Quantifying microbial growth and carbon use efficiency in dry soil environments via 18O water vapor equilibration

Soil microbial physiology controls large fluxes of C to the atmosphere, thus, improving our ability to accurately quantify microbial physiology in soil is essential. However, current methods to determine microbial C metabolism require liquid water addition, which makes it practically impossible to measure microbial physiology in dry soil samples without stimulating microbial growth and respiration (namely, the “Birch effect”). We developed a new method based on in vivo ¹⁸O‐water vapor equilibration to minimize soil rewetting effects. This method allows the isotopic labeling of soil water without direct liquid water addition. This was compared to the main current method (direct ¹⁸O‐liquid water addition) in moist and air‐dry soils. We determined the time kinetics and calculated the average ¹⁸O enrichment of soil water over incubation time, which is necessary to calculate microbial growth from ¹⁸O incorporation in genomic DNA. We tested isotopic equilibration patterns in three natural and six artificially constructed soils covering a wide range of soil texture and soil organic matter content. We then measured microbial growth, respiration and carbon use efficiency (CUE) in three natural soils (either air‐dry or moist). The proposed ¹⁸O‐vapor equilibration method provided similar results as the current method of liquid ¹⁸O‐water addition when used for moist soils. However, when applied to air‐dry soils the liquid ¹⁸O‐water addition method overestimated growth by up to 250%, respiration by up to 500%, and underestimated CUE by up to 40%. We finally describe the new insights into biogeochemical cycling of C that the new method can help uncover, and we consider a range of questions regarding microbial physiology and its response to global change that can now be addressed.     

Population dynamics within a microbial consortium during growth on diesel fuel in saline environments

The diversity and dynamics of a bacterial community extracted from an exploited oil field with high natural soil salinity near Comodoro Rivadavia in Patagonia (Argentina) were investigated. Community shifts during long-term incubation with diesel fuel at four salinities between 0 and 20% NaCl were monitored by single-strand conformation polymorphism community fingerprinting of the PCR-amplified V4-V5 region of the 16S rRNA genes. Information obtained by this qualitative approach was extended by flow cytometric analysis to follow quantitatively the dynamics of community structures at different salinities. Dominant and newly developing clusters of individuals visualized via their DNA patterns versus cell sizes were used to identify the subcommunities primarily involved in the degradation process. To determine the most active species, subcommunities were separated physically by high-resolution cell sorting and subsequent phylogenetic identification by 16S rRNA gene sequencing. Reduced salinity favored the dominance of Sphingomonas spp., whereas at elevated salinities, Ralstonia spp. and a number of halophilic genera, including Halomonas, Dietzia, and Alcanivorax, were identified. The combination of cytometric sorting with molecular characterization allowed us to monitor community adaptation and to identify active and proliferating subcommunities.     

Eco-evolutionary dynamics of dispersal in spatially heterogeneous environments

Ecology Letters (2011) 14: 1025-1034 ABSTRACT: Evolutionary changes in natural populations are often so fast that the evolutionary dynamics may influence ecological population dynamics and vice versa. Here we construct an eco-evolutionary model for dispersal by combining a stochastic patch occupancy metapopulation model with a model for changes in the frequency of fast-dispersing individuals in local populations. We test the model using data on allelic variation in the gene phosphoglucose isomerase (Pgi), which is strongly associated with dispersal rate in the Glanville fritillary butterfly. Population-specific measures of immigration and extinction rates and the frequency of fast-dispersing individuals among the immigrants explained 40% of spatial variation in Pgi allele frequency among 97 local populations. The model clarifies the roles of founder events and gene flow in dispersal evolution and resolves a controversy in the literature about the consequences of habitat loss and fragmentation on the evolution of dispersal.     

