New Perspectives on Microbial Community Distortion after Whole-Genome Amplification

Whole-genome amplification (WGA) has become an important tool to explore the genomic information of microorganisms in an environmental sample with limited biomass, however potential selective biases during the amplification processes are poorly understood. Here, we describe the effects of WGA on 31 different microbial communities from five biotopes that also included low-biomass samples from drinking water and groundwater. Our findings provide evidence that microbiome segregation by biotope was possible despite WGA treatment. Nevertheless, samples from different biotopes revealed different levels of distortion, with genomic GC content significantly correlated with WGA perturbation. Certain phylogenetic clades revealed a homogenous trend across various sample types, for instance Alpha- and Betaproteobacteria showed a decrease in their abundance after WGA treatment. On the other hand, Enterobacteriaceae, an important biomarker group for fecal contamination in groundwater and drinking water, were strongly affected by WGA treatment without a predictable pattern. These novel results describe the impact of WGA on low-biomass samples and may highlight issues to be aware of when designing future metagenomic studies that necessitate preceding WGA treatment.     

Characterization of Hydrocarbon-Degrading Microbial Populations in Contaminated and Pristine Alpine Soils

Biodegradation of petroleum hydrocarbons in cold environments, including Alpine soils, is a result of indigenous cold-adapted microorganisms able to degrade these contaminants. In the present study, the prevalence of seven genotypes involved in the degradation of n-alkanes (Pseudomonas putida GPo1 alkB; Acinetobacter spp. alkM; Rhodococcus spp. alkB1, and Rhodococcus spp. alkB2), aromatic hydrocarbons (P. putida xylE), and polycyclic aromatic hydrocarbons (P. putida ndoB and Mycobacterium sp. strain PYR-1 nidA) was determined in 12 oil-contaminated (428 to 30,644 mg of total petroleum hydrocarbons [TPH]/kg of soil) and 8 pristine Alpine soils from Tyrol (Austria) by PCR hybridization analyses of total soil community DNA, using oligonucleotide primers and DNA probes specific for each genotype. The soils investigated were also analyzed for various physical, chemical, and microbiological parameters, and statistical correlations between all parameters were determined. Genotypes containing genes from gram-negative bacteria (P. putida alkB, xylE, and ndoB and Acinetobacter alkM) were detected to a significantly higher percentage in the contaminated (50 to 75%) than in the pristine (0 to 12.5%) soils, indicating that these organisms had been enriched in soils following contamination. There was a highly significant positive correlation (P < 0.001) between the level of contamination and the number of genotypes containing genes from P. putida and Acinetobacter sp. but no significant correlation between the TPH content and the number of genotypes containing genes from gram-positive bacteria (Rhodococcus alkB1 and alkB2 and Mycobacterium nidA). These genotypes were detected at a high frequency in both contaminated (41.7 to 75%) and pristine (37.5 to 50%) soils, indicating that they are already present in substantial numbers before a contamination event. No correlation was found between the prevalence of hydrocarbon-degradative genotypes and biological activities (respiration, fluorescein diacetate hydrolysis, lipase activity) or numbers of culturable hydrocarbon-degrading soil microorganisms; there also was no correlation between the numbers of hydrocarbon degraders and the contamination level. The measured biological activities showed significant positive correlation with each other, with the organic matter content, and partially with the TPH content and a significant negative correlation with the soil dry-mass content (P < 0.05 to 0.001).     

Investigation of Microbial Populations in the Extremely Metal-Contaminated Coeur d'Alene River Sediments

The deposition of mine tailings generated from 125 years of sulfidic ore mining resulted in the enrichment of Coeur d'Alene River (CdAR) sediments with significant amounts of toxic heavy metals. A review of literature suggests that microbial populations play a pivotal role in the biogeochemical cycling of elements in such mining-impacted sedimentary environments. To assess the indigenous microbial communities associated with metal-enriched sediments of the CdAR, high-density 16S microarray (PhyloChip) and clone libraries specific to bacteria (16S rRNA), ammonia oxidizers (amoA), and methanogens (mcrA) were analyzed. PhyloChip analysis provided a comprehensive assessment of bacterial populations and detected the largest number of phylotypes in Proteobacteria followed by Firmicutes and Actinobacteria. Furthermore, PhyloChip and clone libraries displayed considerable metabolic diversity in indigenous microbial populations by capturing several chemolithotrophic groups such as ammonia oxidizers, iron-reducers and -oxidizers, methanogens, and sulfate-reducers in the CdAR sediments. Twenty-two phylotypes detected on PhyloChip could not be classified even at phylum level thus suggesting the presence of novel microbial populations in the CdAR sediments. Clone libraries demonstrated very limited diversity of ammonia oxidizers and methanogens in the CdAR sediments as evidenced by the fact that only Nitrosospira- and Methanosarcina-related phylotypes were retrieved in amoA and mcrA clone libraries, respectively.     

