Optical fiber-based sensors: application to chemical biology

Optical fibers have been used to develop sensors based on nucleic acids and cells. Sensors employing DNA probes have been developed for various genomics applications and microbial pathogen detection. Live cell-based sensors have enabled the monitoring of environmental toxins, and have been used for fundamental studies on populations of individual cells. Both single-core optical fiber sensors and optical fiber sensor arrays have been used for sensing based on nucleic acids and live cells.     

基因芯片技术在环境微生物群落研究中的应用

基因芯片技术作为一种快速、敏感、高通量的检测技术,近几年来在环境微生物群落研究中的应用越来越广泛并且得到充分的发展.它不仅可以研究环境微生物群落的微生物分布、种类、功能、动力学变化,还能分析环境污染等环境因素改变对其微生物生态的影响.本文按照基因芯片探针的设计方法,将环境样品群落研究基因芯片分为系统寡核苷酸芯片、功能基因芯片、群落基因组芯片、宏基因组芯片,并简要综述了该技术在活性污泥、土壤、水等环境样品微生物群落研究上的应用,最后,本文展望了该技术的研究方向和在寻找不同环境微生物群落之间差异微生物、差异基因或差异表达基因研究中的应用前景.     

Optical-fiber bundles

Optical-fiber bundles have been employed as a versatile substrate for the fabrication of high-density microwell arrays. In this minireview, we discuss the application of optical-fiber-bundle arrays for a variety of biological problems. For genomics studies and microbial pathogen detection, individual beads have been functionalized with DNA probes and then loaded into the microwells. In addition, beads differentially responsive to vapors have been employed in an artificial olfaction system. Microwell arrays have also been loaded with living cells to monitor their individual response to biologically active compounds over long periods. Finally, the microwells have been sealed to enclose single enzyme molecules that can be used to measure individual molecule catalytic activity.     

Detecting single nucleotide polymorphisms using DNA arrays for plant pathogen diagnosis

The lack of a rapid and reliable means for routine pathogen identification has been one of the main limitations in plant disease management, and has pushed the development of culture-independent, molecular approaches. Currently, DNA array technology is the most suitable technique for high-throughput detection and identification, as well as quantification, of multiple pathogens in a single assay. Closely related pathogens that may have completely different host ranges or pathogenicity often differ in only a single to a few base pairs in genes that may be targeted for identification. Therefore, the ability to discriminate single nucleotide polymorphisms (SNPs) should be pursued in any diagnostic assay. In this paper, we demonstrate the utility of DNA array technology to detect SNPs while accounting for specific criteria such as the position of the mismatch, the sequence of the oligonucleotide, and the length and amount of labeled amplicons that are hybridized. When disregarding mismatches at the extreme ends of the oligonucleotides, cross hybridization to single mismatch oligonucleotides is rare when processing environmental samples that contain genetic material from unknown sources. In addition to plant pathology, this study is relevant for any field of research where DNA arrays are used to detect mutations or polymorphisms, ranging from human diagnostics to environmental microbiology and microbial ecology.     

Microarrays for bacterial detection and microbial community analysis

Several types of microarrays have recently been developed and evaluated for bacterial detection and microbial community analysis. These studies demonstrated that specific, sensitive and quantitative detection could be obtained with both functional gene arrays and community genome arrays. Although single-base mismatch can be differentiated with phylogenetic oligonucleotide arrays, reliable specific detection at the single-base level is still problematic. Microarray-based hybridization approaches are also useful for defining genome diversity and bacterial relatedness. However, more rigorous and systematic assessment and development are needed to realize the full potential of microarrays for microbial detection and community analysis.     

基因芯片及其在环境微生物研究中的应用

基因芯片因其具有高密度、高灵敏度、快速 (实时 )检测、经济、自动化和低背景水平等特点 ,而广泛应用于不同的研究领域。目前 ,应用于环境微生物研究的基因芯片主要有功能基因芯片 (FGAs)、系统发育的寡核苷酸芯片 (POAs)和群落基因组芯片 (CGAs)。综述了基因芯片在环境微生物研究中的应用 ,包括自然环境中微生物的基因表达分析、比较基因组分析和混合微生物群落的分析等。讨论了基因芯片面临的挑战和前景展望     

Flash detection/identification of pathogens, bacterial spores and bioterrorism agent biomarkers from clinical and environmental matrices.

