Census of the Viral Metagenome within an Activated Sludge Microbial Assemblage

The viral metagenome within an activated sludge microbial assemblage was sampled using culture-dependent and culture-independent methods and compared to the diversity of activated sludge bacterial taxa. A total of 70 unique cultured bacterial isolates, 24 cultured bacteriophages, 829 bacterial metagenomic clones of 16S rRNA genes, and 1,161 viral metagenomic clones were subjected to a phylogenetic analysis.Bacteriophages play an active role in the ecology of natural environments, influencing prokaryotic population dynamics (5, 15) and mediating lateral gene transfer between diverse bacterial species, for example. Activated sludge (AS) microbial assemblages in wastewater treatment plants have been shown to harbor great numbers of viruses with a wide range of genome sizes (7, 9, 10, 16). Historically, the focus of wastewater viral studies has been on specific host-virus interactions, the application of phages as tools in microbial source tracking, or the use of phages to improve the efficiency of the wastewater treatment process (e.g., foam and pathogen control) (2, 4, 8, 12, 17). Despite the interest in the wastewater viral community, a census of the activated sludge total viral community has not, to our knowledge, been investigated using both culture-based and metagenomic approaches.     

我国微生物肥料产业现状及发展方向

文章为辽宁省微生物学会主办的"微生物与现代农业专题论坛"主题报告,从我国微生物肥料产业的现状、存在的主要问题、发展机遇及微生物肥料的产业化方向4个方面对我国微生物肥料产业现状及发展进行了详细阐述和深入分析。     

微生物污垢检测技术的特点、现状与发展趋势

微生物污垢是工业冷却水污垢的重要组成部分.在适宜条件下,引起污垢的微生物迅速繁殖.不但会显著增大污垢热阻、流动阻力和腐蚀速率,甚至还会堵塞流道而引发停机故障.本文介绍了微生物污垢的概念,阐述了微生物污垢检测技术研究的地位、作用和特点,归纳了目前已知的微生物污垢的形成过程及其主要影响因素,着重分析了目前国内外应用较广的微生物污垢检测方法、优缺点及其最新研究动态.展望了微生物污垢检测技术未来的发展趋势.     

A Comprehensive Census of Microbial Diversity in Hot Springs of Tengchong,Yunnan Province China Using 16S rRNA Gene Pyrosequencing

The Rehai and Ruidian geothermal fields, located in Tengchong County, Yunnan Province, China, host a variety of geochemically distinct hot springs. In this study, we report a comprehensive, cultivation-independent census of microbial communities in 37 samples collected from these geothermal fields, encompassing sites ranging in temperature from 55.1 to 93.6°C, in pH from 2.5 to 9.4, and in mineralogy from silicates in Rehai to carbonates in Ruidian. Richness was low in all samples, with 21–123 species-level OTUs detected. The bacterial phylum Aquificae or archaeal phylum Crenarchaeota were dominant in Rehai samples, yet the dominant taxa within those phyla depended on temperature, pH, and geochemistry. Rehai springs with low pH (2.5–2.6), high temperature (85.1–89.1°C), and high sulfur contents favored the crenarchaeal order Sulfolobales, whereas those with low pH (2.6–4.8) and cooler temperature (55.1–64.5°C) favored the Aquificae genus Hydrogenobaculum. Rehai springs with neutral-alkaline pH (7.2–9.4) and high temperature (>80°C) with high concentrations of silica and salt ions (Na, K, and Cl) favored the Aquificae genus Hydrogenobacter and crenarchaeal orders Desulfurococcales and Thermoproteales. Desulfurococcales and Thermoproteales became predominant in springs with pH much higher than the optimum and even the maximum pH known for these orders. Ruidian water samples harbored a single Aquificae genus Hydrogenobacter, whereas microbial communities in Ruidian sediment samples were more diverse at the phylum level and distinctly different from those in Rehai and Ruidian water samples, with a higher abundance of uncultivated lineages, close relatives of the ammonia-oxidizing archaeon “Candidatus Nitrosocaldus yellowstonii”, and candidate division O1aA90 and OP1. These differences between Ruidian sediments and Rehai samples were likely caused by temperature, pH, and sediment mineralogy. The results of this study significantly expand the current understanding of the microbiology in Tengchong hot springs and provide a basis for comparison with other geothermal systems around the world.     

Census error and the detection of density dependence

1. Studies aiming to identify the prevalence and nature of density dependence in ecological populations have often used statistical analysis of ecological time-series of population counts. Such time-series are also being used increasingly to parameterize models that may be used in population management. 2. If time-series contain measurement errors, tests that rely on detecting a negative relationship between log population change and population size are biased and prone to spuriously detecting density dependence (Type I error). This is because the measurement error in density for a given year appears in the corresponding change in population density, with equal magnitude but opposite sign. 3. This effect introduces bias that may invalidate comparisons of ecological data with density-independent time-series. Unless census error can be accounted for, time-series may appear to show strongly density-dependent dynamics, even though the density-dependent signal may in reality be weak or absent. 4. We distinguish two forms of census error, both of which have serious consequences for detecting density dependence. 5. First, estimates of population density are based rarely on exact counts, but on samples. Hence there exists sampling error, with the level of error depending on the method employed and the number of replicates on which the population estimate is based. 6. Secondly, the group of organisms measured is often not a truly self-contained population, but part of a wider ecological population, defined in terms of location or behaviour. Consequently, the subpopulation studied may effectively be a sample of the population and spurious density dependence may be detected in the dynamics of a single subpopulation. In this case, density dependence is detected erroneously, even if numbers within the subpopulation are censused without sampling error. 7. In order to illustrate how process variation and measurement error may be distinguished we review data sets (counts of numbers of birds by single observers) for which both census error and long-term variance in population density can be estimated. 8. Tests for density dependence need to obviate the problem that measured population sizes are typically estimates rather than exact counts. It is possible that in some cases it may be possible to test for density dependence in the presence of unknown levels of census error, for example by uncovering nonlinearities in the density response. However, it seems likely that these may lack power compared with analyses that are able to explicitly include census error and we review some recently developed methods.