Stochastic population growth in spatially heterogeneous environments

Classical ecological theory predicts that environmental stochasticity increases extinction risk by reducing the average per-capita growth rate of populations. For sedentary populations in a spatially homogeneous yet temporally variable environment, a simple model of population growth is a stochastic differential equation dZ _t = μ Z _t dt + σ Z _t dW _t , t ≥ 0, where the conditional law of Z _t+Δt ? Z _t given Z _t = z has mean and variance approximately z μΔt and z ² σ ²Δt when the time increment Δt is small. The long-term stochastic growth rate ${\lim_{t \to \infty} t^{-1}\log Z_t}$ for such a population equals ${\mu -\frac{\sigma^2}{2}}$ . Most populations, however, experience spatial as well as temporal variability. To understand the interactive effects of environmental stochasticity, spatial heterogeneity, and dispersal on population growth, we study an analogous model ${{\bf X}_t = (X_t^1, \ldots, X_t^n)}$ , t ≥ 0, for the population abundances in n patches: the conditional law of X _t+Δt given X _t = x is such that the conditional mean of ${X_{t+\Delta t}^i - X_t^i}$ is approximately ${[x^i \mu_i + \sum_j (x^j D_{ji} - x^i D_{ij})] \Delta t}$ where μ _i is the per capita growth rate in the ith patch and D _ij is the dispersal rate from the ith patch to the jth patch, and the conditional covariance of ${X_{t+\Delta t}^i - X_t^i}$ and ${X_{t + \Delta t}^j - X_t^j}$ is approximately x ⁱ x ^j σ _ij Δt for some covariance matrix Σ = (σ _ij ). We show for such a spatially extended population that if ${S_t = X_t^1 + \cdots + X_t^n}$ denotes the total population abundance, then Y _t = X _t /S _t , the vector of patch proportions, converges in law to a random vector Y _∞ as ${t \to \infty}$ , and the stochastic growth rate ${\lim_{t \to \infty} t^{-1}\log S_t}$ equals the space-time average per-capita growth rate ${\sum_i \mu_i \mathbb{E}[Y_\infty^i]}$ experienced by the population minus half of the space-time average temporal variation ${\mathbb{E}[\sum_{i,j}\sigma_{ij}Y_\infty^i Y_\infty^j]}$ experienced by the population. Using this characterization of the stochastic growth rate, we derive an explicit expression for the stochastic growth rate for populations living in two patches, determine which choices of the dispersal matrix D produce the maximal stochastic growth rate for a freely dispersing population, derive an analytic approximation of the stochastic growth rate for dispersal limited populations, and use group theoretic techniques to approximate the stochastic growth rate for populations living in multi-scale landscapes (e.g. insects on plants in meadows on islands). Our results provide fundamental insights into “ideal free” movement in the face of uncertainty, the persistence of coupled sink populations, the evolution of dispersal rates, and the single large or several small (SLOSS) debate in conservation biology. For example, our analysis implies that even in the absence of density-dependent feedbacks, ideal-free dispersers occupy multiple patches in spatially heterogeneous environments provided environmental fluctuations are sufficiently strong and sufficiently weakly correlated across space. In contrast, for diffusively dispersing populations living in similar environments, intermediate dispersal rates maximize their stochastic growth rate.     

Simulating tumor growth in confined heterogeneous environments

The holy grail of computational tumor modeling is to develop a simulation tool that can be utilized in the clinic to predict neoplastic progression and propose individualized optimal treatment strategies. In order to develop such a predictive model, one must account for many of the complex processes involved in tumor growth. One interaction that has not been incorporated into computational models of neoplastic progression is the impact that organ-imposed physical confinement and heterogeneity have on tumor growth. For this reason, we have taken a cellular automaton algorithm that was originally designed to simulate spherically symmetric tumor growth and generalized the algorithm to incorporate the effects of tissue shape and structure. We show that models that do not account for organ/tissue geometry and topology lead to false conclusions about tumor spread, shape and size. The impact that confinement has on tumor growth is more pronounced when a neoplasm is growing close to, versus far from, the confining boundary. Thus, any clinical simulation tool of cancer progression must not only consider the shape and structure of the organ in which a tumor is growing, but must also consider the location of the tumor within the organ if it is to accurately predict neoplastic growth dynamics.     