Microbial Populations Involved in Cycling of Dimethyl Sulfide and Methanethiol in Freshwater Sediments

Although several microorganisms that produce and degrade methanethiol (MT) and dimethyl sulfide (DMS) have been isolated from various habitats, little is known about the numbers of these microorganisms in situ. This study reports on the identification and quantification of microorganisms involved in the cycling of MT and DMS in freshwater sediments. Sediment incubation studies revealed that the formation of MT and DMS is well balanced with their degradation. MT formation depends on the concentrations of both sulfide and methyl group-donating compounds. A most-probable number (MPN) dilution series with syringate as the growth substrate showed that methylation of sulfide with methyl groups derived from syringate is a commonly occurring process in situ. MT appeared to be primarily degraded by obligately methylotrophic methanogens, which were found in the highest positive dilutions on DMS and mixed substrates (methanol, trimethylamine [TMA], and DMS). Amplified ribosomal DNA restriction analysis (ARDRA) and 16S rRNA gene sequence analysis of the total DNA isolated from the sediments and of the DNA isolated from the highest positive dilutions of the MPN series (mixed substrates) revealed that the methanogens that are responsible for the degradation of MT, DMS, methanol, and TMA in situ are all phylogenetically closely related to Methanomethylovorans hollandica. This was confirmed by sequence analysis of the product obtained from a nested PCR developed for the selective amplification of the 16S rRNA gene from M. hollandica. The data from sediment incubation experiments, MPN series, and molecular-genetics detection correlated well and provide convincing evidence for the suggested mechanisms for MT and DMS cycling and the common presence of the DMS-degrading methanogen M. hollandica in freshwater sediments.     

Geochemistry and Microbial Populations in Sediments of the Northern Baffin Bay,Arctic

The Northern Baffin Bay between Greenland and Canada is a remote Arctic area restricted in primary production by seasonal ice cover, with presumably low sedimentation rates, carbon content and microbial activities in its sediments. Our aim was to study the so far unknown subseafloor geochemistry and microbial populations driving seafloor ecosystems. Shelf sediments had the highest organic carbon content, numbers of Bacteria and Archaea, and microcosms inoculated from Shelf sediments showed highest sulfate reduction and methane production rates. Sediments in the central deep area and on the southern slope contained less organic carbon and overall lower microbial numbers. Similar 16S rRNA gene copy numbers of Archaea and Bacteria were found for the majority of the sites investigated. Sulfate in pore water correlated with dsrA copy numbers of sulfate-reducing prokaryotes and differed between sites. No methane was found as free gas in the sediments, and mcrA copy numbers of methanogenic Archaea were low. Methanogenic and sulfate-reducing cultures were enriched on a variety of substrates including hydrocarbons. In summary, the Greenlandic shelf sediments contain vital microbial communities adapted to their specific environmental conditions.     

Whole-Genome Sequencing in Microbial Forensic Analysis of Gamma-Irradiated Microbial Materials

Effective microbial forensic analysis of materials used in a potential biological attack requires robust methods of morphological and genetic characterization of the attack materials in order to enable the attribution of the materials to potential sources and to exclude other potential sources. The genetic homogeneity and potential intersample variability of many of the category A to C bioterrorism agents offer a particular challenge to the generation of attributive signatures, potentially requiring whole-genome or proteomic approaches to be utilized. Currently, irradiation of mail is standard practice at several government facilities judged to be at particularly high risk. Thus, initial forensic signatures would need to be recovered from inactivated (nonviable) material. In the study described in this report, we determined the effects of high-dose gamma irradiation on forensic markers of bacterial biothreat agent surrogate organisms with a particular emphasis on the suitability of genomic DNA (gDNA) recovered from such sources as a template for whole-genome analysis. While irradiation of spores and vegetative cells affected the retention of Gram and spore stains and sheared gDNA into small fragments, we found that irradiated material could be utilized to generate accurate whole-genome sequence data on the Illumina and Roche 454 sequencing platforms.     

