Microbes In Environmental Samples Research Articles

Emerging Bioanalytical Devices and Platforms for Rapid Detection of Pathogens in Environmental Samples.

The development of robust bioanalytical devices and biosensors for infectious pathogens is progressing well with the advent of new materials, concepts, and technology. The progress is also stepping towards developing high throughput screening technologies that can quickly identify, differentiate, and determine the concentration of harmful pathogens, facilitating the decision-making process for their elimination and therapeutic interventions in large-scale operations. Recently, much effort has been focused on upgrading these analytical devices to an intelligent technological platform by integrating them with modern communication systems, such as the internet of things (IoT) and machine learning (ML), to expand their application horizon. This review outlines the recent development and applications of bioanalytical devices and biosensors to detect pathogenic microbes in environmental samples. First, the nature of the recent outbreaks of pathogenic microbes such as foodborne, waterborne, and airborne pathogens and microbial toxins are discussed to understand the severity of the problems. Next, the discussion focuses on the detection systems chronologically, starting with the conventional methods, advanced techniques, and emerging technologies, such as biosensors and other portable devices and detection platforms for pathogens. Finally, the progress on multiplex assays, wearable devices, and integration of smartphone technologies to facilitate pathogen detection systems for wider applications are highlighted.

Portable and Rapid Sequencing Device with Microbial Community-Guided Culture Strategies for Precious Field and Environmental Samples.

ABSTRACTCulturing unculturable bacteria is a classic microbiology challenge; to successfully culture unculturable bacteria, microbiologists work hard to create hundreds of culture conditions. To improve the success rate and efficiency of culturing a broad spectrum of environmental microbes, it is helpful to know more about the microbial community composition. Shortening the amount of time spent sequencing, analyzing sequencing data, and predicting suitable culture conditions seems to be a critical step for improving knowledge of microbes in environmental samples and expanding culture collections. In this commentary, we introduce potential strategies using rapid and portable sequencing devices to help scientists design and plan for specific microbial culture media on their way back to the laboratory. Rapid metagenomic sequencing approaches and real-time analysis make it possible to choose microbes we are interested in and discover novel microbes in environments for cultivation.

Optimizing the hybridization chain reaction-fluorescence in situ hybridization (HCR-FISH) protocol for detection of microbes in sediments

Fluorescence in situ hybridization (FISH) is a canonical tool commonly used in environmental microbiology research to visualize targeted cells. However, the problems of low signal intensity and false-positive signals impede its widespread application. Alternatively, the signal intensity can be amplified by incorporating Hybridization Chain Reaction (HCR) with FISH, while the specificity can be improved through protocol modification and proper counterstaining. Here we optimized the HCR-FISH protocol for studying microbes in environmental samples, particularly marine sediments. Firstly, five sets of HCR initiator/amplifier pairs were tested on the laboratory-cultured bacterium Escherichia coli and the archaeon Methanococcoides methylutens, and two sets displayed high hybridization efficiency and specificity. Secondly, we tried to find the best combination of sample pretreatment methods and HCR-FISH protocol for environmental sample analysis with the aim of producing less false positive signals. Various detachment methods, extraction methods and formulas of hybridization buffer were tested using sediment samples. Thirdly, an image processing method was developed to enhance the DAPI signal of microbial cells against that of abiotic particles, providing a reliable reference for FISH imaging. In summary, our optimized HCR-FISH protocol showed promise to serve as an addendum to traditional FISH for research on environmental microbes.

TAMA: improved metagenomic sequence classification through meta-analysis

BackgroundMicroorganisms are important occupants of many different environments. Identifying the composition of microbes and estimating their abundance promote understanding of interactions of microbes in environmental samples. To understand their environments more deeply, the composition of microorganisms in environmental samples has been studied using metagenomes, which are the collections of genomes of the microorganisms. Although many tools have been developed for taxonomy analysis based on different algorithms, variability of analysis outputs of existing tools from the same input metagenome datasets is the main obstacle for many researchers in this field.ResultsHere, we present a novel meta-analysis tool for metagenome taxonomy analysis, called TAMA, by intelligently integrating outputs from three different taxonomy analysis tools. Using an integrated reference database, TAMA performs taxonomy assignment for input metagenome reads based on a meta-score by integrating scores of taxonomy assignment from different taxonomy classification tools. TAMA outperformed existing tools when evaluated using various benchmark datasets. It was also successfully applied to obtain relative species abundance profiles and difference in composition of microorganisms in two types of cheese metagenome and human gut metagenome.ConclusionTAMA can be easily installed and used for metagenome read classification and the prediction of relative species abundance from multiple numbers and types of metagenome read samples. TAMA can be used to more accurately uncover the composition of microorganisms in metagenome samples collected from various environments, especially when the use of a single taxonomy analysis tool is unreliable. TAMA is an open source tool, and can be downloaded at https://github.com/jkimlab/TAMA.