Syntrophic growth on formate: a new microbial niche in anoxic environments

Anaerobic syntrophic associations of fermentative bacteria and methanogenic archaea operate at the thermodynamic limits of life. The interspecies transfer of electrons from formate or hydrogen as a substrate for the methanogens is key. Contrary requirements of syntrophs and methanogens for growth-sustaining product and substrate concentrations keep the formate and hydrogen concentrations low and within a narrow range. Since formate is a direct substrate for methanogens, a niche for microorganisms that grow by the conversion of formate to hydrogen plus bicarbonate--or vice versa--may seem unlikely. Here we report experimental evidence for growth on formate by syntrophic communities of (i) Moorella sp. strain AMP in coculture with a thermophilic hydrogen-consuming Methanothermobacter species and of (ii) Desulfovibrio sp. strain G11 in coculture with a mesophilic hydrogen consumer, Methanobrevibacter arboriphilus AZ. In pure culture, neither Moorella sp. strain AMP, nor Desulfovibrio sp. strain G11, nor the methanogens grow on formate alone. These results imply the existence of a previously unrecognized microbial niche in anoxic environments.     

Stochastic population growth in spatially heterogeneous environments: the density-dependent case

Journal of Mathematical Biology - This work is devoted to studying the dynamics of a structured population that is subject to the combined effects of environmental stochasticity, competition for...     

Effect of various carbon sources on microbial lipases biosynthesis

Summary The aim of these investigations was to study the conditions for the production of extracellular lipases fromPenicillium roqueforti S-86, which was isolated from a commercial sample of roqueforti chese type. As carbon sources there have been used the following compounds: 2% glucose, fructose and sucrosel 1% and 2% butterfat and 2% olive oil. Maximal amount of lipases was produced after six days of incubation grown in the medium with 2% of glucose, initial pH of medium 4.0 at 27°C. Cells ofPenicillium roqueforti grown in the presence of bacto-peptone instead of (NH₄)₂SO₄, as nitrogen source, synthesized maximum quantity of lipases after four days of incubation.The effect of temperature, pH, as well as mono, be and three valent cations: Na⁺, K⁺, Ca⁺⁺, Mn⁺⁺, Mg⁺⁺ and Fe⁺⁺⁺ on lipase activity was followed.     

The temperature sensitivity of soil: microbial biodiversity,growth, and carbon mineralization

Microorganisms drive soil carbon mineralization and changes in their activity with increased temperature could feedback to climate change. Variation in microbial biodiversity and the temperature sensitivities (Q₁₀) of individual taxa may explain differences in the Q₁₀ of soil respiration, a possibility not previously examined due to methodological limitations. Here, we show phylogenetic and taxonomic variation in the Q₁₀ of growth (5–35 °C) among soil bacteria from four sites, one from each of Arctic, boreal, temperate, and tropical biomes. Differences in the temperature sensitivities of taxa and the taxonomic composition of communities determined community-assembled bacterial growth Q₁₀, which was strongly predictive of soil respiration Q₁₀ within and across biomes. Our results suggest community-assembled traits of microbial taxa may enable enhanced prediction of carbon cycling feedbacks to climate change in ecosystems across the globe.Subject terms: Biogeochemistry, Ecosystem ecology, Soil microbiology     

澳大利亚亚热带不同森林土壤微生物群落对碳源的利用

为阐明土壤微生物群落对碳源利用类型和强度,采用MicroRespTM方法研究3种森林类型不同土壤含水量微生物群落对不同类型碳源的利用情况,结果表明：湿地松（Pinus elliottii Engelm. var. elliotttii）林地土壤微生物碳源利用率依次为60%WHC>20%WHC>40%WHC,南洋杉（Araucaria cunninghamii）和贝壳杉（Agathis australis）林地土壤微生物对碳源利用的格局相似,为20%WHC>60%WHC>40%WHC。南洋杉和贝壳杉林地土壤对碳源利用率趋势相同,主要是对L-苹果酸、草酸和L-赖氨酸利用比较高。在40%WHC处理中,3种树种对碳源的利用均很低,差异不明显。除精氨酸和L-赖氨酸外,60%WHC处理土壤微生物利用单一碳源能力的大小顺序为：南洋杉>湿地松>贝壳杉。3种树种土壤Shannon多样性指数(H’)、Shannon均匀度(E)和Simpson指数(D)均无显著差异。土壤pH值影响微生物对L-丙氨酸、精氨酸、D-( )-葡萄糖、N-乙酰基-氨基葡萄糖的利用率较大,这些类群的微生物主要分布在贝壳杉林地;分布在南洋杉林地的微生物对柠檬酸、L-苹果酸和γ-酪氨酸利用率较大,且主要是受TP的影响;D-( )-果糖、柠檬酸和L-半胱氨酸-盐酸等受水分、TN和TC等影响较大,这类微生物类群主要分布在湿地松林地。     