Distribution and Composition of Microbial Populations in a Landfill Leachate Contaminated Aquifer (Grindsted, Denmark)

Abstract To investigate whether landfill leachates affected the microbial biomass and/or community composition of the extant microbiota, 37 samples were collected along a 305-m transect of a shallow landfill-leachate polluted aquifer. The samples were analyzed for total numbers of bacteria by use of the acridine orange direct count method (AODC). Numbers of dominant, specific groups of bacteria and total numbers of protozoa were measured by use of the most probable number method (MPN). Viable biomass estimates were obtained from measures of ATP and ester-linked phospholipid fatty acid (PLFA) concentrations. The estimated numbers of total bacteria by direct counts were relatively constant throughout the aquifer, ranging from a low of 4.8 × 10⁶ cells/g dry weight (dw) to a high of 5.3 × 10⁷ cells/g dw. Viable biomass estimates based on PLFA concentrations were one to three orders of magnitude lower with the greatest concentrations (up to 4 × 10⁵ cells/g dw) occurring at the border of the landfill and in samples collected from thin lenses of clay and silt with sand streaks. Cell number estimates based on ATP concentrations were also found to be lower than the direct count measurements (<2.2 × 10⁶ cells/g dw), and with the greatest concentrations close to the landfill. Methanogens (Archaea) and reducers of sulfate, iron, manganese, and nitrate were all observed in the aquifer. Methanogens were found to be restricted to the most polluted and reduced part of the aquifer at a maximum cell number of 5.4 × 10⁴ cells/g dw. Populations of sulfate reducers decreased with an increase in horizontal distance from the landfill ranging from a high of 9.0 × 10³ cells/g dw to a low of 6 cells/g dw. Iron, manganese, and nitrate reducers were detected throughout the leachate plume all at maximum cell numbers of 10⁶ cells/g dw. Changes in PLFA profiles indicated that a shift in microbial community composition occurred with increasing horizontal distance from the landfill. The types and patterns of lipid biomarkers suggested that increased proportions of sulfate- and iron-reducing bacteria as well as certain microeukaryotes existed at the border of the landfill. The presence of these lipid biomarkers correlated with the MPN results. There was, however, no significant correlation between the abundances of the specific PLFA biomarkers and quantitative measurements of redox processes. The application of AODC, MPN, PLFA, and ATP analyses in the characterization of the extant microbiota within the Grindsted aquifer revealed that as distance increased from the leachate source, viable biomass decreased and community composition shifted. These results led to the conclusion that the landfill leachate induced an increase in microbial cell numbers by altering the subsurface aquifer so that it was conducive to the growth of methanogens and of iron-and sulfate-reducing bacteria and fungi. Received: 11 June 1998; Accepted: 10 December 1998     

Identification of the Microbial Community Structure and Potential Mechanism of Environmental Adaptation in Contaminated Environments

Purpose: To assess the bacterial community structure and the possible mechanism underlying the environmental adaption in contaminated habitats.Methods: The 60-mer oligonucleotide multibacterial microarray (GSE38004) was downloaded from the Gene Expression Omnibus database. The changes in the abundance of bacterial populations and genes in contaminated habitats were assessed based on the gene expression profiles. Then the potential function of the genes and their involved pathways were predicted by a bioinformatics approach.Results: A total of 25 bacterial populations had different abundance between contaminated habitats and uncontaminated area, of which Sphingobium herbicidovorans (S. herbicidovorans) and Pseudomonas pavonaceae (P. pavonaceae) involved in 23 populations were determined to have higher abundance in contaminated areas. Additionally, 184 genes involved in anaerobic respiration and cellular respiration were detected to have potential bioremediation. The genes of MDH (Mannitol-1-phosphate/altronate dehydrogenases) and fadE (acyl-CoA dehydrogenase) may play key roles in the mechanism of metabolic adaption.Conclusions: S. herbicidovorans and P. pavonaceae were the predominant bacterial groups in contaminated sites for the various metabolic potential and antibiotic resistances. The significant genes (MDH, fadE) and pathways (citrate cycle, pyruvate metabolism, fatty acid metabolism) played key roles in the adaptive response of microorganisms in contaminated environment.     