We propose to develop an integrated rapid, semiportable, prototype point microbial detection/identification system for clinical specimens that is also capable of differentiating microbial bioterrorism attacks from threats or hoaxes by defining the pathogen. The system utilizes "flash" extraction/analytical system capable of detection/identification of microbes from environmental and clinical matrices. The system couples demonstrated technologies to provide quantitative analysis of lipid biomarkers of microbes including spores in a system with near-single cell (amol/microl) sensitivity. Tandem mass spectrometry increases specificity by providing the molecular structure of neutral lipids, phospholipids, and derivatized spore-specific bacterial biomarker, 2,6-dipicolinic acid (DPA) as well as the lipopolysaccharide-amide-linked hydroxy-fatty acids (LPS-ALHFA) of Gram-negative bacteria. The extraction should take about an hour for each sample but multiple samples can be processed simultaneously.     

Market analysis of biosensors for food safety

This paper is presented as an overview of the pathogen detection industry. The review includes pathogen detection markets and their prospects for the future. Potential markets include the medical, military, food, and environmental industries. Those industries combined have a market size of $563 million for pathogen detecting biosensors and are expected to grow at a compounded annual growth rate of 4.5%. The food market is further segmented into different food product industries. The overall food-pathogen testing market is expected to grow to $192 million and 34 million tests by 2005. The trend in pathogen testing emphasizes the need to commercialize biosensors for the food safety industry as legislation creates new standards for microbial monitoring. With quicker detection time and reusable features, biosensors will be important to those interested in real time diagnostics of disease causing pathogens. As the world becomes more concerned with safe food and water supply, the demand for rapid detecting biosensors will only increase.     

Homogeneous versus heterogeneous probes for microbial ecological microarrays

Microbial ecological microarrays have been developed for investigating the composition and functions of microorganism communities in environmental niches. These arrays include microbial identification microarrays, which use oligonucleotides, gene fragments or microbial genomes as probes. In this article, the advantages and disadvantages of each type of probe are reviewed. Oligonucleotide probes are currently useful for probing uncultivated bacteria that are not amenable to gene fragment probing, whereas the functional gene fragments amplified randomly from microbial genomes require phylogenetic and hierarchical categorization before use as microbial identification probes, despite their high resolution for both specificity and sensitivity. Until more bacteria are sequenced and gene fragment probes are thoroughly validated, heterogeneous bacterial genome probes will provide a simple, sensitive and quantitative tool for exploring the ecosystem structure.     

Evaluation of 50-mer oligonucleotide arrays for detecting microbial populations in environmental samples

Microarrays fabricated with oligonucleotides longer than 40 bp have been introduced for monitoring whole genome expression but have not been evaluated with environmental samples. To determine the potential of this type of microarray for environmental studies, a 50-mer oligonucleotide microarray was constructed using 763 genes involved in nitrogen cycling: nitrite reductase (nirS and nirK), ammonia monooxygenase (amoA), nitrogenase (nifH), methane monooxygenase (pmoA), and sulfite reductase (dsrAB) from public databases and our own sequence collections. The comparison of the sequences from pure cultures indicated that the developed microarrays could provide species-level resolution for analyzing microorganisms involved in nitrification, denitrification, nitrogen fixation, methane oxidation, and sulfite reduction. Sensitivity tests suggested that the 50-mer oligonucleotide arrays could detect dominant populations in the environments, although sensitivity still needs to be improved. A significant quantitative relationship was also obtained with a mixture of DNAs from eight different bacteria. These results suggest that the 50-mer oligonucleotide array can be used as a specific and quantitative parallel tool for the detection of microbial populations in environmental samples.     

Lessons from interactions within the cassava green mite fungal pathogen Neozygites tanajoae system and prospects for microbial control using Entomophthorales

Most fungal pathogens lack the capacity to search for their host but rather develop sit-and-wait strategies that favour contact with them. The success of these strategies depends upon the interactions of the pathogen with its host, the host plant and the environmental conditions, which altogether determine its transmissibility. Given the limited success that has characterized application of sustainable microbial control, particularly using Entomophthorales, interaction studies have been conducted with the entomophthoralean fungus Neozygites tanajoae, pathogenic to the cassava green mite (CGM), Mononychellus tanajoa, to help understand differences observed between laboratory and field performances of this pathogen. Reciprocal pathogen-host interactions as well as tritrophic interactions involving the host plant were studied. It was found that herbivory triggers the release of volatiles that promote sporulation of isolates of N. tanajoae, whereas the host mite avoids haloes of spores of this pathogen. However, the host mite does not avoid the pathogen when inside the mummified fungus-killed cadaver. The status of microbial control of CGM in Africa is reviewed and implications of these interactions are discussed for prospective application of microbial control using Entomophthorales.     