松嫩草地4种植物功能群土壤微生物碳源利用的差异

为了探讨草地不同植物功能群土壤微生物碳源利用差异,利用Biolog-ECO微平板检测法,研究了松嫩放牧草地禾草(Grass)、羊草(Leymus chinensis)、杂类草(Forb)和豆科牧草(Legume)4种植物功能群土壤微生物碳源代谢的多样性变化特征。结果表明,在培养的240h内,4种不同植物功能群的土壤微生物对碳源的利用程度均随着时间的延长而升高,表明微生物代谢活性随着时间而增强;不同植物功能群土壤微生物总体活性(AWCD)(P0.001)、Shannon-Wiener指数(H)(P0.001)、物种丰富度(R)(P=0.005)、Pielou均匀度指数(E)(P0.001)差异显著;其中禾草的各项指数明显高于其他3种(P0.01),杂类草的各指标均最低。禾草、羊草对糖类、氨基酸类、羧酸类、多聚物类有较好的利用,豆科牧草除羧酸类外对其他碳源都有更好的利用,杂类草只对酚类碳源利用率最高,而对其他碳源利用率极低。总体得出4种功能群土壤微生物的碳源利用率顺序为:禾草羊草豆科牧草杂类草。     

Modeling growth of a heterogeneous tumor

It has long been recognized that the growth of tumor population depends on the initial age distribution of the cells in the tumor and the age-dependent cellular birth rate. Deterministic dual-cell models have been available for sometime; these models take into account the effects of the resultant cell heterogeneity. Nevertheless, these models ignore various variables significantly affecting the growth, such as those characterizing the cells' inherent properties and environmental factors. Uncertainties, or fluctuations, arise when the growth is simulated with the models. Stochastic analysis of these fluctuations is the focus of the current work.Two types of cells are visualized to proliferate separately and to transform mutually during the process. The master equations of the system have been formulated through probabilistic population balance around a particular state by considering all mutually exclusive events. The governing equations for the means, variances, and covariance of the random variables have been derived through the system-size expansion of these nonlinear master equations. The stochastic pathways of the two different types of cells have been numerically simulated by the algorithm derived from the master equation for two different physical situations, one without and, the other, with the chemotherapeutic treatment. The results of the current study illuminate the significance of stochastically modeling the responses of the tumor to a variety of medicinal treatments: The coefficient of variation of the malignant cells' population magnifies with time under chemotherapeutic regimens. Consequently, the impact of the uncertainties in the exact number of malignant cells as expressed by this coefficient of variation is highly unpredictable. For example, it becomes increasingly uncertain if or how fast these cells will reactivate to become a full-blown carcinogenic tumor after treatment.     

Plant influences on soil microbial carbon pump efficiency

Microbe-mediated carbon transformation plays an important role in soil carbon sequestration, which is considered to be one of the key strategies to achieve carbon neutrality in the long term. Assessing the efficiency of microbial necromass accumulation relative to plant carbon input or microbial respiration will help to identify ways to promote soil carbon sequestration from an ecosystem perspective.     