Seasonal Variations in Microbial Populations and Environmental Conditions in an Extreme Acid Mine Drainage Environment

Microbial populations, their distributions, and their aquatic environments were studied over a year (1997) at an acid mine drainage (AMD) site at Iron Mountain, Calif. Populations were quantified by fluorescence in situ hybridizations with group-specific probes. Probes were used for the domains Eucarya, Bacteria, and Archaea and the two species most widely studied and implicated for their role in AMD production, Thiobacillus ferrooxidans and Leptospirillum ferrooxidans. Results show that microbial populations, in relative proportions and absolute numbers, vary spatially and seasonally and correlate with geochemical and physical conditions (pH, temperature, conductivity, and rainfall). Bacterial populations were in the highest proportion (>95%) in January. Conversely, archaeal populations were in the highest proportion in July and September (~50%) and were virtually absent in the winter. Bacterial and archaeal populations correlated with conductivity and rainfall. High concentrations of dissolved solids, as reflected by high conductivity values (up to 125 mS/cm), occurred in the summer and correlated with high archaeal populations and proportionally lower bacterial populations. Eukaryotes were not detected in January, when total microbial cell numbers were lowest (<10⁵ cells/ml), but eukaryotes increased at low-pH sites (~0.5) during the remainder of the year. This correlated with decreasing water temperatures (50 to 30°C; January to November) and increasing numbers of prokaryotes (10⁸ to 10⁹ cells/ml). T. ferrooxidans was in highest abundance (>30%) at moderate pHs and temperatures (~2.5 and 20°C) in sites that were peripheral to primary acid-generating sites and lowest (0 to 5%) at low-pH sites (pH ~0.5) that were in contact with the ore body. L. ferrooxidans was more widely distributed with respect to geochemical conditions (pH = 0 to 3; 20 to 50°C) but was more abundant at higher temperatures and lower pHs (~40°C; pH ~0.5) than T. ferrooxidans.     

Microbial Resource Management: The Road To Go for Environmental Biotechnology

The breakthroughs in microbiology have allowed us to come to terms with the microbial resources present in culture collections and in natural environments. The challenge at present is to manage these microbial resources, particularly when one deals with open systems where the dynamics of microbial ecology are predominant. Hence, to properly address the aspects of Microbial Resource Management (MRM), one needs to handle the questions of who is there, who is doing what with whom and how can one adjust, control and/or steer these mixed cultures and communities. It is argued that microbial ecologists and environmental microbiologists need to address a new mind‐set. The Beijerinck axioma that all microorganisms are everywhere should not be presumed to be generally valid. The Darwin based niche assembly concept needs to be supplemented with the neutral theory of Hubbell. The Pareto 80/20 principle is also applicable to the macro‐economies of microbial communities. Finally, the concept of a “stable” microbial community should be replaced by that of a cooperative community continuum. Overall, MRM is at the basis of a number of new developments in domains such as environmental safety and health, renewable energy production, closing environmental cycles and providing new materials. Specific examples such as, for example, pro‐active immuno‐stimulation by means of drinking water, electro‐microbiology, decreasing global warming by implementation of methanotrophs and generation of nano‐biocatalysts are discussed.     

Successive Microbial Populations in Calimyrna Figs

Smyrna-type (Calimyrna) figs have essentially sterile internal tissue until visited by the pollinating fig wasp, Blastophaga psenes, which introduces a specific microflora consisting of Candida guilliermondii var. carpophila and Serratia plymuthica. This flora persists and develops in numbers throughout the ripening period until maturity of the fruit. These organisms do not cause spoilage. The presence of C. guilliermondii var. carpophila appears to increase the attractiveness of the fruit to drosophilae. Drosophila (mainly D. melanogaster) carry spoilage yeasts and bacteria on their exterior body parts, and introduce these organisms during ovipositing in the fruit cavity. The spoilage yeasts consist almost entirely of apiculate yeasts (Hanseniaspora valbyensis, H. uvarum, and Kloeckera apiculata) and of Torulopsis stellata, which cause active fermentative spoilage. Spoilage bacteria (primarily Acetobacter melanogenus) are also introduced with the yeasts. Organic acids are produced by these yeasts as well as by the Acetobacter. A number of minor spoilage yeasts were also identified.     

