting and quantifying gene expression. Through the use of these methods, researchers can study the influence of soil environmental factors such as nutrients, oxygen status, pH, pollutants, agro-chemicals, moisture and temperature on gene expression and some of the mechanisms involved in the responses of cells to their environment. This review will also address information gaps in bacterial gene expression in soil and possible future research to develop an understanding of microbial activities in soil environments.

Development of a new oligonucleotide array to identify staphylococcal strains at species level

The genus Staphylococcus is made up of 36 validated species which contain strains that are pathogenic, saprophytic, or used as starter cultures for the food industry. An oligonucleotide array targeting the manganese-dependent superoxide dismutase (sodA) gene was developed to overcome the drawbacks of the conventional methods of identification. Divergences of the sodA gene were used to design oligonucleotide probes, and we showed that each of the 36 species had a characteristic pattern of hybridization. To evaluate the array, we analyzed 38 clinical and 38 food or food plant Staphylococcus isolates identified by the phenotype-based system VITEK 2 (bioMérieux). This commercial kit failed to identify 8 (21%) of the clinical isolates and 32 (84%) of the food and food plant isolates. In contrast, the oligonucleotide array we designed provided an accurate and rapid method for the identification of staphylococcal strains, isolated from clinical, environmental, or food samples, at species level.

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