环丙沙星对土壤微生物量碳和土壤微生物 群落碳代谢多样性的影响

采用氯仿熏蒸浸提法和Biolog法,分析环丙沙星作用下的土壤微生物量碳和微生物群落碳代谢多样性,以揭示环丙沙星在环境中残留对土壤微生物学性状的影响.结果表明,环丙沙星(wCIP≥0.1 μg/g)对土壤微生物量碳含量影响显著(P＜0.05),土壤中环丙沙星浓度愈高,微生物量碳含量愈低,100μg/g的环丙沙星处理使土壤微生物量碳含量下降58.69％.环丙沙星对土壤微生物群落碳代谢功能影响显著,环丙沙星降低了土壤微生物对碳水化合物、羧酸、氨基酸、聚合物、酚类和胺类的碳源利用率;环丙沙星(wCIP≥0.1 μg/g)显著影响了土壤微生物群落碳源代谢强度和代谢多样性,但不同浓度的环丙沙星对土壤微生物群落碳代谢功能的影响不同,0.1、1、10 μg/g的环丙沙星处理对土壤微生物群落碳代谢功能的影响主要表现在处理前期(用药第7天、21天),这种影响在处理后期(用药第35天)表现不明显,100μg/g的环丙沙星在用药的前期和后期均显著影响土壤微生物群落碳代谢功能,土壤中环丙沙星积累到该浓度可能对土壤微生物群落碳代谢功能产生难以逆转的长期影响.     

耕作方式对紫色水稻土有机碳和微生物生物量碳的影响

以位于西南大学的农业部紫色土生态环境重点野外科学观测试验站始于1990年的长期定位试验田为对象,研究了冬水田平作(DP)、水旱轮作(SH)、垄作免耕(LM)及垄作翻耕(LF)等4种耕作方式对紫色水稻土有机碳(SOC)和微生物生物量碳(SMBC)的影响。结果表明,4种耕作方式下SOC和SMBC均呈现出在土壤剖面垂直递减趋势,翻耕栽培下其降低较均匀,而免耕栽培下其富集在表层土壤中。同一土层不同耕作方式间SOC和SMBC的差异在表层最大,随着土壤深度的增加,各处理之间的差异逐渐减小。在0—60 cm剖面中,SOC含量依次为:LM(17.6 g/kg)>DP(13.9 g/kg)>LF(12.5 g/kg)>SH(11.3 g/kg),SOC储量也依次为:LM(158.52 Mg C/hm2)>DP(106.74 Mg C/hm2)>LF(93.11 Mg C/hm2)>SH(88.59 Mg C/hm2),而SMBC含量则依次为:LM(259 mg/kg)>SH(213 mg/kg)>LF(160 mg/kg)>DP(144 mg/kg)。与其它3种耕作方式比较,LM处理显著提高SOC含量和储量以及SMBC含量。对土壤微生物商(SMBC/SOC)进行分析发现,耕作方式对SOC和SMBC的影响程度并不一致。SMBC与SOC、全氮、全磷、全硫、碱解氮、有效磷均呈现极显著正相关(P<0.01),与有效硫呈显著正相关(P<0.05);表明SMBC可以作为表征紫色水稻土土壤肥力的敏感因子。     

中亚热带4种林分类型土壤微生物生物量碳氮特征及季节变化

以中亚热带常绿阔叶林及由其改造而来的闽楠、毛竹及杉木人工林为研究对象,采用氯仿熏蒸浸提法测定了4种林分类型表层(0～10 cm)和深层(40～60 cm)土壤微生物生物量碳(MBC)和氮(MBN),并分析了其季节变化及与土壤理化性质之间的关系.结果表明: 4种林分类型表层土壤MBC和MBN均以常绿阔叶林最高,其次为闽楠人工林、毛竹人工林和杉木人工林,且前三者显著高于后者;各林分深层土壤MBC和MBN无显著差异.4种林分类型的表层土壤MBC和MBN均显著高于深层土壤,且各土层MBC和MBN均具有明显的季节变化,总体呈现出“夏高冬低”单峰曲线变化模式.相关分析表明,4种林分类型土壤MBC和MBN与土壤有机碳、全氮及土壤温度呈显著正相关关系,与土壤容重呈显著负相关关系.表明常绿阔叶林改造成人工林30多年后,表层土壤MBC和MBN呈下降趋势,其中杉木人工林下降幅度最大(分别下降39.0%和49.8%),而对深层土壤MBC和MBN的影响较小.凋落物数量和质量、土壤有机碳和总氮含量及土壤温度是导致各林分类型土壤微生物生物量碳氮差异和季节变化的主要因素.