CRISPR-Induced Distributed Immunity in Microbial Populations

In bacteria and archaea, viruses are the primary infectious agents, acting as virulent, often deadly pathogens. A form of adaptive immune defense known as CRISPR-Cas enables microbial cells to acquire immunity to viral pathogens by recognizing specific sequences encoded in viral genomes. The unique biology of this system results in evolutionary dynamics of host and viral diversity that cannot be fully explained by the traditional models used to describe microbe-virus coevolutionary dynamics. Here, we show how the CRISPR-mediated adaptive immune response of hosts to invading viruses facilitates the emergence of an evolutionary mode we call distributed immunity - the coexistence of multiple, equally-fit immune alleles among individuals in a microbial population. We use an eco-evolutionary modeling framework to quantify distributed immunity and demonstrate how it emerges and fluctuates in multi-strain communities of hosts and viruses as a consequence of CRISPR-induced coevolution under conditions of low viral mutation and high relative numbers of viral protospacers. We demonstrate that distributed immunity promotes sustained diversity and stability in host communities and decreased viral population density that can lead to viral extinction. We analyze sequence diversity of experimentally coevolving populations of Streptococcus thermophilus and their viruses where CRISPR-Cas is active, and find the rapid emergence of distributed immunity in the host population, demonstrating the importance of this emergent phenomenon in evolving microbial communities.     

Requirement for a Microbial Consortium To Completely Oxidize Glucose in Fe(III)-Reducing Sediments

In various sediments in which Fe(III) reduction was the terminal electron-accepting process, [¹⁴C]glucose was fermented to ¹⁴C-fatty acids in a manner similar to that observed in methanogenic sediments. These results are consistent with the hypothesis that, in Fe(III)-reducing sediments, fermentable substrates are oxidized to carbon dioxide by the combined activity of fermentative bacteria and fatty acid-oxidizing, Fe(III)-reducing bacteria.     

Searching for Footprints of Positive Selection in Whole-Genome SNP Data From Nonequilibrium Populations

