Diagnostic oligonucleotide microarray fingerprinting of Bacillus isolates

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.

Nonvolatile copolymer compositions for fabricating gel element microarrays

By modifying polymer compositions and cross-linking reagents, we have developed a simple yet effective manufacturing strategy for copolymerized three-dimensional gel element arrays. A new gel-forming monomer, 2-(hydroxyethyl) methacrylamide (HEMAA), was used. HEMAA possesses low volatility and improves the stability of copolymerized gel element arrays to on-chip thermal cycling procedures relative to previously used monomers. Probe immobilization efficiency within the new polymer was 55%, equivalent to that obtained with acrylamide (AA) and methacrylamide (MA) monomers. Nonspecific binding of single-stranded targets was equivalent for all monomers. Increasing cross-linker chain length improved hybridization kinetics and end-point signal intensities relative to N,N-methylenebisacrylamide (Bis). The new copolymer formulation was successfully applied to a model orthopox array. Because HEMAA greatly simplifies gel element array manufacture, we expect it (in combination with new cross-linkers described here) to find widespread application in microarray science.

Microarray expression profiling of Spodoptera litura in response to oxidative stress

To examine the expression profile of oxidative stress responsive genes in Spodoptera litura, we constructed a cDNA library from S. litura injected with hydrogen peroxide (H(2)O(2)). Using a microarray chip composed of 2,964 cDNAs, we screened gene expression at 1, 3, 5, 7, and 9 h post H(2)O(2) injection. Data were clustered into 15 groups of genes that behave similarly across each time course. Seventy-three genes were identified as being at least twofold up- or downregulated after treatment with H(2)O(2) in S. litura. We constructed expressed sequence tags (ESTs) for genes that changed at least twofold after treatment with H(2)O(2) . The functional classification of these ESTs based on Gene Ontology showed that the ESTs are rich in genes involved in oxidoreductase activity (5.7%), defense (14.3%), cellular process (22.9%), and development (17.1%).

Diagnostic microarray for human and animal bacterial diseases and their virulence and antimicrobial resistance genes

Rapid diagnosis and treatment of disease is often based on the identification and characterization of causative agents derived from phenotypic characteristics. Current methods can be laborious and time-consuming, often requiring many skilled personnel and a large amount of lab space. The objective of our study was to develop a spotted microarray for rapid identification and characterization of bacterial pathogens and their antimicrobial resistance genes. Our spotted microarray consists of 489 70mer probes that detect 40 bacterial pathogens of medical, veterinary and zoonotic importance (including 15 NIAID Category A, B and C pathogens); associated genes that encode resistance for antimicrobial and metal resistance; and DNA elements that are important for horizontal gene transfer among bacteria. High specificity and reliability of the microarray was achieved for bacterial pathogens of animal and human importance by validating MDR pathogenic bacteria as pure cultures or by following their inoculation in complex and highly organic sample matrices, such as soil and manure.

Mycobacterial species diversity at a general hospital on the island of Crete: First detection of Mycobacterium lentiflavum in Greece

The objective of the present study was to investigate the diversity of mycobacterial isolates in a general hospital in Crete, Greece. 48 positive Lowenstein-Jensen cultures over a 3-y period were analysed by means of AccuProbe and GenoType assays. Non-tuberculous mycobacteria (NTM) comprised the majority of the isolates (56.3%, 27/48) vs 33.3% (16/48) of M. tuberculosis; 10.4% of the isolates could not be classified. Among NTM, M. lentiflavum was the predominant species isolated (9/27) followed by M. kansasii, M. gordonae and M. peregrinum, whereas no M. avium complex isolates were detected. This is the first detection of M. lentiflavum in Greece. The susceptibilities of the M. lentiflavum isolates to an extended panel of antibiotics were determined by the proportions method and the medical files of the 9 patients were reviewed. Three isolates were from urine, which is an unusual site. All strains exhibited multidrug resistance. The patients were adults with immunosuppression or predisposing conditions for NTM infection. Diagnosis of true infection was either not pursued or the patients died shortly after isolation.

Food microbial pathogen detection and analysis using DNA microarray technologies

Culture-based methods used for microbial detection and identification are simple to use, relatively inexpensive, and sensitive. However, culture-based methods are too time-consuming for high-throughput testing and too tedious for analysis of samples with multiple organisms and provide little clinical information regarding the pathogen (e.g., antibiotic resistance genes, virulence factors, or strain subtype). DNA-based methods, such as polymerase chain reaction (PCR), overcome some these limitations since they are generally faster and can provide more information than culture-based methods. One limitation of traditional PCR-based methods is that they are normally limited to the analysis of a single pathogen, a small group of related pathogens, or a small number of relevant genes. Microarray technology enables a significant expansion of the capability of DNA-based methods in terms of the number of DNA sequences that can be analyzed simultaneously, enabling molecular identification and characterization of multiple pathogens and many genes in a single array assay. Microarray analysis of microbial pathogens has potential uses in research, food safety, medical, agricultural, regulatory, public health, and industrial settings. In this article, we describe the main technical elements of microarray technology and the application and potential use of DNA microarrays for food microbial analysis.

Microarrays in infection and immunity

Over the past decade, microarrays have revolutionized the scientific world as dramatically as the internet has changed everyday life. From the initial applications of DNA microarrays to uncover gene expression patterns that are diagnostic and prognostic of cancer, understanding the interplay between immune responses and disease has been a prime application of this technology. More recent efforts have moved beyond genetic analysis to functional analysis of the molecules involved, including identification of immunodominant antigens and peptides as well as the role of post-translational glycosylation. Here, we focus on recent applications of microarray technology in understanding the detailed chemical biology of immune responses to disease in an effort to guide development of vaccines and other protective therapies.

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.

Oligonucleotide microarray identification of Bacillus anthracis strains using support vector machines

The capability of a custom microarray to discriminate between closely related DNA samples is demonstrated using a set of Bacillus anthracis strains. The microarray was developed as a universal fingerprint device consisting of 390 genome-independent 9mer probes. The genomes of B. anthracis strains are monomorphic and therefore, typically difficult to distinguish using conventional molecular biology tools or microarray data clustering techniques. Using support vector machines (SVMs) as a supervised learning technique, we show that a low-density fingerprint microarray contains enough information to discriminate between B. anthracis strains with 90% sensitivity using a reference library constructed from six replicate arrays and three replicates for new isolates.