A Lipid Extraction and Analysis Method for Characterizing Soil Microbes in Experiments with Many Samples.

Microbial communities are important drivers and regulators of ecosystem processes. To understand how management of ecosystems may affect microbial communities, a relatively precise but effort-intensive technique to assay microbial community composition is phospholipid fatty acid (PLFA) analysis. PLFA was developed to analyze phospholipid biomarkers, which can be used as indicators of microbial biomass and the composition of broad functional groups of fungi and bacteria. It has commonly been used to compare soils under alternative plant communities, ecology, and management regimes. The PLFA method has been shown to be sensitive to detecting shifts in microbial community composition. An alternative method, fatty acid methyl ester extraction and analysis (MIDI-FA) was developed for rapid extraction of total lipids, without separation of the phospholipid fraction, from pure cultures as a microbial identification technique. This method is rapid but is less suited for soil samples because it lacks an initial step separating soil particles and begins instead with a saponification reaction that likely produces artifacts from the background organic matter in the soil. This article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples. The method combines the accuracy achieved through PLFA profiling by extracting and concentrating soil lipids as a first step, and a reduction in effort by saponifying the organic material extracted and processing with the MIDI-FA method as a second step.

Unique honey bee (Apis mellifera) hive component-based communities as detected by a hybrid of phospholipid fatty-acid and fatty-acid methyl ester analyses.

Microbial communities (microbiomes) are associated with almost all metazoans, including the honey bee Apis mellifera. Honey bees are social insects, maintaining complex hive systems composed of a variety of integral components including bees, comb, propolis, honey, and stored pollen. Given that the different components within hives can be physically separated and are nutritionally variable, we hypothesize that unique microbial communities may occur within the different microenvironments of honey bee colonies. To explore this hypothesis and to provide further insights into the microbiome of honey bees, we use a hybrid of fatty acid methyl ester (FAME) and phospholipid-derived fatty acid (PLFA) analysis to produce broad, lipid-based microbial community profiles of stored pollen, adults, pupae, honey, empty comb, and propolis for 11 honey bee hives. Averaging component lipid profiles by hive, we show that, in decreasing order, lipid markers representing fungi, Gram-negative bacteria, and Gram-positive bacteria have the highest relative abundances within honey bee colonies. Our lipid profiles reveal the presence of viable microbial communities in each of the six hive components sampled, with overall microbial community richness varying from lowest to highest in honey, comb, pupae, pollen, adults and propolis, respectively. Finally, microbial community lipid profiles were more similar when compared by component than by hive, location, or sampling year. Specifically, we found that individual hive components typically exhibited several dominant lipids and that these dominant lipids differ between components. Principal component and two-way clustering analyses both support significant grouping of lipids by hive component. Our findings indicate that in addition to the microbial communities present in individual workers, honey bee hives have resident microbial communities associated with different colony components.

GC-based detection of aldononitrile acetate derivatized glucosamine and muramic acid for microbial residue determination in soil.

Quantitative approaches to characterizing microorganisms are crucial for a broader understanding of the microbial status and function within ecosystems. Current strategies for microbial analysis include both traditional laboratory culture-dependent techniques and those based on direct extraction and determination of certain biomarkers. Few among the diversity of microbial species inhabiting soil can be cultured, so culture-dependent methods introduce significant biases, a limitation absent in biomarker analysis. The glucosamine, mannosamine, galactosamine and muramic acid have been well served as measures of both the living and dead microbial mass, of these the glucosamine (most abundant) and muramic acid (uniquely from bacterial cell) are most important constituents in the soil systems. However, the lack of knowledge on the analysis restricts the wide popularization among scientific peers. Among all existing analytical methods, derivatization to aldononitrile acetates followed by GC-based analysis has emerged as a good option with respect to optimally balancing precision, sensitivity, simplicity, good chromatographic separation, and stability upon sample storage. Here, we present a detailed protocol for a reliable and relatively simple analysis of glucosamine and muramic acid from soil after their conversion to aldononitrile acetates. The protocol mainly comprises four steps: acid digestion, sample purification, derivatization and GC determination. The step-by-step procedure is modified according to former publications. In addition, we present a strategy to structurally validate the molecular ion of the derivative and its ion fragments formed upon electron ionization. We applied GC-EI-MS-SIM, LC-ESI-TOF-MS and isotopically labeled reagents to determine the molecular weight of aldononitrile acetate derivatized glucosamine and muramic acid; we used the mass shift of isotope-labeled derivatives in the ion spectrum to investigate ion fragments of each derivatives. In addition to the theoretical elucidation, the validation of molecular ion of the derivative and its ion fragments will be useful to researchers using δ(13)C or ion fragments of these biomarkers in biogeochemical studies.

Reliability of muramic acid as a bacterial biomarker is influenced by methodological artifacts from streptomycin.

The muramic acid (MurA) assay is a powerful tool for the detection and quantification of bacteria with no need to enrich samples by culturing. However, the analysis of MurA in mixed biological and environmental matrices is potentially more complex than analysis in isolated bacterial cells. In this study, we employed one commonly used procedure for extraction of MurA from environmental samples and found that the presence of streptomycin interfered with the determination of MurA by creating chemical species that coeluted with the aldononitrile derivative of MurA prepared in this method. On a molar basis, streptomycin yields a signal that is approximately 0.67 times that of MurA. Mass spectrometry analysis confirmed that the interference from hydrolyzed streptomycin is not actually by MurA, but rather is likely to be N-methyl glucosamine. Because streptomycin is widely applied for selective growth of eukaryotes both in situ and in vitro, our findings may have implications for the significance of results from MurA assays. We conclude that MurA remains an effectual bacterial biomarker due to its unique bacterial origin, but care must be applied in interpreting results from the assay when performed in the presence of streptomycin.

Comparisons of human-cell-based and submitochondrial particle bioassay responses to the MEIC compounds in reference to human toxicity data.

Toxicity results from submitochondrial particle (SMP) bioassays were compared to results from multiple human-cell-based assays to evaluate the SMP tests' ability to indicate cellular toxicity. Correlation analyses between cell-based and SMP responses were conducted on a series of diverse chemicals of human toxicologic interest chosen in the Multicentre Evaluation of In vitro Cytotoxicity (MEIC) study and suggest a high degree of ordering. The r(2) correlation coefficient obtained when comparing the log-transformed SMP results to the average cellular response for all compounds was 0.75 (n=42). When specific mitochondrial inhibitors (to which SMP arc extremely sensitive) and toxic metals (which SMP modeled poorly) were removed, the correlation improved to 0.91 (n=34). When the SMP assay of each individual cell-based assay was compared to the average toxic response of all the cell-based assays for these 34 compounds, the SMP r(2) was greater than the median r(2) of the cell-based assays, indicating its ability to predict cell-based toxic responses with a high degree of accuracy. Comparisons of the SMP data to the human toxicity data are similar to the cell line assays, where removal of the specific mitochondrial toxicants and metals greatly improves the relationship.