A major goal of population genomics is to reconstruct the history of natural populations and to infer the neutral and selective scenarios that can explain the present-day polymorphism patterns. However, the separation between neutral and selective hypotheses has proven hard, mainly because both may predict similar patterns in the genome. This study focuses on the development of methods that can be used to distinguish neutral from selective hypotheses in equilibrium and nonequilibrium populations. These methods utilize a combination of statistics on the basis of the site frequency spectrum (SFS) and linkage disequilibrium (LD). We investigate the patterns of genetic variation along recombining chromosomes using a multitude of comparisons between neutral and selective hypotheses, such as selection or neutrality in equilibrium and nonequilibrium populations and recurrent selection models. We perform hypothesis testing using the classical P-value approach, but we also introduce methods from the machine-learning field. We demonstrate that the combination of SFS- and LD-based statistics increases the power to detect recent positive selection in populations that have experienced past demographic changes.GENOMES contain information related to the history of natural populations. Past neutral and selective processes may have left footprints in the genome. Recent advances in population genetics aim to understand the patterns of genetic diversity and identify events that have led to genetic adaptations. Among them, positive selection has been a focus of many recent studies (Harr et al. 2002; Kim and Stephan 2002; Glinka et al. 2003; Akey et al. 2004; Orengo and Aguadé 2004). Their goal is to (i) provide evidence of positive selection, (ii) estimate the strength and the rate of selection, and (iii) localize the targets of selection. These objectives form the basis of a long-term pursuit, which is the understanding of the molecular basis of adaptation of populations in a changing environment.Positive selection can cause genetic hitchhiking when a beneficial mutation spreads in the population (Maynard Smith and Haigh 1974). When a strongly beneficial mutation occurs and spreads in a population, linked neutral or slightly deleterious variants hitchhike with it, and their frequency increases. According to Maynard Smith and Haigh''s model, three patterns are generated locally around the position of the beneficial mutation. First, the level of variability will be reduced since standing variation of the population that is not linked to the beneficial allele vanishes, and tightly linked polymorphisms may fix (Kaplan et al. 1989; Stephan et al. 1992). Second, the site frequency spectrum (SFS), which describes the frequency of allelic variants, shifts from its neutral expectation toward rare and high-frequency derived variants (Braverman et al. 1995; Fay and Wu 2000). The third signature describes the emergence of specific linkage disequilibrium (LD) patterns around the target of positive selection, such as an elevated level of LD in the early phase of the fixation process of the beneficial mutation and a decay of LD across the selected site at the end of the selective phase (Kim and Nielsen 2004; Stephan et al. 2006).The availability of genome-wide SNP data has made possible the scanning of genomes and the identification of loci that may have been targets of recent selective events. Several approaches have been developed within the last years that can detect the molecular signatures of positive selection (Kim and Stephan 2002; Jensen et al. 2005; Nielsen et al. 2005). While the methods of Kim and Stephan (2002) and Jensen et al. (2005) are designed to analyze subgenomic SNP data, the approach of Nielsen et al. (2005) can be applied to both subgenomic and whole-genome data (reviewed in Pavlidis et al. 2008). For this reason we concentrate here on the latter procedure. This method, called SweepFinder, calculates the probability P(x) that a polymorphism of multiplicity x is linked to a beneficial mutation using a simple selective model and the SFS prior to the selective event. Then, for each location in the genome it compares a selective with a neutral model assuming independence between the SNPs, therefore calculating the composite likelihood ratio Λ. Thus, it identifies regions where the likelihood of the selective sweep is greater than that of the neutral model using the maximum value Λ_MAX of Λ.The ω-statistic, developed by Kim and Nielsen (2004), detects specific LD patterns caused by genetic hitchhiking (described above). In the study by Kim and Nielsen (2004) the maximum value of the ω-statistic was used to identify the targets of selective sweeps. Later, Jensen et al. (2007) studied its performance in separating demographic from selective scenarios. An important result by Jensen et al. (2007) is the demonstration that for demographic parameters relevant to nonequilibrium populations (such as the cosmopolitan populations of Drosophila melanogaster) the ω-statistic can distinguish between neutral and selective scenarios. This article further develops SweepFinder and the ω-statistic such that they can eventually be applied to whole-genome SNP data sets that have been collected from nonequilibrium populations. In particular, populations undergoing population-size bottlenecks are of interest as these size changes may confound the patterns of selective sweeps (Barton 1998). For this reason we use the following approach: first, we theoretically analyze the genealogies of bottlenecked populations under neutrality and show to what extent they resemble the genealogies of single hitchhiking (SHH) events. We also point out the importance of high-frequency-derived variants in the identification of selective sweeps. Second, we study the statistical properties of SweepFinder and the ω-statistic separately and in combination. As the main result, we demonstrate that the combination of these two methods (that include both SFS and LD information) increases the power for detecting recent SHH events in nonequilibrium populations, in particular when machine-learning techniques are employed. Third we analyze the performance of SweepFinder and the ω-statistic in the detection of recurrent hitchhiking (RHH) events.     

Population Dynamics of Polychlorinated Biphenyl-Dechlorinating Microorganisms in Contaminated Sediments

The growth dynamics of polychlorinated biphenyl (PCB)-dechlorinating microorganisms were determined for the first time, along with those of sulfate reducers and methanogens, by using the most-probable-number technique. The time course of Aroclor 1248 dechlorination mirrored the growth of dechlorinators; dechlorination ensued when the dechlorinating population increased by 2 orders of magnitude from 2.5 x 10(sup5) to 4.6 x 10(sup7) cells g of sediment(sup-1), at a specific growth rate of 6.7 day(sup-1) between 2 and 6 weeks. During this period, PCB-dechlorinating microorganisms dechlorinated Aroclor 1248 at a rate of 3.9 x 10(sup-8) mol of Cl g of sediment(sup-1) day(sup-1), reducing the average number of Cl molecules per biphenyl from 3.9 to 2.8. The growth yield was 4.2 x 10(sup13) cells mol of Cl dechlorinated(sup-1). Once dechlorination reached a plateau, after 6 weeks, the number of dechlorinators began to decrease. On the other hand, dechlorinators inoculated into PCB-free sediments decreased over time from their initial level, suggesting that PCBs are required for their selective enrichment. The numbers of sulfate reducers and methanogens increased in both PCB-free and contaminated sediments, showing little difference between them. The maximum population size of sulfate reducers was about an order of magnitude higher than that of dechlorinators, whereas that of methanogens was slightly less. Unlike those of dechlorinators, however, numbers of both sulfate reducers and methanogens remained high even when dechlorination ceased. The results of this study imply that PCB concentrations may have to exceed a certain threshold to maintain the growth of PCB dechlorinators.     