A functional gene array for detection of bacterial virulence elements

Emerging known and unknown pathogens create profound threats to public health. Platforms for rapid detection and characterization of microbial agents are critically needed to prevent and respond to disease outbreaks. Available detection technologies cannot provide broad functional information about known or novel organisms. As a step toward developing such a system, we have produced and tested a series of high-density functional gene arrays to detect elements of virulence and antibiotic resistance mechanisms. Our first generation array targets genes from Escherichia coli strains K12 and CFT073, Enterococcus faecalis and Staphylococcus aureus. We determined optimal probe design parameters for gene family detection and discrimination. When tested with organisms at varying phylogenetic distances from the four target strains, the array detected orthologs for the majority of targeted gene families present in bacteria belonging to the same taxonomic family. In combination with whole-genome amplification, the array detects femtogram concentrations of purified DNA, either spiked in to an aerosol sample background, or in combinations from one or more of the four target organisms. This is the first report of a high density NimbleGen microarray system targeting microbial antibiotic resistance and virulence mechanisms. By targeting virulence gene families as well as genes unique to specific biothreat agents, these arrays will provide important data about the pathogenic potential and drug resistance profiles of unknown organisms in environmental samples.     

Identification of recurrent regions of copy-number variants across multiple individuals

Background  

Algorithms and software for CNV detection have been developed, but they detect the CNV regions sample-by-sample with individual-specific breakpoints, while common CNV regions are likely to occur at the same genomic locations across different individuals in a homogenous population. Current algorithms to detect common CNV regions do not account for the varying reliability of the individual CNVs, typically reported as confidence scores by SNP-based CNV detection algorithms. General methodologies for identifying these recurrent regions, especially those directed at SNP arrays, are still needed.     

Applications of functional gene microarrays for profiling microbial communities

Functional gene arrays (FGAs) have been considered as a specific, sensitive, quantitative, and high throughput metagenomic tool to detect, monitor and characterize microbial communities. Especially GeoChips, the most comprehensive FGAs have been applied to analyze the functional diversity, composition, structure, and metabolic potential or activity of a variety of microbial communities from different habitats, such as aquatic ecosystems, soils, contaminated sites, extreme environments, and bioreactors. FGAs are able to address fundamental questions related to global change, bioremediation, land use, human health, and ecological theories, and link the microbial community structure to environmental properties and ecosystem functioning. This review focuses on applications of FGA technology for profiling microbial communities, including target preparation, hybridization and data processing, and data analysis. We also discuss challenges and future directions of FGA applications.     

Assessing populations of a disease suppressive microorganism and a plant pathogen using DNA arrays

Understanding the relationships between disease suppressive microbial populations and plant pathogens is essential to develop procedures for effective and consistent disease control. Currently, DNA array technology is the most suitable technique to simultaneously detect multiple microorganisms. Although this technology has been successfully applied for diagnostic purposes, its utility to assess different microbial populations, as a basis for further study of population dynamics and their potential interactions, has not yet been investigated. In this study, a DNA macroarray with multiple levels of phylogenetic specificity was developed to measure population densities of a specific disease suppressive microorganism, Trichoderma hamatum isolate 382, and the plant pathogen Rhizoctonia solani. Amongst others, the DNA array contained genus-, species- and isolate-specific detector oligonucleotides and was optimized for sensitive detection and reliable quantification of the target organisms in potting mix samples. Furthermore, this DNA array was used to quantify disease severity as well as incidence of severe disease based on pathogen population densities in the growing medium. Taking into account the unlimited expanding possibilities of DNA arrays to include detector oligonucleotides for other and more microorganisms, this technique has the potential for studying the population dynamics and ecology of several target populations in a single assay.     