The PARE Project: A Short Course-Based Research Project for National Surveillance of Antibiotic-Resistant Microbes in Environmental Samples.

Course-based research experiences (CREs) have been proposed as an inclusive model to expose all students, including those at institutions without a strong research infrastructure, to research at an early stage. Converting an entire semester-long course can be time consuming for instructors and expensive for institutions, so we have developed a short CRE that can be implemented in a variety of life science course types. The Prevalence of Antibiotic Resistance in the Environment (PARE) project uses common microbiology methods and equipment to engage students in nationwide surveillance of environmental soil samples to document the prevalence of antibiotic-resistant bacteria. The project has been implemented at institutions ranging from community colleges to doctoral-granting institutions in 30 states plus Puerto Rico. Programmatic feedback was obtained from instructors over three iterations, and revisions were made based on this feedback. Student learning was measured by pre/post assessment in a subset of institutions. Outcomes indicate that students made significant gains in the project learning goals. Journal of Microbiology & Biology Education

Metatrascriptomic Study of Microbes in Environment: A Community Function Based Approach

Research on microbial ecology in recent years had shifted methodologically from traditional culture based techniques to culture independent molecular techniques. This is due to the fact that it has already been established that less than 1% of microbes in environmental samples are cultivable. In recent years, metagenomics research has developed into analyzing partial or complete genomes as well as other types of molecules in environmental samples, including RNA (transcripts), proteins (translated transcripts), and metabolites (metabolic intermediates and final products). While the analyses of DNA sequences in environmental samples can provide evidence about the diversity and function of specific groups of organisms in the analyzed ecological niches, the relative importance of those genes in environmental functions cannot be determined from DNA sequences alone. To understand which genes are expressed under a specific ecological condition, the analysis of transcripts from the communities is often needed.

A method for purifying high quality and high yield plasmid DNA for metagenomic and deep sequencing approaches

Deep sequencing techniques used in metagenomic approaches have greatly advanced the study of microbial communities in various environments. However, one microbial segment that has remained largely unexplored is the natural plasmids residing within microbial environments. Plasmids are perceived as mobile genetic elements that exist extra-chromosomally and occasionally carry accessory genes that confer an advantage to their host in its ecological niche. They are thus thought to play an important evolutionary role in microbial communities by laterally introducing genes and traits into microbial genomes. Despite their importance, technical obstacles still limit the metagenomic study of natural plasmids using deep sequencing techniques. These include low copy number of the plasmids and heterogeneity of microbes in environmental samples, reflected in the low abundance of each individual plasmid. Furthermore, the extracted plasmids usually contain remnants of chromosomal DNA that can potentially interfere with the analysis of unique plasmid traits. We have recently studied the rumen metagenomic plasmid population using a newly developed procedure that successfully overcomes these obstacles. This procedure enables extraction of pure plasmid DNA suited for deep sequencing studies. Here we present a detailed description and characterization of this procedure which could potentially allow the study of plasmids in other environmental niches.

Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses.

Microbes exist in a range of metabolic states (for example, dormant, active and growing) and analysis of ribosomal RNA (rRNA) is frequently employed to identify the 'active' fraction of microbes in environmental samples. While rRNA analyses are no longer commonly used to quantify a population's growth rate in mixed communities, due to rRNA concentration not scaling linearly with growth rate uniformly across taxa, rRNA analyses are still frequently used toward the more conservative goal of identifying populations that are currently active in a mixed community. Yet, evidence indicates that the general use of rRNA as a reliable indicator of metabolic state in microbial assemblages has serious limitations. This report highlights the complex and often contradictory relationships between rRNA, growth and activity. Potential mechanisms for confounding rRNA patterns are discussed, including differences in life histories, life strategies and non-growth activities. Ways in which rRNA data can be used for useful characterization of microbial assemblages are presented, along with questions to be addressed in future studies.

Development of an Environmental Functional Gene Microarray for Soil Microbial Communities

Functional attributes of microbial communities are difficult to study, and most current techniques rely on DNA- and rRNA-based profiling of taxa and genes, including microarrays containing sequences of known microorganisms. To quantify gene expression in environmental samples in a culture-independent manner, we constructed an environmental functional gene microarray (E-FGA) consisting of 13,056 mRNA-enriched anonymous microbial clones from diverse microbial communities to profile microbial gene transcripts. A new normalization method using internal spot standards was devised to overcome spotting and hybridization bias, enabling direct comparisons of microarrays. To evaluate potential applications of this metatranscriptomic approach for studying microbes in environmental samples, we tested the E-FGA by profiling the microbial activity of agricultural soils with a low or high flux of N₂O. A total of 109 genes displayed expression that differed significantly between soils with low and high N₂O emissions. We conclude that mRNA-based approaches such as the one presented here may complement existing techniques for assessing functional attributes of microbial communities.