Microbial Metal Tolerance in Bermuda Carbonate Sediments

The recovery of aerobic heterotrophic bacteria from Bermuda carbonate sediments on metal-supplemented media varied as much as 44-fold over a 15-cm depth. Distributional relationships with sulfate-reducing bacteria and sediment character indicated that metal tolerance was a function of metal bioavailability.     

Effects of Environmental Factors on Microbial Populations in Brackish Waters off the Southern Coast of Finland

The roles played by environmental factors in seasonal changes in microbial populations were investigated in the Tvärminne area, off the southern coast of Finland. Surface-layer samples were collected at 1- or 2-week intervals in 1976-78, and 14 microbiological and 10 environmental parameters were determined. Stepwise multiple regression analysis was used to explain seasonal variation in the microbiological parameters. Separate analyses were made of the data from the open-water and ice-cover periods. In analyses of data from both periods, the environmental factors included accounted for a significant proportion of the variation in the parameters for community respiration (90%) and bacterial spores (80%), and a smaller proportion (60 to 65%) of the variation in total counts of bacteria and plate counts of psychrophiles and yeasts. Lower values (40 to 55%) were obtained for the variation in the other microbiological parameters. The environmental factors with maximal contributions were organic matter, water temperature, chlorophyll a, and salinity, but rainfall and winds also explained part of the variation in some microbiological parameters. In the winter analysis the results differed from those obtained for the other seasons, the variation being governed by parameters indicating freshwater outflows, namely, humic matter, salinity, water temperature (positive regression coefficient), and rainfall (negative regression coefficient).     

Assessment of Genetic Heterogeneity in Structured Plant Populations Using Multivariate Whole-Genome Regression Models

Plant breeding populations exhibit varying levels of structure and admixture; these features are likely to induce heterogeneity of marker effects across subpopulations. Traditionally, structure has been dealt with as a potential confounder, and various methods exist to “correct” for population stratification. However, these methods induce a mean correction that does not account for heterogeneity of marker effects. The animal breeding literature offers a few recent studies that consider modeling genetic heterogeneity in multibreed data, using multivariate models. However, these methods have received little attention in plant breeding where population structure can have different forms. In this article we address the problem of analyzing data from heterogeneous plant breeding populations, using three approaches: (a) a model that ignores population structure [A-genome-based best linear unbiased prediction (A-GBLUP)], (b) a stratified (i.e., within-group) analysis (W-GBLUP), and (c) a multivariate approach that uses multigroup data and accounts for heterogeneity (MG-GBLUP). The performance of the three models was assessed on three different data sets: a diversity panel of rice (Oryza sativa), a maize (Zea mays L.) half-sib panel, and a wheat (Triticum aestivum L.) data set that originated from plant breeding programs. The estimated genomic correlations between subpopulations varied from null to moderate, depending on the genetic distance between subpopulations and traits. Our assessment of prediction accuracy features cases where ignoring population structure leads to a parsimonious more powerful model as well as others where the multivariate and stratified approaches have higher predictive power. In general, the multivariate approach appeared slightly more robust than either the A- or the W-GBLUP.     

Recombinant Environmental Libraries Provide Access to Microbial Diversity for Drug Discovery from Natural Products

To further explore possible avenues for accessing microbial biodiversity for drug discovery from natural products, we constructed and screened a 5,000-clone “shotgun” environmental DNA library by using an Escherichia coli-Streptomyces lividans shuttle cosmid vector and DNA inserts from microbes derived directly (without cultivation) from soil. The library was analyzed by several means to assess diversity, genetic content, and expression of heterologous genes in both expression hosts. We found that the phylogenetic content of the DNA library was extremely diverse, representing mostly microorganisms that have not been described previously. The library was screened by PCR for sequences similar to parts of type I polyketide synthase genes and tested for the expression of new molecules by screening of live colonies and cell extracts. The results revealed new polyketide synthase genes in at least eight clones. In addition, at least five additional clones were confirmed by high-pressure liquid chromatography analysis and/or biological activity to produce heterologous molecules. These data reinforce the idea that exploiting previously unknown or uncultivated microorganisms for the discovery of novel natural products has potential value and, most importantly, suggest a strategy for developing this technology into a realistic and effective drug discovery tool.