环境微生物研究中机器学习算法及应用

微生物在环境中无处不在，它们不仅是生物地球化学循环和环境演化的关键参与者，也在环境监测、生态治理和保护中发挥着重要作用。随着高通量技术的发展，大量微生物数据产生，运用机器学习对环境微生物大数据进行建模和分析，在微生物标志物识别、污染物预测和环境质量预测等领域的科学研究和社会应用方面均具有重要意义。机器学习可分为监督学习和无监督学习2大类。在微生物组学研究当中，无监督学习通过聚类、降维等方法高效地学习输入数据的特征，进而对微生物数据进行整合和归类。监督学习运用有特征和标记的微生物数据集训练模型，在面对只有特征没有标记的数据时可以判断出标记，从而实现对新数据的分类、识别和预测。然而，复杂的机器学习算法通常以牺牲可解释性为代价来重点关注模型预测的准确性。机器学习模型通常可以看作预测特定结果的“黑匣子”，即对模型如何得出预测所知甚少。为了将机器学习更多地运用于微生物组学研究、提高我们提取有价值的微生物信息的能力，深入了解机器学习算法、提高模型的可解释性尤为重要。本文主要介绍在环境微生物领域常用的机器学习算法和基于微生物组数据的机器学习模型的构建步骤，包括特征选择、算法选择、模型构建和评估等，并对各种机器学习模型在环境微生物领域的应用进行综述，深入探究微生物组与周围环境之间的关联，探讨提高模型可解释性的方法，并为未来环境监测、环境健康预测提供科学参考。     

Application of resequencing microarrays in microbial detection and characterization

Microarrays are powerful, highly parallel assays that are transforming microbiological diagnostics and research. The adaptation of microarray-based resequencing technology for microbial detection and characterization resulted in the development of a number assays that have unique advantages over other existing technologies. This technological platform seems to be especially useful for sensitive and high-resolution multiplexed diagnostics for clinical syndromes with similar symptoms, screening environmental samples for biothreat agents, as well as genotyping and whole-genome analysis of single pathogens.     

Development of functional gene microarrays for microbial community analysis

Functional gene arrays (FGAs) are a special type of microarrays containing probes for key genes involved in microbial functional processes, such as biogeochemical cycling of carbon, nitrogen, sulfur, phosphorus and metals, virulence and antibiotic resistance, biodegradation of environmental contaminants, and stress responses. FGAs have been demonstrated to be a specific, sensitive, and quantitative tool for rapid analysis of microbial communities from different habitats, such as waters, soils, extreme environments, bioreactors, and human microbiomes. In this review, we first summarize currently reported FGAs, and then focus on the FGA development. We will also discuss several key issues of FGA technology as well as challenges and directions in future FGA development.     

An overview of foodborne pathogen detection: In the perspective of biosensors

Food safety is a global health goal and the foodborne diseases take a major crisis on health. Therefore, detection of microbial pathogens in food is the solution to the prevention and recognition of problems related to health and safety. For this reason, a comprehensive literature survey has been carried out aiming to give an overview in the field of foodborne pathogen detection. Conventional and standard bacterial detection methods such as culture and colony counting methods, immunology-based methods and polymerase chain reaction based methods, may take up to several hours or even a few days to yield an answer. Obviously this is inadequate, and recently many researchers are focusing towards the progress of rapid methods. Although new technologies like biosensors show potential approaches, further research and development is essential before biosensors become a real and reliable choice. New bio-molecular techniques for food pathogen detection are being developed to improve the biosensor characteristics such as sensitivity and selectivity, also which is rapid, reliable, effective and suitable for in situ analysis. This paper not only offers an overview in the area of microbial pathogen detection but it also describes the conventional methods, analytical techniques and recent developments in food pathogen detection, identification and quantification, with an emphasis on biosensors.     

The use of NASBA for the detection of microbial pathogens in food and environmental samples

The isothermal amplification method nucleic acid sequence-based amplification (NASBA), which amplifies RNA, has been reported as useful for the detection of microbial pathogens in food and environmental samples. Methods have been published for Campylobacter spp., Listeria monocytogenes and Salmonella enterica ser. Enteritidis in various foods and for Cryptosporidium parvum in water. Both 16S rRNA and various mRNAs have been used as target molecules for detection; the latter may have advantages in allowing specific detection of viable cells. Most of the methods to detect pathogens in foods have employed enrichment in nutrient medium prior to NASBA, as this can ensure sensitivity of detection and encourage the detection of only viable target cells. Although a relatively recent method, NASBA has the potential for adoption as a diagnostic tool for environmental pathogens.